Ras, p63 and Breast Cancer

Kathryn E. Yoh

Submitted in partial fulfillment of the

requirements for the degree of

Doctor of Philosophy

in the Graduate School of Arts and Sciences

COLUMBIA UNIVERSITY

2016

© 2016

Kathryn Yoh

All rights reserved

Abstract: Ras, p63 and Breast Cancer

Kathryn Yoh

As a master regulator of the epithelial state, p63 is a family member of the well-

known tumor suppressor p53. It has previously been connected to a cancer-associated

process, epithelial-to-mesenchymal transition (EMT), and here we find that it can be

regulated by oncogenes involved in breast tumorigenesis. Specifically, activated forms

of PIK3CA and H-RAS are able to strongly repress expression of ∆Np63α, which is the

major p63 isoform in epithelial cells. In mammary epithelial lines, this oncogene

downregulation occurs at the transcriptional level, and complete repression occurs over

the course of several days.

As p63 is repressed, the cells undergo EMT and acquire the ability to invade

individually through a 3D collagen matrix. Strikingly, even when p63 is suppressed but

no oncogene action is present, these cells undergo a mesenchymal shift, suggesting

the importance of this gene in maintaining the epithelial state. Furthermore, it is

particularly interesting that p63 protein and RNA levels are often low in breast tumors.

By connecting H-RAS and PIK3CA signaling to p63, it is hypothesized that such

oncogene suppression could account for tumor progression in cases where p63 levels

are low. Here, it is proposed that p63 acts in a tumor-suppressive manner, although it

can be overcome by oncogenes leading to changes in differentiation state and

migratory capability, therefore drastically affecting breast carcinogenesis.

i

Table of Contents

List of Figures and Tables…………………………………………………….….…ii

Preface……………………………………………………………….……………….iv

Chapter 1………………………………………………………………….…………..1

“Breast Cancer, Current Outcomes and Therapies”…………………….…..…...1

“Breast Cancer: Molecular Subtypes, Grading and Staging, Heterogeneity”.…5

“Mutations frequently found in breast and other cancers”………………….…..9

“An Introduction to Epithelial-to-Mesenchymal Transition (EMT)”………….….15

“Regulators of EMT”…………………………………..………………………….…18

“Breast Cancer Stem Cells, Progenitor Cells, and Mammary Stem Cells”.......22

“Model cell lines for cancer research”………………………………………….….28

“An introduction to the p53 family, and p53 target genes”……………….……...29

“The p53 protein undergoes many post-translational modifications”……….….32

“All in the family: how p63 differs from p53”………………………………….…...35

“Post-translational modifications and target genes”………………………….…..41

“MicroRNAs and p63”……………………………….………………………….……46

“An introduction to the Ras signaling pathway”………………….…………..……49

References………………………………………………………….………….……..59

Chapter 2………………………………………………………………….…...…….158

References…………………………………………………………………………..175

Chapter 3………………………………………………………………….………….208

References………………………….………………………………………………..231

Chapter 4……………………………………………………………………………..256

References…………………………………………………………………………...272

ii

List of Figures and Tables

Chapter 1

Figure 1 “Human breast diagram”………………………………….146

Figure 2 “HER2 signaling pathway”………………………………..147

Figure 3 “Hallmarks of cancer”……………………………………..148

Figure 4 “Metastatic cascade”………………………………………149

Figure 5 “Selected p63 targets”…………………………………….150

Figure 6 “Multistep model for colorectal cancer progression”…...151

Figure 7 “MAPK signaling cascades”………………………………152

Chapter 2

Figures 1-6………………………………………………………...….182

Figures S1-6…………………………………………………………..189

Chapter 3

Figures 1-5………………………………………………………...….245

Figures S1-5…………………………………………………………..251

Chapter 4

Figure 1………………………………………………………………..262

iii

Acknowledgements

I am incredibly grateful to Carol Prives and Ron Prywes for mentoring me. I also

received guidance from so many people - Rachel Beckerman, Seungmin Lee, Ella

Freulich, Jamie Lee Elliott, and Jerry Yin are just some of them. Finally, I want to thank

my parents for being supportive and wonderful.

iv

Preface

Women with basal-like breast cancers, which usually have triple-negative status, have

the worst prognosis among all breast cancer patients. These cancers are aggressive and there

is a lack of targeted therapies, since the signaling pathways that go awry in these cancers have

yielded few druggable targets. Herein, I discuss the current situation in this field, including the

various subtypes, treatments, and common mutations in this tissue. Tumorigenic processes

such as EMT (epithelial-to-mesenchymal transition) are discussed, along with the circuits

regulating these pathways. The evidence for cancer stem cells, metastatic niches and

progenitor cells will be presented.

After this, the p53 family is described in depth, which includes homologues p63 and p73.

The impact of p53 and p63 in governing an array of cellular processes, especially those

contributing to tumor-related processes, is summarized. As p63 is of great interest, I provide a

detailed analysis of its history, associated diseases and advances in the understanding of this

regulator of epithelial tissues. Its impact on target genes, including microRNAs, is analyzed,

along with any published connections to cancer (including EMT).

Following this, the Ras signaling pathway is expounded in detail. Interactions between

the Ras and p53 families are described, illuminating the interconnectedness of these signaling

pathways. Finally, our work concerning the regulation of p63 and p53 is detailed in Chapters 2

and 3, along with a review of p63 regulation (Chapter 4).

1

Chapter 1

Breast Cancer, Current Outcomes and Therapies

The human breast is thought to have evolved differently from that of other primates, as

evidenced by the finding that human females have larger deposits of adipose tissue (see review

by Dixson et al., 2015). Both adipose tissue and connective tissue (consisting the stromal

tissue) surround a network of ducts that end in a network of terminal ductal lobular units or

TDLUs (see Figure 1). The terminal end buds are club-shaped outgrowths forming from the duct

that will eventually form new lobes or alveoli as the mammary gland differentiates. The outer

layer of each duct or lobe is comprised of basal/myoepithelial cells, characterized by keratins 5

and 6, as well as p63 expression (Dairkee et al. 1985; Nagle et al. 1986; Trask et al. 1990;

Nylander et al. 2002). There is also an inner layer of luminal cells, which typically express

keratins 8, 18 and 19 (Bartek et al. 1985; Taylor-Papadimitriou et al. 1989). Luminal secretory

cells in the lobes are responsible for producing milk, involving the action of hormones such as

prolactin and insulin (Borellini & Oka 1989; Qian & Zhao 2014). When a woman is lactating, milk

is produced there and carried via the ducts to the nipple. Clearly, the breast is a complex and

important tissue, essential for the wellbeing of human progeny.

However, breast cancer is one of the leading cancer-related killers of women worldwide

(Stewart et al., 2014). In the United States alone, it is estimated that around 230,000 women

were diagnosed in 2015 (Siegel et al., 2015, American Cancer Society, 2015). Nearly 12% of all

women will be diagnosed with breast cancer at some point in their lifetime, although 89% of

those women will survive at least 5 years post-diagnosis, up from 75% in 1975 (Howlader N,

2015). In summary, diagnoses of breast cancer are common in the U.S. and worldwide, so there

is great incentive to reduce incidence rates and improve standards of care.

2

As with many cancers, early diagnosis is key to improving prognosis. If a mammogram,

other type of imaging or a physical examination identifies a suspicious lump within the breast, a

biopsy can be taken and analyzed. One of the least invasive ways to do this is with Fine Needle

Aspiration (FNA), where cells are taken from a suspicious lump or area of the breast with a very

thin needle (Willems et al. 2012). It can also be done with the help of MRI or other locating

technology (stereotactic biopsy). Slightly riskier methods include using a vacuum-assisted

needle that gathers samples or a hollow core needle (Dahabreh et al. 2014). Of course, it is

possible to remove some tissue surgically, which might be assisted by needle (wire) or

radioactive seed localization (Diego et al. 2014). With all of these, there is some risk of false

negatives, if normal cells are sampled, or along with tumor cells.

Once biopsies are taken, they can be stained for various proteins and examined under

the microscope. For example, two hormones that play a role in normal breast development and

tumor processes are estrogen and progesterone (Graham & Clarke 1997; Platet et al. 2004).

Quite early on, it was noted that expression of their receptors could be correlated with cancer

prognosis and/or survival (Pike et al. 1983; McGuire 1978). As an example, testing for Estrogen

Receptor (ESR1 or simply ER) status predicates whether treatment with the estrogen antagonist

tamoxifen might be successful (Rutqvist et al. 1989; EBCTCG 1998). Progesterone Receptor

(PR) and the oncogene Epidermal Growth Factor Type II Receptor (EGFR2, also known as

ERBB2/HER2/neu) are the two other receptors routinely tested (Clark et al. 1983; Schechter et

al. 1984; Bargmann et al. 1986). HER2 amplification/overexpression was a prognostic factor

and found in 15-20% of breast cancers (Andrulis et al. 1998; Sjogren et al. 1998; Ross &

Fletcher 1998). As HER2 signals to a large network of genes regulating processes like growth

and the cell cycle progression (see Figure 2), deregulation of HER2 has negative consequences

in various tissues, not just in the breast (Hirsch et al. 2003; Hechtman & Polydorides 2012; Sholl

et al. 2015).

3

Nevertheless, patients with HER2-enriched tumors often have the best prognosis of all

breast cancer patients, due to treatments like Herceptin and its derivatives. Herceptin

(trastuzumab) is a monoclonal antibody targeting HER2 used for adjuvant treatment of

metastatic breast cancers (MBC) overexpressing that gene (Slamon et al. 2001; Slamon et al.

2011; Romond et al. 2005; Piccart-Gebhart et al. 2005). Herceptin not only suppresses HER2

activation and downstream Akt kinase phosphorylation; it also induces antibody-dependent

cellular cytotoxicity (ADCC), stimulating natural killer cells (Arnould et al. 2006; Junttila et al.

2009). And yet, most patients on this treatment eventually relapse. Potential mechanisms

include upregulation of IGF-IR, alterations in the cyclin-dependent kinase inhibitor p27kip1, or

modulation of other signaling pathways, thus subverting the blockade of HER2 (see reviews by

Fiszman and Jasnis 2011; Robinson et al. 2013).

Another antibody, Avastin (bevucimab), is an anti-angiogenesis drug that was shown to

improve progression-free survival in patients (Miller et al. 2007). It was indicated for the

treatment of MBC, including the HER2-negative type, from 2008 to 2011 when its approval was

revoked by the FDA. However, Avastin is still being used to treat other types of cancers, and it

can still be prescribed off-label for MBC. Additionally, Tykerb (lapatinib) is a tyrosine kinase

inhibitor (TKI) of HER2 and EGFR. It can be used in combination with chemotherapy, hormone

therapy or Herceptin (Blackwell et al. 2010; Blackwell et al. 2012; Guan et al. 2013).

Meanwhile, ER-positive tumors have been treated with tamoxifen, an estrogen

antagonist which competitively binds to estrogen receptors (ESR1/ER-α and ESR2/ER-β).

These are both expressed in breast and other tissues, however ER-α has a well-established

role in regulation of breast cancers, whereas the impact of ER-β is less clear (Platet et al. 2004).

ER-α has two activation function domains, AF-1 and AF-2, found at the N terminus and C

terminus of the protein. Agonist binding to ER-α allows for conformational changes leading to a

4

transcriptionally active receptor, whereas antagonist binding reduces coactivator binding and

AF-2-dependent activity (Shiau et al. 1998; Pike et al. 1999). In the case of antagonist binding,

there is some residual agonist activity, although there is reduced ER-dependent gene

transcription as compared to when estrogen binds (Berry et al. 1990; Johnston and Dowsett

2003). Because of this, therapies that fully silence ER transactivation have been developed.

Currently, the 3rd generation of aromatase inhibitors, so named for the enzyme that converts

androgen to estrogen, is used as a first-line approach in ER-positive or PR-positive cancers

(reviewed in Fabian 2007). These do not allow any ER activity, and use of these hormonal

treatments generally leads to improved outcomes when compared to tamoxifen (Coombes et al.

2007; EBCTCG 2016). However, some patients develop resistance to endocrine therapy, for

instance by losing ER expression or activating HER2 signaling (S. R. Johnston et al. 1995;

Sabnis et al. 2010). Such activation of HER2 signaling might be an alternate technique to

strengthen AF-1-mediated transactivation, as it is a known ER cofactor (Johnston et al. 1999;

Schiff et al. 2000).

An inconvenient truth is that mortality rates differ significantly based on the type of breast

cancer a woman is diagnosed with (Dawood et al. 2011; Engstrøm et al. 2013). Even today,

standard chemotherapeutic drugs like Abraxane (paclitaxel) and Halaven (eribulin) are still used

to treat MBC. There is one targeted treatment, an inhibitor of the kinase mTor, that can be used

in combination with an aromatase inhibitor to treat ER-positive, HER2-negative MBCs (Baselga

et al. 2012). However, there are only limited targeted therapies available for triple-negative

breast cancers, which do not appreciable levels of ER, PR, and HER2. These comprise about

15% of breast cancers, are considered an aggressive subtype and are associated with poor

prognosis (see review by Chacon and Costanzo 2010). Therefore, it is of great relevance to

study oncogenic signaling pathways in order to find new drug targets.

5

Breast Cancer: Molecular Subtypes, Grading and Staging, Heterogeneity

In the last few decades, we have gained the ability to scan for certain cancer-associated

mutations or to conduct whole genome sequencing of patient tumors. This may add greater

specificity towards treating individual diseases. Such sequencing of such tumors has generated

a new categorization of tumors, called ‘molecular’ or ‘intrinsic subtypes’ (Perou et al. 2000;

Sorlie et al. 2003; Parker et al. 2009; The Cancer Genome Atlas 2012). These include at least

five subtypes, including basal-like, luminal A, luminal B, HER2-enriched and normal-like

(Wirapati et al. 2008; Reis-Filho & Pusztai 2011). Luminal A and luminal B had distinct gene

patterns (the A subtype might have higher ER-α and GATA3 expression) although they could be

PR-positive or PR-negative. Her2-enriched tumors do not always show receptor positivity based

on immunohistochemistry (IHC) analysis; however these are correlated with worse prognosis

(Parker et al. 2009; Haque et al. 2012). Luminal A usually has low proliferation marker (Ki-67)

expression, while luminal B exhibits high Ki67 - associated with poor prognosis for these

patients (Domagala et al. 1996; Trihia et al. 2003; Cheang et al. 2009). Also, there is some

evidence of a claudin-low subtype, which resembles basal-like breast cancer in that it is

frequently triple-negative (Herschkowitz et al. 2007; Prat et al. 2010; Sabatier et al. 2014).

These cancers typically have low expression of E-cadherin and claudins such as 3, 4 and 7, and

high mesenchymal marker expression, although they are less well-defined than the other five

subtypes. Generally, these molecular-based subtypes are considered more reliable than the

IHC-based method of receptor staining (reviewed in O’Reilly et al. 2015).

Basal-like breast cancers comprise about 15-20% of all breast cancers. Most are triple

receptor-negative, positive for basal keratins, integrin-β4 (ITGB4) and high grade; although 10-

15% do not fit this characterization (Perou et al. 2000; M-J. Kim et al. 2006; Cheang et al.

2008). In Chapter 2, we examine publically available breast cancer data from The Cancer

Genome Atlas (TCGA), and the basal marker p63 is low in all cancers (discussed in depth later

6

in this section). But it is especially interesting that they are lost in basal-like cancers, despite

these cells typically expressing p63. There have been many other reports of low p63 expression

in breast cancers, although the significance of this is largely unknown (Barbareschi et al. 2001;

Wang et al. 2002; D. Stefanou et al. 2004; Matos et al. 2005; Tse et al. 2007; Hanker et al.

2010). As basal-like cancers resemble TNBCs, only more heterogeneous, they are also hard to

treat effectively. It is obvious that we must strive to understand the signaling pathways that can

be targeted in these cancers.

Although sequencing of tumors has become more routine, pathology-based analysis of

cancer is still a main source of clinical information about cancers. A standard is pathological

grading of the tumor, relying on a point system for characteristics such as degree of

differentiation or tubule formation. One such system that was used historically is Bloom-

Richardson, describing Grades I-III in a large cohort of breast cancer patients (Bloom &

Richardson 1957). A recent paper modified this system (now called Nottingham or Elston-Ellis)

and confirmed that grading is correlated to prognosis in a long-term study (Elston & Ellis 1991).

Specifically, grade I (well-differentiated) patients have the longer survival and event-free times,

whereas grade III (low differentiation) is associated with the worst prognosis.

Clinical staging is a different classification that takes the location and spread of the

cancer into account, relying on pathology and imaging technology (see AJCC 7th edition). These

are graded on the TNM system, standing for Tumor, Node, and Metastases. T0-T4 describes

the primary tumor, whether it is ductal or lobular (DCIS or LCIS), how large it is and whether it

reaches the skin or the chest wall (in which case, it is T4). T4d is inflammatory breast carcinoma

(IBC), which is the most aggressive type and may contain dermal lymphatic metastases, also

known as tumor emboli (Rosen 2001). As for N, while N0 indicates no nodal metastasis, N1-N3

indicate axillary lymph node and possibly supraclavicular/infraclavicular lymph node metastasis.

These lymph nodes run from the armpit to the collarbone (clavicle), and can drain lymph fluid

7

from the breast and surrounding areas. Because of the location of the axillary lymph node, right

behind the breast, historically this was the first place metastases were detected (Nemoto et al.

1980; Giuliano et al. 1994). And finally for M, M0 means there are no detectable metastases,

while M1 indicates there is one or more detected (larger than 0.2 mm). Staging (I-IV) takes all of

these into account, although any tumor with M1 is considered Stage IV, regardless of T or N

status. Stage IV breast cancers have the worst prognosis, with a median survival of 26 months -

although advances in imaging and therapy have improved this considerably from several

decades ago (Thomas et al. 2015).

It should be clarified that many of the above studies use endpoints like ‘disease-free

survival’, which encompasses any local or distant evidence of disease, or any death, even if not

attributable to cancer (Hudis et al. 2007). Progression-free survival or recurrence-free interval

would refer to evidence of breast cancer-specific disease or death due to that cancer. Overall

survival is probably the clearest, while another often-used term is ‘pathologic complete

response’ or pCR. This may describe a situation in which there is no residual disease in the

breast or nodes, or minimal non-invasive disease in the breast only (Fisher et al. 1998). pCR

was found to be a good endpoint and statistically higher in luminal B, HER2-enriched and triple-

negative cancers (von Minckwitz et al. 2012; Cortazar et al. 2014). Usually pCR is associated

with longer survival time, unfortunately, only a fraction of patients will attain this state.

A complication in treating cancer is that tumor heterogeneity commonly occurs and adds

difficulty in determining proper treatment regimens (see review by Bedard et al. 2013). There is

intertumor heterogeneity, where patients with the same subtype display large genetic variation.

Then there is intratumor heterogeneity, wherein areas of the same tumor display genetic or

phenotypic differences. Single cell sequencing has been used to detect these differences; and

to follow clonal evolution and oncogenic drivers in primary breast cancers (Shah et al. 2012;

8

Nik-Zainal et al. 2012). Indeed, the first report indicated there is larger intratumor and intertumor

heterogeneity in TNBCs, suggesting these cancers should not be treated as a single entity

(Shah et al. 2012). While clonal evolution contributes to heterogeneity, differentiation of cells

within tumors may be another mechanism (reviewed in Meacham and Morrison 2013).

In addition, genomic instability is frequent in cancers and is likely to contribute to

heterogeneity (see review by Giam and Rancati 2015). This can occur at two levels: nucleotide

or chromosome instability, where large sections of chromosomes may be gained or lost and

rearrangements may take place. Chromosomal instability (CIN) often leads to aneuploidy,

where total chromosome numbers are abnormal. CIN has been correlated to poor prognosis,

but with aneuploidy, correlation with prognosis is less clear (McGranahan et al. 2012).

Additionally, localized regions undergoing high rates of mutations were discovered in breast

cancers (Nik-Zainal et al. 2012; Alexandrov et al. 2013). Recently, a new mutational event has

been described, termed ‘chromothripsis’, wherein large amounts of chromosomal

rearrangements occur in a single event (Stephens et al. 2011; Rausch et al. 2012). So far, only

a subset of cancers have been found to undergo this process, but this might explain the greater

aggressiveness in cancers where this is prevalent.

Finally, therapies can provoke larger tumor heterogeneity, which may be correlated to

poor prognosis (Robinson et al. 2013; Janiszewska et al. 2015). This is called temporal

heterogeneity, and it suggests that comparison to patient samples from before treatment may

be irrelevant in some cases. This is a conundrum in cancers where it is not easy to get a biopsy

after each treatment regime, or there is a slight risk associated with such procedures. Hence,

there is a need for less invasive assays that can paint a clear picture of what is happening

during tumor progression in vivo.

Circulating tumor cells (CTCs) have been isolated from patient cancers, and these

behaved like cancer stem cells (CSCs) in that they were able to form tumors in mice (Baccelli et

9

al. 2013). CTCs show promise for the tracking of tumor progression. For instance, they can be

analyzed for HER2 status after treatment with Herceptin, so treatment can be adjusted if

necessary (Munzone et al. 2010). Many assays are being developed to capture or detect CTCs,

and to see whether biomarkers correlate to various endpoints (reviewed in Bystricky and Mego

2015). Some groups have even developed PCR-based methods to assay circulating tumor DNA

(ctDNA) from small cohorts of breast cancer patients undergoing treatment (S-J. Dawson et al.

2013; Olsson et al. 2015). In these cases, levels of ctDNA predicted relapse and disease-free

survival, suggesting that they are excellent biomarkers. In another report, similar to the work

with HER2 tracking above, specific patient mutations was tracked via ctDNA sequencing

(Garcia-Murillas et al. 2015). This group demonstrated that such mutation tracking indicated

whether treatment was effective; as mutation abundance was correlated with clinical relapse.

Clearly, these technologies are promising and can give us more tools with which to fight cancer.

The needle has shifted towards personalized/precision medicine, which relies on

sequencing of patient tumors. Disease stratification by mutations will certainly provide more

information about proper courses of treatment. However, the data is mixed as to whether

targeted treatment leads to improved response rate (see review by Chen and Bedard 2013). In

some trials, specific treatments add improvement in endpoints measured, while in others they

do not (Gelmon et al. 2011; Andre et al. 2013; Bieniasz et al. 2015). So far, many of the trials

use inhibitors of common alterations, like the ones discussed below.

Mutations frequently found in breast and other cancers

A gene closely associated with breast cancer is BRCA1, for ‘breast cancer 1’. Mutations

in BRCA1 and another gene BRCA2 were first identified in familial breast cancers, although

perturbations in these also occur rarely in sporadic cases (Hall et al. 1990; Easton et al. 1993;

Futreal et al. 1994; Rebbeck et al. 1996). These genes are tumor suppressors involved in repair

10

pathways like double-strand break and nucleotide excision, such that alterations in these lead to

genomic instability (Sharan et al. 1997; Scully et al. 1997; Shen et al. 1998; Xu et al. 1999;

Hartman & Ford 2002). Additionally, they modulate cell cycle progression by interacting with

proteins like cyclin E1 (CCNE1) and p21 (CDKN1A), among many others (Hakem et al. 1996;

Somasundaram et al. 1997; Xu et al. 1999). Interestingly, BRCA1 mutation carriers were more

likely to present with the basal-like breast cancer subtype (Sorlie et al. 2003).

A key receptor discussed earlier, EGFR2 forms heterodimers with other EGFR family

members, leading to downstream signaling through the Ras pathway. A major effector

downstream of Ras is the PI3K/Akt pathway. There are 8 mammalian PI3K enzymes, however

four of them fall into class I PI3Ks, each of them playing a role in tumorigenesis (see review by

Fruman and Rommel 2014). While all PI3Ks can synthesize phosphoinositides, class I alone is

able to generate phosphatidylinositol-3,4,5-trisphosphates (PIP3) (reviewed in Jean and Kiger

2014) . The four class I members are p110α, p110β, p110γ, and leukocyte-specific p110δ, and

of these p110α (PIK3CA) mainly receives signaling input from Ras. In mammals, any of the

catalytic subunit p110s can form a complex with one of five regulatory subunit p85s (Carpenter

et al. 1990; Holt et al. 1994). Upon stimulation, these complexes are brought to the membrane,

where they can catalyze formation of phosphatidylinositol (4,5)-bisphosphates (PIP2) to PIP3.

Opposite of this is the phosphatase PTEN, which converts PIP3 back to PIP2. Mutations in

PIK3CA and PTEN are frequently found in breast cancers, especially those of the basal variety

(Depowski, Rosenthal, and Ross 2001; Samuels et al. 2004; Wu et al. 2005; Stemke-Hale et al.

2008; Cancer Genome Atlas 2012). The two most common hotspot mutations in PIK3CA

include H1047R and E345K, which lead to increased Akt kinase activity and transformative

properties in mammary cell lines (Zhao et al. 2005; Isakoff et al. 2005).

Various PI3K inhibitors, including buparlisib (BKM120), a class I phosphoinositide-3-

kinase inhibitor, are undergoing trials in breast cancer patients, particularly those with PIK3CA

11

or PTEN mutations. However, most trials have reported only limited clinical activity of these

inhibitors, with the exception of the p110δ inhibitor Zydelig (idelalisib) which gained approval in

blood cancers (reviewed in Fruman and Rommel 2014). Additionally, mutations in these genes

do not seem to predict prognosis, at least as reported in the trials so far. It is possible that using

isoform-specific inhibitors or combination therapies will be more successful in staving off PI3K-

dependent cancers (see review by Thorpe, Yuzugullu, and Zhao 2015).

The tumor suppressor p53 is often mutated in a range of cancers, including a quarter of

breast tumors (Hollstein et al. 1994; Petitjean et al. 2007). Loss of an allele or mutation of this

gene generally leads to tumor-promoting activity (see reviews by Ko and Prives 1996; J. Liu et

al. 2014). Along with this, there is an increased likelihood of eliminating the remaining allele

(called loss of heterozygosity or LOH). This likely explains why TP53 mutation status is a

prognostic factor in breast cancer – that and the finding that mutations were frequently found in

the most aggressive subtypes, basal-like and HER2-enriched (Bergh et al. 1995; Langerød et

al. 2007). The recent TCGA study suggested p53 function was often lost in basal-like breast

cancers (nonsense and frameshift mutations), whereas mutant p53 (missense mutations) might

be more prevalent in the other subtypes (Cancer Genome Atlas 2012). The reason behind this

is unclear, but it might be speculated that different cancers have a selective growth advantage

in the context of mutant p53 or lacking p53 altogether. For instance, lymphomas and sarcomas

commonly occur in mice homozygous for p53 deletion, whereas the p53mut mice display a more

varied tumor spectrum (Donehower et al. 1992; Jacks et al. 1994).

The main function of p53 is to act as a DNA damage sensor, minimizing damage to the

genome (reviewed in M. R. Junttila and Evan 2009). It becomes stabilized after stresses

including damaging drugs, nutrient deprivation, and others. These differentially influence post-

translational modification of p53, contributing to greater stability (see review by Lavin and

12

Gueven 2006). Opposite of this, the E3 ubiquitin ligase Mdm2 targets p53 for degradation, and

the two form an autoregulatory feedback loop (Prives 1998; Wade et al. 2013).

Germline mutations in hotspots like R248 and R273 are often found in patients with Li-

Fraumeni syndrome, a familial cancer syndrome marked by early onset of various malignancies

(Frebourg et al. 1995; Varley et al. 1997). There are at least two major classes of p53 mutations

– the two above are ‘contact mutants’ since they affect core domain-DNA interactions (Cho et

al. 1994). Meanwhile R175, R249 and other hotspots are ‘conformational’ mutants leading to

local or global structural deviations from WT-p53 (Sigal & Rotter 2000). Even within the same

class, there are differences in transactivation abilities and properties including cooperation with

Ras (Weisz et al. 2007; Solomon et al. 2012). Also, mutant p53 has been shown to have gain-

of-function effects, which may subvert the effects of WT-p53 (reviewed in H. Song et al. 2007;

Muller et al. 2014). Among these are transactivation of genes involved in angiogenesis,

inflammatory pathways and metabolism (Dittmer et al. 1993; Yeudall et al. 2012; Zhang et al.

2013; Zhao et al. 2015). Furthermore, work from our lab identified mutant p53-specific effects

upon the sterol biosynthesis and chromatin remodeling pathways (Freed-Pastor et al. 2012;

Pfister et al. 2015). All of these effects could explain why this gene is frequently altered in breast

and other cancers (Cancer Genome Atlas 2012; Kandoth et al. 2013).

Finally, retinoblastoma 1 (RB1) is a tumor suppressor frequently altered in cancers, and

especially in breast cancers of luminal B and TNBC subtype (Lee et al. 1988; T’Ang et al. 1988;

Jiang et al. 2010). Inactivating mutations, usually deletions, led to predisposition to

retinoblastomas, hence its name (Sparkes et al. 1980; Sparkes et al. 1983). The protein, termed

pRb, forms complexes with E2F family members and controls aspects of the cell cycle, affecting

tissue homeostasis, erythroid development, and many other dependent processes (reviewed in

H. Liu et al. 2004). Deregulation of RB1 also leads to chromosomal instability, which is likely

13

caused by mitotic defects (Sage & Straight 2010; Nath et al. 2015). Interestingly, targeting of

both RB1 and p53 has suggested a cooperative effect in tumorigenesis (Williams et al. 1994;

Meuwissen et al. 2003). Additionally, there is crosstalk between many of the above pathways,

which can have wide-ranging effects on tumorigenesis (Sherr & McCormick 2002; Scata & El-

Deiry 2007).

An Introduction to Epithelial-to-Mesenchymal Transition (EMT)

The original hallmarks of cancer, as written by Hanahan and Weinberg in 2000, detailed

six capabilities that cancers acquire over the course of tumorigenic development (Hanahan &

Weinberg 2000). These include evading cell death and senescence, while sustaining chronic

proliferation, which can be achieved by deregulation of growth suppressive pathways. Increased

angiogenesis and invasive/metastatic abilities are also traits of the most aggressive cancers.

Such qualities were well-substantiated by the time the review was written, but of course the field

has evolved considerably since then.

Based on work done largely during this century, an update in 2011 gave us an additional

two ‘emerging hallmarks’ of cancer: metabolic reprogramming and evasion of immune

surveillance (see Figure 3) (Hanahan and Weinberg 2011). Tumor masses often initiate a

metabolic switch, relying on increased aerobic glycolysis and secreting lactate - called the

Warburg effect (Warburg 1956; Christofk et al. 2008; Hsu & Sabatini 2008). Cancer cells

increase glucose uptake and usage, using this to generate NADPH and a steady metabolite

pool (DeBerardinis et al. 2007; DeBerardinis et al. 2008; Boroughs & DeBerardinis 2015).

Additionally, there is evidence that a functional immune system has multiple mechanisms to

keep tumors in check (reviewed in Zou 2005). Many tumors show evidence of T cell-infiltration,

and the presence of these cells within a tumor often correlates with clinical response (L. Zhang

et al. 2003; Mahmoud et al. 2011). However, tumors can suppress such infiltrating T cells, as

14

well as disrupt T cell homing by deregulating chemokine expression (Curiel et al. 2004; Pivarcsi

et al. 2007; Molon et al. 2011). Along with these, there are other pathways tumors have evolved

to bypass immune surveillance (reviewed in Gajewski et al. 2013).

A key question is how do cancers acquire these capabilities? Some may gain increased

invasive and proliferative abilities by undergoing a program called “epithelial-to-mesenchymal

transition” or EMT. Cells at the invasive front (the interface between normal stroma and the

tumor) may go through this transition, to better migrate and disseminate (see review by Brabletz

et al. 2005). Cell-cell attachment is mediated by factors including cadherins, which are Ca2+

transmembrane receptors (reviewed in Stemmler 2008). When extracellular Ca2+ is available,

cadherins can form dynamic interactions with a range of molecules including catenins (α, β, γ

and p120), anchoring the cytoskeleton to the plasma membrane (Aberle et al. 1996; Kobielak

and Fuchs 2004). In this manner, E-cadherin can provoke assembly of adherens junctions and

tight junctions, promoting close cell-cell contact (Gumbiner 2005; Hartsock & Nelson 2008). The

cells grow closely to one another and resemble a cobblestone pattern when confluent.

The transition process is characterized by loss of the classical Type I cadherin, epithelial

cadherin (E-cadherin, CDH1). Once this happens, neuronal cadherin is induced (N-cadherin,

CDH2) along with other mesenchymal markers (Hennig et al. 1995; Perl et al. 1998; Thiery

2002). This process is also known as cadherin switching. E-cadherin acts as an invasion

suppressor, possibly through its stabilizing interaction with β-catenin (Meiners et al. 1998; J. Luo

et al. 1999; Wong and Gumbiner 2003). Perturbation of E-cadherin generally leads to disruption

of the junctions, so that cells are no longer tightly attached. In addition, apical-basal polarity, a

hallmark of epithelial cells and required for the correct patterning of tissue layers, is lost

(reviewed in R. Y. Huang et al. 2012). These activities are dependent specifically on E-cadherin.

How does loss of E-cadherin occur? Older studies suggested it was due to mutations

within the gene, or possibly promoter hypermethylation (Graff et al. 1995; Berx et al. 1998).

