Stem Cells, Cancer, And Cancer Stem Cells-Lorne Cancer Conference 2012

Presenter

Presentation Notes

5 yrs ago, even though our work on the p53 patway was going along well, I had this same feeling, which I saw in a shop window today in Melbourne. This brought back memories of when I started at Salk 30 years ago and wanted to work on Frog devel to clone the thyroid hormone receptor-I was advised not to because I didn’t know anything about development, frogs, or the thyroid hormone receptor! I listened, but have always wanted to work on development and cancer-and figured when I grew up I would do that. I want to thank trhe Organizers of the conference, Murray Norris and David Huang fo their bery generous invitation to enable me to present the results of this big expt in not playing it safe—and to Jane Visvader for putting my name forth to be considered to attend and present at this great conference in your beautiful country.

Stem Cells, Cancer, and Cancer Stem Cells

Lorne Cancer

Conference

February 10, 2012 Geoffrey M. Wahl

The Salk Institute for Biological Studies

Dedicated to Evelyn Lauder

founder Breast Cancer

Research Foundation

(Cell Stem Cell 10:183-197, 2012)

Design: Jamie Simon, Ben Spike, Geoff Wahl

Stem-Like Cells in Breast Cancer defined by enrichment of ES/iPSC expression signatures

(Ben-Porath et al, 2008; Mizuno, Spike, et al, 2010)

•Origin(s) ?

•Characterization • molecular markers for prospective identification • analyses of contributions to tumor initiation, progression, heterogeneity

•Prognostic/Predictive potential

•Relationship to Cancer Stem Cells (defined operationally)

• Cancer IS a caricature of development (processes, cell/microenvironment)

Approaching Stem Cells and Cancer from a Developmental Perspective---Why?

Justin La

Dannielle Engle

Jennifer Lin

Benjamin Spike

Samantha Cheung

• Co-option of development pathways in cancer cells (Wnt, Notch, Hh, etc)

• Expression of embryonic alternative splice isoforms in cancer (Ponta et al, 1998-CD44; Christofk et al, 2008-Pyr Kin MII)

• Expression of embryonic miRNAs in cancer (Nimmo and Slack, 2009)

• Presence of fetal and adult cell types in some cancers • (e.g., teratocarcinomas--Virchow, ~1863; Cohnheim,

“embryonic rest hypothesis, 1875) “…before we shall ever be able to understand

the question of why or how tumors form … we

must be able to solve the problem of normal

growth and development.” 1884. W.S. Savory

Presenter

Presentation Notes

Why is organogenesis/embryonic development relevant? Wound healing-artificial-reads out a lot of prolif but has prognostic power

Isolating Mammary Stem Cells from the Embryo

Artist: D Engle

E11.5 E12.5 E13.5 E15.5 E16.5 E18.5

~1000 cells

placode bud

~10,000 cells Per rudiment

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

1.6%

1.8%

E13.5 E15.5 E16 E16.5 E18.5 adult

MaS

C F

requ

ency

(%)

No Matrigel Matrigel

Jennifer Lin, Samantha Cheung, Dannielle Engle, Benjamin Spike

Fetal Mammary Stem Cell (fMaSC) Activity during Development Intact fragment = 70 – 100% transplant efficiency

Limiting dilution transplantation Full, functional outgrowths Serial transplantation->”self-renewal”

Presenter

Presentation Notes

Add transplant criteria to right—full outgrowth, serial Trans, functionality How is Y axis calculated? % of bulk cells— Look at MSC freq in pops consisting of stroma + epith but depleted of RBC. What about Adult—% of BULK cells (adip, mesench, epith, etc) M repop actiivty is increased by Matrigel significanty at all times, and is essential to detect any activity during the bud stages where we estimate very crudely that there may be at most ONE MRU per rudiment. We use the term MRU as this is this is the operational term that reflects what is being done—Also use of the term MRU opens up the possibility that to see SC activity, one needs either additional cells or additional factors to detect it—thus MRU’s contain everyting required for SC activity SC frequency—probably an underestimate—because contam cells diff –e15 is 50% mamary epith, but at e18.5 is much less as more fat, endothelial, fibro, etc. Look at K14/K8—68% fMRU exp Ker and 21% non fMRU –so could be 10% 3-10% of lin neg cells are fMaSC by sorting— 10-20% are lin + endoth and blood Should we have gotten better % transplant—either because dec transplantability after proessing OR HELP is needed from other cell types that are removed during sorting At 18.5 eepith cel freq about 20%---eval by k14/k8 on counted and sorted cells For early stages is based on visual inspection and can be est by k14/k8 and by fraction of rudiment within the bud may be ~50%--so the MaSC freq may be 3-5% on an epith cell basis

Lineage depleted cells from Adult no. 4 mammary gland +

E18.5 eGFP+ mammary rudiments

CD49f

CD2

4

All stem

cell activity

No stem

cell activity

Jennifer Lin, Dannielle Engle, Benjamin Spike, Samantha Cheung

CD49f+ CD24Hi Cells Contain All fMaSC Activity At E18.5

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

fStromal fMaSC aMaSC

MaS

C F

requ

ency

(%)

MaSC Frequency

CD49f+CD24lo/- CD49f+CD24hi CD49f=α6 Integrin CD24=Heat Stable Ag.

Presenter

Presentation Notes

The adult masc enrichment strategy uses the expression of CD24 and CD49f. In this plot, the mammary stem cell activity is enriched to a level of about 1 in 60 in this CD24 medium, cd49f high MYO designated population, although activity is also detectable in their stromal designated population at levels 50 fold more rare. Upon application of this fractionation strategy to the E18.5 rudiment, you can see that a novel population exists with high levels of CD24 and CD49f expression. These markers localize to the basal layer of the E18.5 mammary rudiment with very little background staining in the surrounding stroma. We evaluated the stem cell activity in this novel population and found that it contains all stem cell activity with no detectable stem cell activity from the cd24 med to neg cells hereon designated as the fetal stromal enriched population. As this cd24hicd49hi population contained all stem cell activity we designated it as the fMRU enriched population and upon quantification, found that its MRU frequency of 1 in 14 was roughly four fold higher than achievable using this strategy in the adult gland. At this point, we chose to further characterize the expression patterns of the fMRU enriched population in order to compare its characteristics to breast cancer Stingl—reported MASCs at tip of Myo-CD24+CD49fhi Stem Cell tech—kit for MaSCs—call at same place but note that there are cells where fetal ones are (in CD24hi)—as tip that extends up in adult pop and may be strain dependent Are there two types of SC in adult—one adult and one fetal like—is one more tumor prone than the other? Blanpain—LGR5 labels from early but dies out by nipple—in lineage tracing paper- Cardiff-diff in mouse models in aggressiveness of tumors at front and at extremity of gland-

Which cells are the stem cells? Quantitation of Putative Bipotential CK14+ CK8+ Cells

Keratin 8 Keratin 14 Double + Double -

0%

10%

20%

30%

40%

50%

60%

K14-K8- K14+K8- K14-K8+ K14+K8+

Keratin Cell Types - fMaSC

Vimentin Negative

Vimentin Positive

~70% Vim+

Dannielle Engle and Samantha Cheung

?

K14+

K8+

K8+ K14+

Bipotent?

Myo Luminal

Presenter

Presentation Notes

Y axis = % of cells in fMaSc population (CD24hi/Cd49f+) K14/K8 double pos ar 70% Vim pos—this is very interesting as such cells have been reported to correlate with poor prognosis and have been found in therapy resistant Most Claudin low were originally classified as basal-like, and basal-like have a high % of triple negatives- Metaplastic –most recently Perou—refers to same Tu as claudin-low with metapl features Hennesy-takes profiles from Metapl and looks like claudin low Ben went back to Metapl and found 2 classes-one has metapl-like signature up reg—All are claudin low

Sphere Characteristics

1. Clonal a. Single cell plating b. GFP + non-GFP cells c. Methyl-cellulose 2. Polarized, Multipotent 3. Self Renewing (dissociation / replating)

Sphere forming efficiency

fMaSC Frequency

E15.5 (bulk) E18.5 (sorted) 0.1

1.0

10

(%)

0.08 0.06

8.5 9.2

fMaSCs Generate Polarized, Multi-lineage Spheres in vitro Reflecting in vivo Transplantation Efficiencies

? 1 cell

Basal Cells (K14)

Luminal Cell (K8)

Lumen (milk?)