15

However, the recent TCGA study suggested that the CDH1 gene is altered in only 7% of breast

cancers, although germline mutations are slightly more common in lobular breast cancers

(Cancer Genome Atlas 2012; J. S. Ross et al. 2013). Currently, it is accepted that in the majority

of cases, E-cadherin expression is silenced by a range of transcription factors. This may be

modulated by signaling pathways like transforming growth factor β (TGF-β), fibroblast growth

factor receptor (FGFR), Notch, and Hedgehog, among others (reviewed in the next section).

These pathways all lead to repression of E-cadherin, and subsequently mesenchymal transition.

What is the function of N-cadherin? Although it is able to bind to many of the molecules

as E-cadherin, it acts as a weaker intracellullar adhesion system (Monier-Gavelle & Duband

1995; Chu et al. 2006). Cells expressing N-cadherin are more motile and invasive, even if they

also have E-cadherin (Nieman et al. 1999; Hazan et al. 2000; J-B. Kim et al. 2000). This may be

due to increased levels of cytokines such as interleukin-6 (IL6) and matrix metalloproteinases

(MMPs) like MMP-9 (Suyama et al. 2002; Sullivan et al. 2009). Cancer cells typically secrete

IL6, which plays a role in the immune response as well as migration and invasive ability

(Boxman et al. 1993; Chavey et al. 2007). High levels of MMPs have also been linked to

invasive properties and stimulation of angiogenesis (reviewed in Folgueras et al. 2004). MMPs

function as zinc-dependent endopeptidases that cleave ECM components, thereby participating

in tissue remodeling and modulation of the immune response. Besides MMP-9, MMP-2 and

MMP-14 (also called MT1-MMP) have been connected to breast cancer, although there are

many other family members, some of which are not increased in cancers (reviewed in Brown

and Murray 2015). Together with expression of N-cadherin, these factors allows cancer cells to

gain greater migratory, invasive and metastatic abilities (Hazan et al. 2000; Derycke & Bracke

2004; Brown & Murray 2015). It should be noted that N-cadherin is normally present in

16

mesodermal tissues including the brain, where it affects neurite outgrowth and synapse

plasticity (Paradies & Grunwald 1993; Jungling et al. 2006).

Along with upregulation of N-cadherin, other typical mesenchymal markers include

fibronectin, vimentin, and α-smooth muscle actin (αSMA, transcribed from the gene ACTA2).

Different combinations of these genes, along with other markers, may be induced depending on

the setting (reviewed in Lamouille et al., 2014). Mesenchymal cell migration is aided by

fibronectin-dependent processes, like integrin and actin binding (Ridley et al. 2003; Veevers-

Lowe et al. 2011). Vimentin is an intermediate filament protein of fibroblasts, also linked to cell-

matrix interaction and focal adhesion dynamics (Ivaska et al. 2007). Perhaps because it

mediates these processes, a distinct morphology and increased motility are seen in the event of

vimentin expression (Hendrix et al. 1997; Mendez et al. 2010). Meanwhile, αSMA is present in

stress fibers of fibroblasts, mediating ECM remodeling to allow cells greater contractibility (Hinz

et al. 2001; Kondo et al. 2004).

It should be noted that EMT can occur normally in development, such as at the

gastrulation stage (Nakaya & Sheng 2008). In metazoans (kingdom Animalia), this process

gives rise to three distinct germ layers (endoderm, mesoderm and ectoderm) which will form all

the tissues of the body. The mechanisms for this vary across animals, but the similarity is that

the epitheliod ectoderm gives rise to a mesenchymal-like mesoderm after a morphogenetic

reorganization of the embryo (Morali et al. 2000; Ohta et al. 2007). The reorganization step may

involve one or several of the following: involution, invagination and ingression. These refer to

the precise movement of the cells involved. Additionally, EMT is essential during normal organ

formation (Tam et al. 1997; Rivera-Feliciano et al. 2006).

There are at least three types of EMT, differing on context (reviewed in Lamouille et al.

2014). Type 1 covers development, including embryogenesis; as described above. Type 2

occurs in tissue homeostasis, and it includes endothelial-to-mesenchymal transition (EndMT), a

17

similar process that generates fibroblasts leading to cardiac or renal fibrosis (Zeisberg et al.

2007a; Zeisberg et al. 2008). Type 3 takes place in cancer settings, and has been linked to

increased migratory and invasive properties, along with self-renewal capabilities (Friedl and

Wolf 2003; L. Wan et al. 2013; Clark and Vignjevic 2015). In Type 3 EMT, the gain of N-

cadherin and the corresponding dispersed phenotype allows cancer cells greater invasive

abilities (Frixen et al. 1991; Vleminckx et al. 1991). A key step of the metastatic cascade is

intravasation; the process wherein primary tumor cells migrate through the basement

membrane into the blood vessels (see Figure 4). EMT-enhanced invasive abilities likely

contribute to cancer cell intravasation, and possibly other steps of the metastatic cascade

(reviewed in Kalluri and Weinberg 2009, Bravo-Cordero 2012).

It should be noted that EMT is usually reversible. There are many reports of in vitro EMT

being revoked by drugs targeting the MAPK pathway, ROCK, and other important kinases

(Janda et al. 2002; Rhyu et al. 2005; Das et al. 2009). Plasticity of this process is thought to be

required for cells to undergo the opposing process, mesenchymal-to-epithelial transition (MET)

once within the target organ. This is a logical reason for why it is hard to track such cells in

metastatic tumors (reviewed in Brabletz et al. 2005; Gunasinghe et al. 2012). The evidence for

MET at metastatic sites is limited, but includes re-expression of E-cadherin, allowing renewed

cell-cell adhesion and proliferation (Oltean et al. 2006; Y. L. Chao et al. 2010; Y. Chao et al.

2012; Wendt et al. 2011). On the other hand, some studies suggest EMT is altogether

dispensible for metastasis, but it mediates chemoresistance and stemness (Steinestel et al.

2014; Fischer et al. 2015; Zheng et al. 2015).

Cells also undergo EMT to escape immune surveillance, another hallmark of cancer

mentioned above. HER2-driven tumors underwent immunoediting, as defined by loss of

antigenic variants and MHC expression (Knutson et al. 2006). Targeted immunoediting using

HER2/neu-treated dendritic cells in DCIS patients also affected their immune response

18

(Czerniecki et al. 2007). Snail-induced EMT was shown to subdue the immune system by

inducing suppressor T (Treg) cells and TSP1, an inhibitor of dendritic cells (Kudo-Saito et al.

2009). Gaining a mesenchymal phenotype also protected cells against lysis by cytotoxic T

lymphocytes (Akalay et al. 2013). These results and others suggest that the EMT pathway holds

many advantages for cancers, allowing them to proliferate free of immune regulation.

Regulators of EMT

There are several canonical transcription factors known to repress E-cadherin by binding

to E-boxes within its promoter - including Snail, Slug, and Zeb1 (Cano et al. 2000; Comijn et al.

2001; Hajra et al. 2002). Twist and FoxC2 are also EMT inducers, although they indirectly

repress E-cadherin (Yang et al. 2004; Thiery et al. 2009). Interestingly, some factors like Twist

and Snail were first discovered in Drosophila melanogaster, the common fruit fly, where they are

required for dorsoventral patterning of the embryos (Simpson 1983). Deletion of either of these

genes is lethal at the embryonic stage, as gastrulation does not occur (Nüsslein-Volhard et al.

1984). Slug (SNAI2, or escargot in D. melanogaster) belongs to the Snail family and is required

for mesoderm formation and neural crest development in some animals (Nieto et al. 1994; R.

Mayor et al. 1995). Interestingly, mutant p53 can activate Slug and Twist, while there seems to

be a negative feedback loop between WT-p53 and Twist (Shiota et al. 2008; Wang et al. 2009;

Kogan-Sakin et al. 2011).

Some recently discovered factors include YB-1, which allows for increased translation of

Slug, Zeb2 and other canonical TFs, thereby inducing EMT (Evdokimova et al. 2009; Khan et al.

2014). Lbx1 can similarly induce Snail, Zeb1 and Zeb2; increasing the stem cell/progenitor

population (Yu, Smolen, et al. 2009). And FoxC2, mentioned earlier, is a member of the

forkhead family of transcription factors, affecting EMT through PDGFR-β transactivation and

likely other methods (Mani et al. 2007; Hollier et al. 2013). This gene was reported to be highly

19

expressed in the claudin-low subset of basal-like breast cancers (Hollier et al. 2013).

Meanwhile, the Six1 transcription factor strengthens TGF-β-dependent signaling, leading to

mesenchymal transition and increased metastasis in vivo (Micalizzi et al. 2009; McCoy et al.

2009). Additional new inducers of EMT are still being elucidated.

Many microRNAs regulators have also come to light, including miR-34a and miR-200

family members (de Craene 2013, Jolly 2015). MiR-34a is a well-known p53 target and acts as

a tumor suppressor to induce cellular outcomes like arrest or apoptosis (Chang 2007, Tarasov

2007). A recent report indicated miR-34a normally represses Snail, and by compromising p53 or

miR-34a, Snail-dependent EMT is induced (Kim 2011). This microRNA is also known to interact

with pluripotency factors like Nanog, Sox2 and N-Myc, along with Wnt1 and β-catenin, thereby

affecting processes like EMT (Choi 2011, Kim 2011). As it involves these stem-like elements

and phenotypic switching, EMT can be considered akin to transdifferentiation (Jopling 2011).

Members of the miR-200 family, including miR-141, miR-200b, and miR-200c are well-

known EMT suppressors, acting to inhibit Zeb1 and Zeb2 (Gregory et al. 2008; Korpal et al.

2008; Park et al. 2008). MiR-203 and miR-205 also possess these abilities (Tucci et al. 2012;

Tran et al. 2013; Moes et al. 2012). In these cases, overexpression of one of these miRNAs was

sufficient to repress EMT, suggesting that they are quite powerful regulators. MiR-203 even

exists in a feedback loop with Snail, and some miR-200 family members are reciprocally

regulated by Zeb1 (Burk et al. 2008; Wellner et al. 2009; Moes et al. 2012). Additionally, these

miRNAs are targets of p53 (T. Kim et al. 2011; Chang et al. 2011). Furthermore, miR-192 is a

mediator of TGF-β-mediated EMT via Zeb2 (T. Kim et al. 2011; Hong et al. 2013). MiR-145 is

another p53 target that plays a role in the mesenchymal transition, leading to pancreatic cancer

invasiveness (Ren et al. 2013). In summary, microRNAs are powerful regulators of EMT, and

they may act alone or in concert with other factors to drive phenotypic switching.

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One of the major signaling pathways leading to EMT is the TGF-β pathway. TGF-β

comprises a family of serine/threonine kinase receptors, and alteration of these is associated

with developmentally induced- and cancer associated-EMT induction (reviewed in Thiery et al.

2009). In the canonical TGF-β pathway, ligand binding to a complex of receptors allows for

downstream phosphorylation of transcription factors Smad2 and Smad3 (reviewed in Derynck

and Zhang 2003). These activated Smads form a complex with Smad4 and are translocated to

the nucleus, where they can activate or repress target genes. Transfection of either receptors I,

II or III, or downstream Smad2 or Smad3, is sufficient to elicit EMT, suggesting that these

Smads are key mediators of this pathway (Valcourt et al. 2005). The Smad3-Smad4 complex

may even act as a cofactor for certain EMT regulators to provoke this effect (Vincent et al.

2009). Also, the TGF-β pathway has extensive crosstalk with PI3K and MAPK signaling,

suggesting additional sources of regulation (Bakin et al. 2000; Derynck & Zhang 2003).

Several other growth factor pathways, including FGFR, can induce EMT possibly

through factors such as Slug and Notch1 (Boyer and Thiery 1993; Savagner et al. 1997; Billottet

et al. 2008). FGFR signaling mediates cell survival and angiogenesis, among other processes

(reviewed in Turner and Grose 2010). Some FGFR members are also altered in cancers,

particularly of the breast (Luqmani et al. 1992; Relf et al. 1997). In fact, SNPs within FGFR2 are

strongly associated with breast tumors, indicating that this gene contributes to oncogenesis

(Hunter et al. 2007; Easton et al. 2007).

Meanwhile, Notch signaling directs cell fate in metazoan development; harnessing both

proliferative and growth-suppressive effects to achieve correct patterning of the organism

(Artavanis-Tsakonas et al. 1999). After ligands such as Jagged-1 or Delta-like 1 bind to the

Notch receptor, cleavage results in an active fragment, Notch intracellular domain (NICD)

(Blaumueller et al. 1997). NICD is translocated to the nucleus, activating many downstream

21

targets such as the HEY family of transcription factors, Gata3, c-Myc, and others (reviewed in

Borggrefe and Oswald 2009). It can also interact with Snail and Slug in mediating mesenchymal

transition, even cooperating with TGF-β for this effect (Zavadil et al. 2004; K. G. Leong et al.

2007; Sahlgren et al. 2008). Besides this, Notch signaling is linked to hypoxia, and some reports

suggest hypoxia induces Notch-mediated EMT (Gustafsson et al. 2005; Sahlgren et al. 2008;

Zheng et al. 2008; Xing et al. 2011).

Hedgehog (HH) signaling also works to regulate cell fate and patterning, and is essential

for normal mammary gland development (Nusslein-Volhard & Wieschaus 1980; Lewis et al.

1999; Michno et al. 2003). This pathway is associated with proliferation control, especially in the

context of stem/progenitor cells (Liu et al. 2006; N. Li et al. 2008; Vied & Kalderon 2009; Tanaka

et al. 2009). Components of the pathway are often altered in breast cancers, and especially in

TNBCs (Tao et al. 2011; Souzaki et al. 2011). As HH ligands are secreted proteins, paracrine

interactions have been studied in many contexts, including the breast (Yu, Gipp, et al. 2009;

Garcia-Zaragoza et al. 2012). In the latter study, a HH ligand is secreted by breast luminal cells

and received by nearby basal progenitor cells, implicating intraepithelial signaling in the

aforementioned processes. There may also be epithelial-stromal HH signaling, affecting tumor

growth and metastasis (O’Toole et al. 2011). HH signaling was shown to enhance EMT and

metastatic growth, possibly through stimulation of the PI3K/Akt pathway (Kawahira et al. 2005;

Morton et al. 2007; Yoo et al. 2011). Other reports suggest that Gli1, a key HH effector, can

repress E-cadherin via induction of Snail (Louro et al. 2002; X. Li et al. 2006).

In summary, there are many pathways that can induce EMT, and some of these act in

concert to drive cells towards the mesenchymal state. This also suggests that the reverse

process can occur via alterations of these pathways. For more about other regulators of this

important process, see the excellent review by Lamouille et al., 2014.

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Breast Cancer Stem Cells, Progenitor Cells, and Mammary Stem Cells

Stem cells are a rare subset of cells capable of self-renewal and differentiation into all

cell types in a particular tissue (see review by Reya et al. 2001). These may be found in a stem

cell niche, which was first described several decades ago (Schofield 1978). The niche is a

specialized microenvironment composed of cells supporting the stem cells, or a mixture of cells

and ECM components (reviewed in Lander et al. 2012). It is also a signaling hub where stem

cells can receive cues from other tissues, thereby influencing whether they stay dormant, self-

renew or differentiate.

Niches for specialized stem cells have been described, such as the niche for hair follicle

stem cells or intestinal stem cells (Oshima et al. 2001; Barker et al. 2007). The hematopoietic

stem cell (HSC) niche, which resides in bone marrow, is by now well-characterized (Lo Celso &

Scadden 2011). Wnt and Notch signaling seem to play a role in regulating the HSC niche, as do

adhesion molecules and surrounding chemicals. Importantly, niches can protect stem cells from

damaging agents and/or immune attack (Eliasson & Jönsson 2010; Fujisaki et al. 2011).

Stem cells have been found to drive both liquid and solid tumors, giving rise to the theory

of cancer stem cells (CSCs) (Bonnet & Dick 1997; Al-Hajj et al. 2003). There is some evidence

that CSCs, also called cancer-initiating cells (CICs) are formed after cells undergo EMT and

gain stem-like markers (Mani et al. 2008; Morel et al. 2008). EMT regulators like Zeb1 and

FoxC2 can regulate factors involved in stemness, including miRNAs (Mani et al. 2007; Wellner

et al. 2009; Hollier et al. 2013). Another report suggested cooperation between Slug and

stemness factor Sox9 can increase the metastatic potential of breast cancer lines (Guo et al.

2012). These two factors can also convert differentiated luminal cells into mammary stem cells

(MaSC), highlighting the similarity between these and breast cancer stem cells (BCSC).

Polycomb group (PcG) proteins like Bmi1 and EZH2 also regulate self-renewal and

tumorigenesis (O’Carroll et al. 2001; Lessard & Sauvageau 2003; Boyer et al. 2006). These act

in Polycomb repressive complexes (PRCs), epigenetic modulators that can silence or activate

23

transcription of certain genes (reviewed in Di Croce and Helin 2013). Bmi1, EZH2 and other

PcG members are often altered in cancers and in CSCs, and have also been found to enhance

chemoresistance (Chase & Cross 2011; Siddique & Saleem 2012; Michalak et al. 2013).

CSCs may reside in typical stem cell niches, or they may produce niche-like proteins

and have the support of nearby factors (reviewed in Oskarsson et al. 2014). Cancer-associated

fibroblasts (CAFs) are recruited to these sites or generated through mesenchymal transition,

aiding in ECM remodeling and tumorigenic processes (Zeisberg, Potenta, et al. 2007; Erez et al.

2010; Dumont et al. 2013). Stromal factors like periostin (Postn) and osteopontin (Spp1) are

essential for metastatic niche colonization, regulating stromal-niche interactions (Malanchi et al.

2012; Sangaletti et al. 2014; Squadrito & De Palma 2015). Macrophages are often recruited,

likely to co-opt the native immune response and to assist in tumor invasion (Condeelis & Pollard

2006; Wyckoff et al. 2007). The EMT program is also associated with niche activation, and vice

versa (Sleeman 2012; Del Pozo Martin et al. 2015). This could be due to niche proteins like

tenascin C, MMPs and the S100 family members aiding metastatic formation (Calvo et al. 2008;

Oskarsson et al. 2011; Lukanidin & Sleeman 2012). In particular, S100A4 is secreted from

tumor cells, upregulating MMPs and angiogenic factors (Bjørnland et al. 1999; Schmidt-Hansen

et al. 2004). Other family members S100A8 and S100A9 prepare the niche by inducing

inflammatory cytokines, attracting myeloid cells (see review by Lukanidin and Sleeman 2012).

As mentioned before, infiltration of tumors with these cells is associated with increased

vasculature and poor prognosis (reviewed in Murdoch et al. 2008).

At the same time, niches are often hypoxic environments, so that stem cells can be kept

dormant and protected from damage (reviewed in Cabarcas et al. 2011). Hypoxia-inducible

factor 1, α subunit (HIF-1α) and its targets, such as Oct4 and Notch, are induced, and maintain

the stem cell pool. ECM remodeling may take place, including upregulation of CXCR4, a

chemokine receptor and CSC homing molecule (Burger & Kipps 2005; Ishikawa et al. 2009).

24

The hypoxic environment allows for increased Vascular Endothelial Growth Factor A (VEGF-A)

and Lysyl Oxidase (LOX) production, driving further vascularization (Erler et al. 2009; Wong et

al. 2011). It was noticed quite early on that CSCs are known to reside near blood vessels or

areas with extensive vascularization, perhaps because of these effects (Bao et al. 2006;

Calabrese et al. 2007). All of this can ensure that the CSCs are in a nutrient-rich environment,

where they may be sheltered from harm.

But, how are BCSCs, in particular, distinguishable from other CSCs? Like other CSCs,

they are generally refractory to treatment (M. Diehn et al. 2009; Smalley et al. 2013). Some

reports indicate BCSCs are CD44-positive and CD24-negative, with high aldehyde

dehydrogenase (ALDH1) activity (Al-Hajj et al. 2003; Ginestier et al. 2007; Honeth et al. 2008).

CD44 and CD24 are not only stem cell surface markers, they regulate cell-matrix interactions,

often facilitating metastases (reviewed in Jaggupilli and Elkord 2012). There is some evidence

that ALDH1, a detoxification enzyme, could play a role in BSCS resistance to chemotherapy

(Sakakibara et al. 2012; Alamgeer et al. 2014). Another possibility is that altered levels of ATP-

binding cassette (ABC) transporters increase drug efflux; however targeting these factors

remains problematic due to their expression in normal tissues as well as cancerous ones

(reviewed in Dean 2009; Choi and Yu 2014). Additionally, various chemokines such as CXCR4,

CXCL7 and IL6 are important regulators/markers of BCSCs (Sansone et al. 2007; Hwang-

Verslues et al. 2009; Liu et al. 2011).

Despite the BCSC model, the cell-of-origin leading to breast cancers is still controversial

(see review by Petersen and Polyak 2010). This may be due to the various strategies/assays

used to isolate and investigate such cells, as well the likelihood that breast cancer subtypes

differ in their originating cells. It is also important to note that the cell-of-origin may or may not

resemble the BCSC, or even the MaSC (Visvader 2011). Most studies endeavoring to isolate

25

such cells use flow cytometry, based on the markers above. Other surface markers that may be

associated with these cells include CD326 (EpCAM), the epithelial cell adhesion molecule, and

CD133 (also known as prominin-1) (O’Brien et al. 2007; Wright et al. 2008; Sarrio et al. 2012).

Additionally there is CD29 (Integrin-β1), which is highly expressed in cap cells of the terminal

end buds, where stem cells reside (Kenney et al. 2001; Shackleton et al. 2006). Finally, Mucin-1

(MUC1) is a glycoprotein prominently expressed in breast cancers and is a marker of BCSCs

(McGuckin et al. 1995; Christgen et al. 2007; Engelmann et al. 2008). Not only can MUC1

enhance ALDH1 activity; it may also heighten Zeb1-dependent EMT (Alam et al. 2013; Rajabi et

al. 2014).

Once a subset of cells have been isolated, it is common to test whether they can

recapitulate mammary architecture in vivo. If the mammary gland is surgically removed from the

fat pads of mice of 3 weeks age, full mammary development will not occur and the fat pads are

considered cleared (Brill et al. 2008; Dunphy et al. 2010). In a pioneering study, transplantation

of mammary tissue into cleared fat pads showed mammary fragments or dissociated cells could

regenerate the full mammary gland (DeOme et al. 1959). This important assay can also be used

to confirm the potential of MaSCs or progenitor cells in an orthotopic model. Seminal studies

have indicated that a small amount of such cells could reconstitute a functional mammary gland

(Kordon & Smith 1998; Shackleton et al. 2006). The latter study found that CD29-high CD24-

positive cells were capable of self-renewal and could form both ductal and lobular tissue in the

fat pad (Shackleton et al. 2006). A very similar study had earlier found that CD44-positive

CD24-negative cells formed tumors when injected into mammary fat pads of mice, and these

cells could be expanded by serial passaging (Al-Hajj et al. 2003). These results and others

would suggest a single lineage of stem cells gives rise to basal and luminal cells, suggesting a

hierarchy in development. This is consistent with reports indicating the bipotent progenitor is

26

positive for Keratin 14 and Keratin 19 (Villadsen et al. 2007; LaBarge et al. 2009). A review

proposed that bipotent progenitors are EPCAM-positive CD133-negtive and CD49f-positive

whilst the luminal and basal progenitors express other markers (Stingl et al. 2005). These

bipotent progenitors may also express the Wnt targets Lgr5 and Notch1, the latter receptor often

being highly expressed in breast cancers (Rios et al. 2014; Rodilla et al. 2015; Yuan et al.

2015).

As for the luminal and basal/myoepithelial progenitors, these are described in other

studies (Smith 1996; Stingl et al. 2005; Van Keymeulen et al. 2011). Consideration of these

various progenitors might explain why there are somewhat conflicting results in this field. Adding

to that, there is also evidence that luminal or basal progenitors can be reprogrammed to the

other type (LaBarge et al. 2009; Van Keymeulen et al. 2015). This might happen as a result of

oncogenic stimulus or microenvironmental changes. Interestingly, studies indicate BRCA1-

associated basal cancers have expanded luminal progenitor cell populations, and that gene

expression profiles of luminal progenitors and basal cancers are quite similar (Lim et al. 2009;

Shehata et al. 2012). This evidence suggests BRCA1-mutated basal cancers actually derive

from luminal progenitor cells (Molyneux et al. 2010), although this may not be the case for other

basal-like breast cancers.

While the mammary fat pad assay is extremely useful, it is not exactly similar to the

human breast. This is evidenced by the fact that some human mammary epithelial cells do not

grow well in cleared fat pads, likely due to stromal interactions (reviewed in Hovey et al. 1999).

Several groups have humanized the mouse fat pad by adding fibroblasts or immune cells like

macrophages, resulting in greater progenitor self-renewal or enhanced tumor outgrowths

(Kuperwasser et al. 2004; Proia & Kuperwasser 2006; Ginestier et al. 2007; Fleming et al.

2012). One group describes another in vivo method where single epithelial cells and fibroblasts

are embedded together in collagen gels, and after some time these were injected under the

27

kidney capsule of hormone-supplemented immunodeficient mice (Eirew et al. 2008). This

causes bilayered ducts to form that resemble the human breast in distribution of proteins and

regenerative potential. MaSCs can also be isolated, suggesting that epithelial-stromal

interactions are key for progenitor cell development (Parmar 2002, Eirew 2008).

Mammospheres can also be grown in gels made of collagen or Matrigel™, a

reconstituted basement membrane secreted by Engelbreth-Holm-Swarm (EHS) sarcomas. This

is an in vitro assay that can enrich for MaSCs (Dontu et al. 2003; Eirew et al. 2008). It is also

feasible to qualitatively tell the differences in lineage of the cells tested (Eirew et al. 2008; Smart

et al. 2013). Moreover, normal mammary cells or breast cancer cells are typically tested in

Matrigel, as they usually grow well in this environment (O. W. Petersen et al. 1992; Janke et al.

2000). Fascinatingly, they are several different morphologies seen in this 3D setting, including

disorganized (grape-like) from the luminal lines and round colonies from most of the basal lines

tested (Kenny et al. 2007).

Additionally, spheroid assays in these 3D environments have yielded different toxicity

data from 2D drug screens. It is commonly thought that 3D assays can better simulate the

normal/tumor microenviroment and in vivo growth (Cioce et al. 2010; Oak et al. 2012). High-

throughput methods have been developed to screen large libraries of compounds, and cell line

growth in 3D can be assessed to optimize treatments (Ranga et al. 2014; Li et al. 2015). It is

noted that the cells become more heterogenous in 3D cultures with varying gene expression

changes, and this, together with the microenvironment, mimics the situation in the human body

(Celli et al. 2010; Smith et al. 2012; Tew & Fisher 2014). The hope is that such screens will

allow for better synchronization with patient response, so that we will have more therapies

available for the average cancer patient.

28

Model cell lines for cancer research

Mammalian cell lines and humanized mice are commonly used in cancer research. As

there are a number of human-derived lines, both normal and tumorigenic, it is typical to use

several of these for any study looking at a purported oncogenic or tumor suppressive effect. For

our work in Chapter 2, well-characterized mammary epithelial lines were utilized. MCF-10A and

MCF-12A are derived from spontaneously immortalized reduction mammoplasty samples from

two separate women (Soule et al. 1990; Paine et al. 1992). These lines are non-tumorigenic and

triple receptor-negative. Because of these attributes, experimental evidence with these lines

may give us insight into patient TNBCs. However, they cannot be considered ‘normal’ cell lines

due to the women having fibrocystic disease, which leads to benign lumps in the breasts

(though this disease is not cancer-associated) (Guinebretière et al. 2005). The MCF-10A line

strongly expresses myoepithelial/basal characteristics, including associated keratins and p63.

The MCF-12A line is reported to represent either a basal population or a mixed basal/luminal

population, depending on the paper (Paine et al. 1992; Subik et al. 2010). Transcriptional

profiling of these cell lines suggests that they most resemble the basal-like subset of cancers,

suggesting that both arise from basal progenitors (Neve et al. 2006; Kao et al. 2009).

Neither the MCF-10A nor the MCF-12A cells form tumors when xenografted into nude

mice, which are engineered to have a defective immune system response. They also do not

form colonies in soft agar. However, several groups have described the characteristics of MCF-

10A cells transformed with H-RasV12, also known as MCF-10AT cells (Miller et al. 1993; Pauley

et al. 1993; Dawson et al. 1996; Santner et al. 2001). When these cells are xenografted into the

nude mice, a subset of the mice present with carcinomas; therefore the MCF10AT line is

thought to closely resemble DCIS, a pre-neoplastic state. Additionally, they exhibit anchorage-

independent growth in soft agar and disorganized stuctures in Matrigel. Similarly, MCF-10A cell

lines expressing hotspot mutations in PIK3CA undergo transformation, exhibiting enhanced

29

proliferation and colony forming ability (Isakoff et al. 2005). A separate derivative cell line

MCF10CA1a had both the H-RasV12 and hotspot PIK3CA mutation, along with other cancer-

associated mutations (Santner et al. 2001; Kadota et al. 2010). This line was much more

oncogenic than the parental MCF-10A cells, forming undifferentiated tumors in nude mice 100%

of the time. Finally, another mutagenic study showed p53 mutations in MCF-10A lines were

associated with anchorage-independent growth and tumor forming ability in vivo (Zientek-

Targosz et al. 2008).

For our work in Chapter 3, NCI-H1299 lung carcinoma cells were used (from here,

H1299). This line was derived from the lymph node metastasis of a patient with large cell

carcinoma (Maneckjee & Minna 1990; Giaccone et al. 1992). It is positive for vimentin and has a

homozygous deletion in p53, so that it lacks full-length p53 protein (Bodner et al. 1992). This

must be an important growth requirement for these cells, as re-expressing p53 here leads to

growth arrest, or inhibition of colony formation (Zakut & Givol 1995; Wang et al. 1998). So, in

order to avoid senescence or arrest in these cells, we chose to use the tetracycline-inducible

system to transiently express p53 in this background. These systems allow for the tight control

of a protein of interest, by regulating the levels of tetracycline antibiotic in the media (Gossen &

Bujard 1992; Gossen et al. 1995).

An introduction to the p53 family, and p53 target genes

The p63 gene was the most recently discovered among its family members, p53 and

p73, although an ancient gene resembling p63/p73 actually predates the appearance of a p53-

like gene (reviewed in A. Yang et al. 2002; Belyi et al. 2010). The ancestral p63/p73 gene

seems to be important for germline integrity; and gene duplication into the separate genes is not

30

seen until cartilaginous fish appear. However, the p53-like gene does not appear until the

advent of bony fish like zebrafish, and continuing through vertebrates, some of the family

members take on novel functions (Belyi et al. 2010, Yang et al. 1999, Mills et al. 1999, Yang et

al. 2000). In this vein, analysis of the DNA-binding domains of these proteins demonstrates p53

has changed considerably throughout its evolution in vertebrates, whereas p63 evolved the

least and p73, an intermediate amount.

There are general processes that each family member regulates, such as cell death,

senescence and proliferation (Osada et al. 1998; M Dohn et al. 2001). This is not surprising as

they share a similar modular architecture, with an N-terminus consisting of a transactivation

domain (TAD), DNA-binding domain (DBD), and proline-enriched region. The C-terminal domain

(CTD) also contains an oligomerization domain (OD), required for stabilization of the proteins

(Sturzbecher et al. 1992; Iwabuchi et al. 1993). The p63 and p73 genes additionally contain a

sterile α-motif (SAM) domain and the transcription inhibition domain (TID) at the C-terminal end.

P53 and its family members regulate genes by binding to response elements usually

within the promoters of genes, although there are some exceptions (Riley et al. 2008). This

significant review also describes how p53 can acts as transcriptional activator, by recruiting

histone acetyltransferases (HATs) to the promoter, or as a repressor, through various

mechanisms. For instance, p53 can inhibit transcription factors like Sp1 and CAAT enhancer-

binding protein α (CEBPA) and β (CEBPB), repressing genes they control like albumin and

cyclin B1 (Kubicka et al. 1999; Innocente & Lee 2005). Binding of p53 can also result in

recruitment of histone deacetylases (HDACs), leading to the repression of genes like

microtubule-associated protein 4 (MAP4) and stathmin (STMN1) (Murphy et al. 1999).

Usually, the p53 binding site is composed of two half-sites with the sequence 5'-

PuPuPuC(A/T)(T/A)GPyPyPy-3' separated by a variable spacer, although some genes contain

many half-sites within close proximity (termed ‘cluster sites’) (Riley et al. 2008). Near-perfect

31

sites exist within the promoter of the p21 (CDKN1A) gene, a canonical p53 target also regulated

by BRCA proteins (el-Deiry et al. 1993). The protein p21 can bind to various cyclin-dependent

kinases (CDKs) and inhibit phosphorylation of Rb, thus limiting cell cycle progression (Harper et

al. 1993). Interestingly, later work identified the necessity of p21 in facilitating p53’s repression

of certain target genes like survivin (BIRC5) (Löhr et al. 2003). Another G1/S transition regulator

is B-cell translocation gene 2 (BTG2), a member of the BTG/Tob family (Rouault et al. 1996).

This family acts through various transcription factor complexes, altogether having an

antiproliferative effect (Bogdan et al. 1998; Prevot et al. 2000). However GADD45A (growth

arrest and DNA damage-inducible protein 45, alpha) leads to G2/M arrest, partly through

inhibition of the Cdc2/Cyclin B1 complex (Kastan et al. 1992; Wang et al. 1999; Zhan et al.

1999). The microRNA mentioned in an earlier section, miR-34a, is also a p53 target and can

trigger apoptosis, arrest, and other outcomes (reviewed in Misso et al. 2014).

In addition, there are pro-apoptotic p53 targets such as Puma, Noxa, and Bid, all

members of the Bcl-2 family (Oda et al. 2000; Nakano & Vousden 2001; Sax et al. 2002).

Usually, a combination of these are induced, leading to mitochondrial outer membrane

permeabilization and caspase activation (reviewed in Vaseva and Moll 2009). Some evidence

points to these factors being key to p53’s tumor suppressive abilities, as their deficiency was

found to accelerate Myc-driven lymphomas (Garrison et al. 2008; Michalak et al. 2009).

However, there is also data suggesting p53 has anti-tumorigenic effects outside of those

mediated by p21, Puma and Noxa (Jiang et al. 2011; Valente et al. 2013).

Certain p53 targets can affect the redox state of the cell. The aptly named TP53-

induced Glycolysis and Apoptosis Regulator (TIGAR) can reduce ROS levels, thus modulating

the apoptotic response (Bensaad et al. 2006). Other p53 direct targets like Sestrin1 (SESN1)

and Sestrin 2 (SESN2) also can protect cells from high ROS and chemotherapies (Velasco-

32

Miguel et al. 1999; Budanov et al. 2002). The mitochondrial glutaminase 2 (GLS2) not only

controls ROS levels, it also alters energy metabolism by increasing ATP generation (Hu et al.

2010; Suzuki et al. 2010). These diverse properties of p53 have been shown to defend against

in vivo tumorigenesis (Sablina et al. 2005; J. Liu, Zhang, Lin, et al. 2014; Bartesaghi et al.

2015).

The control of autophagy, a self-digestive form of cell death and organismal survival

mechanism, may also be taken into consideration. DRAM (damage-regulated autophagy

modulator) was found to be required for p53-induced autophagy (Crighton et al. 2006). Control

of SESN2 and kinases Ulk1 and Ulk2 also contribute to regulation of this process (Maiuri et al.

2009; Gao et al. 2011). Opposite of this, cytoplasmic p53 actually inhibits autophagy, possibly

via interaction with RB1CC1 (RB1-inducible coiled-coil protein 1) or other autophagy proteins

like Atg5 or Atg10 (Tasdemir et al. 2008; Morselli et al. 2011). In summary, p53 is an influential

transcription factor, and its myriad impacts on cellular processes and outcomes are still being

illuminated.

The p53 protein undergoes many post-translational modifications

As written earlier, p53 becomes stabilized after various stress stimuli, aided by post-

translational modifications (PTMs). These increase tetramerization and the stability of tetrameric

p53, allowing it to act as a nuclear transcription factor and activate expression of a wide variety

of genes. Among the factors acting upon p53 are ATM (ataxia telangiectasia -mutated) kinase,

which phosphorylates several N-terminal serines of p53 after radiation treatment (Banin et al.

1998; Saito et al. 2002). Some of these serines are also phosphorylated by p38 kinase,

especially after radiation (Bulavin et al. 1999). Various damaging agents lead to Jun NH2-

terminal kinase (JNK) phosphorylation of Thr-81 (Buschmann et al. 2001). Homeodomain-

interacting protein kinase-2 (HIPK2) can phosphorylate Ser46, affecting the stability of p53

33

(D’Orazi et al. 2002; Stefano et al. 2004). As these sites are within the transactivation domain of

p53, transcriptional activity is altered in these cases.

The p53 CTD is also subjected to various modifications that can affect transcriptional

activity, and sometimes stability of the protein itself. Early work showed that p53 forms a

complex with the acetyltransferases p300 and CREB-binding protein (CBP), and these can

acetylate lysines within p53 (Gu et al. 1997; Avantaggiati et al. 1997). Another acetyltransferase

is p300/CBP-associated factor (PCAF/KAT2B), which associates with p300 to modify residues

upon p53 activation (Liu et al. 1999). Fascinatingly, Mdm2 can regulate these factors,

suppressing p53 acetylation (Jin et al. 2002; Legube et al. 2002). Still, various signaling

pathways are affected by activated p53, including Wnt/β-catenin (reviewed in Harris and Levine

2005). This review also describes how several feedback loops from p53 to other pathways

require the presence of Mdm2.

Specific studies that delve into the consequences of CTD-mutant p53 will now be

discussed. Many of these involve mutation of specific lysines to arginine, which preserves their

positive charge but is not generally modified. Mutation to alanine similarly blocks modification,

but neutralizes the positive charge. However, lysine mutation to glutamine, a negative amino

acid, neutralizes the charge and is theorized to mimic acetylated lysine (Megee et al. 1990;

Zhang et al. 1998). Of course, these residue switches may or may not affect the structure or

stability of the mutant protein (reviewed in Barnes and Gray 2003).

In the extreme C-terminal domain, past the ODi, changing 6 lysines to arginines (K6R) in

mouse p53 led to impaired transactivation ability, but it did not affect protein stability (Feng et al.

2005). Work from our lab also indicated that human 6KR p53 (analogous mutations to mouse

i OD = oligomerization domain

34

p53) bound significantly less targets than WT-p53, and 6KQ an even smaller subset (Laptenko

et al. 2015). On the other hand, a mouse with similar mutations in 7 lysines (7KR) was viable

and appeared phenotypically normal, with this mutant p53 acting equivalently to WT-p53

(Krummel et al. 2005). Similarly, mutation of 4 lysines to alanine, neutralizing their positive

charge, did not affect binding to target genes, although it affected Mdm2-dependent

ubiquitination (Nakamura et al. 2000). Other studies found that upon altering PTM sites of p53,

interaction with coactivators was affected (Barlev et al. 2001; Knights et al. 2006; A. G. Li et al.

2007; Kurash et al. 2008). Some of these find a linkage between acetylation, phosphorylation,

and/or methylation, suggesting such modifications might occur in directed cascades to alter

function (Sakaguchi et al. 1998; Ivanov et al. 2007; Knights et al. 2006; Kurash et al. 2008). In

this manner, even modification of one residue in p53, which may or may not cause modification

of other residues, can be sufficient to alter target gene specificity (Chuikov et al. 2004; Chao et

al. 2006; Tang et al. 2006).

Furthermore, deletion of the last 30 residues of WT-p53 (termed ‘∆30 p53’) altered target

gene binding (Espinosa & Emerson 2001; McKinney et al. 2004; Laptenko et al. 2015). Mice

engineered with a synonymous deletion, ∆24 p53, exhibit severe developmental defects, dying

soon after birth (Hamard et al. 2013). The heart, kidney and other organs are abnormal,

ostensibly due to altered induction of target genes like p21, Puma and others (Hamard et al.

2012; Hamard et al. 2013). Interestingly, a mouse expressing a ∆31 p53 actually displays

greater p53 stabilization, along with increased repression of targets involved in telomere

metabolism (Simeonova et al. 2013). In sum, PTMs in the CTD, as well as the CTD itself, are

greatly important for the function of p53 protein and its control of cellular outcomes.

The motivation to study specific residues K351 and K357 within the OD came from

studies showing that these were modified in various settings (Kruse & Gu 2009; Joubel et al.

35

2009; W. Kim et al. 2011; DeHart et al. 2014). One of these reports found that MSL2, the human

equivalent of D. melanogaster male-specific lethal-2, acts as an E3 ligase to ubiquitinate these

lysines (Kruse and Gu 2009). This increases the levels of cytoplasmic p53 suggesting export is

affected; although surprisingly this p53 is not targeted for proteasomal degradation. A

proteomics study found that lysines 351 and 357 may also be methylated and acetylated,

although the consequences of this have not been studied (DeHart et al. 2014). Mining of TCGA

data also showed there are various human cancers containing mutations at these sites,

suggesting they are relevant to disease. Therefore, we also mutated these residues to arginine

or glutamine, and set out to characterize the effect this would have on gene transactivation and

general processes (see Chapter 3).

All in the family: how p63 differs from p53

While p63 can bind many of the same target genes as p53, its story is complicated by

the fact that it is uniquely a regulator of the epithelial state. First, there is evidence from various

studies where the p63 gene is knocked out in mice. In the first, exons 6-8 encoding the DBD

were targeted and replaced with a neomycin-resistance cassette (Yang et al. 1999). In these

mice, squamous epithelia of the esophagus, mammary gland and other tissues are absent; and

limb/craniofacial development is abnormal. Clumps of differentiated keratinocytes are found

instead of a normal epidermis, and the mice die soon after birth, likely due to dehydration. In

another study, mouse models called Brdm1 and Brdm2 were constructed by targeting exons 6

or 10, resulting in a loss of functional p63 protein (Mills et al. 1999). These mice were born with

“shiny, transparent skin” that again showed lack of epithelial stratification and died of

dehydration soon after birth. Here, the teeth, hair follicles and mammary glands are absent, with

the forelimbs being truncated. It is clear that p63 is important for proper formation of these

diverse features. One report hypothesized that p63 was necessary for the self-renewal of

36

progenitor/stem cells, which was verified by later work in various tissues (Yang et al. 1999;

Senoo et al. 2007; Fletcher et al. 2011; Chakrabarti et al. 2014).

Of course, the distinctive architecture of the p63 and p73 genes was realized quite early,

along with the mixture of isoforms that could be generated (Kaghad et al. 1997; Yang et al.

1998). We shall discuss the major isoforms of these genes as they have been studied for some

time, although several novel isoforms have been revealed (Engelmann & Pützer 2014). For

reference, this review also discusses p53 isoforms, and the biological context in which they may

function.

Meanwhile, for p63 and p73, there are the major classes TA and ΔN which are

transcribed from alternative promoters (Osada et al. 1998; Yang et al. 1998). The ∆N isoforms

lack the classical N-terminal transactivation domain and it was thought that they mainly regulate

genes by interacting with TAp63 or p53 (Yang et al. 1998; Celli et al. 1999; Lee & Kimelman

2002). However, it was soon determined that these proteins have a unique N-terminal

transactivation domain, rendering them capable of target gene activation in their own right (M

Dohn et al. 2001; Ghioni et al. 2002). Alternative splicing also occurs at the 3’ end, generating

isoforms such as α, β, and γ, with slightly different targeting specificities.

Most studies concentrate on the major classes and α/β isoforms, while the γ isoforms

are comparatively less-studied. They alone lack the transactivation-inhibitory domain (TID) at

the C-terminal end, which might explain the high apoptotic target gene induction in cells

expressing this isoform (A. Yang et al. 1998). There is evidence p63γ induces target genes

more strongly than the related proteins (A. Yang et al. 1998, Ghioni et al. 2002). Also, there is

evidence that TAp63γ regulates expression of ∆Np63 in a p53-independent manner (N. Li et al.

2006). TAp63γ may even signal to maspin (SERPINB5), in the absence of p53 (Spiesbach et al.

2005). Both TAp63α and γ can induce Bcl-2, which is also a p53 target gene that controls the

37

intrinsic apoptotic pathway (Miyashita et al. 1994; Lantner et al. 2007). On the other hand, some

studies suggest that the ΔNp63α/β isoforms have much stronger transactivation activity than the

γ isoform (Helton et al. 2006; Marcel et al. 2012).

Perhaps this is not surprising because in some studies, ∆Np63α was found to be the

most stable isoform. Tracking of the six major isoforms after ectopic expression revealed each

had a half-life of about 1 hour, except for ∆Np63α at around 8 hours (Petitjean et al. 2008). The

half-life of endogenous ∆Np63α seems to be similarly long (Galli et al. 2010). ΔNp63 is also the

predominant isoform expressed in epithelial cells (Yang et al. 1998; Nylander et al. 2002).

Furthermore, it was also determined to be responsible for the characteristics of p63-knockout

mice, as was clarified by isoform-specific knockdowns and genetic complementation (Koster et

al. 2004; Candi et al. 2006; R-A. Romano et al. 2012). In these studies, expression of ∆Np63

alone was required for normal epithelial stratification. In developed tissue, though, it is only

expressed in the basal/proliferative layer of epithelial tissue, and its expression decreases at the

suprabasal/luminal level (Parsa et al. 1999; Hall et al. 2000).

In these suprabasal cells, TAp63 levels increase, suggesting the balance between the

isoforms is important for epidermal differentiation (Candi et al. 2006, Su et al. 2009). The exact

role of TAp63 in epithelial cells is controversial, as the mouse models have conflicting

phenotypes. A general knockout model showed normal epithelia, but the oocytes displayed a

deficient apoptotic response after various damages (Suh et al. 2006). However, germline

deletion of TAp63 led to skin ulcers, blisters, and a compromised wound healing response (Su

et al. 2009). These mice also displayed signs of accelerating aging like kyphosis (hunchback),

and significantly reduced survival. Surprisingly, deletion of TAp63 in K14-positive cells did not

phenocopy the above mice, suggesting they might be expressed in other populations.

Consistent with this, one recent study found TAp63 transcripts were not expressed in epithelial

cell lines, but TAp63α was expressed in some Burkitt’s lymphoma cell lines (Sethi et al. 2015).

38

This finding clarifies many earlier IHC studies that found p63 expression, based on an antibody

that could not discriminate between isoforms, in a variety of lymphoma lines (Di Como et al.

2002; Park & Oh 2005). Even if TAp63 is not normally present in epithelial tissues, it still has a

role in the induction of neuronal cell death and protecting germline tissues from chemotherapy

(Jacobs et al. 2005; Suh et al. 2006; Gonfloni et al. 2009). As such, p63 can be considered a

“guardian of human reproduction”, although it is true that p53 and p73 contribute to this process

as well (reviewed in Levine et al. 2011; Amelio et al. 2012).

As for p73, targeted disruption of the gene in mice had a very different phenotype. The

p73-/- mice died soon after birth, but in this case it was due to gastrointestinal hemorrhages or

intracranial bleeding (Yang et al. 2000). They displayed a defect in the localization of neurons

responsible for cerebrum development, along with hydrocephalus; likely explanations for the

bleeding. Later work confirmed that p73 is required for the maintenance of CNS neurons, and

that the ∆Np73 isoform is particularly vital for the survival of mature neurons (Pozniak et al.

2002; Talos et al. 2010).

As p63 and p73 belong to the p53 family, their contribution to tumorigenesis was

examined. One study suggested lacking a copy of either gene leads to lesion formation, and

LOH, explaining why the mice have a significantly shorter lifespan (Flores et al. 2005). The

double heterozygote (p63+/-; p73+/-) mice have even higher tumor burden than p53+/- mice,

display an aged phenotype and a greater variety of tumors (mammary and lung

adenocarcinomas, sarcomas) (Flores et al. 2005). However, another p63+/- mouse model (using

the Brdm alleles described previously) displayed less tumor burden than their WT littermates,

suggesting a protective effect for p63 (Mills et al. 1999; Keyes et al. 2006). Of the cancers that

did develop in these mice, there was also a larger spectrum, and these mice displayed some

epithelial hyperplasia and inflammation. How are these two very different phenotypes

39

explicable? It may be due to the different targeting strategies these groups used; also one study

used mouse strains C57BL/6J and 129S5, while the other used strains C57BL/6 and 129SvJae.

Both the 129 and C57BL/6 lines are genetically distinct (Simpson et al. 1997; Mekada et al.

2009). In any case, p63 clearly has an impact on tumorigenesis.

Interestingly, one group found that the Brdm2 mouse described previously (Mills et al.

1999), with the insertional mutagenesis at exon 10, may not be a complete knockout as

previously thought (Wolff et al. 2009). These mice actually expressed truncated TA and ∆N

isoforms resembling p63γ, along with incomplete epidermal/limb development. This would

suggest that the α and/or β isoforms are responsible for progression of these areas. However,

this work must be taken with a grain of salt, since spontaneous reversion events are known to

occur frequently in heterozygote mice (Zheng et al. 1999; Mikkola et al. 2010).

Mutations in p63 lead to several disorders in humans; most of which are characterized

by abnormal epithelial and craniofacial development, and possibly limb defects (reviewed in T.

Rinne et al. 2007). Essentially, these are the same phenotype seen in the p63-/- mice described

earlier (Mills et al. 1999, Yang et al. 1999). The human syndromes all show autosomal dominant

inheritance, so many were first described in families. Additionally, many of the symptoms

overlap, so separate disorders have now been classified within the same spectrum. For

instance, EEC/AEC (ectrodactylyii or ankyloblepharoniii, ectodermal dysplasia, and cleft

lip/palate) seems to be fundamentally the same as Rapp-Hodgkin syndrome (Rapp & Hodgkin

1968; Rodini & Richieri-Costa 1990; Cambiaghi et al. 1994). EEC also encompasses Hay-Wells

syndrome, named for its discoverers (Hay & Wells 1976). The ectodermal component covers

symptoms like reduced ability to sweat (hydrosis), sparse or absent eyebrows and hair, and

ii Ectrodactyly is split hand/foot, with a deficiency of one or more central digits of the hand or central toes iii Ankyloblepharon filiforme adnatum, or adhesion of the eyelids to one another

40

tooth agenesis/hypodontiaiv, among others. These syndromes, and the ones described below,

are typically caused by mutations within the DNA-binding domain of p63, although there are a

few exceptions (reviewed by T. Rinne et al. 2007).

Other p63-associated disorders include limb mammary syndrome (LMS), wherein hand

and/or foot malformations and mammary hypoplasia/aplasiav are present (van Bokhoven et al.

2001). These patients do not have any ectodermal abnormalities, however, those are observed

in patients with acro-dermato-ungual-lacrimal-tooth (ADULT) syndrome (Propping et al. 2000;

Amiel et al. 2001). ADULT syndrome not only manifests with hand/foot/mammary abnormalities,

it also variably presents with nail dysplasia, obstruction of tooth development and the lacrimal

duct, hair loss and freckling. Finally, about a tenth of patients with split-hand/split-foot

malformation (SHFM) have p63 mutations (Ianakiev et al. 2000; van Bokhoven et al. 2001).

Interestingly, none of these disorders is known to be associated with increased cancer risk.

Of course, ectodermal abnormalities may be caused by alterations in many other genes,

such as interferon regulatory factor 6 (IRF6). Mutations in this gene are associated with several

human syndromes that present with cleft lip and/or palate (Kondo et al. 2002). Interestingly, the

IRF6-/- mouse phenocopies the p63-/- mouse, with a lack of stratified epidermis, along with

craniofacial and skeletal defects (Ingraham et al. 2006). These two genes also display a similar

pattern of expression in tissues. As it turns out, p63 directly binds to and induces IRF6

transcription, and this is required for normal palate and ectoderm development (Moretti et al.

2010; Thomason et al. 2010). Also, members of the Dlx homeobox family are required for palate

development, and can lead to cleft lip/palate when mutated in humans (Sabóia et al. 2015; Wu

et al. 2015). A member of this family will be discussed further below, as it is a p53 target. Along

with this, we will discuss how alteration of p63 contributes to epithelial phenotypes, and affects

tumorigenesis.

iv One or more teeth are missing, due to abnormal development v This condition denotes little to no development of the breast, which may affect milk production.

41

Post-translational modifications and target genes

Early on, it was noted that genotoxic stresses, including UV radiation, generally resulted

in p63 protein loss (Liefer et al. 2000; Hall et al. 2000; Westfall et al. 2005). There are some

exceptions, especially when it comes to TAp63, which is stabilized after damage much like p53

(Okada et al. 2002; Petitjean et al. 2005). However, the E3 ubiquitin ligases discussed below

were usually shown to bind both classes of p63, translocating them to the cytoplasm where they

are ubiquitinated for proteasomal degradation. For example, Mdm2 not only translocates p53, it

also exports p63 to the cytoplasm, allowing Fbw7 (FBXW7) ligase to target it for destruction

(Kadakia et al. 2001; Galli et al. 2010). The ligases Itch and Pirh2 regulate steady-state levels of

p63 in epithelial cells (Rossi et al. 2006; Jung et al. 2013). However, the peptidyl-prolyl

isomerase Pin1 stabilizes p63 levels by disrupting its interaction with the WWP1 ubiquitin ligase

(Y Li et al. 2008; C. Li et al. 2013). Interestingly, Pin1 levels are high in many cancers, and it is

known to cooperate with both mutant p53 and Ras signaling (reviewed in Lu and Hunter 2014).

At least two mechanisms were described for p63 disintegration by the scaffold protein

Rack1. After cisplatin usage, Stratifin (SFN) targets phosphorylated ΔNp63α to the cytoplasm,

where Rack1 initiates its degradation (Fomenkov et al. 2004). Our lab showed another scaffold

protein, Stxbp4, suppresses Rack1 except in stressed conditions, allowing for Rack1-mediated

p63 degradation (Y. Li et al. 2009). ΔNp63α may also undergo sumoylation by Ubc9, except in

the case of disease-causing mutations, leading to altered transcriptional regulation (Y-P. Huang

et al. 2004; Bakkers et al. 2005). Finally, both IKKα and IKKβ can stabilize p63, and p63 is

reciprocally able to sustain IKKα expression (Koster et al. 2007; MacPartlin et al. 2008).

Similar to p53, p63 is regulated by a plethora of post-translational modifications. This

often facilitates its degradation by the factors listed above. The kinases ATM, CDK2 and

42

p70S6K (RPS6KB1) phosphorylate specific sites of ΔNp63α, and degradation is dependent on

these phosphorylated sites (Huang et al. 2008). Cisplatin’s induction of TAp63 is mediated by c-

Abl kinase phosphorylating specific tyrosine residues, and this is essential for the apoptotic

response (Gonfloni et al. 2009). The interaction between c-Abl and p63 may also be essential

for its acetylation by p300 (Restelli et al. 2015). Another important factor is Dlx3, a homeobox

gene and p63 target that plays a key role in epidermal differentiation (Morasso et al. 1996;

Radoja et al. 2007). Raf1 kinase can phosphorylate certain C-terminal residues of ΔNp63α,

leading to degradation by Dlx3 (Di Costanzo et al. 2009). Interestingly, Dlx3 was recently

identified as a potential cofactor for p63, as they regulate a similar network of genes (Sethi et al.

2015). Last but not least, ISG15 is an interferon-induced ubiquitin-like factor that modulates the

immune response (Jeon et al. 2010). ΔNp63α is ISGylated in the TID following chemotherapy,

leading to degradation by caspase-2, whereas surprisingly, TAp63 transactivation is increased

by the same modifications (Jeon et al. 2012). For more on regulators of the p63 protein, see the

excellent review by C. Li and Xiao, 2014.

It is clear that p63 can be altered in a number of ways, which might explain why it is so

frequently misregulated in cancers. In many squamous carcinomas, p63 (especially ∆Np63) is

amplified or overexpressed, suggesting an oncogenic role (Yamaguchi et al. 2000; Wang et al.

2001). In these cases, ∆Np63 overexpression leads to properties such as anchorage-

independent growth or increased tumor size in nude mice (Hibi et al. 2000; Kumar Ram et al.

2014). The antibody p40, specific for this isoform, is a valuable diagnostic marker for squamous

cell carcinomas (SCC) of the lung, as well as for basal cells of the prostate and breast (Bishop

et al. 2012; Nobre et al. 2013; Sailer et al. 2013; Sailer et al. 2015). Other than those of

squamous origin, however, p63 is rarely mutated in cancerous lines (Hagiwara et al. 1999;

Barretina et al. 2012). In fact, as written earlier, many groups, including ours, found that p63

RNA levels are low in breast cancers. However, p63 levels may be high in specific rare

43

mammary neoplasms, such as phyllodes tumors and adenoid cystic carcinomas (Emanuel et al.

2004; Cimino-Mathews et al. 2014). Taking this into account, it is reasonable to conclude that

this gene is a common target of upstream signaling in various tumors.

Some ChIP-seq and RNA-seq analyses suggest that p63 regulates a large subset of

genes, perhaps even more than p53 (Yang et al. 2006; Viganò et al. 2006; Barton et al. 2010;

Kouwenhoven et al. 2010; McDade et al. 2012). This may be due to its role as a regulator of

epithelial tissues. In this vein, targets such as the apoptosis effector Perp, Keratin-5, Keratin-14,

and Claudin-1, all play roles in epithelial development (Ihrie et al. 2005; Lopardo et al. 2008;

Romano et al. 2009). Mutations in Keratin-5 and Keratin-14 cause a disorder leading to fragile

skin and blistering, especially after trauma (Lane & McLean 2004). Interleukin-1-α (IL1A) and

Transcription Factor 7-Like 1 (TCF7L1), a Wnt family member, are also p63 target genes that

regulate epidermal homeostasis (Barton et al. 2010). On top of these targets, both p63 and p73

can regulate Jagged1 and Jagged2, the Notch ligands (Sasaki et al. 2002; Fontemaggi et al.

2002). Interestingly, Jagged1 has been associated with breast cancers, especially the basal-like

subtype (Dickson et al. 2007; Reedijk et al. 2008).

Importantly, one of the above ChiP-seq studies found that the p63 binding site

resembles that of p53, only more degenerate, and these sites have enhancer-like histone

profiles (McDade et al. 2012). This paper also identified transcription factor AP-2α (TFAP2A)

and another family member TFAP2C as coregulators of various targets with p63, the former

being the causative mutation for Branchiooculofacial Syndrome (Milunsky et al. 2008; McDade

et al. 2012). Both TFAP2A and p63 can bind to a long-range enhancer element, which

modulates p63 expression in epithelial cells (Antonini et al. 2006; Antonini et al. 2015).

Surprisingly, BRCA1 is recruited to this enhancer and controls p63 expression, suggesting

ΔNp63 is a downstream effector of BRCA1 signaling (Buckley et al. 2011). Another report

44

suggested that sites bound by both p63 and H3K27ac were indicative of active transcription

(Kouwenhoven et al. 2015). About half of the p63-bound sites were also occupied by H3K27ac,

which correlated with gene expression. From this analysis, conducted over the course of

epidermal differentiation, they determined that p63 is poised at these sites to allow transcription

at specific time points.

Additionally, as can be seen from one of the TAp63 mouse models, p63 alteration is

linked to aging, possibly due to its regulation of proliferation (Su et al. 2009). In these mice, the

dermal and epidermal precursors become senescent, likely due to lowered p57Kip2 levels, a

known cell cycle regulator. An earlier paper noted that in the absence of p63, PML and p16INK4A

are induced, encouraging a senescent response (Keyes et al. 2005; de Jesus & Blasco 2012).

Strangely, a follow-up paper suggested TAp63-dependent senescence might occur through p21

and Rb (Guo et al. 2009). Meanwhile, ∆Np63 can control sirtuin-1 (SIRT1) and a lymphoid-

specific helicase (HELLS, also known as LSH), which are regulators of cell survival and

senescence (Sommer et al. 2006; Keyes et al. 2011). HELLS is a member of the SNF2 family of

helicases, and was shown to repress p16INK4A via histone deacetylases (HDACs) (Zhou et al.

2009). Of course, modulation of p53 sometimes leads to premature aging in mouse models, so

the balance between the family members may be important in mediating the cell cycle (Tyner et

al. 2002; Maier et al. 2004).

Both p53 and p63 can regulate CD44, the transmembrane cell surface marker discussed

above that also delineates stem cells (Carroll, Carroll, Leong, Cheng, Brown, A. A. Mills, et al.

2006; Boldrup et al. 2007; Godar et al. 2008). This protein not only associates with growth factor

receptors like HER2, it also plays a role in tumor initiation and growth (Ponta et al. 2003; Godar

et al. 2008; Louderbough and Schroeder 2011). Interestingly, another surface/stemness marker,

CD24, was found to be associated with inactivating p53 mutations in various cancers (Wang et

al. 2015). TRAF4 is another p53/p63 target that is overexpressed in breast carcinomas (Régnier

45

et al. 1995; Sax & El-Deiry 2003; Gu et al. 2007). It can activate the NF-κB pathway, and it also

destabilizes tight junctions, leading to cell migration (reviewed in Chung et al. 2002; Rousseau

et al. 2013). Furthermore, ∆Np63 can induce NF-κB targets like IL-8 and CSF2: inflammatory

cytokines that may be responsible for its malignancy in SCC lines (Yang et al. 2011; Du et al.

2014). ∆Np63 can also positively affect β-catenin nuclear accumulation, and therefore

downstream signaling (Patturajan et al. 2002; Lee et al. 2014). Of course, the Wnt/β-catenin

pathway includes tumor suppressors like Axin1, as well as famed oncogenes like Myc, which is

high in certain types of breast cancers (Katoh and Katoh 2007; Cancer Genome Atlas 2012).

Perhaps because it can regulate all of these targets, p63 has been shown to have both

oncogenic and tumor suppressive effects in carcinomas (see Figure 5, Graziano and De

Laurenzi 2011). Broadly, there is evidence to support an oncogenic role for ∆Np63 in various

tumor types. For instance, p63 regulation of a Hedgehog pathway factor and STAT3 could be

responsible for its transformative properties in osteosarcomas (Bid et al. 2014; Kumar Ram et

al. 2014). FGFR2-mediated induction of p63 might be responsible for the survival of SCCs,

explaining why these cancers often have high p63 levels (Ramsey et al. 2013). In melanomas,

both major isoforms of p63 were correlated to poor survival, possibly via deregulation of p53

stabilization and/or apoptotic signaling (Matin et al. 2013). And strangely, p63 has been

associated with opposing effects in bladder cancers, depending on whether it has invaded the

detrusor muscle. In muscle-invasive cases, p63 expression is associated with poor survival,

while in non-muscle invasive disease, it is a positive prognostic factor (Karni-Schmidt et al.

2011; Choi et al. 2012). It is clear that cellular context can affect p63 signaling, and in some

cancers the expression of p63 itself can be correlated with outcome.

46

In an earlier section, mutant p53 was discussed, as it is a common alteration in cancers

including those originating from mammary tissue. This factor has been shown to limit the effects

of p63 via several mechanisms; suggesting p63 could inhibit tumorigenesis. First, mutant p53

can bind to and sequester p63 to assist TGF-β in driving cell invasion/metastasis (Adorno et al.

2009). Fascinatingly, it worked in concert with oncogenic Ras, whereas p63 (specifically ∆Np63)

was necessary to oppose this migratory/invasive phenotype. Other evidence pointed to a TGF-

β-independent role for mutant p53 in suppressing p63 (Muller et al. 2009). In this context,

integrin recycling/EGFR signaling is enhanced, promoting 3D invasion. As alluded to above,

Pin1 and mutant p53 are inversely correlated to survival in breast cancers, and these factors

also inhibit p63 (Girardini et al. 2011). Interestingly, Mdm2 was recently shown to decrease the

mutant p53-p63 interaction by binding to the mutant p53, and not p63 (Stindt et al. 2015). It is

not known whether this signaling takes place in human cancers with mutant p53, although this

could be a potential mechanism to combat this oncogenic alteration.

For other factors that are known to regulate p63, see our review (Appendix). Many p63

targets feed back into regulation of p63, so those reciprocal loops are discussed. Note that our

research paper in Chapter 2 delineates a novel role for Ras/PI3K pathways in directing p63

expression, previously unknown. As for how p63 acts against carcinogenesis, along with the

targets listed above, it also regulates a number of tumor-suppressive microRNAs.

microRNA processing and p63

MicroRNAs are short noncoding RNAs that have the ability to silence a wide variety of

genes by binding to and targeting the 3’ UTR of mRNAs for destruction. There have been more

than 1,000 of these validated and several thousand more candidates are in evaluation; many

playing roles in cellular processes and tumorigenesis (Friedlander et al. 2014). Most are

transcribed by RNA Polymerase II and undergo capping and polyadenylation, just like mRNAs,

47

only these are called primary miRNAs (pri-miRNAs). These transcripts may comprise a single

miRNA or several, if they are transcribed from the same locus. In any case, these are cleaved

into individual stem-loop transcripts (pre-miRNAs) by the Microprocessor complex, which

includes the RNase III enzyme Drosha and a cofactor, the double-stranded RNA (dsRNA)-

binding protein DiGeorge syndrome critical region 8 (DGCR8) (Gregory et al. 2004; Han et al.

2004). There are other factors involved in this complex, but these are the two essential

elements.

The pre-miRNAs can then be translocated into the cytoplasm by exportin 5 (XPO5),

where they undergo further processing by a ribonuclease type III enzyme, DICER1 (Bernstein et

al. 2001). This enzyme cleaves the stem-loop structure so that a mature miRNA duplex is

formed, complete with overhanging ends. These ends are particularly important for binding to

Argonaute proteins (AGO1-AGO4 in humans), which are major components of the miRNA-

induced silencing complex (miRISC) (Ma et al. 2004; J.-B. Ma et al. 2005). The miRNA then

acts as a guide for RISC to identify complementary RNA to cleave (reviewed in Pratt and

MacRae 2009).

Interestingly, there are multiple links between p63 and miRNA processing factors.

∆Np63 was found to induce DGCR8, and this axis was responsible for suppressing stemness

factors like Sox2 and Nanog (Chakravarti et al. 2014). This paper delineated at least seven

miRNAs, including miR-141 and miR-205, which are strongly regulated by that axis. Another

finding is that TAp63 binds to and induces DICER1 transcription, and in the absence of TAp63,

DICER1 is low and there is defective processing of several microRNAs (Su et al. 2010). As

modulation of DICER1 levels in TAp63-/- cells affected invasion, it seems that miRNAs strongly

contribute to TAp63’s anti-metastatic effects. Of course, DGCR8, DICER1 and other processing

components are altered in tumors and likely have multiple ways to affect oncogenesis (reviewed

in Lin and Gregory 2015).