Stem Cell?

Matrigel

50µΜ

Presenter

Presentation Notes

Add graph showing sphere freq as a function of Dev stage

Clonal Growth Multi-lineage Differentiation

Aggregation (Motility; Anoikis

Resistance)

Measures?

Clonal Growth, Multi-lineage Differentiation

Sphere Formation: What does it actually measure? fMaSCs: All Stem Cell

Activity (1 Cell) fStroma: No Stem Cell Activity (20,000 Cells)

Mam

mos

pher

e (S

uspe

nsio

n)

Sem

i-Sus

pens

ion

(2%

Mat

rigel

) M

ethy

lcel

lulo

se

NO SPHERE FORMATION

50um

Small, limited sphere formation

K14 K8 DAPI

50um

Large, bi-lineage sphere formation

NO SPHERE FORMATION

Highest Expression Similarity: CD44hiCD24lo “CSC”, Claudin Low

Large, infrequent sphere formation K14 K8 Vim DAPI

K14 K8 DAPI

Large, bi-lineage sphere formation

Presenter

Presentation Notes

This result gives pause when attempting to interpret the meaning of sphere assays-or the implication that they measure clonal growth=-=to demonstrate this, one needs to show that the spheres do NOT result from aggregation and instead do form from clonal expansion of a single cell—also holds fro Self renewal

Links between fMaSC gene expression profiles and human breast cancer

(87) Wnt/Hedgehog signaling Axon guidance Cancer MAPK signaling

(1238) Energy metabolism Leukocyte transendothelial migr. MAPK signaling Calcium signaling Adhesion Regulation of actin cytoskeleton

(1171) Nucleic acid metabolism/repair Leukocyte transendo. migration Metabolism Axon guidance Cancer

(1557) Cell cycle p53 signaling pathway Nucleic acid metabolism/repair Cancer Homologous recombination Amino acid turnover Metabolism PPAR signaling pathway ECM-receptor interaction Axon guidance

(1803) Melanogenesis Cancer Axon guidance Complement cascades Wnt/Hedgehog signaling Calcium signaling pathway Toll-like receptor signaling Hematopoiesis Depression Gap junction NK cell mediated cytotoxicity

aMaSC (2047)

fMaSC (3450)

fStromal (3764)

1.5fold

Total Mammary Epithelium (Lin-)

Pathway and Process enrichment analysis

Expression in sub-populations

fMaSC Microarray Analysis: Identifying Cancers with fMaSC signature, therapeutic targets, antigens for purification

Benjamin Spike, Dannielle Engle, Jennifer Lin, Samantha Cheung

Presenter

Presentation Notes

First, we performed microarray analysis on the different fetal populations. We designated fMaSC and fStromal specific signatures by comparing the genes up-regulated in both populations. Genes that were shared between these profiles were eliminated from the specific signatures in order to mitigate the contribution of shared cell types’ to the fMaSC and fStromal signatures. Therefore, the fMaSC signature consists of the blue and purple genes while the fStromal signature consists of yellow and orange. Next, we asked how these specific signatures were represented in breast cancer. Genes are differentially exp from stroma or not—if pathways are listed in different categories, then they are by definition different— Green is what is shared between fMaSC and Stroma- Blue—unique to fMaSC Rich data source with many gene exp diffs between fetal and adult-notably Ca relevant pathways—come up in several categories: Overlap with adult—purple Unique to fMaSC-blue Unique to Stroma-Yellow- BY definition, these are all different, and therefore imply presence of different targets