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A recent report used computational analysis to identify 39 microRNAs putatively

regulated by p63 (see Figure 5, C. Lin et al. 2015). Some were previously examined, including

miR-200a/b and miR-429 (Knouf et al. 2012). As stated in an earlier section, these are members

of the miR-200 family that are known to suppress EMT, suggesting an anti-tumorigenic role for

p63. Interestingly, cisplatin-induced phosphorylation of ∆Np63 can influence several miRNAs

like miR-181a in SCC cells (Huang et al. 2013). Another paper identified ∆Np63’s direct

repression of miR-34a and miR-34c, which exert significant control over the cell cycle (Antonini

et al. 2010). On the other hand, TAp63 can induce miR-34a, along with miR-130b, contributing

to an anti-invasive phenotype (Su et al. 2010; Dong et al. 2013). Meanwhile, ∆Np63 can inhibit

expression of miR-30b, 138, -181a, and -181b, which protects keratinocytes from senescence

(Rivetti di Val Cervo et al. 2012). In these cases, it seems that TAp63 and ∆Np63 have

opposing effects. Most importantly, ∆Np63 was required for miR-205 expression, but loss of

either of these led to increased invasion in prostate and bladder cancer cells (Tucci et al. 2012;

Tran et al. 2013). The EMT regulator Zeb1 was induced by the depletion of ∆Np63 or miR-205,

suggesting one mechanism by which p63 controls EMT.

There are several connections suggesting that p63 is an important regulatory factor of

mesenchymal transition. For example, ΔNp63 is downregulated during Snail-mediated and/or

Slug-mediated EMT in SCC cells, allowing TAp63 stabilization (Higashikawa et al. 2007; Herfs

et al. 2010). The loss of ΔNp63 also occurs in prostate cells that undergo EMT or cancers from

this tissue, and re-expression of it leads to gain of epithelial characteristics (Tucci et al. 2012;

Olsen et al. 2013). As mentioned previously, negative feedback loops occur between the p63-

controlled miR-200 family and EMT. There is also the p63 target Grainyhead-like-2 (GRHL2), a

transcription factor that normally keeps Zeb1 expression in check (Werner et al. 2013; Cieply et

al. 2013; Mehrazarin et al. 2015). A perplexing result is that knockdown of ∆Np63α and -β led to

EMT in mammary cells, mediated by Twist and/or Slug, while expression of the γ isoform

49

supports TGF-β-mediated EMT (Lindsay et al. 2011). If this is the case, then it seems these

isoforms have entirely contradictory roles in mediating tumorigenesis. Similar to the ∆Np63γ

evidence, another paper found that overexpression of ΔNp63α enhances TGF-β signaling to

drive mesenchymal transition in human keratinocytes (Oh et al. 2011).

In conclusion, ΔNp63 can control almost all of the major regulators of EMT, along with

various other cancer-related genes (refer to Figure 5). Although TAp63 is somewhat less

studied, because it has the same architecture it is possible that it also regulates many of these

same targets. A number of tumor suppressive targets may play a role in cell cycle control (p21,

HELLS, miR-34f, p57), whereas some are microRNAs that inhibit EMT (see Figure 5). There are

also oncogenic targets that lie in a variety of signaling pathways (CD44, FGFR2, VDR, SOX2).

The presence of associated factors and balance between isoforms also complicates p63

signaling. As it is still unknown how this important factor is downregulated in some cancers, we

endeavored to study regulation of ΔNp63α by oncogenes, and how this might influence

tumorigenesis.

An introduction to the Ras signaling pathway

Alterations in the tumor suppressor APC (Adenomatous Polyposis Coli) and oncogene

K-Ras (V-Ki-Ras2 or Kirsten Rat Sarcoma 2 Viral Oncogene Homolog) were suggested to be

early mutations in one model for multistep colorectal tumorigenesis (see Figure 6) (Vogelstein et

al. 1988). The 1988 paper found these genes were frequently perturbed in carcinomas but

infrequently in adenomas. Furthermore, a smaller subset of carcinomas had p53 mutations,

suggesting alterations in this gene occurs late in tumorigenesis. However, some studies found

50

that only rare colorectal tumors harbor modifications in all of these genes; yet the fact is these

are important genes involved in carcinogenesis (Smith et al. 2002; Conlin et al. 2005).

Although K-Ras is a transforming protein from a rat sarcoma virus, H-Ras, another

transforming protein from a similar rodent virus was actually the first family member described

(Harvey 1964; Kirsten & Mayer 1967; Anderson & Robbins 1976). The other classical family

member is N-Ras, so named because it was discovered in a neuroblastoma; although it is

expressed in multiple tissues (Shimizu et al. 1983; Hall et al. 1983). Furthermore there are

related family proteins like M-Ras, E-Ras and R-Ras, which are also known to utilize some of

the canonical Ras effector pathways (see review by Rajalingam et al. 2007).

The Ras family members are membrane-bound guanosine triphosphate

(GTP)/guanosine diphosphate (GDP)-binding proteins, also called GTPases. They transduce

signals from receptor tyrosine kinases (RTKs) like EGFR to the nucleus (Satoh et al. 1990a;

Satoh et al. 1990b). Upon ligand binding, RTKs become phosphorylated, initiating formation of a

complex with adaptor proteins like Grb2 and SOS1 (a guanine nucleotide exchange factor or

GEF) (Buday & Downward 1993; Li et al. 1993). The RTK-Grb2-SOS1 complex promotes Ras

nucleotide exchange, so that Ras-GDP (“Inactive”) becomes Ras-GTP (“Active”). The opposite

step, hydrolysis of the GTP to GDP, is controlled by GAPs (GTPase activating proteins) like

p120GAP. Yet, hotspot mutations in Ras disturb the ability of GAPs to hydrolyze GTP, thus

ensuring constitutively active GTP-Ras (Scheffzek et al. 1997). Such mutations are found in a

variety of tumors, with K-Ras the most common alteration at around 20% and H-Ras/N-Ras

under 10% (Bos 1989; Prior et al. 2012). The three common hotspots are G12, G13 and Q61,

with G12 alterations most prevalent in H-Ras and K-Ras (reviewed in Cox and Der 2010). Since

Ras signaling affects processes like proliferation, migration and differentiation, alterations can

have profound consequences at the organismal level (Downward 2003; Kim & Choi 2010).

51

One of the main branches downstream of Ras is PI3K/Akt signaling, which was

discussed earlier (refer to Figure 2). Beyond PTEN, the PI3Ks and downstream Akt kinases; a

major downstream player is mammalian target of rapamycin (mTOR), a serine/threonine kinase.

Interestingly, mTOR is a member of at least two complexes that generally regulate different

processes, called mTORC1 and mTORC2 (reviewed in Laplante and Sabatini 2009). For

instance, mTORC1 activates SREBP-1 (sterol regulatory element binding protein 1) and PGC1-

α (PPARγ coactivator 1), affecting lipid synthesis and energy metabolism (Cunningham et al.

2007; Porstmann et al. 2008). mTORC1 also modulates mRNA biogenesis through

phosphorylation of 4E-BP1 and S6 Kinase (S6K), leading to initiation of translation (reviewed in

X. M. Ma and Blenis 2009). On other hand, mTORC2 loops back to fully activate Akt kinase, as

well as a similar kinase SGK1 (serum- and glucocorticoid-induced protein kinase 1), a key gene

in cellular homeostasis (Sarbassov et al. 2005; García-Martínez & Alessi 2008; Yan et al. 2008).

Furthermore, both complexes can affect the actin cytoskeleton either through interactions with

small G proteins like RhoA and Rac1, or by phosphorylating targets like PKC-α (protein kinase

C-α) (Jacinto et al. 2004; Sarbassov et al. 2004; Liu et al. 2010).

Another layer of complexity lies with the TSC1-TSC2 complex, named for the disease

caused when either gene is mutated - tuberous sclerosis complex. In this disease, benign

tumors form in various organs and developmental problems are prevalent (European Tuberous

Sclerosis Consortium 1993; Slegtenhorst et al. 1997). Fascinatingly, the TSC1-TSC2 complex

can inhibit mTORC1, while increasing the activity of mTORC2 (Huang & Manning 2009). The

inhibition of mTORC1 functions through reduced GTP loading of Rheb (Ras homolog enriched

in brain), with TSC2 acting as a GAP for this small G protein (Tee et al. 2003; Y. Zhang et al.

2003). Moreover, TSC1-TSC2 receives input from kinases such as Akt, AMP-activated protein

kinase (AMPK) and ERK1 (extracellular-signal-regulated kinase 1) (Inoki et al. 2002; Inoki et al.

2003; L. Ma et al. 2005).

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This brings us to another branch of Ras signaling, the Raf/MAPK pathway where ERK1

and family members lie. These two kinases are known as conventional mitogen-activated

protein kinases (MAPKs), along with some c-Jun amino (N)-terminal kinases (JNKs) and p38

isoforms (reviewed in Cargnello & Roux 2011). They are serine/threonine kinases containing a

Thr–X–Tyr motif, where phosphorylation of these residues is required for activation (Johnson et

al. 1996). Atypical MAPKs seem to contain a different phospho-acceptor motif, which may

explain their altered substrate specificity and properties (see review by Coulombe & Meloche

2007).

Also, conventional MAPKs are composed of a cascade of three kinases (see figure 7). In

the canonical activation of ERK1, the activating signal is Ras-GTP. It binds to one of the Raf

proteins (A-Raf, B-Raf or C-Raf/Raf1) which can act as a MAPKKK (MAP kinase kinase kinase)

to phosphorylate MEK1. Then, MEK1 functions as a MAPKK to phosphorylate ERK1, the last

MAPK in the cascade. Activated ERK1 can phosphorylate various targets like p90RSK, Elk1, c-

Fos and p53 (reviewed in Shaul & Seger 2007). This can result in the transcription of immediate

early genes, or factors involved in the cellular stress response (Buchwalter et al. 2004; Anjum &

Blenis 2008). Similar cascades also exist for JNKs and p38 isoforms, even involving some of

the same activators and/or target substrates (refer to Raman et al. 2007).

Another branch of GTPases affected by Ras includes the Rho family, which

encompasses RhoA, Rac1 and Cdc42 (some of these reinforce canonical MAPK cascades).

Like Ras, most of these are responsive to GAPs and GEFs that regulate their activation state,

although there are some exceptions. This class of GTPases is also bound by GDIs (guanine-

nucleotide-dissociation inhibitors); sequestering them in the inactive state (-GDP) and

preventing conversion to the active (-GTP) conformation (reviewed in DerMardirossian &

53

Bokoch 2005). Various kinases can phosphorylate the GDI or GTPase, either enhancing

complex formation or releasing the GTPase from inhibition. Thus, the control of these Rho

GTPases is quite complex, ensuring activation only after various stimuli have been assimilated.

The classical studies on RhoA and Rac1 established that they are necessary for the

formation of actin stress fibers and membrane ruffles (or lamellipodia) (Ridley & Hall 1992;

Ridley et al. 1992). Meanwhile, Cdc42 was found to regulate actin-rich projections called

filopodia, which together with lamellipodia, are involved in cell migration (Nobes & Hall 1995). Of

course, these descriptions are simplistic - not only are there multiple members of each group

(Rho-like, Rac-like, etc), there is also crosstalk among the family members (reviewed in

Burridge & Wennerberg 2004). For example, a combination of the three GTPases is needed in

order for focal adhesion complexes to form (Hotchin & Hall 1995; Allen et al. 1997). These

complexes are integrin-rich structures at the cellular periphery that serve as mechano-sensors,

influencing cell behavior. Similarly, the three G proteins are required for apicobasal polarity and

the integrity of adherens junctions (reviewed in Mack & Georgiou 2014). In particular, RhoA and

Rac1 regulate factors involved in vascular permeability, such as VE-cadherin (Vascular

Epithelium) (Hordijk et al. 1999; Wojciak-Stothard et al. 2001; Garcia-Ponce et al. 2015).

The Rho-like family includes not only RhoA, but also RhoB and RhoC, which can affect

cancer cell invasion and progression (reviewed in Ridley 2013). The preponderance of evidence

suggests RhoA and RhoC are oncogenic, whereas RhoB may act as a tumor suppressor

through its induction of apoptosis. Among the three, the most is known about RhoA, including its

effectors ROCK1 (Rho kinase 1) and ROCK2. These are serine/threonine kinases that interact

with regulators of actomyosin contractibility, including the LIM kinase family (Maekawa et al.

1999). Another effector, mDia1, links Rho to profilin, which promotes actin filament assembly

(Watanabe et al. 1997). Interestingly, activated Rho promotes phosphorylation of myosin light

54

chain, increasing actomyosin contractibility; whereas Rac opposes this via its effector PAK1

(p21-activated kinase 1) (Sanders et al. 1999).

PAK1 and its family member PAK2 are actually effector kinases for both Rac1 and

Cdc42 (Manser et al. 1994). When activated PAK1 was microinjected into cells, both filopodia

and lamellipodia formed (Sells et al. 1997). Similarly, Rac and Cdc42 bind to various members

of the WASP (Wiskott–Aldrich syndrome protein) and WAVE (WASP-family verprolin-

homologous) family (reviewed in Takenawa & Suetsugu 2007). Binding to these factors

activates the ARP2/3 complex, inducing actin polymerization (Miki et al. 1998; Machesky et al.

1999). ADP-ribosylation factor (ARF) family members like ARF6 are regulated by Rac1,

increasing membrane trafficking leading to cytoskeletal rearrangements (Radhakrishna et al.

1999). However, these effects are just the tip of the iceberg when it comes to the Rho family of

GTPases. To be precise, they can mediate many processes beyond migration and polarity, as

summarized in many excellent reviews (Hanna & El-Sibai 2013; Wilson et al. 2013).

Many of the Ras family members are subject to various post-translational modifications

which determine their activation and localization (reviewed in Ahearn 2012). The GTPases have

a CAAX motif, which is required for attachment to polyisoprenoids, intermediates from the

mevalonate pathway, as well as to GDIs (Casey 1989, Seabra 1991). Depending on the Ras

protein in question, they may be regulated by one or both prenyltransferases:

farnesyltransferase (FTase) or geranylgeranyltransferase type I (GGTase I). As indicated by

their names, they are responsible for attaching either a farnesyl pyrophosphate (FPP) or

geranylgeranyl pyrophosphate (GGPP) to the cysteine of the CAAX sequence. Farnesylation

targets the protein to the endoplasmic reticulum (ER), where further processing cleaves the

AAX off and carboxymethylates the farnesyl cysteine (Kim et al. 1999; Bergo et al. 2000). This

imparts the C-terminus with hydrophobicity, which (along with palmitoylation for some family

55

members) allows Ras to efficiently localize to the plasma membrane (Shahinian & Silvius 1995;

Choy et al. 1999). Alternatively, geranylgeranylation can be undergone by K-Ras and N-Ras

(but not H-Ras), after which the same processing steps and localization occur.

As these modifications have been shown to affect the activation of these oncogenes, a

key objective has been to target these pathways. Statins limit the availability of the isoprenoid

moieties, decreasing Ras prenylation, although not at the therapeutic dose (Demierre et al.

2005; Cho et al. 2011). FTase and GGTase inhibitors are of limited clinical efficacy as single

agents, due to the presence of the other prenylation pathway (reviewed in Cox et al. 2015). Dual

inhibition has not been successful so far, but it is possible inhibitors of post-prenylation

processing enzymes may be efficacious (reviewed in Tamanoi & Lu 2013).

Although A-Raf and C-Raf/Raf1 mutations are not common in cancers, B-Raf mutations

are frequent, especially in melanomas (Davies et al. 2002; Anon 2015). Most of these B-Raf

mutations take place within the kinase domain (often V600), and result in a conformational

change to the active state, irrespective of outside signaling (Davies et al. 2002; Wan et al.

2004). Thus, there is constitutive activation of downstream targets like ERK1/2. Whereas Ras

inhibitors have had limited success, there are two specific inhibitors of B-Raf V600E approved

for metastatic melanoma patients (Chapman et al. 2011; Hauschild et al. 2012). Additionally,

another drug Nexavar (sorafenib) that targets Raf and other kinases, is used to treat renal cell

carcinomas and several other cancers (Wilhelm et al. 2008). Moreover, the MEK1/2 inhibitor

Mekinist (trametinib) was approved for melanoma patients with the same mutation, and

combination therapy with both B-Raf and MEK inhibition improves survival more than MEK1/2

inhibition alone (Flaherty et al. 2012; Long et al. 2015). Interestingly, there have been many

MEK and ERK inhibitors tested for clinical efficacy, but only Mekinist has received FDA approval

for cancer (Frémin & Meloche 2010; Samatar & Poulikakos 2014). Some tumors with Raf

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mutations are more sensitive to MEK inhibition, suggesting the need for more combinatorial

targeted therapy. In conclusion, although Ras has proven difficult to inhibit, there have been

successes when it comes to targeting its effectors.

Ras has been shown to influence various processes that increase metastasis (reviewed

in Pylayeva-Gupta et al. 2011). Among these are alterations in the levels of EMT regulators. For

example, Snail and Slug are induced by activated Ras, possibly through the MAPK and/or

PI3K/Akt pathway (Conacci-Sorrell et al. 2003; Z. Wang et al. 2006; Saegusa et al. 2009; Wang

et al. 2010). Recently a feedback loop was described between Ras, Rb and Zeb1, leading to

either oncogene-induced senescence or tumor initiation (Y. Liu et al. 2014). It is possible that

Ras-dependent regulation of Zeb1 and Zeb2 takes place through the ERK1/2 target, Fra1 (Shin

et al. 2010; Bakiri et al. 2015). Glycogen synthase kinase 3 beta (GSK3β), which is inhibited by

MAPK and PI3K signaling, phosphorylates Snail leading to its export from the nucleus and

degradation by the proteasome (Cohen & Frame 2001; Zhou et al. 2004). Therefore when

GSK3β is suppressed, Snail protein can accumulate in the nucleus and act upon its targets.

Interestingly, ERK1/2 may also phosphorylate Slug, enhancing upregulation of vimentin and

cellular invasion (Virtakoivu et al. 2015).

Previously, I described how TGF-β signaling can drive EMT. In some cases, activated

Ras in conjunction with TGF-β treatment was required to produce invasive transformation (Oft et

al. 1996; Oft et al. 2002; Janda et al. 2002). There is some evidence that Raf/MAPK signaling is

particularly important in enhancing TGF-β signaling, perhaps by increasing secretion of the

ligand (Lehmann et al. 2000; Janda et al. 2002). It is also possible that the MAPK pathway

increases accumulation of Smads, key transcription factors that transactivate TGF-β targets

(Saha et al. 2001; Oft et al. 2002; Davies et al. 2005). Ras-upregulated BLT2 (leukotriene B4

receptor-2) was found to cooperates with TGF-β via NF-κB and ROS induction (Yoo et al. 2004;

57

Kim et al. 2014). Furthermore, various MAPKs are stimulated by TGF-β treatment, and may

increase TGF-β-dependent upregulation of targets like MMP-2 (Xu et al. 2006; Burch et al.

2010; Li et al. 2010). Ras also promotes complex formation between p53 and TGF-β family

members, to various effects (Cordenonsi et al. 2007; Adorno et al. 2009).

Herein, a role for Ras in regulating p63 is described, but there are some prior studies

linking the two factors. Fascinatingly, TAp63 was found to be required for Ras-induced

senescence (Guo et al. 2009). In mouse embryonic fibroblasts, overexpression of TAp63 and H-

RasV12 led to a strong senescence response. The aforementioned group found that genetic

ablation of endogenous TAp63 in the context of oncogenic H-Ras led to increased proliferation

and larger tumors after subcutaneous injection in mice. On the other hand, another report in

primary mouse keratinocytes indicated ∆Np63 loss with H-Ras leads to oncogene-induced

senescence (Keyes et al. 2011). In this case, ∆Np63 actually cooperates with H-Ras to promote

self-renewal and tumor formation. This is consistent with other work indicating ∆Np63

expression with H-Ras leads to more malignant skin tumor formation in nude mice than with H-

Ras alone (Ha et al. 2011). There was evidence that this was due to p63 suppression of

p16INK4A and p19ARF; two key cell cycle regulators.

Furthermore, Ras signaling can regulate p53 and/or its targets through various

mechanisms. Activated Ras promotes upregulation of the tumor suppressor PML (associated

with acute promyelocytic leukaemia) (Ferbeyre et al. 2000; Pearson et al. 2000). This results in

the formation of complexes with p53, PML and CBP that promote the induction of senescence.

Ras also affects p21CDKN1A, p16INK4A, p27KIP1 and other CDK inhibitors (reviewed in McMahon &

Woods 2001). Similarly, there is evidence of crosstalk between Ras GTPases and the Bcl-2

family (reviewed in Kang & Pervaiz 2012). It is puzzling that activated Ras both increases Mdm2

transcription and induces p19ARF accumulation, which negatively regulates Mdm2 (Palmero et

58

al. 1998; Ries et al. 2000). The accrual of p19 (also known as p14ARF in humans) may be due to

Ras upregulation of the transcription factor Dmp1 (Sreeramaneni et al. 2005). The net effect is

that when p19 is not present, Mdm2 levels are increased and it can degrade p53; but if p19 is

expressed, Mdm2 is sequestered to the nucleolus.

There is also evidence that p53 can regulate some Ras targets - which is not surprising

given perturbations in p53 can cooperate with mutant Ras to further metastasis (Hingorani et al.

2005; Y. Wang et al. 2006; Tsumura et al. 2006). Several studies have identified factors that

may be responsible for this synergistic effect (McMurray et al. 2008; Buganim et al. 2010;

Solomon et al. 2012). For instance, BTG2 is not only a p53 target gene, it also suppresses GTP

loading of Ras (Boiko et al. 2006; Buganim et al. 2010). Other p53 target genes include the

phosphatases Pac1/Dusp2, Dusp5 and Mkp1, which can activate conventional MAPK signaling

cascades (reviewed in Wu 2004). The powerful tumor suppressor also influences RhoA and

ROCK, affecting the cytoskeleton and cellular movement (Mizuarai et al. 2006; Gadea et al.

2007; Xia & Land 2007; Lefort et al. 2007). Additionally, a feedback loop occurs between p53

and the phosphatase PTEN (Stambolic et al. 2001; Tang & Eng 2006). Finally, p53 can directly

bind to H-Ras, activating gene transcription (Zhang et al. 1995). One study found that after

treatment with a DNA topoisomerase I inhibitor, camptothecin, p53 is stabilized and a particular

H-Ras splice variant is induced (Barbier et al. 2007). However, the splice variant is targeted by

the nonsense-mediated decay pathway, and total H-Ras protein levels do not change, so the

purpose of this conduit is unknown.

It is not surprising there are multiple links between these genes, as they each regulate a

large number of targets. Our work adds to the literature involving the regulation of p63 and p53.

Since these genes are important to human development and disease, this work can have an

impact on treatment and achieving a healthier populace.

59

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Figures from Introduction

Figure 1

Human breast diagram. Duct inset is shown to the right. TDLU = Terminal ductal lobular units.

The human mammary gland usually consists of 15-20 lobes per breast, not depicted here.

Outside of the ducts and lobular units, the stroma is composed of fatty and connective tissue.

151

Figure 2: The HER2 Signaling Pathway

The HER2 signaling pathway. Ligand binding induces dimerization, leading to activation of the

intracellular tyrosine kinase. On auto- and cross-phosporylation of the receptor complex, key

downstream effectors are recruited. This figure illustrates a HER2-HER3 heterodimer, but HER2

can also form homodimers or heterodimerize with other members of the HER2 family. FKHR,

forkhead in rhabdomyosarcoma; Grb2, growth factor receptor-bound protein 2; GSK-3, glycogen

kinase synthase-3; MAPK, mitogen-activated protein kinase; mTOR, molecular target of

rapamycin; PI3K, phospatidyl-inositol 3-kinase; PTEN, phosphatase and tensin homologue

deleted on chromosome 10; SOS, son-of-sevenless guanine nucleotide exchange factor (© Lin

and Winer 2007, Clin Cancer Res).

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Figure 3

The original six hallmarks of cancers, plus two new emerging hallmarks (in black font) are

shown (adapted from Hanahan and Weinberg, 2000; 2011). Not all cancers display these

abilities, but many late-stage cancers are found to have a mix of these.

153

Figure 4

In the metastatic cascade, a primary tumor undergoes these steps to seed a tumor in a distant

organ. Tumor cells must become motile, invade the basement membrane to reach the blood

vessels (intravasation), translocate through the blood system and extravasate into the target

organ. These cancer cells can then form a micrometastatic tumor (© Valastyan and Weinberg,

2011, Cell).

154

Figure 5

Selected p63 targets play a role in cell cycle control (arrest, proliferation, senescence), with

some coordinately regulating antitumorigenic mechanisms including the repression of EMT

factors. For some microRNAs, ‘f’ is used to denote their family members. Meanwhile, some p63

forms alter the levels of known oncogenes or stemness factors like SOX2, which is upregulated

in some cancers.

155

Figure 6

Multistep model of colorectal cancer: Certain genetic alterations are seen early or late in

tumor development. Alterations in the oncogene K-Ras are generally not seen in polyps

(dysplatic tissue), however they are found in a high percentage of adenomas and carcinomas (©

G Smith et al. 2002, Proc Natl Acad Sci).

156

Figure 7

Simplistic model of MAPK cascades. At left, the categories including Activator, MAPK-MAP3K

and Targets. There are many targets for each MAPK and often several upstream activators for

each MAPK cascade, not shown here.

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158

Chapter 2

Repression of p63 and induction of EMT by mutant Ras in mammary

epithelial cells

Kathryn E. Yoh1, Kausik Regunath1, Asja Guzman2, Seung-Min Lee3, Neil T. Pfister1,4, Olutosin

Akanni1,4, Laura J. Kaufman2, Carol L. Prives1* and Ron Prywes1*.

1Department of Biological Sciences, Columbia University, New York, NY, USA

2Department of Chemistry, Columbia University, New York, NY, USA

3Department of Food and Nutritional Sciences, College of Human Ecology, Yonsei University,

Seoul, South Korea

*Co-corresponding authors

4Present address: College of Physicians and Surgeons, Columbia University, New York, NY,

USA

[Author’s Note: At the time of deposit, this paper was not in its final form. The reader is

encouraged to read the finalized paper online.]

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Abstract

The p53-related transcription factor p63 is required for maintenance of epithelial cell

differentiation. We found that activated forms of the H-RAS and PIK3CA oncogenes strongly

repress expression of ∆Np63α, the predominant p63 isoform in mammary epithelial cells. This

regulation occurred at the transcriptional level and a short promoter sequence is sufficient for

altered expression induced by H-RasV12. The suppression of ∆Np63α expression by these

oncogenes concomitantly leads to an epithelial-to-mesenchymal transition (EMT). In addition,

the depletion of ∆Np63α alone is sufficient to induce EMT. Both H-RasV12 expression and

∆Np63α depletion induce individual cell invasion in a 3D collagen gel system, thereby

demonstrating how Ras can drive the mammary epithelial cell state towards greater invasive

ability. Together these results suggest a pathway by which RAS and PIK3CA oncogenes induce

EMT through regulation of ∆Np63α.

Significance

The H-RAS and PIK3CA oncogenes are well-known for altering cell growth and

morphology. We show here that they are also able to modify the differentiation state of

mammary epithelial cells by inducing an epithelial-to-mesenchymal transition (EMT). This

transition leads to greater invasiveness, a hallmark of the progression of tumors towards

metastasis. Expression of p63, a gene required for the development of mammary epithelial

cells, is strongly repressed by these oncogenes. In turn, loss of p63, which occurs at the

transcriptional level, causes a shift in microRNAs and transcription factors that control EMT.

Screening for changes in these factors may help in evaluating therapies that will halt breast

cancer progression.

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Introduction

H-Ras, a member of the Ras family of GTPases, was originally identified as the

transforming protein encoded by the Harvey rat sarcoma retrovirus (DeFeo et al. 1981).

Activating mutations in H-Ras and its family members, K-Ras and N-Ras, were identified in a

variety of cancers, and nearly 30% of all cancers have mutations in one of the Ras genes

(Forbes et al. 2011; Prior et al. 2012). The EGFR2 receptor, also known as HER2/neu, is a main

signal transducer to Ras and is amplified in about 20-30% of breast cancers (Slamon et al.

1987; Varley et al. 1987). While many HER2+ cancers are sensitive to the drug Herceptin

(trastuzumab), resistance usually develops (Narayan et al. 2009; Gajria & Chandarlapaty 2011).

Therefore, it is of great importance to understand signaling pathways that are downstream of

EGFR2 and Ras in order to develop new therapeutics.

A major effector downstream of Ras is the phosphatidylinositol 3-kinase (PI3K) pathway.

The catalytic subunit of PI3K, PIK3CA, is frequently found mutated in cancers, especially those

originating in the breast (Samuels et al. 2004; Cancer Genome Atlas 2012). Like mutant Ras,

expression of mutant PIK3CA can cause non-tumorigenic cells to undergo transformation and

gain invasive abilities (Zhao et al. 2005; Kang et al. 2005). Cancerous cells may gain increased

invasive abilities by undergoing an epithelial-to-mesenchymal transition (EMT), where adherens

junctions mediated by E-cadherin are disrupted (Perl et al. 1998; Krakhmal et al. 2015).

Canonical transcription factors that repress E-cadherin include Twist, Snail, Slug, and Zeb1,

although this network has grown more complex in recent years (Craene & Berx 2013; Comijn et

al. 2001).

The transcription factor p63 not only induces transcription of canonical p53 targets, but is

a master regulator of epithelial cells (Koster et al. 2004; Barbieri & Pietenpol 2006). Mice losing

both alleles of p63 display complications due to the loss of epithelial stratification, including

absence of mammary glands (Yang et al. 1999; Mills et al. 1999). They also have significant

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craniofacial and limb abnormalities, indicating that p63 plays a key role in embryonic

development. There are many isoforms of p63, including the major types TAp63 and ΔNp63,

which are transcribed from alternative start sites (Yang et al. 1998). There is also alternative

splicing at the 3’ end, resulting in the isoforms α, β, and γ (Yang et al. 1998). ΔNp63 was

determined to be responsible for the aforementioned characteristics in mice, as isoform-specific

knockdown led to similar epidermal defects (Koster et al. 2007; Romano et al. 2012).

Interestingly, p63 has a controversial role in cancer, possibly due to the different

activities of its various isoforms and/or tissue specificity. In head and neck squamous cell

carcinomas (HNSCC), the p63 locus is often amplified, suggesting an oncogenic role (Hibi et al.

2000). However in other types of tumors, basal epithelial markers like p63 and Keratin 14 are

lost (Urist et al. 2002; Dairkee et al. 1985; Nylander et al. 2002) and ∆Np63 has been found to

suppress EMT in prostate and bladder cancer cells (Tucci et al. 2012; Tran et al. 2013).

Surprisingly, in one report, ∆Np63 and were found to inhibit, while ∆Np63 promoted EMT in

MCF10A mammary epithelial cells (Lindsay et al. 2011). These conflicting results make it

important to determine the effects of p63 on cell growth, differentiation and invasiveness in

different cell types.

We endeavored to study gene and network changes downstream of Ras signaling

pathways in mammary epithelial cells. Our analysis of these changes indicates that Ras and

PIK3CA oncogenes can induce EMT via repression of p63.

Results

We were originally interested in how the H-Ras and p53 pathways might interact to

regulate gene expression. For this purpose we utilized a pair of isogenic MCF10A cell lines, one

with wild-type (WT) p53 and another with a deletion of p53’s second exon, leading to loss of p53

protein (termed ‘p53-del’ here; clone 1A from (Weiss et al. 2010)). MCF10A is a non-

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transformed mammary epithelial cell line that was spontaneously immortalized after derivation

from a healthy woman who underwent reduction mammoplasty (Soule et al. 1990). This line is

thought to derive from myoepithelial cells as they express p63, keratin 5 and keratin 14

(Barbareschi et al. 2001). Activated H-RasV12 or the empty Vector (pBabepuro) were

introduced into MCF10A cells by retroviral transduction. Expression of H-Ras was confirmed by

immunoblots and quantitative RT-PCR (qPCR) (Figures 1A and S1A). The morphology of the H-

Ras cells was altered, as previously seen for the comparable cell line MCF10AT (Figure S1B)

(Miller et al. 1993). While the Vector lines were cobblestone-like when confluent, characteristic

of epithelial cells, the two lines with overexpressed H-Ras displayed an elongated shape with

loss of cell-cell attachments (Figure S1B). We also found that the H-Ras, but not the Vector cell

lines, formed large colonies in soft agar, demonstrating anchorage-independent growth (Figure

S1B), indicating their transformed properties.

In order to assess gene expression changes and identify novel targets downstream of H-

Ras and p53, we performed RNA-Seq for the four cell lines. A Venn diagram of genes

significantly regulated by H-RasV12 (fold change > 2 and p < 0.05) is shown for the p53-del and

WT cell lines (Figure 1B). Interestingly, the total number of significantly changed targets was

smaller in the WT-p53 set of cell lines than in the p53-del set of cell lines, suggesting that the

presence of p53 has a dampening effect on H-Ras signaling pathways.

There was an overlap of 821 genes up- or down-regulated by H-Ras in both the p53-del

and WT cell lines, listed in Table S1A. Genes uniquely regulated by H-Ras in the p53-del and

WT cell lines are listed in Tables S1B and C. In addition to H-Ras, which was high due to its

introduction into these cell lines, a number of mesenchymal genes, including Zeb1, Zeb2,

Vimentin (VIM), N-cadherin (CDH2) and Fibronectin (FN1), were within the top 100 significantly

upregulated targets (Figure S1C, Supplemental Table 1A). This suggests that the cells have

undergone EMT. Correspondingly, genes associated with the epithelial state were significantly

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downregulated, including E-cadherin (CDH1), miR-205 host gene (MIR205HG), keratin 5

(KRT5), grainyhead-like transcription factor 2 (GRHL2), and epithelial splicing regulatory protein

1 (ESRP1) (Figure 1C, Table S1A). Interestingly, TP63, a transcription factor associated with

the epithelial state, was the 2nd and 6th most significantly downregulated target in the WT-p53

and p53-del cells, respectively.

We performed a gene ontology (GO) cluster analysis and found that mutant H-Ras-

downregulated genes were strongly associated with epidermis and ectoderm development, as

well as epithelial cell differentiation, further suggesting that H-Ras causes changes in epithelial

differentiation (Figure 1B). There was weaker, but significant association of Ras-upregulated

genes with ‘response to wounding’ and vasculature development (Figure S1D). Due to this

regulation of epithelial-related genes in both the p53-del and WT-p53 lines, we chose to focus

on this p53-independent aspect of H-Ras signaling. (For future reference, Tables S1B and C

provide lists of genes regulated by H-Ras uniquely in either the WT-p53 or p53-del

backgrounds.)

We validated by qPCR that p63, specifically the ΔNp63α isoform, is strongly reduced in

the H-Ras cells and found that the ΔNp63α isoform is the predominant p63 isoform expressed

in MCF10A cells, as has been described previously (Figure 1D and Table S1D) (Carroll, Carroll,

Leong, Cheng, Brown, A. a Mills, et al. 2006). Furthermore, using intronic primers in order to

measure pre-mRNA in the nucleus we showed that p63 pre-mRNA was similarly repressed by

H-RasV12 indicating that ΔNp63α expression is regulated at the transcriptional level (Figure

1E).

To investigate how ΔNp63α expression is repressed by H-RasV12, we constructed

promoter reporter genes with various lengths of upstream p63 genomic sequence, starting with

a construct containing -3043 to +139 of the ΔNp63α promoter (Lanza et al. 2006). These

reporters were transfected into the MCF10A cells (WT-p53) expressing Vector or H-Ras. We

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found that there was 2-3 fold higher expression of the promoter constructs in Vector vs. H-Ras

cells, after normalizing to the internal control of an SV40 promoter-Renilla luciferase plasmid

(pRLSV40) (Figure 2A). Progressive deletion from the 5’ end resulted in reduced expression;

however each construct down to -100 showed similar differential expression between Vector

and H-Ras cells. As controls, we found that the promoter activity of a p53 response element

(p53 RE) plasmid was not altered, while a minimal E-cadherin promoter was significantly

lowered in Ras cells as expected given the lower E-cadherin expression in these cells

discussed below (Figure 2C).

In order to separate the effects of the upstream promoter from that of the sequence

surrounding the transcriptional start site, we tested the -100 to -30 region (just upstream of a

TATA box) using a reporter with a minimal c-fos promoter. This sequence was similarly

preferentially expressed in Vector cells (Figure S2A). We also tested a trimer of the -100 to -30

region and found even stronger Vector-specific activity (Figure S2B). The -83 to -30 region was

also sufficient for this regulation, however deletion of either the 5’ or 3’ parts of this sequence

caused a loss of promoter activity, suggesting that multiple sequences within this region are

required for expression (Figure S2A).

We were able to further narrow the region required for differential expression to -83 to -

44 (Figure 2B). Examination of the sequence of this region showed a CCAAT box and potential

SP1 site. When we made specific mutations along the -83 to -44 sequence (Figure 2B, left),

altering CCAAT Box and Sp1 sites, promoter activity was completely abolished, while mutations

in the other sites had only partial effects on expression (Figure 2B, right). We also tested the

CCAAT Box and Sp1 mutations in the context of a longer promoter sequence, -500 to +139,

and these also displayed strongly decreased promoter activity (Figure S2C). Collectively, these

results suggest the importance of the entire -83 to -44 region for regulation, and point to the

CCAAT box and SP1 sites as being particularly critical.

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Given the strong effects of the CCAAT box and SP1 site mutations, we tested whether

the main factors associated with these sites bind to them in MCF10A cells using chromatin

immunoprecipitation followed by qPCR. For the CCAAT box, a trimer of NF-YA/B/C is the

primary binding factor (Dorn et al. 1987; Sinha et al. 1995), while SP1 binds to GC-rich boxes

(Dynan & Tjian 1983). While there were no differences in the expression levels of NF-YA or SP1

from immunoblotting, we were able to detect their binding by chromatin immunoprecipitation to a

sequence near the transcriptional start site (TSS) of a known target, Thymidine Kinase 1 (Figure

2D, right). However, no binding was detected at the TSS of the ΔNp63α promoter above that of

a non-specific IgG control (Figure 2D, left). These results argue against an involvement of these

factors in ΔNp63α regulation. It is possible that factors other than Sp1 and NF-YA bind to the

above-mentioned sequences in the ΔNp63 promoter, in contrast to the case in mouse

keratinocytes. Importantly, when we measured binding of activated RNA polymerase II at the

ΔNp63α TSS using antibodies to phospho-serine 5 of the C-terminal domain, there was a strong

signal in Vector cells, but greatly decreased binding in H-Ras cells that was close to the

background of the IgG control (Figure 2D). These results further confirm that ΔNp63α is

regulated by H-RasV12 at the transcriptional level, affecting RNA polymerase II activity and

leading to repression of ΔNp63α expression.

Given the changes in epithelial-specific genes seen by RNA- The hallmark epithelial

marker E-cadherin was suppressed, while the mesenchymal marker N-cadherin was activated

by H-Ras at both levels (Figure 3A, S1E). Similar to the mRNA levels, ∆Np63α protein levels

were suppressed, while mesenchymal proteins Fibronectin and Vimentin were increased in H-

Ras cells (Figures 1D, 3A), demonstrating that these cells have undergone EMT.

As many reports have suggested that the transcription factors Twist, Snail and Slug are

regulators of the cadherin switch, it is likely they are not EMT inducers in this model due to the

lack of RNA regulation (although we cannot rule out post-transcriptional regulation). Other

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known inducers of EMT and E-cadherin repression are the ZEB1 and ZEB2 transcription factors

(Comijn et al. 2001; Guaita et al. 2002; Vandewalle et al. 2005). In contrast to the other

regulators, these factors were strongly upregulated in the H-Ras cells, suggesting that they are

the likely regulators of EMT in these cells (Figure 3B, C).

A well-known mechanism of suppression of the ZEB factors is by miR-205 and members

of the miR-200 family (including miR-200a, miR-200b, miR-200c, miR-141 and miR-429; from

here on, miR-200f) (Gregory et al. 2008; Korpal et al. 2008). These microRNAs have also been

reported to be regulated by p63 (Knouf et al. 2012; Tucci et al. 2012; Tran et al. 2013),

suggesting a pathway for ∆Np63α regulation of ZEB1/2. We validated that expression of miR-

200b, miR-200c and miR-205 was suppressed by H-Ras in the four cell lines, while there was

no change in expression of a control miRNA, miR-375 (Figure 3D). These results are consistent

with p63 regulation of ZEB1/2 through these miRNAs, though additional mechanisms are

possible.

To test whether the effect of H-RasV12 was specific to the cell line used, we expressed

H-RasV12 in an independent mammary epithelial cell line, MCF12A. This is an immortalized,

non-tumorigenic breast epithelial cell line from a different patient than MCF10A, who also

underwent reduction mammoplasty. The MCF12A H-RasV12 line similarly underwent EMT,

displaying the same loss of E-cadherin and ∆Np63α, along with induction of N-cadherin,

Fibronectin, Zeb1 and Zeb2 (Figures S3A, B, and data not shown). Therefore, the downstream

effects of H-Ras activation may be generalizable to multiple mammary epithelial cell lines.

While mutant H-Ras is significantly overexpressed in the MCF10A cells, it is also

possible that endogenous H-Ras is expressed at low levels in MCF10A cells. We therefore

compared the levels of total Ras proteins using a pan-Ras antibody. In this case, the

overexpression of Ras in our MCF10A cells is only 3-4 fold (Figure S3C). We also found that the

levels of mutant Ras expressed in our engineered cell lines were only 2.5-7 fold higher than the

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level in a breast adenocarcinoma line Hs578T containing mutant H-Ras. It was also only 1-4.4x

higher than the level in a colorectal carcinoma line HT-29 containing wild-type Ras (Figure

S3C). These results suggest that the expression of mutant H-Ras in the MCF10A lines are

within a range of physiological expression and that the observed effects cannot be attributed to

grossly overexpressed Ras oncoprotein.

In order to understand more about the kinetics of Ras’ effects, we engineered a stable

cell line with doxycycline-inducible H-RasV12, termed ‘TR-Hras’. We found that it took about 5

days of H-Ras induction by doxycycline to cause ∆Np63 and E-cadherin repression, and to

induce mesenchymal markers N-cadherin, Fibronectin and Zeb1 (Figure S4A, B). These results

further confirm the coordinate regulation of p63 and EMT by H-Ras. Also, these are not

immediate effects of H-RasV12 activity, but rather necessitate several days, as is common for

induction of mesenchymal transition (Grunert et al. 2003).

Since activation of PI3K and loss of the phosphoinositide phosphatase PTEN (which

leads to hyperactive PI3K) are more common in breast cancers than Ras mutations, we

expressed mutant H1047R of PIK3CA in MCF10A cells by retroviral transduction, establishing

two clonal cell lines, P1 and P2 (Figure 4A). Hotspot mutations in the PI3K catalytic subunit

PIK3CA, like H1047R, are found in nearly a quarter of breast cancers, and H1047R in particular

was shown to lead to strong activation of the Akt kinase, driving mammary tumorigenesis

(Bachman et al. 2004; Zhao et al. 2005; Samuels et al. 2004). Clones P1 and P2 showed

induced phosphorylation of Akt1, demonstrating their activation downstream of PIK3CA (Figure

4A). They also displayed fibroblast-like morphology, along with repression of ∆Np63α and E-

cadherin, similar to the H-Ras cell lines (Figure 4A, B, data not shown). Mesenchymal genes

were again upregulated in these clones (Figure 4B). These results show that PIK3CA, a

common breast cancer oncogene, can induce EMT and ΔNp63α repression and suggest that H-

Ras’ effects may be due to its activation of PI3K/Akt signaling.

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In order to determine whether repression of ΔNp63α is sufficient to induce EMT, we

made stable cell lines with shRNA to inhibit ΔNp63α expression. We established two clonal cell

lines with independent shRNAs targeting the unique N-terminus of ΔNp63. These clones

displayed near total inhibition of ∆Np63 mRNA and protein levels (Figure 5A). Concordantly,

they exhibited characteristics of EMT, including loss of E-cadherin and gain of N-cadherin,

Vimentin, Fibronectin and Zeb1 expression (Figures 5A and S5A). In addition, epithelial-

associated miRNAs showed strongly reduced expression (Figure 5B) and the cells displayed a

loss of epithelial morphology (data not shown).

We also performed RNA-seq on the control (LMP vector) and sh-ΔNp63 lines and

identified 2,255 genes that were significantly altered by both shRNAs (Table S2A). GO analysis

found that many of the same epithelial gene signatures as in H-Ras cells were prominently

altered, along with cell migration and adhesion (Figure S5B). Upon comparison with the mutant

Ras-regulated genes, 380 overlapping targets were identified (Figure 5C, Table S2B), indicating

that p63 regulates an appreciable subset of the genes that H-Ras controls. We performed GO

analysis on the genes downregulated by both perturbations and again found many processes

related to an epithelial phenotype (Figure 5C). Epithelial-specific genes such as MIR205HG,

KRT5, CDH1, ESRP1, GRHL2 were significantly downregulated following both alterations. The

loss of these genes further demonstrated that the induction of EMT in these cells is due to

circuits controlled by Ras and p63.

When we analyzed several transcription factors among genes uniquely regulated by H-

Ras (Table S2C), from preliminary data, we did not identify any one factor as being sufficient to

control p63 or induce EMT. It is possible that several factors coordinately regulate this pathway,

or it is feasible that other targets are regulated post-transcriptionally to control EMT.

Collagen is a major component of extracellular matrix (ECM) in human breast tissue and

known to support growth of MCF10A mammospheres (Krause et al. 2008). As H-Ras controlled

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EMT has been associated with metastasis (Thiery et al. 2009; Zheng & Kang 2014), we

examined 3D cell invasiveness in 3D collagen I gels. For these assays, we embedded

spheroids consisting of 5000 cells into collagen I matrices and then monitored any outward

cellular invasion into the fibrillar matrix at 24 hours. Under these conditions proliferation is

minimal. We found that Vector cells only spread from the spheroid in a sheet-like manner,

exhibiting a closed front characteristic of epithelial growth (Figure 6A). In contrast, H-RasV12

and sh-ΔNp63α cells demonstrated extensive individual invasion of cells into the ECM (Figure

6A). We quantified the change in invasive behavior by counting the number of single cells that

left the spheroid. While few cells invaded beyond the spheroid core with the Vector cells, a

significant number left the core of H-RasV12 and sh-ΔNp63α spheroids (Figure 6B). This

invasive behavior is consistent with a shift to a mesenchymal phenotype.

Adhesion to and force generation on ECM via integrin receptors is a prerequisite for

mesenchymal locomotion in 3D environments such as the collagen I matrices used here (Friedl

& Wolf 2010). Radial alignment of collagen fibers at the spheroid surface is a robust indicator of

integrin-dependent interaction with the ECM (Schiro et al. 1991; Friedl et al. 1997). To exclude

the possibility that invasion differences were caused by variances in the cells’ abilities to

establish contacts with the collagen matrix, we imaged them soon after embedment (T = 2h)

with confocal reflectance microscopy, which allows for the visualization of collagen fibers. In

each case we observed collagen fibers radially aligned to the spheroid surface, indicating

comparable cell-matrix attachments (Figure S6).

In order to determine whether p63 levels in tumors might be similarly regulated as in

Ras-transformed MCF10A cells, we looked at expression of the p53 family in paired

normal/tumor samples from the TCGA Breast Cancer dataset (Cancer Genome Atlas 2012). In

fact, p63 expression was significantly lower in tumor samples as compared to their normal

tissue counterparts, while there was little change in p53 and p73 expression (Figure 7A). This

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suggests that, although p63 is not frequently mutated in cancers, its expression is commonly

lowered by tumorigenic mechanisms.

It was also possible that since p63 is expressed in basal but not luminal epithelial cells

(Yang et al. 1998), the low p63 expression is simply due to a predominance of non-basal

tumors. However after stratifying a larger breast cancer dataset of tumors by molecular subtype

according to the PAM50 gene set (Parker et al. 2009), p63 expression in basal-like breast

cancers is still close to zero and significantly lower than that in normal tissue (Figure 7B). In fact,

expression of p63 in basal tumors was comparable to that in luminal-type tumors, which would

not be expected to express p63. Finally, we examined a tissue microarray (TMA) of triple-

negative breast cancer (TNBC) tissues for p63 protein expression. Various studies have shown

that the majority of basal-like breast cancers fall within the TNBC subtype, and these express

basal keratins (Bertucci et al. 2008; Cheang et al. 2008). However, in 94% of these patient

samples, p63 was expressed in ≤5% of tumor nuclei (Table S3). Together, these results show

that p63 expression is greatly reduced in a preponderance of breast cancers and support our

model that oncogene downregulation of this gene is important for tumor progression. We cannot

rule out, however, that basal tumors originate in luminal progenitors as has been reported for

basal tumors from BRCA1 carriers (Lim et al. 2009; Molyneux et al. 2010).

Discussion

We found that ectopic expression of mutant H-Ras and PIK3CA in MCF10A cells leads

to downregulation of ∆Np63 and differentiation to a mesenchymal phenotype. This was

accompanied by a distinct change in invasive behavior, suggesting that the transdifferentiation

process leads to greater metastatic potential. Depletion of ∆Np63 with shRNA also caused

EMT, demonstrating that this depletion is sufficient to induce the transition. Along with other

evidence that ∆Np63 is required for epithelial stratification (Yang et al. 1999; Koster et al. 2004),

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these results confirm that ∆Np63 is a master regulator of the epithelial state. ∆Np63 activates

expression of epithelial-specific miRNAs that are repressors of the mesenchymal-inducing

transcription factors Zeb1 and Zeb2. These factors directly repress E-cadherin expression,

suggesting a pathway for induction of EMT from Ras to ∆Np63 to E-cadherin (Figure 7C).

There are many afferent pathways that can induce EMT (Kalluri & Weinberg 2009).

While TGF-β was one of the first characterized inducers of this transition, signaling from Ras

and TGF-β can synergistically drive cells towards the mesenchymal state (Oft et al. 1996; Janda

et al. 2002). We have not observed activation of the TGF-β pathway in our MCF10A-Ras cells,

suggesting that they are driven to EMT independent of TGF-β. As we found that mutant PIK3CA

phenocopied the H-Ras cells, we postulate that a factor(s) within the PI3K/Akt signaling

pathway are responsible for direct repression of the ΔNp63 promoter and induction of EMT,

independent of the TGF-β pathway.

We were concerned that the MCF10A cell lines we generated markedly overexpress mutant H-

Ras when compared to the endogenous H-Ras in the control cell lines and that this

overexpression might cause non-physiological effects. However, the levels of total Ras proteins

(H-, K- and N-Ras) were more modestly overexpressed and there was 1-7 fold higher

expression than endogenous Ras compared to two human cancer cell lines that were tested

(Figure S3C). While we cannot rule out spurious effects of high levels of ectopic mutant H-Ras

in MCF10A cells, the PIK3CA cell lines we generated expressed p110α protein at levels similar

to the endogenous protein. As PIK3CA mutations are common in breast cancer, our results

suggest that physiological levels of mutant PIK3CA can have effects on EMT during breast

cancer progression and that the similar effects of H-Ras overexpression in our cell lines on EMT

are physiological. Further work will be required in mammary tumor models to test the

importance of oncogene repression of p63 and activation of EMT.

172

We mapped a short region of the ∆Np63 promoter required for differential expression in

Ras transformed cells. While the entire region of -83 to -44 is required, the CCAAT box and SP1

sites were particularly critical. A similar requirement for these sites for ∆Np63 expression was

also found in mouse keratinocytes (Romano et al. 2006). However, while Romano et al.

detected binding of NF-Y and Sp1 to the ∆Np63 promoter in these cells, we did not detect

binding by chromatin immunoprecipitation in MCF10A mammary epithelial cells, suggesting that

other factors are required. A number of transcription factors have been reported to regulate

∆Np63 expression (reviewed in 58). More work will be required to determine which transcription

factors directly regulate the ∆Np63 promoter in mammary cells, and how Ras and PI3K

signaling modulate their activity.

Among all of the canonical EMT regulators, Zeb1 and Zeb2 expression was induced in

both H-Ras and sh-∆Np63 cells. One mechanism for ∆Np63 regulation of Zeb1/2 is through

miR-200f and miR-205 (Gregory et al. 2008; Korpal et al. 2008; Tucci et al. 2012; Tran et al.

2013) (Fig. 7C). We found that these miRNAs are strongly repressed by H-Ras and ∆Np63

depletion in MCF10A cells, consistent with this model. Intriguingly, we also found that Zeb1/2

pre-mRNA levels were increased by H-Ras, suggesting that there may also be regulation at the

transcriptional level (data not shown).

Consistent with our data linking repression of ∆Np63 with occurrence of EMT, there is a

precipitous loss of ∆Np63 expression in the TCGA breast cancer dataset, suggesting that this

repression is a common occurrence in breast cancer progression. In particular, separate

analyses revealed that basal-like cancers and TNBCs have low ∆Np63 expression. The

importance of these results is that triple negative breast cancers are the most resistant to

therapy (Hudis & Gianni 2011). There have been many other reports of low p63 expression in

breast cancers (Barbareschi et al. 2001; D Stefanou et al. 2004; Hanker et al. 2010), however

there are also cases where higher expression is seen (C.-O. Leong et al. 2007; H. Li et al. 2007;

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Sailer et al. 2015). The latter examples may be similar to the situation in squamous carcinomas;

indicating either the different cellular origin of breast carcinomas with elevated p63, or a

separate pathway for oncogenesis (Hu et al. 2002; Reis-Filho et al. 2003; King et al. 2013).

The cell-of-origin of basal–like breast cancers from BRCA1 mutation carriers may

actually be a luminal progenitor cell; such cells have similar gene expression patterns to patient

basal-like tumors (Lim et al. 2009; Molyneux et al. 2010). It is currently unknown as to whether

all basal-like breast cancers derive from luminal progenitors, not just those with BRCA1

alterations. Indeed, there may be several differentiation pathways for mammary cancers since

multipotent and unipotent progenitors have been characterized (Fu et al. 2014). Some basal

myoepithelial cells even have mammary stem cell properties in that they are able to repopulate

a mammary gland in vivo (Prater et al. 2014), raising the possibility that these stem cells could

be targeted for transformation resulting in basal-like tumors. It is possible that oncogenes such

as H-Ras and PIK3CA could participate in this transformation to a basal-like cancer state, which

might result in the gain of mesenchymal properties.

There is considerable evidence that EMT contributes to cancer progression and

metastasis as well as inducing stem cell properties (Wan et al. 2013). EMT is also known to

induce individual cell migration, which is a property we observed after Ras pathway activation

and ∆Np63 depletion (Krakhmal et al. 2015; Wan et al. 2013; Friedl & Alexander 2011).

However, two recent papers have concluded rather provocatively that EMT is not required for

metastasis, although it increases chemoresistance (Fischer et al. 2015; Zheng et al. 2015). It is

unclear if these conclusions hold true for human breast cancer; so further manipulation of EMT

will be required to determine its role in different cancer types.

In conclusion, the strong repression of ∆Np63 by H-Ras and PIK3CA and induction of

EMT suggest that this process is critical for mammary tumorigenesis. Future studies must

determine how important this pathway is to human breast cancer progression and how p63

174

regulation influences pathogenesis. A more detailed understanding of this pathway may lead to

an alternate approach to block this extremely prevalent disease.

Materials and Methods

See supplementary information for a complete description of materials and methods.

Acknowledgements

We thank Drs. Ella Freulich, Yan Zhu, Wen Zhou, and members of the Prives and

Prywes laboratories for their assistance and advice. We thank Dr. Scott Lowe for the

pBabepuro, pBabepuro-H-RasV12, and LMP vectors and Phoenix-Ampho cells. We are grateful

for the MCF10A-TR cells from Dr. Rosalie Sears, and the E-cadherin minimal reporter from Dr.

Kumiko Ui-Tei. We thank Dr. Antonio Costanzo for the gift of the -3000 ∆Np63 promoter

construct. This work was supported by P01 CA87497 from the National Institutes of Health. This

work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is

supported by National Science Foundation grant number ACI-1053575. The XSEDE

computational grant awarded to C.L.P. and K.R. helped support data analysis carried out on the

Stampede and Blacklight supercomputers. The authors acknowledge the Texas Advanced

Computing Center (TACC) at The University of Texas at Austin for providing access to the high

performance computing system Stampede and the Pittsburgh Supercomputing Center (PSC) for

the Blacklight system.

Author Contributions

K.E.Y., A.G. and R.P. designed and performed experiments. N.T.P. contributed cell lines and

expert advice. O.A. assisted with experiments. S-M.L. prepared RNA-Seq experiments and K.R.

175

and R.P. performed data analysis. K.R. also performed TCGA analysis. L.K., C.L.P. and R.P.

provided intellectual contribution.

Conflict of Interest

The authors declare there is no conflict of interest.

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Figures for Chapter 2

Figure 1: Differential expression in H-Ras transformed MCF10A cells.

A) Western blot of MCF10A cells with WT-p53 or p53-del background, with H-RasV12 (R) or

Vector (V). B) A Venn diagram is shown for differentially expressed genes (DEG) identified by

RNA-seq in H-RasV12 compared to Vector cells in either background. Top gene ontology (GO)

categories are shown for genes downregulated in H-Ras cells in both WT and p53-del settings.

C) The top 10 downregulated genes from the overlap seen in B (values from WT-p53 set); p

values shown are always adjusted for multiple testing using the Benjamini–Hochberg procedure.

D) Validation of ΔNp63 mRNA levels by qPCR. Error bars are the standard deviation from three

experiments. E) Levels of p63 pre-mRNA assayed by qPCR using intron-specific primers.

183

Figure 2. Analysis of the ∆Np63 promoter.

A) Luciferase reporter assays were performed in the Vector (V) and H-Ras (R) cell lines (WT-

p53 background) with ∆Np63 promoter constructs with the indicated sequence upstream of the

transcriptional start site. B) Left, sequence of the -83 to -44 ∆Np63 promoter region with the

indicated transcription factor binding sites and mutations. Right, reporter assays of -83 to -44

region on a Fos minimal promoter and indicated mutants. C) Luciferase reporter assays of

control E-cadherin and p53 Response Element reporters. These are the means of at least three

experiments with the standard deviation. D) Chromatin immunoprecipitation of the ∆Np63 and

TK1 promoters. The indicated antibodies were used to detect transcription factor or RNA

polymerase II phospho-S5 binding to the ∆Np63 TSS (left) or control Thymidine Kinase 1 (TK1)

TSS (right) in Vector of H-RasV12 cells. Non-specific IgG was used as a control antibody. The

error bars indicate the standard deviation of three experiments. X-axis is Arbitrary Units

normalized to input DNA for each cell line.

184

Figure 3. Regulation of EMT factors in H-Ras cells.

A) Protein levels of p63 and EMT markers. Western blots were performed with the indicated

antibodies. The results are representative of at least three experiments. B) Zeb1 and Zeb2

mRNA levels were measured by qPCR. C) Western blots of Vector and Ras cells were

performed with the indicated antibodies. D) Expression of the indicated miRNA levels were

measured by qPCR. The results are normalized to 1.0 in the p53-del Vector cell line for each

miRNA.

185

Figure 4. Activation of the PI3K pathway causes EMT.

A) Phase contrast (left) and Western blots with the indicated antibodies (right) of MCF10A cells

infected with Vector (V) or PI3KCA mutant H1047R (P1 and P2). The endogenous and 3X Flag-

tagged p110α can be seen separately in the P1 and P2 cells. B) Western blots and qPCR of

p63 and EMT markers. For qPCR, the results are normalized to 1.0 in Vector cells for E-

cadherin and ∆Np63 and in P1 cells for N-cadherin, Zeb1 and Zeb2; error bars show the

standard deviation of three experiments.

186

Figure 5. Depletion of ∆Np63 induces an EMT phenotype.

A) MCF 10A cells stably expressing shRNA (#1, #2) to ∆Np63 or a control shRNA (V) were

immunoblotted with the indicated antibodies (left) or analyzed for mRNA expression by qPCR

(right). B) Levels of the indicated miRNAs in the control and sh-∆Np63 cell lines were

determined by qPCR. C) A Venn diagram is shown for genes identified by RNA-seq that are

differentially expressed in H-RasV12 and ∆Np63 shRNA cell lines compared to control cells.

Top GO processes are shown for genes downregulated in both H-Ras and ∆Np63 shRNA cells.

187

Figure 6. Invasiveness of H-RasV12 and p63-depleted cells.

A) Multicellular spheroid invasion was measured in 3D collagen I gels. After cells invaded for 24

hours, spheroids were fixed and stained with fluorescently-labeled phalloidin (scale bar = 200

µM). B) Individual cell invasion was quantified by counting single cells beyond the spheroid core

(at least 20 spheroids were measured from 2 independent experiments; *** indicates p < 0.0001

from a one-tailed t-test).

188

Figure 7. p63 expression in human breast cancers.

A) p53 family expression in breast tumors. TP53, TP63 and TP73 gene expression in paired

normal and tumor samples (n = 110) from the TCGA Breast Cancer dataset are shown as

normalized counts from RSEM. The p value for the difference in TP63 expression between

Normal and Tumor cells is shown. B) Breast tumors (n = 795) were stratified by molecular

subtype using the PAM50 gene set and compared to normal samples, from above (n = 110).

‘HER2-Enr’ stands for HER2-Enriched tumors. The normalized expression for ΔNp63α is shown

(*** = p < 0.0001 using Welch’s t-Test for basal compared to normal). C) Model of signaling from

activated Ras leading to ∆Np63α repression and EMT.

189

Supporting Information

Figure S1: Characterization of MCF10A lines with and without H-RasV12.

A) H-Ras mRNA levels were measured in transduced Vector (V) or H-RasV12 (R) MCF10A

cells by qPCR. B) Top, differential interference contrast images of Vector and H-Ras cells (scale

bar = 100 µM). Bottom, colony growth of WT-p53 cells in soft agar after 11 days (scale bar =

200 µM); representative of three independent experiments. C) Top 10 H-RasV12 upregulated

genes from the overlapping gene set of p53-del and WT-p53 cells, sorted by adjusted p value.

D) Top GO biological pathways associated with genes upregulated after H-RasV12 expression.

E) Levels of E-cadherin and N-cadherin mRNA measured by qPCR.

C Top Upregulated

190

Figure S2: Luciferase reporter assays.

A) The indicated regions of the ΔNp63 promoter were cloned upstream of the c-fos minimal

promoter and assayed for luciferase activity in Vector (V) or H-RasV12 (R) MCF10A cells. B)

The 3x(-100 to -30) sequence was cloned upstream of the c-fos minimal promoter and assayed

for luciferase activity as in A (A, B are the average of 3 experiments). C) The indicated mutants

were constructed in the -500 to +139 background, replicating the results in Figure 2B (average

of 2 experiments).

191

Figure S3: Induction of EMT after H-RasV12 was transduced into MCF12A cells.

A) Western blot with the indicated antibodies. B) ΔNp63 and E-cadherin mRNA levels were

determined by qPCR. Error bars represent the standard deviation of three experiments.

192

Figure S4: EMT is induced in a “Tet-on” cell line, TR-Hras.

A) Left, TR-Hras cells with and without doxycycline treatment for the indicated days were lysed

and immunoblotted with the indicated antibodies. Right, qPCR analysis for the indicated genes

after four days of doxycycline treatment. B) The indicated mesenchymal gene expression was

measured by qPCR after four days of doxycycline treatment.

193

Figure S5: Characterization of the ΔNp63 shRNA cell lines.

A) The indicated mesenchymal genes were measured by qPCR. B) The expression of genes in

the ΔNp63 shRNA and control cell lines was determined by RNA-seq. Selected GO categories

that are associated with genes significantly downregulated in the ΔNp63 shRNA cell lines are

shown.

194

Figure S6: Collagen attachment of spheroids.

Confocal reflectance microscopy (CRM) was used to assess cell-ECM contact and visualize

collagen fibers in MCF10A-derived cell lines two hours after the spheroids were embedded in

the matrix. Collagen fibers radially aligned to the spheroid surface (arrows) indicate integrin-

mediated cell-matrix adhesion (scale bar = 50 µm). The bright spot in the images is an optical

artifact associated with CRM.

195

Supplemental Materials and Methods

Plasmids, cell culture and generation of stable cell lines

pBabe puro HA PIK3CA H1047R was a gift from Dr. Jean Zhou (Addgene plasmid

#12524) (1). The MSCV LTRmiR30-PIG (LMP) vector, pBabepuro, pBabepuro-H-RasV12

(Addgene plasmid #18744) and Phoenix-Ampho cell line were generous gifts from Dr. Scott

Lowe. The -3000 ∆Np63 promoter construct was a gift from Dr. Antonio Costanzo and contains -

3043 to +139 of the human ∆Np63 gene in the pGL3-basic luciferase plasmid (2). The -1500, -

500, -165 and -100 constructs were made by PCR from these bases to +139 and cloning the

fragments between the KpnI and HindIII sites of pGL3-basic. The Cdh1 (E-cadherin) reporter

contains -178 to +91 of the E-cadherin promoter and was a gift from Dr. Kumiko Ui-Tei (3). The

c-fos minimal promoter, pOF-GL3, was previously described (4). The fragments of the ∆Np63

promoter were made by PCR and cloned upstream of the c-fos minimal promoter into pOF-GL3

containing -54 to +49 of the human c-fos promoter using KpnI and HindIII sites. The 3x (-100 to

-30) construct was made by synthesizing the 3x fragment (Genewiz) before cloning into pOF-

FL3. The -83 to -44 fragment and mutations were made by synthesizing oligonucleotides

(Integrated DNA Technologies) that were annealed before cloning into pOF-GL3. The p53 RE

promoter plasmid contains the p53 response element from the p21 promoter (-2.3 kB) cloned in

front of the c-fos minimal promoter of pOF-GL3 (Dr. Kristine McKinney, unpublished).