Prat et al. BCR 2010 Neve et al. Cancer Cell 2006

Cancer cell lines differentially express fMaSC and fStromal genes

HCC70 fMaSC/fSTR-Like HCC3153 fMaSC/fSTR-Like HCC1569 fMaSC/fSTR-Like SUM225CWN fMaSC-Like HCC1143 fMaSC-Like HCC1599 fMaSC-Like HCC2157 fMaSC-Like MDAMB468 fMaSC-Like HCC1937 fMaSC-Like HCC1954 fMaSC-Like SUM190PT fMaSC-Like MDAMB157 fStromal-Like HS578T fStromal-Like BT549 fStromal-Like MDAMD436 fStromal-Like SUM1315 fStromal-Like MDAMB231 fStromal-Like SUM159PT fStromal-Like HBL100 fStromal-Like HCC38 fStromal-Like SUM14 9PT fStromal-Like MDAMB435 neither MCF10A

Basal A

Basal B Claudin Low

Benjamin Spike

Presenter

Presentation Notes

The Prat at al is the paper that describes the differentiation score. They acknowledge that Claudin low have a significant enrichment in Stromal genes. But given our findings it is not clear wether it is the stem cell aspect that underlies their character and the scheme may need to be revised. Take cell lines-appeared to be 2 types basal from Neve—based on hierarch cluster arrays from cell lines-clustered into 3 grps—2basal and 1 lum and her2 dist across both—basal A and B not known how they distinguished at that time-our sig discriminates—Claudin low then disting Basal B into clauding like—other basals are the real ones—and they are the ones with the stem-like signatures— Did babasl B result from EMT as they exp epith keratins??

Some Breast Cancers Exhibit fMRU Signature Enrichment

fMaS

C

fSTR

Pr

olife

ratio

n

Rand

om

-prolif -prolif

aMaS

C

aStro

mal

aLum

inal

Pro

gen.

aMat

ure

Lum

inal

Basal Her2

Normal-like Caludin-low

Luminal A

Luminal B

*

* From Lim et al. 2010 >1200 Samples from Ben-Porath et al, 2008

Benjamin Spike

Presenter

Presentation Notes

In this analysis you can see that the fMaSC signature is strongly enriched in basal, Her2 positive, and normal like breast cancer and depleted in the claudin low subtype. The fSTR signature performs differently from the fMRU signature in that it is depleted in basal like breast cancer and enriched in the claudin low subtype. The enrichment and depletion of the fetal signatures is not due to the influence of proliferation as you can see here that they do not follow the pattern of proliferation associated genes and moreover, upon subtraction of proliferation genes from the fetal signatures, their enrichment and depletion patterns do not change We note that the adult Stromal and MASC signatures enrich for similar tumors. This likely reflects the difficulty of purifying adult MaSCs away from their assoc stroma and myo-epithelial cells, which is consistent with the similarities in the gene expression signatures reported for these populations. This may explain why little clinical impact has come from the gene expression data from these adult populations. In lumA-does stroma induce prolif block? Does this explain why less aggressive? Mirror the normal developmental state better—Normal like tu looks red in fMaSC and red in Stroma— Adult luminal signature—has 20-30% prolif genes so this does contribute, but even if substract they are still enriched—Lum progen pop is CD24hi so this may actually contain some fMaSC like cells— Adult lum porgen does have SOME gene overlap—eg 31 genes—but see Gata3 which is lum gene but is also in fMaSC—which has lum charact as indicsted by K8 exp== Why do they say is lum progen vs stem like? Because their SC sig is not sufficiently precise to specify a stem cell Lum Progen—signature is from Lindeman/Visvader group—not from Smalley-though he came to similar conclusion in Brca- mouse model Surprising that aMaSC does not show enrichment in basal- -may be more myoepi like Why does Lum Progen show enrichment in basal-like tumors-=-Visvader showed this too—get enrichment for basal like compart— Cabveats—looked at most enrichment of 4 sig they developed—aMaSC they showed also did not enrich strongly—lum progen looked most like basal-like tu—but Lum Porgen is heterogeneous—Stem cell compartment is not very stem like, is more mesench like—basal like tu really dio have enrich for our fMaSC and LumProgen—BUT need to reall have more pure Adult MaSC to see if these are more Lum Porgen like— Look at genes that provide enrichment that see btwn Lum Progen and Basal—Lum sig and basal tu—not all genes most highly exp in Lum progen—no tu looks exactly like fMaSC—line up all 4 pops with most (aMaSC, Lum Prpgen, Mat Lum, Stroma)—no good adult Mamm Stem cell sig—Mamm Sig repr mostly non-stem cells—most similar to Myoepi-cells with diff functional output but no sig diff btwn aMaSC and Myoepi signatures---Shackleton 4 Signatures—Stingl, Smalley (Kendrick(—assumed cant dist Myo for Stem cell then did lumER- and +; Lim (Visvader and Lindemann---did parallel sorting between Hu and Mo and got similar but non ID signatures—difficult to dist SC in adult from contam cells or myoepi/str cells that co-purify—NO ONE KNOWS YET WHAT ADULT MAMM SC Looks like. Raouf—(Rosen)—Pelicci (sig look very good with ours)—isol cells from suspension We use mouse to mouse conversion—so we use papers that did have human and mouse comparisons so we can infer those genes with homology in both species— Lim et al—human Visvader—Mouse—both human and mouse sig light up same tu—human do better as are homologous to hu tumors—you lose genes in translation from hu to mouse as they are not ID—so we use human orthologs of mouise genes to look at humans—