MCF10A and MCF10A clone 1A (p53 -/-) were a kind gift from Dr. David Weber (5). We

received the MCF12A cells from Dr. Jose Silva. These breast cell lines and their derivatives

were grown in DME/F-12 media (Thermo Fisher #11765) with 5% horse serum (Thermo Fisher

#16050122), 0.5 μg/mL hydrocortisone (Sigma-Aldrich #H0888), 10 μg/mL insulin (Sigma-

Aldrich #91077C), and 20 ng/mL EGF (Peprotech #AF-100-15) in a 37°C incubator with 5%

CO2. For stable expression lines, the Phoenix-Ampho packaging cell line was grown in DME

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with 10% FBS (Gemini #900108H). To package virus, they were transfected with 10 μg of

plasmid with Lipofectamine 2000 (Thermo Fisher). After three days, retroviral supernatant was

filtered (0.45 microns) and added to MCF10A or MCF12A cells with 5 μg/mL polybrene (Sigma-

Aldrich #107689). After two days, cell lines were selected with 2 μg/mL puromycin (InvivoGen)

and individual clones were picked (for PIK3CA) or surviving cells were pooled (for H-RasV12).

After selection was completed (about 7 days), all cells were kept in 1 μg/mL puromycin for

maintenance.

MCF10A-TR cells (containing the tetracycline repressor TetR) were from Dr. Rosalie

Sears (6) and pLenti CMV/TO-RasV12-Puro vector was from Dr. Eric Campeau (Addgene

plasmid #22262). For the TR-Hras cell line, the pLenti CMV/TO-RasV12-Puro vector was

transfected into 293 cells with the lentiviral packaging plasmids (p∆8.9 and pVSVG) and the

viral supernatant was then used to infect MCF10A-TR cells. The infected cells were selected

with 5 µg/mL blasticidin (Thermo Fisher).

For the OEΔN line, the FLAG-∆Np63α sequence was taken from pCMV7.1-∆Np63

previously described (7) and inserted in pBabebleo at the BamHI site to make the pBabebleo-

Flag-∆Np63α vector. pBabebleo was a gift from Drs. Hartmut Land, Jay Morgenstern, and Bob

Weinberg (Addgene plasmid #1766) (8). The same transduction procedure was used as above

for pBabepuro-H-RasV12, except that these cells were selected with 5 μg/mL phleomycin

(Invivogen) and maintained in 2 μg/mL.

For the shRNA cell lines, short hairpin sequences to ∆Np63 (unique to the N-terminal

coding region) were designed as previously described (9) (Table S4). These were subcloned

into the LMP vector. As above, Phoenix packaging cells were transfected using Lipofectamine

2000 with the vector of interest. Retroviruses were harvested 48-60 hours post-transfection, and

the viral supernatant was filtered before it was added to plates of MCF10A cells, in the presence

of 8 μg/mL polybrene (Sigma-Aldrich). Cells were grown for 1 day before selection was begun

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using 1 μg/ml puromycin (Sigma-Aldrich). Antibiotic selection was complete 24 hours after the

last cell on the untransduced plate was eliminated, and cells were grown without any further

antibiotic usage.

Protein extraction and Western blotting

Cells grown on 10 cm plates to 80-90% confluency were washed twice with cold PBS

and scraped into cold TEGN buffer (10 mM Tris at pH 7.9, 1 mM EDTA, 10% glycerol, 40 mM

NaCl, 0.5% NP-40 and 100 μM DTT with protease inhibitors (10 μM benzamidine, 70 nM

leupeptin, 10 μg/mL α2-macroglobulin, 0.7 μM bacitracin, 0.5 μM PMSF)). The lysates were kept

on ice for 30 minutes with vortexing every 10 minutes. These were centrifuged at 13000 RPM

for 10 minutes after which the supernatant was isolated. Finally, the protein concentrations of

the lysates were quantitated using Bradford assays (Bio-Rad). These whole-cell lysates (40 µg)

were run on 7-15% polyacrylamide gels (depending on the proteins to be detected), and

transferred to nitrocellulose filters followed by blocking for one hour at RT (5% nonfat dry milk in

phosphate-buffered saline (PBS) with 0.05% Tween-20). Antibodies used include anti-ΔNp63

(polyclonal rabbit serum raised from peptide sequence MLYLENNAQTQFSEP, by Covance),

anti-N-cadherin (BD Biosciences 561553), anti-Flag (Sigma-Aldrich F1804), anti-Fibronectin

(Sigma-Aldrich F3648) and anti-β-actin (Sigma-Aldrich A5316). The following antibodies were

from Santa Cruz Biotechnology: p63α (H-129), Zeb1 (H-102), Vimentin (C-20), E-cadherin (H-

108), H-Ras (F235), pS473-Akt1 (11E6), Akt1 (5C10), p110α (H-201), NF-YA (H-209 X), and

Sp1 (PEP2 X).

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qRT-PCR

For quantitative RT-PCR (qPCR) RNA was extracted from cell pellets using TRIzol

reagent according to the manufacturer’s protocol (Thermo Fisher) and quantitated by ultraviolet

spectrophotometry. cDNA was created from 1 µg of total RNA using the ImProm-II Reverse

Transcription System (Promega), following the protocol for random hexamer priming. PCR was

then performed using a dNTP mix (Roche) and Sybr Green Master Mix (Life Technologies) on

an Applied Biosystems Step One Plus. Conditions for linear amplification were established

through template and cycle curves. The cycling conditions were as follows: a denaturation step

at 95°C for 10 min followed by 40 cycles: 95°C for 15 seconds, 60°C for 1 minute and 72°C for

30 sec, with a final extension of 72°C for 7 min. The cDNA quantity was normalized to GAPDH

or 18s rRNA levels. Primer sets are listed in Supplementary Table 4. The following miScript

Primer Assays were used to quantitate miRNA levels following the Qiagen protocol: Hs_miR-

205_1, Hs_miR-200b_3 and Hs_miR-200c_1, all of which were normalized to RNU6B using

Hs_RNU6-2_11.

Luciferase reporter assays

Cells were plated in 24-well plates in triplicate for each point, and transfected at 50%

confluency with 400 ng promoter plasmid and 200 ng pRLSV40 following the protocol for

Lipofectamine LTX with Plus reagent in 50% optimem/media (Thermo Fisher). After one day,

cells were washed in PBS, then gently swirled with 1x PLB (Promega) for 30 minutes at RT.

Firefly and Renilla luciferease activites were measured following the protocol for Dual-

Luciferase Reporter Assays (Promega) using a Turner 20/20 Luminometer (TD-20/20).

Background readings for Firefly and Renilla luciferase in the untransfected control cells were

subtracted from experimental readings; then ratios were quantitated and averaged for at least

three experiments.

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Invasion and soft agar assays

For the invasion assay, cells were grown to 80-90% confluency, Accutase (MP

Biomedicals) was used to detach cells from the plates, and spheroids were formed for 24h at

37oC and 5% CO2 using a previously described centrifugation method (10) that results in

multicellular spheroids surrounded by a shell of basement membrane. Spheroids were

immersed in Cell Recovery Solution (Corning) to remove the basement membrane shell. Single

spheroids were collected and each was added to collagen solution prepared from pepsin-

treated (PT) bovine collagen I (Advanced BioMatrix). This solution (1.0 mg/ml collagen) was

prepared by diluting the stock solution with DMEM (Sigma-Aldrich), 2.5% HEPES buffer

(Invitrogen), 2.5% sodium bicarbonate (Sigma-Aldrich) and double distilled (dd) H2O. Just

before addition of the spheroid, the collagen solution was neutralized with NaOH to adjust the

pH to 7.4. The samples were then transferred to an incubator at 37°C and 5% CO2. After 1 hour

that allowed for complete collagen gelation, the gels were overlaid with 50 µl growth medium.

For immunocytochemical staining, cells were fixed in neutrally buffered 4% formaline solution

(Fisher Scientific) for 20 min at 24°C and washed extensively with PBS. For staining of the actin

cytoskeleton, cells were permeabilized with 0.5% Triton-X, washed with PBS, and then

fluorescently-labelled phalloidin (Invitrogen) was added to the samples for 16 h at 4°C. Then this

solution was removed and spheroids were rinsed several times with PBS. For assessment of

cell invasion, live cell imaging was performed using a custom-built microscope incubation

chamber and objective heater to keep cells at 37°C and 5% CO2. A 10x air objective (NA = 0.4)

was used during this imaging. At least 20 spheroids were imaged for each condition from two

independent experiments. Only cells completely distinct from the spheroid core at t = 24h were

counted. For more detailed characterization of cell morphology and collagen structure, a 60x oil

objective (NA = 1.42) was used. For all imaging, an inverted confocal laser-scanning

200

microscope (Olympus Fluoview 300) was used in transmitted light or confocal

fluorescence/reflectance modes. For fluorescence imaging of cells, a 543 nm HeNe laser was

used for excitation and detection was performed through a longpass 570 nm filter. Black-and-

white inverted images are shown for maximum clarity. Collagen was imaged via confocal

reflectance microscopy (CRM) with the 488 nm laser at t = 2 hours after spheroid embedding.

For soft agar plates, 4% agarose (Invitrogen) was diluted to 1.2% and mixed with 2x

DME/F-12 to make a solution of 0.6% agarose DME/F-12 media. To each 35 mm plate, 1 mL of

this solution was added and allowed to harden for at least 30 minutes at 4°C. Cells (500) were

plated in a final concentration of 0.3% agarose in DME/F-12 media (1 mL). Complete (1 mL)

media was added, and this media was changed every 3 days. Differential interference contrast

(DIC) micrographs were taken 11 days after seeding on a Nikon Diaphot 300 microscope.

Morphology pictures of passaging cells were taken in DIC or phase contrast mode.

Bioinformatics/RNA-Seq

The Cancer Genome Atlas (TCGA) datasets were downloaded directly from the TCGA

data portal (http://cancergenome.nih.gov/). We used the Breast Invasive Carcinoma (BRCA)

(12) TCGA Provisional dataset for analysis (February 2014). Data analysis was performed using

Matlab (13). The RNA-sequence V2 (RNA-SeqV2) dataset was downloaded and analyzed to

determine the expression levels of genes of interest. In the TCGA portal, the RNA-SeqV2

dataset includes the normalized gene expression of all genes and isoforms as estimated by

upper quartile normalization procedure using the RSEM software package (14). The median

gene expression was calculated for each of the genes of interest following sample stratification

based on metadata (normal tissue or tumor sample) and plotted using the box plot function. The

statistical significance of the findings was determined by Welch’s t-test (15), which is a

modification in which the two samples may have unequal variances (heteroscedastic). The

201

results were corrected for multiple testing by using the false discovery rate procedure (FDR) of

Benjamini and Hochberg to obtain the adjusted p-values (16, 17). The box plots (18, 19) in the

figures were plotted in Matlab and are standard box plots with the notch to show the confidence

intervals of the median of gene expression. In the plots, asterisks (***) are used to denote

statistical significance (p-value <0.0001). The accuracy of the analytical procedure was verified

by corroborating several key genes to the results obtained from the cBioPortal website (20).

Then, the samples were further stratified on the basis of the PAM50 classifier of 50 genes (21,

22) based on the datasets downloaded from the UCSC Cancer Genome Browser (23, 24). The

calculation of the median of gene expression and statistical significance of the findings was

determined as described for the previous analysis. Some of the computationally intensive

analyses were carried out using the high performance computing systems of the XSEDE

network (25).

For RNA-seq, mRNA was isolated from total RNA using oligo-dT beads (Thermo Fisher).

Construction of the RNA-seq library was carried out based on the Illumina HiSeq2000 protocols

in order to generate 101 pair-end RNA-seq reads. RNA-Seq was carried out by C&K Genomics

(Vector and H-Ras cells) and Macrogen (LMP and sh-∆Np63 cells). The raw data quality was

checked using FastQC, and adapter sequences were trimmed using Trimmomatic before further

analysis. All quality-filtered reads were aligned to the human reference genome (GRCh37) from

the Ensembl database using Tophat. Aligned reads were sorted using Samtools, and the

number of reads for each gene were counted using HTseq. Differentially expressed genes

(DEG) were filtered using the criteria of fold change > 2.0 and p value (adjusted using the

Benjamini-Hochberg procedure) < 0.05. Only genes above a threshold for expression in the

positive cell lines were included. These genes were categorized for gene ontology with the

DAVID v6.7 program restricting to categories of biological processes (https://david.ncifcrf.gov)

202

(26, 27). BioVenn was used to generate area-proportional Venn diagrams

(http://www.cmbi.ru.nl/cdd/biovenn/) (28).

Chromatin immunoprecipitation

Cell lines were grown to 80-90% confluence in 10 cm plates, washed twice with PBS

and crosslinked with 1% formaldehyde in PBS for 15 minutes at room temperature (RT). Glycine

was added to 0.125 M for 5 minutes to quench the crosslinker; then cells were washed twice

with PBS. Cells were scraped into PBS with protease inhibitors (same as above) and

centrifuged at 8000 x g for 5 minutes at 4°C. Cells were then resuspended in lysis buffer (50

mM Tris-HCl, pH 8.0, 150 mM NaCl, 1% NP-40 with the same protease inhibitors as above).

Batches of cell lysates (2 mL) were sonicated for 4 minutes each, consisting of 20 second

pulses at 24 Watts with a Misonix Sonicator 3000. Debris was removed by centrifugation for 10

minutes at 4°C at 3000 RPM. Supernatant was isolated, and protein levels were quantitated

using Bradford reagent and normalized to 1.5 mg/mL aliquots. An aliquot (500 μL) was saved as

input for each cell line and was frozen with liquid nitrogen and stored at -80oC. Aliquots for ChIP

were precleared with 1 μg/mL BSA and salmon sperm DNA (Invitrogen) for two hours.

Previously, Protein A-protein G beads (Thermo Fisher, GE Healthcare) were washed three

times and resuspended in the same lysis buffer as above, and rotated overnight at 4°C with

control IgG or specific antibodies, as described above for western blots and with anti-RNA

polymerase II p-Ser5 CTD (Abcam, ab5408). For the ChIP, 100 μL antibody-bound beads (1 μg

antibody) were added to precleared aliquots of sonicated lysates and rotated overnight at 4°C.

The following day, beads were washed five times with RIPA buffer (150 mM sodium chloride,

1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS, and 50 mM Tris at pH 8.0) and then once

with TE (10 mM Tris-Cl, at pH 7.5, 1 mM EDTA), with 5 minutes rotation in between each wash.

Beads were resuspended in 100 μL TE and 200 μL 1.5x Talianidis Elution Buffer (105 mM Tris-

203

Cl at pH 8.0, 1.5 mM EDTA pH 8.0, 2.25% SDS) was added, along with 1 μg/mL RNase A

(Qiagen). Beads were left to elute at RT for one hour, then for 10 minutes at 65°C. At this point,

NaCl was added to 0.2 M to both inputs and samples, and they were incubated at 65°C for two

hours to reverse crosslinking. Proteinase K (20 μg/mL) was added for the last hour. Finally,

samples and inputs were subjected to ethanol precipitation and resuspended in 300 μL ddH2O.

Eluates (8 μL) were analyzed by qPCR as above (see primer sets in Table S4). Normalization

was set to 2.5 ng of input DNA for each sample.

Tissue microarrays

Three triple-negative breast cancer tissue microarrays were constructed under Columbia

University Medical Center (CUMC) IRB protocol AAAB2667 (Research with Human Surgical

Specimens). Cases were selected for inclusion from the clinical records at CUMC matching the

following criteria: invasive breast cases negative for estrogen and progesterone receptor

expression (<10% nuclear staining) by immunohistochemistry (IHC) and Her2-negative for

overexpression by IHC (HercepTest score 0, 1 or 2 with the latter also FISH- or SISH-negative

for Her2 amplification). Three cores of invasive breast carcinoma (1 mm in diameter), and, in

many cases, three cores of adjacent normal (non-neoplastic) breast tissue were represented

per case in the array. Regarding staining, a 10 mM citrate buffer (pH = 6.0) was used for heat-

induced antigen retrieval adapted from (29). The ΔNp63 antibody from Santa Cruz

Biotechnology (sc-71825) was utilized at 1:100 for 90 minutes at RT. The secondary antibody

was mouse EnVision (Dako), and hematoxylin was counterstained.

204

Supplemental References

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mutant p110α and p110β phosphatidylinositol 3-kinases in human mammary epithelial

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talks in the p53 family: deltaNp63 is an anti-apoptotic target for deltaNp73alpha and p53

gain-of-function mutants. Cell Cycle 5(17):1996–2004.

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suppression of ZEB1 transcriptional repressor by small RNA-mediated gene silencing.

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pathway through two sequence elements: the c-fos AP-1 and p62TCF sites. Oncogene

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5. Weiss MB, Vitolo MI, Mohseni M, Rosen DM, Denmeade SR, et al. (2010) Deletion of p53

in human mammary epithelial cells causes chromosomal instability and altered therapeutic

response. Oncogene 29(33):4715–24.

6. Farrell AS, Pelz C, Wang X, Daniel CJ, Wang Z, et al. (2013) Pin1 Regulates the

Dynamics of c-Myc DNA Binding To Facilitate Target Gene Regulation and Oncogenesis.

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7. Li Y, Peart MJ, Prives C. (2009) Stxbp4 regulates DeltaNp63 stability by suppression of

RACK1-dependent degradation. Mol Cell Biol 29(14):3953–63.

8. Morgenstern JP, Land H. (1990) Advanced mammalian gene transfer: high titre retroviral

vectors with multiple drug selection markers and a complementary helper-free packaging

cell line. Nucleic Acids Res 18(12):3587–96.

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9. Zuber J, McJunkin K, Fellmann C, Dow LE, Taylor MJ, et al. (2011) Toolkit for evaluating

genes required for proliferation and survival using tetracycline-regulated RNAi. Nat Biotech

29(1):79–83.

10. Ivascu A, Kubbies M. (2006) Rapid generation of single-tumor spheroids for high-

throughput cell function and toxicity analysis. J Biomol Screen 11(8):922–32.

11. Pfister NT, Fomin V, Regunath K, Zhou JY, Zhou W, et al. (2015) Mutant p53 cooperates

with the SWI/SNF chromatin remodeling complex to regulate VEGFR2 in breast cancer

cells. Genes Dev 29(12):1298–315.

12. Cancer Genome Atlas, N., Comprehensive molecular portraits of human breast tumours.

Nature, 2012. 490(7418): p. 61-70.

13. Sobie EA. (2011) An introduction to MATLAB. Sci Signal 4(191): p. tr7.

14. Li B, Dewey CN. (2011) RSEM: accurate transcript quantification from RNA-Seq data with

or without a reference genome. BMC Bioinformatics 12: p. 323.

15. Jeanmougin M, de Reynies A, Marisa L, Paccard C, Nuel G, et al. (2010) Should we

abandon the t-test in the analysis of gene expression microarray data: a comparison of

variance modeling strategies. PLoS One 5(9):e12336.

16. Hochberg Y, Benjamini Y. (1990) More powerful procedures for multiple significance

testing. Stat med 9(7): p. 811-8.

17. Benjamini Y, Hochberg Y. (1995) Controlling the False Discovery Rate: A Practical and

Powerful Approach to Multiple Testing. J Royal Stat Soc: Series B (Methodological), 57(1):

p. 289-300.

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119-20.

19. Streit M, Gehlenborg N. (2014) Bar charts and box plots. Nat Methods 11(2): p. 117.

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20. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, et al. (2013) Integrative analysis of

complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6(269):pl1.

21. Bastien RRL, Rodriguez-Lescure A, Ebbert MTW, Prat A, Munarriz B, et al. (2012) PAM50

breast cancer subtyping by RT-qPCR and concordance with standard clinical molecular

markers. BMC Med Genomics 5:44.

22. Parker JS, Mullins M, Cheang MCU, Leung S, Voduc D, et al. (2009) Supervised risk

predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27(8):1160–7.

23. Zhu J, Sanborn JZ, Benz S, Szeto C, Hsu F, et al. (2009) The UCSC Cancer Genomics

Browser. Nature methods 6 (4): p. 239–40.

24. Goldman M, Craft B, Swatloski T, Cline M, Morozova O, et al. (2015) The UCSC Cancer

Genomics Browser: update 2015. Nucleic Acids Res 43(Database issue):D812–7.

25. Towns J, Cockerill T, Dahan M, Foster I, Gaither K, et al. (2014) XSEDE: Accelerating

Scientific Discovery. Computing in Science & Engineering 16(5): p. 62-74.

26. Huang DW, Sherman BT, Lempicki RA. (2009) Systematic and integrative analysis of

large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57.

27. Huang DW, Sherman BT, Lempicki RA. (2009) Bioinformatics enrichment tools: paths

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and visualization of biological lists using area-proportional Venn diagrams. BMC Genomics

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Chapter 3

Lysines in the tetramerization domain of p53 selectively modulate G1 arrest

Rachel Beckerman1§, Kathryn Yoh2§, Melissa Mattia-Sansobrino3§, Andrew Zupnick4§, Oleg

Laptenko2, Orit Karni-Schmidt2, Jinwoo Ahn5, In-Ja Byeon5, Susan Keezer6, and Carol Prives2†

1Current address: Maple Health Group, LLC, New York, NY, USA

2Department of Biological Sciences, Columbia University, New York, NY

3Current address: Department of Oncological Sciences, Icahn School of Medicine at Mount

Sinai, New York, NY 10029

4Current address: Novella Clinical, Morrisville, NC, USA

5Department of Structural Biology, University of Pittsburgh, Pittsburgh, PA, USA

6Cell Signaling Technology, Inc., Danvers, MA, USA

§ First authors.

† Corresponding author

clp3@columbia.edu

© Cell Cycle

2016

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ABSTRACT

Functional in a tetrameric state, the protein product of the p53 tumor suppressor gene

confers its tumor-suppressive activity by transactivating genes which promote cell-cycle arrest,

senescence, or programmed cell death. How p53 distinguishes between these divergent

outcomes is still a matter of considerable interest. Here we discuss the impact of two mutations

in the tetramerization domain that confer unique properties onto p53. By changing lysines 351

and 357 to arginine, thereby blocking all post-translational modifications of these residues, DNA

binding and transcriptional regulation by p53 remain virtually unchanged. On the other hand, by

changing these lysines to glutamine (2KQ-p53), thereby neutralizing their positive charge and

potentially mimicking acetylation, p53 is impaired in the induction of cell cycle arrest.

Surprisingly, when 2KQ-p53 is expressed at high levels in H1299 cells, it can bind to and

transactivate numerous p53 target genes including p21, but not others such as miR-34a and

cyclin G1 - and yet it still cannot arrest cells in the G1 phase of the cell cycle. Our findings show

that strong induction of p21 is not sufficient to block H1299 cells in G1, and imply that

modification of one or both of the lysines within the tetramerization domain may serve as a

mechanism to shunt p53 from inducing cell cycle arrest.

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INTRODUCTION

p53 is a DNA-binding transcription factor that carries out its tumor suppressive function

by playing roles in cell cycle arrest, apoptosis, genomic stability and DNA repair, as well as

other pathways (see refs. 1-5). The p53 protein consists of 393 amino acid residues with a

number of functional domains and isoforms of various lengths.6 The full-length wild-type (WT)

protein contains a bipartite transactivation domain within the N-terminal region (residues 20-40

and 40-60), a proline-rich domain with a pro-apoptotic role (residues 60-90), a sequence-

specific DNA binding domain (DBD) where most of the tumor-derived mutations reside (residues

100-300), an oligomerization domain which confers the tetrameric structure necessary for p53

function (residues 320-357), and a highly basic C-terminal domain (CTD) (residues 363-393)

which possesses the ability to interact with DNA in a sequence non-specific manner.7, 8

Well-studied as a sequence-specific DNA-binding transcription factor, p53 is most active

in that regard in its tetrameric state. Maintenance of its proper conformation is controlled by the

tetramerization domain.9, 10 Although not nearly as frequently mutated in cancer as the DBD, the

tetramerization domain of p53 has sustained certain tumor-derived mutations including L344

and R337. These are found mutated predominantly in Li-Fraumeni patients, affecting

oligomerization and transactivation abilities.11 In particular, an inherited mutation in p53

(R337H) was found to be associated strongly with familial pediatric adrenocortical carcinoma.12

Most relevantly, it was shown that an ovarian carcinoma mutation, K351N, attenuates p53

function via a similar mechanism.13 This same mutation was shown to compromise p53

ubiquitination and subsequent mitochondrial localization, affecting the apoptotic response.14

The tetramerization domain contains a nuclear export signal (NES) (residues 340 to

351), which is masked upon tetramerization of p53, allowing it to accumulate in the nucleus.15

Two residues in the NES (L348 and L350) appear to be critical both for nuclear export and

efficient tetramerization, suggesting an interplay between these two processes and optimal p53

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function. Biochemical studies suggested phosphorylation of S392 could increase tetramer

formation, while phosphorylation of S315 (within the linker region) might counteract this effect.16

Additionally, two reports conflictingly implicate CTD acetylation in either the promotion of

tetramerization and maintenance of high acetyl-p53 levels by PTEN, or the disruption of

oligomerization, followed by nuclear export.17-18

Following a wide range of cellular stresses, p53 becomes extensively modified at both

the N-termini and C-termini by a number of phosphorylating, acetylating, ubiquitinating,

sumoylating, methylating and neddylating enzymes.19-21 Acetylation and ubiquitination occur

predominantly within the CTD of p53 and there is an important balance between ubiquitination

and acetylation since acetylated lysines cannot simultaneously be ubiquitinated by Mdm2.22

Few modifications have been reported in the tetramerization domain of p53. One report

implicated PRMT5-mediated arginine methylation of three residues, R333, R335 and R337 as

being required for full induction of the GADD45, p21 and APAF1 genes.23 Another paper

identified K357 by mass spectrometry as undergoing acetylation in COS-1 cells,24 although no

biological consequence of the modification was reported. Lysine residues 351 and 357 have

been reported to be ubiquitinated by MSL2, a novel E3 ligase for p53 that promotes the

cytoplasmic localization of the protein, but not its degradation.25 A large screen to identify

ubiquitin-modified proteins confirmed the modification of lysine 357, but not lysine 351.26

However, mass spectrometry analysis of COS-1 p53 or etoposide-induced p53 from human

foreskin fibroblasts indicates acetylation and methylation take place at lysines 351 and 357. 27

From mining the TCGA database, we found various human cancers with alterations in

K351, including one kidney carcinoma with a K351N mutation and a lung carcinoma with a

mutation in 351 leading to a nonsense codon, as well as a malignant melanoma and an adrenal

cortical carcinoma with K351E mutations (see Table I).

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Additionally, the modification of other residues within p53 requires an intact quaternary

structure. For example, in a p53 protein where the tetramerization domain has been deleted,

Chk1 can no longer phosphorylate p53.28 One study reported that oligomerization of p53 is

essential for the acetylation of the protein’s CTD lysines.29

Since the functional consequences of modifications at K351 and K357 are still being

elucidated, and they are clearly of physiological interest, we generated cell lines expressing

mutations to either glutamine or arginine for both residues. Our results indicate that these

lysines are involved in differential regulation of p53 target genes and ensuing cellular outcomes,

notably cell cycle arrest.

RESULTS

Mutation of tetramerization domain lysines does not affect p53 localization or

oligomerization.

Since conflicting data have come from studies in which p53 was overexpressed

ectopically by transient transfection, which in some cases masked true in vivo function,30 we

analyzed the effects of lysine mutations at residues 351 and 357 in the more physiological

setting of inducible cell lines. Expression of p53 protein was regulated (by reducing or omitting

tetracycline) to levels comparable with endogenous expression.31, 32 When we undertook clonal

selection of cells expressing lysine residues 351/357 mutations to arginine (2KR-p53) or

glutamine (2KQ-p53), we obtained far fewer clones that expressed 2KR-p53 than their 2KQ-p53

counterparts. In fact, only two of the 2KR-p53 clones survived expansion and these expressed

significant amounts of p53 protein (Table II). This result suggests that, even though protein

expression should be completely silent in the presence of tetracycline,33, 34 there may be slight

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leakiness from the inducible promoter that expressed a hyperactive p53 that can block cell

survival and clonal isolation. This phenomenon was previously observed when we attempted to

clonally isolate an apoptotically hyperactive mutant of p53 (Table II, ref. 30). We proceeded to

characterize the p53 proteins with mutated tetramerization domain lysines (2KQ-p53 and 2KR-

p53).

A diagram of p53 organization with the location of the two lysines in the tetramerization

domain (denoted by asterisks) is shown in Figure 1A. As deduced from the solution structure of

the tetramerization domain,34, 35 both of these residues are solvent-accessible, and thus

potentially amenable to modification (Figure S1A). Since mutations at other residues in the

tetramerization region have previously been shown to disrupt p53 quaternary structure and

expose a nuclear export signal,11, 15 we first wanted to investigate the subcellular localization of

our mutants. Immunofluorescence experiments with two clones each of 2KR-p53 and 2KQ-p53

expressing cells showed strong nuclear staining that was virtually identical to WT-p53 (Figure

1B). The mutants further behaved like WT-p53 by showing nucleolar exclusion36 as evidenced

both by DIC imaging and a lack of colocalization with nucleolin (data not shown).

In general, misfolded proteins may be quickly degraded by multiple degradation

pathways via quality control mechanisms (as reviewed in ref. 37). We performed half-life

experiments to confirm that the tetramerization domain mutations were not causing hyperactive

degradation of p53. Figure S1B shows that when transfected into H1299 cells, WT-p53, 2KQ-

p53, and 2KR-p53 proteins each have a similar half-life (~9 hours) as determined by

cycloheximide chase. Although endogenous p53 has a half-life of about 20-30 minutes,

ectopically p53 is generally known to be much more stable for as yet unknown reasons 38.

Further, information about the tetramerization region of the wild-type and mutant protein

was derived from NMR analysis. 1H-15N TROSY-HSQC experiments were performed with WT-

p53, 2KR-p53 or 2KQ-p53 tetramerization domain proteins (310-362) to assess whether double

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mutations of the K351 and K357 residues perturbed the local structure of the protein. For both

the 2KQ-p53 and 2KR-p53 mutants (310-362), the overall appearance of the HSQC spectra

was similar to WT-p53 (Figure 1D and E), indicating the mutations did not cause global

unfolding and/or gross aggregation of the protein. The assignments of the 1H-15N TROSY-

HSQC resonances of the mutants were straightforward because of close similarity of the

chemical shifts to those of WT-p53 protein, except for a couple of resonances. Specifically, the

2KR-p53 spectrum, in contrast to the 2KQ-p53 spectrum, did not show a peak for the Leu350

resonance at the chemical shifts similar to the WT-p53 resonance (8.24, 123.8ppm). However,

one new unassigned peak (8.51, 123.1ppm) was detected in the vicinity of the spectral region,

allowing us to tentatively assign this peak as the L350 resonance. In summary, 1H-15N amide

backbone resonances for all but five N-terminal residues (310-315) were assigned.

In Figure S1C, chemical shift differences between WT-p53 and the 2KR-p53 or 2KQ-p53

mutants are plotted versus residue number. The largest differences observed were spatially

close to the mutated residues. The residues with great (>0.10 ppm) chemical shift deviations

from WT-p53 were F341, A347, L350 and A355. However, the differences between 2KR and

2KQ at these residues are minor (≤0.05 ppm), with the exception of L350. Due to their similar

spectra, and overall deviation from WT-p53 being minimal, we conclude that the quaternary

structure of p53 is not significantly affected by these mutations.

To determine whether these mutations affected tetramerization of p53, we performed

gluteraldehyde crosslinking gel electrophoresis comparing WT-p53, 2KR-p53 and 2KQ-p53

expressed in transfected H1299 cells and showed that variants formed equivalent populations of

tetramers and dimers under denaturing conditions (Figure 1C). Additionally, analytical size

exclusion column chromatography analysis on Thioredoxin-fusion p53 (tetramerization domain

only) showed that WT, 2KR and 2KQ have similar quaternary states at 150 – 500 mM NaCl

(data not shown).

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Together these results demonstrate that mutation of the lysines within p53’s

tetramerization domain to either arginine or glutamine may cause minor localized alterations,

however these do not seem to affect the tetramerization status, localization or degradation of

the protein itself.

2KQ-p53 is deficient in binding to and transactivation of p53 target genes.

We examined the abilities of tetramerization domain lysine p53 mutants to function as

transcription factors. By regulating the concentration of tetracycline in the medium, p53 protein

expression was normalized for each cell line so that equivalent protein levels could be

compared (Figure 2A). Immediately a difference in activities between 2KR-p53 and 2KQ-p53

could be seen: 2KR-p53 was able to induce similar levels of p21 protein as WT-p53 while p21

resulting from 2KQ-p53 induction was barely increased above background basal levels. When

looking at a direct readout of transcriptional activity by analyzing mRNA production via RT-PCR

experiments, we saw similar results. Two clones of 2KR-p53 (2KR-13 and 2KR-17) induced

equivalent amounts of p21, PIG3 and Mdm2 mRNA as WT-p53, yet two clones of 2KQ-p53

(2KQ-5 and 2KQ-12) were significantly impaired in this respect (Figure 2B and C).

We next evaluated DNA binding by these p53 variants in vivo in a ChIP assay. Again,

immunoblot analysis of the lysates indicated similar levels of p53 expressed for each line along

with weaker induction of p21 protein by 2KQ-p53 (Figure 2D). In fact, binding by the 2KR and

2KQ mutants to three different p53 target gene promoters (p21-5’, PIG3 and Mdm2) in ChIP

assays correlated well with the downstream induction of mRNA and protein; 2KR-p53

reproducibly bound as well as WT-p53, and 2KQ-p53 had decreased affinity for these sites

(Figure 2E and F).

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Overall, our findings indicate that blocking lysine modification within the tetramerization

domain does not impact DNA binding, whereas neutralizing charge at these residues decreases

p53’s overall affinity for DNA in vivo.

2KQ-p53 cannot arrest cells in G1 despite high levels of p21.

We investigated the ability of the p53 variants to affect two well studied downstream

events in the p53 pathway, namely cell cycle arrest and apoptosis. As before, cells were

induced to express equivalent amounts of p53 protein prior to determining their cell-cycle profile

analyzed by FACS. A typical effect of WT-p53 expression in these cells is evidenced by a drop

in S-phase and an increase in either G1 or the G2 population (Figure 3A, top row). A mild

increase in sub-G1 content was also seen, as reflected by the amount of fragmented DNA from

apoptotic cells.39

Results with the tetramerization domain mutants were quite striking. Similar to cells

expressing WT-p53, expression of 2KR-p53 caused a drop in S-phase and a strong arrest

(Figure 3A). 2KQ-p53, on the other hand, showed the opposite phenotype. Upon expression of

this mutant, there was no discernable cell cycle arrest. Notably, the 2KQ-p53 mutant was able

to induce apoptosis, both alone (weakly) and, more strongly in response to 5-FU (Figure 3B) to

a comparable degree as did WT-p53, indicating a selective deficiency in the ability of 2KQ-p53

to function within the cell cycle arrest pathway. We wondered if this phenotype was due to

abnormal localization of the key cell cycle regulator, p21. However, immunofluorescence of this

protein in the three cell lines in tetracycline-free conditions indicated that it is still localized in the

nucleus (Figure 3C).

It is interesting that 2KQ-p53, when regulated to express a similar amount of protein as

WT-p53 was able to cause apoptosis despite its deficiency in inducing apoptotic target genes

such as PIG3 (Figure 2) as well as Bax,, Puma, Noxa, and PIDD (Figure S2). The ability of

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2KQ-p53 to transactivate these genes was not significantly increased after treatment with either

daunorubicin or 5-FU (data not shown). Under these conditions, then, either one or more

unidentified key pro-apoptotic targets of p53 can be well induced by 2KQ-p53 or this mutant is

competent in regulating a p53-mediated transcription-independent apoptotic pathway.

The inability of 2KQ-p53 to arrest cells was an intriguing finding that called for further

investigation. Since p21 is thought to be the major effector of p53-mediated cell-cycle arrest, 40-

42 it is possible that 2KQ-p53 was unable to cause a G1 arrest simply because it did not induce

sufficient p21 mRNA and protein. To address this question we adjusted the amounts of

tetracycline in the culture media of WT-p53- and 2KQ-p53-expressing cells to obtain points

where the mutant induced markedly more p21 protein than did the WT-p53 cells (Figure 3D).

While WT-p53 was still capable of causing a robust arrest, surprisingly, 2KQ-p53 was

completely inert in this regard even when expressed at higher levels than the wild-type protein

(Figure 3E). This effect was not unique to this clone of cells, as we observed this phenotype

with three additional 2KQ-p53-expressing clones (Figure 3F). In line with the FACS analysis,

turning on WT-p53 caused a decrease in the levels of phospho-Rb, consistent with a G1 arrest,

while overexpressing 2KQ-p53 did not lead to such a decrease (Figure 3D). Furthermore, when

WT-p53 was expressed in the background of p21 downregulation by siRNA (Figure S3), cells

were nonetheless able to arrest in G1 to a similar extent to those cells with control siRNA. Thus,

in this system, expression of p21 alone is not sufficient (and may not be necessary) to induce a

G1 arrest.

Even when overexpressed in vivo, 2KQ-p53 is deficient in binding and inducing miR-34a

and CCNG1.

Since levels of p21 were clearly not the sole determinant of G1 arrest in this system, we

next wanted to investigate how 2KQ-p53 regulates other p53 target genes when expressed at

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high levels. As before, cells were plated and conditions were set to express more 2KQ-p53

protein than WT-p53 protein, and the induction of a panel of p53 targets was assessed by qRT-

PCR (Figure 4A). We found that when overexpressed, 2KQ-p53 was as competent as WT p53

in inducing several targets, with the notable exceptions of CCNG1 and miR-34a. Intriguingly,

both of these targets have been implicated in the G1 arrest pathway.43-45 We saw similar if not

more dramatic results with another clone of 2KQ-p53 (Figure S4A).

p53 DNA binding can directly correlate with mRNA induction32 and so we next asked

whether the marked deficiency of 2KQ-p53 in inducing CCNG1 and miR-34a mRNA was due to

an impaired ability to bind the p53 canonical response elements at these loci. This question

was especially important because many alterations to the C-terminus of p53 have been shown

to impact binding by the core domain in vitro (reviewed in ref. 46). As before, cells were plated

and induced with tetracycline for 24 hours to express more 2KQ-p53 protein than WT-p53

protein, and a ChIP assay was performed to assay for p53 binding to its distal p21, miR34-a,

and CCNG1 response elements. Nicely mimicking our RNA data, when 2KQ-p53 was

overexpressed, it was capable of binding the p21 distal response element to a comparable

degree as WT-p53. However, even when it was expressed at these high levels, 2KQ-p53 was

still markedly deficient in binding the miR-34a and CCNG1 response elements in a ChIP assay

(Figure 4B). Similar results were observed in a transient transfection/ChIP assay (Figure

S4B,C).

We examined whether the 2KQ-p53 binding deficiency observed in vivo was due to

impaired direct binding to DNA by performing an electrophoretic mobility shift assay (EMSA)

with purified WT-p53 and 2KQ-p53 proteins. WT-p53 was capable of strong binding to a probe

derived from the p21 5’ response element, as well as a probe derived from the miR-34a

response element, as evidenced by a shift of IRDye-labelled DNA (Figure 4C). Excess short

oligonucleotide was used as a carrier to compete away any nonspecific interactions in these

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reactions since long carrier can engage the p53 C-terminus and has led to confusing

interpretations of EMSA data in the past. 47, 48 Surprisingly, given our ChIP data, at high

concentrations, 2KQ-p53 protein bound probes derived from p21 and miR-34a response

elements somewhat better than WT-p53 (Figure 4C). That WT-p53 and 2KQ-p53 binding to p21

and miR-34a probes were nearly identical, suggests that whatever is responsible for the

differential binding profiles of WT-p53 and 2KQ-p53 to these sites in a ChIP assay is not

recapitulated in vitro. Even though EMSA results do not always reflect cellular DNA binding, 32,

49 it was nonetheless surprising that at high concentrations 2KQ-p53 appeared to bind DNA

better than WT-p53, however it transactivated p53 targets to a lesser degree in vivo. It is

possible that the differential binding of WT-p53 and 2KQ-p53 were related to the slight structural

perturbations seen in our NMR spectra, as were the somewhat different display of p53:DNA

complexes formed by WT and 2KQ p53 proteins on the miR-34a probe. Nonetheless, since the

overall binding deficiency of 2KQ-p53 at high concentrations was not observed on naked DNA

spanning the miR-34a binding site in vitro, these results suggest that the binding impairment of

2KQ-p53 is not inherent to the sequence of the p53 RE at this locus.

Lysine 357 can be acetylated by p300/CBP

Given our results with acetyl-mimicking mutants and previously reported findings 24, 26-27

we sought to confirm that lysine residues 351 or 357 could be acetylated in vivo. We first

transfected constructs expressing WT-p53 alone or with either HA-tagged p300 or CBP, or Flag-

tagged pCAF into H1299 cells. The cells were harvested and subjected to immunoblot analysis

with a rabbit polyclonal antibody that recognizes doubly acetylated p53 at K351 and K357 (anti-

Ac-351/7). Both p300 and CBP were able to acetylate these residues (Figure 5A). While pCAF

was not apparently able to do so when expressed in these cells, it is acknowledged that a firm

conclusion cannot be drawn since the version of pCAF we had available has a different epitope

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tag and so we cannot compare its levels to those of p300 and CBP. Tip60, a HAT implicated in

the acetylation of p53 at Lys120,50 was also apparently unable to acetylate these residues (data

not shown). Although our p300 construct expressed poorly, on a per mole basis, it is possible

that it could acetylate K351 and K357 to a similar extent as did CBP.

To determine if the antibody was specific to these residues, we performed a similar

transfection experiment in which either WT-p53 or 2KR-p53 was transfected with or without

CBP. The signal from the Ac351/7 antibody was markedly reduced when 2KR-p53

cotransfected with CBP was compared with WT-p53 cotransfected with CBP (Figure 5B).

Mutations to arginine within these residues of the tetramerization domain did not appear to

compromise p53 reactivity with a pan-acetyl antibody, while reactivity with an Ac-K382-specific

antibody was decreased as compared to WT-p53 (Figure 5C).

While the anti-Ac351/357 antibody could be used to detect ectopically overexpressed

p53, unfortunately, it was not strong enough to detect endogenously expressed p53. A rabbit

polyclonal antibody was generated that specifically recognized p53 acetylated at K357 (Ac357;

Figure S5). HCT116 cells, which contain wild-type p53, were treated with either MG132, a

proteasome inhibitor that stabilizes p53 in the absence of DNA damage, or with 5-FU, to induce

damage-stabilized p53. The cells were harvested and subjected to immunoblot analysis with

anti-Ac357 (Figure 5C). Acetylation of K357 was induced in response to damage by 5-FU, but

was not visible when p53 was stabilized in the absence of damage by MG132, indicating that 5-

FU initiates a signaling pathway that results in acetylation of p53 at K357.

Our results extend previous findings from mass spectrometric analysis that p53 can be

acetylated at K357.26-27 Taken together, these data indicate that CBP (and likely p300), at least

when they are overexpressed, are capable of acetylating p53 at K351 and/or K357 in

mammalian cells, and that acetylation of K357 occurs endogenously in response to genotoxic

stress, unlocking the exciting possibility of a physiological importance for these modifications.

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Given that our data thus far demonstrated that 2KQ-p53 was largely defective in binding

and transactivating p53 target genes (Figure 2), it was perhaps counterintuitive that acetylation

of p53 at K357 was increased after DNA damage, when p53 function is amplified. We therefore

constructed single mutants of p53-K351 (R or Q) and p53-K357 (R or Q). To determine whether

the individual p53 mutants of these lysines gave rise to the same phenotype as the double

mutant, their transactivation ability was assessed in a transient transfection assay (Figure 5D).

Interestingly, at comparable levels of p53 protein (Fig 5D), both K351Q-p53 and K357R-p53

showed reduced transcription of p21 mRNA when compared with WT-p53, while K351R-p53

and K357Q-p53 could transactivate p21 as well as, or better than, WT-p53 (Figure 5E). To date,

we have not yet been able to confirm whether K351 is also acetylated. These data indicate that

if K351 is in fact acetylated such modification may be the reason for the defects in 2KQ binding

and transactivation of select p53 targets. In that case, modification of K351 and K357 would

play opposing roles in regulating p53 function, and the negative role of K351 modification would

be dominant over a positive role of K357 modification.

DISCUSSION

While the extreme CTD of p53 is highly modified, few modifications have been described

within p53’s tetramerization domain.19, 51 We sought to investigate the potential role of

modification within this region by mutating the only two lysines in this domain of p53 (351 and

357, Figure 1A) to either block all post-translational modification (lysine to arginine, 2KR-p53) or

to neutralize basic charge (lysine to glutamine, 2KQ-p53).

Although mutation of other residues within this region can drastically impact p53

tetramer formation and function, 52, 53 we found that neither 2KR-p53 nor 2KQ-p53 possessed

unusual localization or tetramerization ability. Our data strongly suggest that alteration of the

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lysines in the tetramerization domain does not have a deleterious effect on the correct folding of

p53. To that end, the cellular phenotypes we observe seem to be a direct effect of the lysine

mutations and not caused by perturbations in overall structure.

When examining the effects of lysines 351 and 357 mutation on p53’s role as a DNA-

binding transcription factor, some interesting results were observed. Mutation to arginine did

not have any observable impact on transactivation or in vivo DNA binding (Figure 2). On the

other hand, mutation to glutamine had a significant impact on both of these functions, reducing

p53’s ability to bind response elements in vivo and induce mRNA production by about half.

Although acetylation on lysines in regions flanking the tetramerization domain have been shown

to enhance p53 transcriptional activity, our results point to a requirement of the positively

charged residues in this region for proper recognition of DNA response elements by the core

domain in the context of chromatin and efficient transcription. It is possible that even though

neutralization of these lysines does not disrupt tetramerization and localization they could impart

some subtle effects on the quaternary structure of p53 which impairs its access to DNA, as

suggested by our NMR data. However, since at higher concentrations, the 2KQ-p53 mutant was

not deficient in binding naked p21 or miR-34a response elements in an EMSA assay (Figure

5C), it is also possible that the altered promoter specificity in vivo was due to the ability of 2KQ-

p53 to interact in an altered fashion with p53 binding partners. Many proteins have been shown

to bind to p53 and to affect its propensity to bind select response elements,5, 20 and further

experiments are underway to explore the differential association of WT-p53 and 2KQ-p53 with

such proteins. Alternately, the chromatin landscape at p53 target promoters may account for

different interactions of WT and 2KQ-p53.

How p53 discriminates between inducing cell-cycle arrest or apoptosis, the so-called

“molecular switch,” has been attributed to a number of players, yet none of them are sufficient to

drive this process in all cases. In fact, mouse studies have conflictingly suggested that p53’s

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arrest or apoptotic functions are individually dispensable for tumor suppression.54, 55

Mechanisms underlying this decision making process could be as simple as levels of p53

protein, presumably dictated by the extent of cellular stress, 32 or as complex as a coordination

of modifications, interactions and conformational changes to the protein in a cell-type specific

context. A growing list of mutations within various regions of p53 has been shown to have an

impact on the downstream function of p53. Some of these mutations exhibit increased

apoptotic activity (S121F, S46F), 56, 57 while others have been shown to drastically decrease the

apoptotic potential of p53 (R213Q, A175P, A143P, ∆300-308).58-61 In these cases, the

combined effect of the mutation on target gene activation, cell cycle arrest and apoptosis has

been variable, supporting the idea that p53 serves as a “master gene” in which a single change

can lead to simultaneous downstream changes.62 Even naturally-occurring polymorphisms

within p53 can influence serine-46 phosphorylation (P47S)63 or cause a propensity for apoptosis

by increasing p53’s export from the nucleus (P72R) and binding to the mitochondria which

activates a transcription-independent death pathway (reviewed in ref. 64). Other interacting

proteins such as the ASPP family of proteins which bind to the core and proline-rich regions of

p53, 65, 66 or kinases such as PKCdelta and HIPK2 which are recruited to p53 by p53DINP1 can

selectively upregulate genes involved in cell death.67-69

With 2KQ-p53, a global defect in transcriptional ability could certainly explain the lack of

arrest. However, since p21 is thought to be the major effector of p53-mediated G1 arrest, our

findings that 2KQ-p53 still failed to arrest cells even after inducing wild-type (or greater) levels of

p21 induction were surprising. That 2KQ-p53 was still capable of killing cells after 5-FU

treatment was also unexpected, considering that apoptotic targets like PIG3 are known to

possess generally weaker binding sites70 and that in our hands 2KQ-p53 was less capable of

inducing a number of apoptotic targets. This is a rare instance of a version of p53 incapable of

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arrest yet still functional for programmed cell death and implicates a role for this mutant in the

transcription-independent apoptotic program of p53. The 2KQ mutant p53 that we describe here

functions to the contrary of several previously described p53 mutants, including K120R, R175P

and E177R, which are defective in apoptotic induction but are still able to regulate cell cycle

arrest. 50, 71-73 It is also unknown whether our mutant would be incapable of senescence, since it

cannot arrest, and future work will look into these capabilities.74, 75

Data with the 2KQ-p53 mutants point to some exciting possibilities. First, there may be

additional p53 transcriptional targets required for inducing G1 arrest other than p21. Lending

support to this notion, p21-null mice develop normally, but curiously only partially fail to arrest in

response to irradiation, indicating that a parallel, p21-independent pathway for G1 arrest must

also exist.41 Based on data described herein and elsewhere, it is possible that this parallel

pathway involves CCNG1 and/or miR34a as well as other targets that we have not identified.

Along these lines, in certain contexts, miR-34a can inhibit cellular proliferation even when p21 is

absent. 43 Notably, miR-34a functions by downregulating various cell-cycle related targets,

including CDK4, CDK6, c-Myc, cyclin D1 and cyclin E2 mRNA, leading to decreased levels of

phosphorylated Rb.43 CCNG1 was one of the earliest p53 targets to be discovered,74 yet its

function remains controversial. It is known that CCNG1 can either induce a G1 arrest or

apoptosis when expressed at high levels, in a fashion partially dependent on the Rb protein.45

However, at low levels of expression, CCNG1 appears to promote proliferation.47 Also of note, a

p21-independent, p53-mediated repression of c-Myc was shown to be necessary for human and

mouse cells to arrest in G1.75 C-Myc is an oncogene that drives cellular proliferation,76 and can

function by overcoming p53-mediated activation of p21 and GADD45.77, 78 It is possible that

while WT-p53 can effect this repression of c-Myc, 2KQ-p53 cannot, thus preventing an efficient

arrest of cells in G1. Another possibility is the existence of a pathway activated by 2KQ-p53

which can selectively block p21’s inhibition of cyclin/CDK complexes. Next-generation

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sequencing studies would undoubtedly be worthwhile to look for additional targets induced or

repressed by these mutants and to find novel targets directly involved in these phenotypes.

An impetus for this study was the discovery that p53 can be modified on lysines 351 and

357, however except for in the case of ubiquitination, the implications of this are largely

unknown.24, 26-27 Here, we show that p53 can be acetylated in vivo on K357, although the

functional significance of this modification alone is not yet clear. While our finding that

acetylation of K357 increases after 5-FU treatment might seem at odds with our results that

2KQ is impaired in transactivating p53 target genes, we cannot exclude the possibility that

modification of K351 negatively regulates p53 target gene expression and is dominant over

K357. Indeed, given our transfection data with the tetramerization domain single mutants

(Figure 5D, E), this appears to be the case, at least for p21. It is also possible that there are

differential kinetics and outcomes of modification of these residues. Such possibilities await

future experimentation and further work is required to elucidate how these and potentially other

modifications on lysines 351 and 357 can essentially “flip the molecular switch” of p53 to drive

either cell cycle arrest or apoptosis.

MATERIALS & METHODS

Cell line creation and cell culture

H1299 cells expressing tetracycline (tet) regulated (“tet-off”) WT-p53 were previously

described.30, 31 Mammalian expression constructs in the pTRE2 backbone (Invitrogen)

expressing p53-(K351R/K357R; 2KR) and p53-(K351Q/K351Q; 2KQ) were mutated from the

wild-type sequence using the QuikChange protocol (Qiagen) according to the manufacturer’s

specifications. H1299-derived inducible cell lines expressing 2KR-p53 and 2KQ-p53 were then

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created using a two-step tetracycline-regulated system and clonally selected with 400 µg/ml

Hygromycin B (Invitrogen) as previously described.31 The cells were grown in Dulbecco’s

Modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum, 5 µg/ml

tetracycline (Sigma), 100 µg/ml Hygromycin B (BD Biosciences), and 200 µg/ml G418 (Gibco).

At least 25 clones were picked and screened for each line and all lines used in this study had

their p53 sequence confirmed. Transfection experiments were performed with Lipofectamine

2000 (Invitrogen), according to the manufacturer’s protocols.

Immunofluorescence (IF)

Cells were plated without drugs to 90% confluency in a 35-mm dish with a glass coverslip,

washed twice 22 hours after seeding, and induced to express p53 (with tetracycline amounts

indicated in the figure legends). Twenty four hours after induction, cells were washed twice with

PBS followed by incubation with 4% paraformaldehyde (Sigma-Aldrich) for 15 minutes then

washed three times with PBS, treated with PBS/0.5% Triton X-100 for one and a half minutes

and blocked with 0.5% BSA (Sigma-Aldrich) in PBS for 30 minutes prior to the incubation with

the antibodies. Samples were incubated with monoclonal antibodies PAb 1801 and PAb DO-1

(50 µl) for one hour at room temperature. The coverslips were then washed three times with

PBS followed by incubation for another hour with 50 µl of diluted (1:100) secondary Cy5 goat

anti-mouse IgG antibody (Jackson ImmunoResearch) and washed three times with PBS. DNA

staining was performed using 10 nM SYTOX Green nucleic acid dye (Molecular Probes) for 10

minutes. The coverslips were then mounted with 10 µl cold 50% glycerol. The images were

collected using confocal laser scanning microscopy (Olympus Model 1X70) with Fluoview

software. In order to directly visualize cells and nuclei, Differential Interference Contrast (DIC)

images were taken in parallel. For p21 IF, the indicated cell lines were grown in tetracycline-free

media for 48 hours before fixation, same as above. They were incubated with PAb p21 (F-5,

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Santa Cruz Biotechnology) for one hour and then with Alexa Fluor 488 (sc-6246) for one hour.

Cover slips were mounted with 20 μl Vectashield Mounting Medium with DAPI (Vector

Laboratories, H-1200, Burlingame, CA) and visualized as above.

Creation of acetyl-specific antibodies

Polyclonal antibodies were generated by Cell Signaling Technologies and produced by

immunizing animals with synthetic acetylated peptides (KLH-coupled) corresponding to residues

surrounding lysine 351 and lysine 357 of human p53. Antibodies were purified using protein A

and peptide affinity chromatography.

Protein Purification and NMR Spectroscopy

N-terminally Flag-tagged WT-p53 and 2KQ-p53 mutant proteins that contain an inserted site at

their N-termini were affinity purified as previously described from Sf-9 cells infected with the

corresponding baculovirus for p53.81 Constructs coding for p53 residues 310-362 were

amplified from WT-p53, 2KQ-p53, and 2KR-p53 mutants respectively. The cDNAs were

subcloned into pET32a (EMD Chemicals, Inc.) using EcoRI and XhoI sites. A TEV protease

recognition sequence (ENLYFQS) was created between the BamHI and EcoRI sites in pET32,

at the C-terminus of Thioredoxin. After TEV protease cleavage, p53 proteins contained Ser-

Glu-Phe at their N-termini. For isotopic labeling, proteins were expressed in E. coli Rosetta 2

(DE3), using 0.4 mM IPTG for induction and growth at 23 ºC for 16 h in modified minimal media

using 15NH4Cl and U-13C6-Glucose or U-[13C6,2H7]-Glucose as sole nitrogen and/or carbon

sources. Soluble forms of His-tagged proteins were first purified using 5 mL Ni-NTA columns

and followed by gel-filtration column chromatography using Hi-Load Superdex200 16/60 (GE

Healthcare) with a buffer containing 25 mM sodium phosphate, pH 7.5, 50 mM NaCl, 1 mM

DTT, and 0.02 % sodium azide. After removal of Thioredoxin, final purifications of p53 proteins

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were performed over a Hi-Trap QP column (GE Healthcare) at pH 7.5 using a 0 – 1 M NaCl

gradient. Buffer exchange was carried out using Amicon concentrators (Millipore).

All NMR experiments were conducted at 17°C using protein samples in 25 mM sodium

phosphate buffer, pH 7.5, 150 mM NaCl, 2 mM DTT, 0.02% sodium azide and 7% D2O, using a

Bruker Avance 700 MHz spectrometer, equipped with 5 mm, triple resonance and z-axis

gradient cryoprobes. For backbone chemical shift assignments of WT-p53(310-362), 2D 1H-

15N TROSY-HSQC and 3D TROSY-HNCACB and TROSY-HN(CO)CACB experiments were

performed on a U-[13C,15N,2H]-labeled protein. The 1H-15N assignments of the 2KQ-p53 and

2KR-p53 mutants were obtained by comparing the 2D 1H-15N TROSY-HSQC spectra of U-

[15N]-labeled mutant proteins to that of U-[13C,15N,2H]-labeled wild-type protein.

Immunoblotting analysis

Cells were seeded and induced as for immunofluorescence but on a 60 mm culture dish. 24

hours after induction, cells were harvested and split into pellets for immunoblotting or RT-PCR.

Lysis for immunoblotting was performed as previously described.82 p53 protein was visualized

by separating 40 µg of whole-cell lysate on a 12% polyacrylamide gel, transferring to

nitrocellulose followed by immunoblotting with DO-1 antibody (hybridoma supernatant, 1:1

dilution with 5% milk) for 1 hour at room temperature. Other antibodies used were anti-p21

(C19, Oncogene Research Products), anti-pRb (XZ.131 supernatant), anti-HA (Covance), anti-

Flag (Sigma-Aldrich), anti-pan-acetyl lysine (Cell Signaling Technology), anti-p53 Ac382 (a gift

from Y. Taya), and anti-actin (Sigma-Aldrich).

Gluteraldehyde crosslinking

H1299 cells were transfected with pCDNA3 vectors expressing WT-p53, or mutants 2KR-p53,

or 2KQ-p53 (500 ng). 24 hours later, samples were harvested in TEGN buffer, and

229

gluteraldehyde was added to a final concentration of .005% to .020%. Samples were incubated

on ice for 20 minutes, and the crosslinking reaction was stopped by the addition of protein

sample buffer. Multimeric complexes were separated by SDS-PAGE and analyzed by Western

Blotting.

RT-PCR and qRT-PCR

RNA was extracted from cell pellets using RNeasy Mini Kit (Qiagen) and quantitated by

ultraviolet spectrophotometry. cDNA was created from 1 µg of total RNA using the Superscript

III First Strand Synthesis System for RT-PCR (Invitrogen), following the protocol for oligo-dT

priming. RT-PCR was performed using a dNTP mix (Roche) and Taq 2000 DNA Polymerase

(Stratagene). Conditions for linear amplification were established through template and cycle

curves. The cycling conditions were as follows: a denaturation step at 95°C for 5 min followed

by 20 cycles (for p21, GADPH, and PIG3) or 19 cycles (for Mdm2) at 95°C for 30 sec, 56°C for

30 sec and 70°C for 30 sec, with a final extension of 72°C for 7 min. PCR products were then

separated on 2.5% agarose gels and bands were visualized with ethidium bromide and the Gel

Logix 100 imaging system (Kodak). qRT-PCR was performed essentially as described. 83

Sequences for the primers are available upon request.

Electrophoretic mobility shift assay (EMSA)

WT- p53 and 2KQ-p53 purified proteins from Sf9 cells (0 to 80 ug of total protein) were

incubated for 25 minutes at room temperature in 1X EMSA buffer (12.5 mM Tris-HCl pH 6.8, 25

mM KCl, 10% glycerol, 0.05% Triton-X, 0.5mg/ml BSA, 1 mM DTT, 250 ng mutant p21

oligonucleotide) with 10 ng of a p21 5' binding site-containing 44 base pair oligonucleotide (5’-

AGC TAG TAG AGC GAA TAT ATC CCA ATA TAT TGG CGT GCT GCA GC-3’)or a miR-34a

p53 binding site-containing 44- mer (5’-CGG GCT CTG CCT GGG CTT GCC TGG GCT TGT

230

TCC GAG CCG GGC TG-3’), labeled at the 5’ end with IRDye 800. Oligonucleotides were

manufactured by Integrated DNA Technologies. The samples were run on a 4% native

polyacrylamide gel at 200 Volts. The gel was visualized with a Licor Odyssey apparatus

according to the manufacturer’s protocols. Oligonucleotide sequences used for EMSA are

available upon request.

Chromatin immunoprecipitation (ChIP)

Inducible cells were seeded to a 50% density in 10 cm plates and protein expression was

induced for 24 hours as described above. Crosslinking, lysis, sonication, immunoprecipitation,

purification, and semi-quantitative or qRT-PCR were performed as previously described. 82, 84

Primer sequences are available upon request.

Fluorescent-activated cell Sorting (FACS) analysis

For cell cycle analysis, 2x105 cells were seeded per 60-mm plate without tetracycline. 22 hours

after plating, the cells were washed and tetracycline added as needed to equilibrate protein

levels. 0.5 mM 5-fluorouracil (5-FU) was added 24 hours after induction as indicated and 48

hours after that, cells were harvested by trypsinization and fixed overnight with methanol at –

20°C as previously described.85 Fixed cells and fragmented DNA were spun down for 5 minutes

at 1660 g, resuspended with 1 ml cold PBS, and rehydrated for 30 minutes on ice. 86 After

another spin, cells were resuspended in 0.5 ml of PBS solution containing RNase (50 µg/ml)

and propidium iodide (PI) (60 µg/ml, Sigma), and incubated in the dark for 20 minutes at room

temperature. Stained cells were analyzed in a fluorescence-activated cell sorter (FACSCalibur,

Becton Dickinson), gating away the debris and aggregates. Cell cycle profiles and apoptotic

sub-G1 content were quantitated using the ModFit LT program.

231

Acknowledgements

We would like to thank Dr. Ella Freulich for her technical expertise and support, Dr. Masha

Poyurovsky for her valuable input on the manuscript, Dr. Song Xiang (now at Shanghai’s

Institute for Nutritional Sciences) for his assistance with PyMOL, and other members of the

Prives lab for helpful discussion and input. This work was supported by NIH grant CA77742.

Author contributions

RB, KY, AZ and MM-S made cell lines and performed experiments. OL contributed intellectually

and performed glutaraldehyde crosslinking experiments, while OK-S provided the

immunofluorescence data. JW and IB performed NMR, chromatography, and analysis. SK

created the acetyl-specific reagents. CP provided intellectual contribution.

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Figure Legends

Figure 1. Mutation of the two lysine residues within the tetramerization domain of p53

does not alter subcellular localization or oligomerization.

A) Schematic representation of the domains of p53. Location of tumor-derived mutations and

relative frequency are indicated by the height of the line found at each amino acid position.

The primary sequence encompassing lysine residues 351 and 357 in the tetramerization

domain is listed and asterisks denote the positions of these residues within the sequence.

(B) Subcellular localization of tetramerization domain mutants. Cell lines listed on left were

induced to express p53 by removal of tetracycline for 24 hours. DNA was visualized by

staining with Sytox Green (‘Sytox’) and p53 localization was detected by incubation with

DO-1 monoclonal antibody followed by goat anti-mouse secondary antibody conjugated to

Cy5 (‘p53’, red). The ‘+ tet’ image shows p53 staining without protein induction. DIC

image of wild-type shows the whole cell including its nucleus and nucleoli.

(C) H1299 cells were transfected with WT-p53, 2KR-p53, or 2KQ-p53 expression constructs.

24 hours later, cells were harvested and crosslinked with 0.005%, 0.01%, or 0.02%

gluteraldehyde. Complexes were separated by SDS-page and subjected to immunoblot

analysis.

(D) Superposition of the 1H-15N TROSY-HSQC spectra of WT-p53 (black) and the 2KQ-p53

mutant (red).

(E) Superposition of the 1H-15N TROSY-HSQC spectra of WT-p53 (black) and the 2KR-p53

mutant (green).

Figure 2. 2KQ-p53 mutants show reduced ability to bind to and transactivate canonical

p53 target genes, while 2KR-p53 mutants behave similarly to WT-p53.

242

(A) Western Blot analysis. Tetracycline levels were regulated (as indicated) to express

equivalent amounts of wild-type or mutant p53 protein, as determined by immunoblotting

with 40 g of whole cell extract. Levels of p21 protein induction were determined for each

cell line and actin levels were assessed as a loading control.

(B) mRNA induction by p53 variants. Total cellular RNA was prepared with cells from the

same plate as (A) and cDNA was generated from Poly-A mRNA by oligo-dT priming. 2 l

of each cDNA reaction was then used as a template in semi-quantitative RT-PCR to

amplify the indicated endogenous target. 20 l PCR reactions were resolved by agarose

gel electrophoresis, stained with ethidium bromide, and visualized on a Kodak gel imaging

system.

(C) Summary chart showing the average of three independent RT-PCR experiments, graphing

arbitrary mRNA induction after normalizing to GAPDH and uninduced basal levels. Error

bars indicate standard deviation.

(D and E) Chromatin binding in vivo. Wild-type p53 (WT-p53), 2KR-17, or 2KQ-12 cell lines

were induced with indicated tet amounts for 24 hours, crosslinked, lysed, sonicated, and

processed for immunoblotting of p53, actin, or p21 (D) or ChIP (E). p53

immunoprecipitations were performed using a mixture of protein A and protein G beads

preincubated with 1801 and DO-1 antibodies. In vivo DNA binding to p21 5’, PIG3 and

Mdm2 response elements was determined by PCR using primers specific for regions

within these genes. An aliquot of chromatin was taken before the immunoprecipitation and

amplified by PCR to determine the relative number of cells in each ChIP sample (‘Input’).

(F) Chart representing the average binding of each cell line to the indicated promoter,

normalized to input and uninduced (+ tet) basal levels. Error bars indicate the standard

deviation of at least three independent ChIP experiments.

243

Figure 3. Even when overexpressed, 2KQ-p53 is unable to arrest but can induce cell

death.

(A) FACS analysis of wild-type and mutant p53 cell lines, expressing either no p53 or

equivalent levels of protein. Percentage of cells in G1, S, or G2-phase and/or apoptotic

sub-G1 is indicated. S-phase is highlighted to indicate arrest.

(B) FACS analysis of WT-p53 and 2KQ-p53 cell lines, expressing either no p53 or equivalent

levels of protein as indicated. Cells were treated for 48 hours with 0.5 mM 5-FU. Sub-G1

content is indicated to highlight apoptosis.

(C) p21 localization in H1299 cell lines. Cell lines listed at top were grown in tetracycline-free

media for 48 hours. DNA was visualized by staining with DAPI and p21 was detected by

incubation with p21 antibody (Santa Cruz Biotechnology, F-5), then with Alexa Fluor 488

(sc-6246).

(D and E) Western blot (D) and FACS analysis (E) at low WT-p53 and maximal 2KQ-p53

protein levels to determine relative p21 induction and cell cycle arrest respectively.

(F) FACS analysis of other 2KQ-p53 clones induced to express maximal levels of p53 and p21.

Figure 4. When overexpressed in vivo, 2KQ-p53 is deficient in binding and inducing

miR-34a and CCNG1.

(A) mRNA induction at low WT-p53 and maximal 2KQ-p53 p53 protein levels (as in Fig 3C) to

analyze transactivation potential of the 2KQ-p53 mutant. Cells were induced or not with tet

for 24 hours, harvested, and RNA was extracted. After cDNA was synthesized, samples

were amplified for indicated p53 target genes by qRT-PCR.

(B) ChIP analysis at low WT-p53 and maximal 2KQ-p53 protein levels to assess in vivo DNA

binding. Samples were processed for ChIP as in Figure 2D, except that immunoprecipitated

244

DNA was analyzed by qRT-PCR. For (A) and (B), the average of three experiments is

shown, and error bars show standard deviation of three experiments.

(C) Purified p53 WT-p53 or 2KQ-p53 protein was incubated with 10 ng of p21 5’ RE or miR34a

RE fluorescently labeled 44mer probe, in the presence of excess 44mer mutant p21

competitor. P53:DNA complexes were separated by electrophoresis and visualized by the

Licor Odyssey system.

Figure 5. Lysine 357 can be acetylated in vivo.

(A) H1299 cells were transfected with WT-p53 and either p300-HA, CBP-HA, or pCAF-Flag

expression plasmids. Cells were lysed and proteins were separated by SDS-PAGE and

transferred to a nitrocellulose membrane. Immunoblot analysis with indicated antibodies

was performed.

(B) H1299 cells were transfected with CBP-HA and either WT-p53 or 2KR-p53. After 24 hours,

cells were lysed and analyzed as in A.

(C) HCT116 cells expressing p53 (+/+) were treated with 5FU (0.5 mM ) for 24 hours or

MG132 (25 uM) for 6 hours, lysed, and subjected to immunoblot analysis with antibodies

recognizing p53, p53-Ac357, or actin. HCT116 p53-null cells (-/-) were run alongside as a

control.

(D) H1299 cells were transfected with constructs expressing single tetramerization domain

lysine p53 mutants as indicated. Cells were lysed and immunoblot analysis to detect p53

level and actin as indicated. (E) Parallel cultures of H1299 cells were treated as in D. RNA

was extracted and analyzed by qPCR for p21 expression, which was normalized to hprt1

levels. Error bars are the standard deviation of three experiments.

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Figures for Chapter 3

FIGURE 1

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FIGURE 2

247

FIGURE 3

248

FIGURE 4

249

FIGURE 5

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TABLES

Cancer Alteration(s) Mut. Type

Renal cell (RCC) K351N Missense

Lung K351* Nonsense

Melanoma K351E Missense

Adrenocortical (ACC) K351E Missense

Bladder Δ349-351 In-frame deletion

Table I. Mutations in p53 tetramerization domain. Table showing selected mutations in p53

tetramerization domain from cancers across all TCGA datasets (Accessed from cBioPortal Sept

2015).

Cell Line # Positive % Positive

WT-p53* 13/37 35%

2KR-p53 2/28 7%

2KQ-p53 9/20 45%

T123A-p53* 2/52 4%

Table II. Isolation of inducible clones. Table showing the number and percent positive of

isolated expression-positive tet-off inducible clones. Starred cell lines were isolated previously.30

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SUPPLEMENTAL FIGURES

Figure S1. The two lysine residues within the tetramerization domain of p53 are solvent-

accessible and their mutation does not alter p53 stability or global structure. (A) Cartoon

representation of the NMR structure of the tetramerization domain created with PyMOL (PDB =

3SAK, (Clore et al., 1995)). Location of lysines 351 and 357 on all four chains are represented

by a ball-and-stick model. (B) H1299 cells were transfected with WT-p53 or 2KQ-p53

expression constructs. 24 hours later, cells were treated with cycloheximide for the indicated

time points, harvested, and analyzed by immunoblotting. Quantification of bands was performed

with the Odyssey Licor system. (C) Combined amide 1H,15N chemical shift differences are

plotted versus residue number. The combined chemical shift difference () is calculated using

22)1.0( NHN with HN and N representing the 1HN and 15N chemical shift

differences, respectively, between WT-p53 and the 2KQ-p53 (red bars) and 2KR-p53 (green

bars) mutants.

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Figure S2. 2KQ-p53 is deficient in inducing pro-apoptotic targets. (A) WT-p53, 2KQ-12, or 2KQ-

5 tetracycline inducible cells were treated to express equivalent amounts of physiological levels

of p53, lysed, separated by SDS-PAGE and analyzed by immunoblotting. (B) RNA was

extracted and analyzed as in Figure 5A, for the mRNAs indicated in the figure.

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Figure S3. WT-p53 can effect a G1 arrest even if p21 has been downregulated. (A) WT-p53

tetracycline inducible cells were exposed to p21 siRNA for 48 hours, and treated or not to

express maximal amounts of p53 protein. Samples were lysed, separated by SDS-PAGE and

analyzed by immunoblotting to check p21 knockdown. (B) WT-p53 tetracycline inducible cells

were treated as in A, and subjected to FACS analysis. The cell cycle curves were analyzed by

ModFit LT and and are depicted in the graph.

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Figure S4. 2KQ-p53 is deficient in binding and inducing miR-34a and CCNG1. (A) mRNA was

analyzed from cells expressing low physiological levels of WT-p53 and maximal supra-

physiological 2KQ-p53 protein that was expressed from another clone (2KQ-12) of H1299 2KQ

inducible cells than shown in Figure 4. Cells were induced or not with tet for 24 hours,

harvested, and RNA was extracted. After cDNA was synthesized, samples were amplified for

indicated p53 target genes by qRT-PCR. Graph is representative of three independent

experiments. (B, C) H1299 cells were transfected with pTRE2 empty vector or 1 ug of either

WT-p53 or 2KQ-p53. 24 hours later, cells were harvested and processed for a ChIP assay.

Samples were processed for ChIP as in Figure 2D, except that immunoprecipitated DNA was

analyzed by qRT-PCR. Graph is representative of two independent experiments

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Figure S5. Specificity of rabbit polyclonal antibodies generated against acetylated p53-K357.

H1299 cells were transfected with either WT-p53 or 2KR-p53 and with or without CBP-HA

expression plasmids. Cells were lysed and proteins were separated by SDS-PAGE and

transferred to a nitrocellulose membrane. Immunoblot analysis with indicated antibodies was

performed.

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Chapter 4

Pathway regulation of p63, a director of epithelial cell fate

Kathryn Yoh and Ron Prywes

Department of Biological Sciences, Columbia University, New York, NY, USA

Review - Front. Endocrinol., 28 April 2015 | http://dx.doi.org/10.3389/fendo.2015.00051

Copyright © 2015 Yoh and Prywes

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Abstract

The p53-related gene p63 is required for epithelial cell establishment and its expression

is often altered in tumor cells. Great strides have been made in understanding the pathways and

mechanisms that regulate p63 levels, such as the Wnt, Hedgehog, Notch, and EGFR pathways.

We discuss here the multiple signaling pathways that control p63 expression as well as

transcription factors and post-transcriptional mechanisms that regulate p63 levels. While a

unified picture has not emerged, it is clear that the fine-tuning of p63 has evolved to carefully

control epithelial cell differentiation and fate.

Introduction

At first glance, the tumor suppressor p53 and its family member p63 seem quite similar

in function and exhibit a high degree of evolutionary conservation. In particular, the DNA-binding

domains are about 60% identical at the amino acid level; however, the adjacent domains and C-

termini diverge drastically (1). While it was first thought that p63 and p53 could regulate similar

sets of genes, it has become clear that these potent transcription factors possess some partially

redundant functions, and some that are entirely unique (2–4).

p63 is also unlike its family member p53 in that it is rarely mutated in human cancers.

Instead, mutations in p63 lead to disorders with ectodermal dysplasia such as ankyloblepharon-

ectodermal dysplasia-clefting (AEC)/Hay–Wells syndrome, which can include symptoms like

cleft lip/palate and skin erosions (5, 6). Other p63 syndromes can include split hand/foot

malformation and alopecia as symptoms, but cancer predisposition is generally not seen (7–9).

Due to differential promoter usage and splicing, there are at least six common isoforms.

There are two classes that arise from different promoters, one with the N-terminal

transactivation domain (TA), and the other set lacking the N-terminal transactivation domain

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(ΔN). While the ΔN form can be dominant negative to the TA isoforms (2), the ΔNp63α isoform

has been shown to contain an alternate transcriptional activation domain, suggesting it can also

directly activate target genes (3, 10). Alternative splicing of the 3′ end of the TA and ΔNp63

mRNAs produces the α, β, and γ isoforms, although only the α isoforms contain the sterile-α

motif (SAM) domain and the transcription-inhibitory (TI) domain. Mutations in these domains can

disrupt binding to the target Apobec-1-binding protein-1 (ABBP1), and deletion of both domains

led to increased p21Waf1/Cip1 signaling, indicating that these domains can modulate target gene

specificity (11, 12).

As to the specific functions of these isoforms, mouse models have been instrumental in

providing us with clues. Two groups reported that p63−/− mice were found to have severe limb

and epithelial defects, including partial or missing epithelial stratification, and truncated

forelimbs (13, 14). More recently, both the whole animal- and epidermal-specific deletion of

ΔNp63α in mice led to skin erosions and impaired terminal differentiation of keratinocytes,

demonstrating the importance of this isoform in the epithelial stratification process (15–17). It is

possible that deregulation of p63 targets linked to cell–matrix adhesion and epithelial

morphogenesis causes these skin abnormalities (18–20).

Furthermore, loss of epithelial cells in ΔNp63-null mice suggested that this isoform is

essential for the establishment of epidermal progenitor cells (13). Pellegrini et al. (21) suggested

that p63 is found in the stem cells of the proliferative compartment, but not in the transit

amplifying keratinocytes that have exited the compartment. When it comes to the caudal

endoderm, Pignon et al. (22) revealed that the p63-expressing cells are capable of

differentiating into prostate, bladder, and colorectal epithelia. Another report found p63 to be

essential for the proliferative ability and differentiation of the epidermis; however, in a thymic

model, p63 was only required for clonogenicity but not for lineage commitment or differentiation

(23, 24). Intriguingly, depletion of ΔNp63 or its target DGCR8, an miRNA processing factor,

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allowed keratinocytes to enter a multipotent stem cell state, suggesting that ΔNp63 is needed to

maintain the keratinocyte differentiation state (25). Finally, an AEC-like mutation in p63 led to

reduced proliferative and clonogenic potential in epithelial cells (26). Together these studies

make a compelling case for p63 in the maintenance and regulation of epithelial stem cells.

Meanwhile, TAp63 ablation demonstrated that this isoform monitors the integrity of the

germline after cellular stresses (27, 28). In particular, γ-irradiation was shown to induce

tetramerization of TAp63α from inactive dimers, leading to greatly increased target binding

ability (29), and inducing cell cycle arrest or an apoptotic response.

Yet, p63 levels are sometimes altered in tumors. Many groups have reported increased

expression in cancers, especially in head and neck squamous cell carcinomas (HNSCC) (30,

31). Indeed, amplification or overexpression of p63 has frequently been observed in lung

cancers, and more rarely in HNSCC (32–34). However, p63 expression is lost in more invasive

prostate and breast cancers, and this loss is associated with worse prognosis in some cases

(35, 36). It has been theorized that the tissue context, as well as the balance between TA and

ΔN isoforms, could partially explain this dichotomy.

So how does p63 impact cancer formation? The last decade has seen a preponderance

of direct targets unearthed, including adhesion-related β4 integrin, the tissue integrity factor

Perp, the Notch ligands Jagged1 and Jagged2, keratins 5 and 14, and EGF receptor (18, 19,

37–41). Cancer-related targets like N-cadherin, Id3, MMP13, and Wnt-4 can be activated by

p63; however, p63 can also induce Sharp1 and Cyclin G2 expression, which have been shown

to be suppressors of breast cancer metastasis (42–45). Additionally, phosphorylated ΔNp63α

was found to associate with components of the splicing machinery, as well as transcription

factors SREBP1 and E2F1, in regulation of metabolic and cell cycle-related processes (46).

p63 is also known to regulate a diverse set of microRNAs. A prominent target is miR-

205, a repressor of epithelial–mesenchymal transition (EMT) and metastasis in bladder and

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pancreatic cancers (36, 47, 48). In contrast to the role of miR-205, members of the miR-17

family (miR-17, miR-20b, and miR-106a) are regulated by p63 and Myc, and were found to

target Rb, p21, and JNK2, suggesting that they are oncomirs (49–51). Additionally, p63 can

repress the prominent cell cycle regulators miR-34a and miR-34c, thereby affecting cellular

progression in a p53-independent manner (52).

A data mining approach also identified p63 and the p53-related p73 gene as key

regulators of microRNAs differentially expressed in ovarian carcinomas, including miR-200a,

miR-200b, and miR-429 (53). Similarly, mir-193a was repressed by both p63 and p73, although

its induction leads to p73 inhibition (54). For more on p63 regulation of microRNAs, see the

review by Candi et al. (55).

Taken together, p63, like p53 and p73, can regulate a host of processes, some of which

are known regulators for or against tumor growth. As suggested by the opposite expression of

p63 in different tumor types, the context of the cell type appears to be critical to which p63

targets have the dominant effects in each cell. Whether targets are differentially expressed or

have different activities in different cell types needs to be investigated further.

As ΔNp63 is required for the formation of stratified epithelial layers and is the primary

isoform expressed in the basal layer of epithelial tissues, it is subject to multiple modes of

tissue-specific regulation (13, 14). As described below, a number of signaling pathways and

transcription factors have been identified that affect p63 expression in epithelial cells (Figure 1).

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Figure 1. Signaling pathway regulation of ΔNp63. Signaling pathways reported to

regulate ΔNp63 levels are indicated. The thick, blue arrow indicates autoregulation. *Note

that the Hedgehog and NF-κB pathways repress ΔNp63 levels while simultaneously

activating expression from the TAp63 promoter. See text for feedback regulation where

p63 regulates multiple components of these pathways (not shown here).

Notch signaling

One prominent pathway is Notch, which can control epidermal differentiation as well as

other developmental pathways (56, 57). Notch activation was found to suppress p63 expression

in keratinocytes, ectodermal progenitor cells, and mammary epithelial cells (58–60). The

repression in keratinocytes was dependent on the IRF3 and IRF7 transcription factors (59). In

mouse mammary epithelial cells, the Notch-mediated repression of p63 functions through the

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CBF1/RBP-Jk transcription factor (60). In addition to these cases, there has been a report of

Notch activation of p63 in fibroblasts (61), suggesting differing cell-specific modes of regulation.

The Notch-to-p63 pathway is subject to feedback regulation by ΔNp63, as it can activate

Notch pathway gene expression (58, 60, 62). This loop could delineate the boundary between

basal and luminal mammary cells as well as allow for ectodermal specification during

development (58, 60).

As with p63 mutations, alterations in interferon regulatory factor 6 (IRF6) are associated

with craniofacial abnormalities like cleft lip and/or palate (63, 64). Both IRF6 and p63 are

required for normal palate development, so the finding that ΔNp63 induces IRF6 expression is

logical, but surprisingly, IRF6 in turn causes proteasomal degradation of ΔNp63 (65, 66). Notch

has also been found to activate IRF6 expression in keratinocytes (67). Together, these results

suggest a Notch/p63/IRF6 axis regulates genes involved in epithelial development. Importantly,

Notch, p63, and IRF6 genes were found mutated in about 30% of HNSCC cases, suggesting

that this developmental pathway can be hijacked to promote tumor growth (68).

Hedgehog signaling

Hedgehog is another essential pathway for development (69, 70), and it is reported to

regulate p63 expression. Hedgehog activation is seen in various cancers including lung,

prostate, and breast (71, 72). Hedgehog ligands including Indian Hedgehog (IHH) can lead to

activation of the Gli3 transcription factor, while absence of these ligands leads to a repressive

form of the Gli3 transcription factor, termed Gli3R (73, 74). This balance of Gli3 forms can

control p63 isoform formation, as IHH induction of Gli3 actually upregulates TAp63 expression

while reducing ΔNp63 promoter usage (75). Again, there is a regulatory loop here since TAp63

expression can increase IHH expression. Similarly, both TA and ΔNp63 β and γ isoforms can

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activate Sonic Hedgehog (SHH) expression and recently ΔNp63 was found to induce

expression of Gli2 and the Hedgehog receptor Ptch1, affecting mammary stem cell renewal (76,

77). In addition, it was posited that some of the developmental defects observed in the p63−/−

mice may occur due to subsequent repression of SHH and other Hedgehog pathway genes

(76). Other connections between the p63 and Hedgehog pathways include ΔNp63 activation of

Gli2 and Gli3 as well as suppressor of fused (SUFU) (78–80). As SUFU is an inhibitor of the Gli

proteins, these contrasting effects show the complexity of this signaling system. Nevertheless,

together these results suggest a strong connection between the Hedgehog and p63 signaling

pathways that could control normal epithelial differentiation or cancer progression.

Wnt signaling

Strikingly, mutations in the WNT genes also cause similar craniofacial abnormalities as

p63 and IRF6 mutations (81, 82). Moreover, mutations in the Pbx genes in mice resulted in a

similar phenotype and perturbed Wnt signaling (82). Further analysis demonstrated a Pbx-

Wnt9b/Wnt3-p63-IRF6 signaling axis controlling development of the midfacial ectoderm (82).

Chromatin immunoprecipitation and reporter genes suggested that p63 is directly regulated by

the Wnt pathway through binding of Lef1/Tcf with β-catenin to a region between the TA and

ΔNp63 promoters (82), although another report identified a β-catenin responsive site within the

proximal ΔNp63 promoter (83). Recently, the Hedgehog pathway was also shown to be

connected to craniofacial defects (84). Compound mutations in the Hedgehog pathway genes

Hedgehog acyltransferase (Hhat) and Patched 1 (Ptch1) led to a cleft lip-like phenotype and

these acted through reduced Wnt-p63-IRF6 signaling.

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Analysis of keratinocyte differentiation has led to a different characterization of the p63,

Wnt, and Notch signaling pathways. Knockdown of p63 caused reduced Wnt and Notch

signaling (50, 51), suggesting that they lie downstream of p63 in contrast to the models of

craniofacial development. This could be reconciled as part of a feedback regulation pathway as

described above for Notch and p63. Additionally, the activation of Wnt and Notch by p63 may be

dependent upon the availability of other transcription factors. For instance, the depletion of p63

led to reduced Myc gene expression via lowered Wnt/β-catenin and Notch signaling, and this is

consistent with the requirement of both p63 and Myc for keratinocyte proliferation (50, 51). p63

was also found to regulate the expression of Myc and β-catenin in esophageal squamous cell

carcinomas, suggesting the general functioning of a p63/β-catenin/Myc pathway in

tumorigenesis (85). Finally, ΔNp63 was shown to upregulate the Wnt receptor Fzd7, leading to

enhanced mammary stem cell formation and clonogenic potential (86).

FGFR2/EGFR signaling

Mutations in the FRGR2 gene (also known as KGFR) can also lead to craniofacial

disorders such as cleft lip and Crouzon’s syndrome (87, 88). The splice variant FGFR2-2b is an

epithelial-specific receptor for ligands like FGF1 and FGF7 (KGF), and is required for

embryogenesis and adult tissue homeostasis (89). FGFR signaling and ΔNp63 can influence

each other, as ΔNp63 activates expression of FGFR2 in thymic epithelial cells (90) and KGF-

induced ΔNp63 expression in limbal epithelial cells (91). KGF’s effects on ΔNp63 require p38

MAPK, suggesting a novel pathway for regulation of p63 (91). Furthermore, mutations in p63

that cause AEC syndrome led to impaired FGFR2 gene expression and increased splicing of

the mesenchymal FGFR2-2c isoform (11, 26). Together, the combination of FGFR2 activation of

ΔNp63 and ΔNp63 induction of specific isoforms of FGFR2 are likely to lead to increased

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proliferation of specific epithelial cell types. This could enhance proliferation of progenitor cells,

but might block progression of specific epithelial cancers.

Interestingly, FGFR2 can induce expression of the epithelial-specific transcription factor

Elf5, and deletion of Elf5 causes altered expression of ΔNp63 in the luminal compartment of

mouse mammary tissue (92–94). This suggests a pathway for cell type-specific expression of

ΔNp63 mediated by Elf5.

The tyrosine kinase receptor EGFR has also been found to induce ΔNp63 expression. In

one case, this was through phosphatidylinositol-3-kinase (PI3K) signaling in keratinocytes (42),

while in two types of carcinomas EGFR activation of ΔNp63 was found to be mediated by

STAT3 (95, 96). STAT3 was also required for ΔNp63 expression in limbal keratinocytes (97).

The inhibition of the STAT3 growth-stimulatory pathway allowed the concomitant differentiation

of the limbal keratinocytes, further suggesting the importance of ΔNp63 regulation in these and

likely other epithelial cells. The PI3K and STAT3 pathways may be connected through mTOR

signaling, as Ma et al. (62) found that PI3K activation of mTOR led to mTOR-dependent

activation of the STAT3-p63-Jagged pathway. This highlights the interconnectedness of these

signaling pathways, and the role of STAT3 as a key regulator of p63. However, a clear

mechanism for how STAT3 directly regulates p63 remains to be determined.

Regulation of ΔNp63 during the Epithelial to Mesenchymal Transition

Epithelial cells can undergo an EMT during development and during carcinogenesis,

progressing to a more invasive and metastatic phenotype. This differentiation is thought to allow

the cancerous cells greater motility and increased metastatic potential [see reviews by Thiery

(98) and Kang and Massagué (99)]. The expression of ΔNp63 is repressed during this transition

(100, 101). Transcription factors that can induce EMT include Snail, Slug (also known as

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Snail2), and Zeb1, and all of these can repress ΔNp63 in epithelial cells (100, 102–104). This

inhibition, however, may be due to a feedback loop, as ΔNp63 expression can inhibit EMT by

activation of miR-205, which suppresses Zeb1 and Zeb2 expression (36, 48).

Other transcription factors involved in control of EMT are Ovol1 and Ovol2 (85, 105).

These factors can repress Zeb1 expression; however, it was also found that ΔNp63 expression

increased in Ovol1- and Ovol2-deficient cells, and that Ovol2 could bind to several sites within

the ΔNp63 promoter (85). Ovol2 may be upstream of ΔNp63 in an EMT-inducing pathway;

alternatively, there may be feedback of Ovol2 to ΔNp63 (as there is with Zeb1 and ΔNp63) with

ΔNp63 being an activator of Ovol2. In general, it remains to be characterized how ΔNp63 is

regulated during EMT in different epithelial cell types.

Transcription Factor Control

While we have mentioned a number of transcription factors as regulators of ΔNp63, a

clear picture has yet to emerge on which factors are critical direct regulators of ΔNp63 and

through which sequence elements they act near the ΔNp63 gene.

It is possible that multiple pathways regulate p63 through the C/EBP family of

transcription factors, as they have been repeatedly found to regulate p63. C/EBPδ was found to

bind to multiple regions of the ΔNp63 gene in human keratinocytes (106, 107). Antonini et al.

(108, 109) assayed all conserved regions throughout the p63 gene and identified two, termed as

C38 and C40, in the second intron of the ΔNp63 gene that affect expression in mouse

keratinocytes. The C40 region was needed for expression in keratinocytes, while C38 provided

repression during calcium-dependent differentiation. They found that C/EBPα and β bound to

the C38 and C40 regions, and that overexpression of these factors repressed reporter gene

expression. In addition, siRNA depletion of C/EBPα and β slightly increased p63 mRNA levels in

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differentiating cells, suggesting that C/EBPα and β are direct repressors of p63 expression.

Furthermore, these investigators found AP-2 to be an activator of the C40 region and the POU

domain protein Pou3f1 to be a repressor (108, 109). In contrast to the repression by C/EBPα

and β described above, another group described a C/EBP site within the proximal human

ΔNp63 promoter, which was required for expression in A431 epidermal carcinoma cells (100).

C/EBPα was also found to positively activate a site within the mouse ΔNp63 promoter in mouse

keratinocytes (110). Finally, after chemical stress, the cytosolic NAD(P)H:quinone

oxidoreductase 1 (NQO1) was found to bind to and inhibit C/EBPα, partially accounting for its

inhibition of ΔNp63 expression (110, 111). These contrasting effects of C/EBP may reflect

different family members, DNA-binding sites or cell types used, suggesting that further studies

are needed to better understanding of the roles these factors play in regulating p63.

Other transcription factors have also been found to regulate the p63 gene. An OCT4

binding site within the TAp63 promoter activates its expression, suggesting its involvement in

stem cell regulation (112). Another pluripotency factor, Sox2, bound to p63 protein and localized

with it to common gene loci in chromatin immunoprecipitation experiments. This binding

occurred in squamous cell carcinoma cells, but not in embryonic stem cells, suggesting that p63

may co-opt pluripotency factors for differentiated cell-specific expression (105).

p63 Autoregulation and Interaction with p53

p63 positively activates its own expression through binding to the C38 and C40 intronic

enhancers as well as to its own proximal promoter (108, 109, 113). Overexpression of the

ΔNp63γ isoform increased expression of ΔNp63α in HeLa cells, and of a promoter reporter

gene in keratinocytes (108, 113). Overexpression of ΔNp63 was also found to increase

expression of endogenous ΔNp63 in a nasopharyngeal carcinoma cell line where activation was

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dependent upon the STAT3 transcription factor (95). Whether binding of p63 to its promoter is

direct or through another transcription factor, the evidence consistently shows that it positively

feeds back to augment its own expression.

Initially, p63 expression was found to be suppressed by stresses, such as UV irradiation,

that stimulate p53 expression (114–116). Binding of p53 to the ΔNp63 proximal promoter was

detected in a mammary epithelial cell line, suggesting direct regulation by p53 of ΔNp63

expression (116). Mutant p53 proteins could also bind to the p63 protein in tumor cell lines and

inhibit its activity (117), while in carcinoma cells it was shown that mutant p53 together with

SMADs could sequester p63, resulting in inhibition of p63 and increased metastatic potential

(45). While these results suggest that wild-type and mutant p53 can repress p63 expression and

function, more work is needed to demonstrate the significance of this effect in human cancers,

and exactly how this could contribute to tumorigenesis.

Post-Transcriptional Regulation

p63 levels are also regulated by miRNA, ubiquitin-dependent proteasomal degradation,

and protein phosphorylation. Notably, miR-203 can repress p63 expression in supra-basal

epithelial cells, contributing to definition of the border between progenitor and differentiated

epithelial cells (118, 119). In addition, miR-203 expression was activated during luminal

mammary epithelial differentiation and ectopic expression of miR-203 stimulated EMT (120).

These results suggest that miR-203 is an essential part of the epithelial differentiation pathway.

Other miRNAs have also been found to regulate p63 expression. miR-92 targets

ΔNp63α and β in the HaCaT keratinocyte cell line and in myeloid cells, respectively, and miR-

302 suppressed p63 expression in germ cells (121, 122). The apotosis stimulating protein of

p53 (ASPP) family of p53 coactivators has similarities with protein phosphatases (123). A

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related family member iASPP (also known as PPP1R13L) is an inhibitor of apoptosis and can

also bind to p63 (124). The expression of iASPP in the basal layer of skin cells is strikingly

similar to that of p63, and knockdown of iASPP promoted epithelial differentiation (125).

However, rather than regulating p63 by protein–protein interaction, Chikh et al. (125) found that

iASPP inhibits the expression of two miRNAs, miR-574-3p and miR-720, which inhibit p63

expression. There is an auto-regulatory loop as p63 is needed for expression of these miRNAs

and binds to the promoter of the iASPP gene. These experiments point to a critical role of

iASPP and repression of its target miRNAs in maintenance of p63 expression and the epithelial

phenotype.

p63 protein stability is also regulated by the ubiquitin–proteasome system, adding

another layer of regulation (126). One example is p53-induced RING-H2 (Pirh2), an E3 ubiquitin

ligase, which can directly bind to p63 and cause its poly ubiquitination and degradation in

keratinocytes (127, 128). Pirh2 was also a transcriptional target of ΔNp63, establishing an auto-

regulatory loop, and was required for epithelial differentiation. Another E3 ubiquitin ligase,

Ring1B, part of the polycomb repressive complex 1 (PRC1), was found to target p63 (129).

Ring1b is overexpressed in breast and pancreatic cancer cells (129, 130), suggesting a possible

mechanism for p63 suppression in these tumors.

While p53 is stabilized by DNA damaging agents, such as UV irradiation, ΔNp63 is

degraded (115, 131, 132). Two mechanisms related to the NF-κB pathway have been found to

mediate this degradation. In the first, IKKβ binds to ΔNp63 and phosphorylates it to induce

ubiquitination and degradation (133). A second mechanism is direct binding of the p65 subunit

of NF-κB to ΔNp63 in cisplatin-treated cells, leading to proteasomal degradation of ΔNp63

(134). The reduction of ΔNp63 augmented activation of p53 target genes and may contribute to

cell death in UV-damaged cells. NF-κB repression of p63 may also have a role in epithelial cell

differentiation, as overexpression of the NF-κB factor p65 in epithelial cells led to p63

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downregulation and increased EMT (103). ΔNp63 also bound to target genes with p65,

suggesting that these two factors coordinately regulate a gene program promoting cell survival

(135).

NF-κB can also activate the TAp63 promoter, suggesting that a shift to the TAp63 form

could also underlie the DNA damage response (136). Again, there is an auto-regulatory loop

where TAp63 activates p65 expression as well as stabilizes p65 protein by direct binding (137,

138).

An alternative mechanism for ΔNp63 degradation as part of the DNA damage response

is phosphorylation on threonine 397 by the protein kinase HIPK2 (139). HIPK2 has previously

been identified as a DNA damage-induced kinase targeting p53 (140), such that it provides a

mechanism to coordinate p63 levels with p53 and other aspects of the DNA damage response.

For more regulators of p63 protein stability, see the review by Li and Xiao (126).

Conclusion

p63 has been termed a master regulator of epithelial cells, and it is often suppressed in

order for these cells to differentiate (21, 141, 142). We now understand more about how p63 is

regulated, uncovering a large array of signaling pathways (Figure 1) and feedback regulation

that controls expression of components of the signaling pathway as well as p63. Besides the

processes of differentiation and development, p63 is also regulated during the DNA damage

response, suggesting that it can mediate the more immediate fate of cells. The regulation of

ΔNp63 expression, the predominant form in epithelial cells, includes transcriptional and post-

transcriptional components. The relative importance of each pathway is still unclear and their

usage will likely vary in different cell types and developmental stages. While there are multiple

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reports of some pathways and mechanisms, common regulatory sequence element(s) for

control of the p63 gene across systems have yet to be established.

It will also be important to understand how modulation of p63 levels affects cancer

formation. The combination of heterozygous p63 and p53 genotypes in mice yielded conflicting

results, giving either greater or reduced tumor burdens (143, 144). Additionally, while ΔNp63 is

often highly expressed or amplified in squamous carcinomas, other tumors such as esophageal

adenocarcinomas and hepatocellular carcinomas generally lack expression (145–147).

Naturally, these cancers arise from diverse tissues, but it is confounding that p63 can have

oncogenic effects in some cases and tumor suppressive ones in others. As EMT is part of

metastatic progression of some carcinomas, it is interesting that repression of p63 was seen

during this differentiation process - is this regulation critical for progression of tumor cells to a

more aggressive state? Further, which of the pathways described here, if any, are altered in

cancer cells and modulate p63 levels in a critical manner?

Other open questions concern p63 promoter usage and splicing – what factors

determine the balance of usage of the TA and ΔN promoters, and what governs the presence of

different 3′ splicing isoforms? How does the balance of these 3′ isoforms lead to differences in

development or oncogenesis? Finally, can the signaling pathways that control p63 levels be

controlled to provide a therapeutic benefit in specific cancers? We can hope that the following

years will bring a greater understanding of this master regulator of epithelial biology.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial

or financial relationships that could be construed as a potential conflict of interest. The Guest

Associate Editor Wen Zhou declares that, despite being affiliated to the same institution as

272

authors Ron Prywes and Kathryn Yoh, the review process was handled objectively and no

conflict of interest exists.

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