% overlap

fMaSCs Expressed Genes: A Signature Unlike Others

Benjamin Spike

Presenter

Presentation Notes

Existing expression based metrics being used or developed for the clinic have significant gene content relating to proliferation and/or ER and ErbB status. Portions of the fMaSC signature correlate with ESR1 expression but otherwise the signature appears to reflect other types of biology. This reliance on proliferation and ER genes is common to many of the expression based signatures used for breast cancer classification in the past. And these signatures are highly related to one another. [ Notes: the AurKA, ESR1 and ERBB2 signatures in the left comparison are composites from Desmedt and Wirapati (both from Sotiriou) Where they use the genes in the parentheses as the prototype and make a list of correlating genes in archived microarrays to build the signatures. Also note the colors on left correlate with % while the colors on right correlate with p-value in hypergeometric overlap.] Some or all of this could be purelycoorelative and not really directly dependent on ER signaling.Alternatively, It could be that the luminal type is emerging at this timeor is a portion of the bipotent phenotype. Here are a few notes/plausibleexplanations for specific genes: Sfrp1 is profoundly induced by estrogens in some tissues but is alsoinducible by oxygen or nutrient deprivation and by hedgehog signaling inothers. BCAS1 is elevated in tamoxifen resistant BC cell lines. So,several genes may be ER regulatABLE but also have other modes ofexpression.Gata3 and FoxA1 are classic partnering TFs in ER signaling so they veryoften correlate. But are up stream or parralel to ER.Molecules like CD24 are typically associated with luminal cells which arepredominantly ER expressing so molecules like this may reflect guilt byassociation and nothing more.I am also reminded of a paper (I think it was by visvader) that pointed toE2 sensitivity in the mammary stem cell pool but proposed a circuitousexplanation for how this could work given the proposed receptor status ofthese cells. complete set of ER correlated genes in the fMaSC unique set:ALCAM, BCAS1, BCL11A, C1orf106, CA12, CD24, CRYAB, CX3CL1, FGFBP1, FOXA1,FOXC1, GABRP, GALNT3, GALNT6, GATA3, KCNN4, KRT7, LAD1, MARCO, MID1, MYB,NOVA1, PLAT, PTGFR, SERPINB5, SFRP1, SOX10, TJP3,TM4SF1, TTYH1 Perou 2000: Sorlie 203 Perreard 2006 Hu 2006 Hu 2006 Wirapati ESR1 Desmedt ESR1 Wirapati-AURK—proliferation Desmedt AURK—Proliferation Chang PlOS 2004 Van’t Veer 2002—70 gene mammaprint—ROR—unbiased-”6 Hallmarks of Cancer” Captured Pam50- Oncotype Dx-20 gene Wirapati-ErbB2 Desmedt ErbB2

Years

Surv

ival

Pro

babi

lity

Fetal gene subsets have prognostic value in uni- And multi-variate analyses

• Potential Molecular Targets—ErbB signaling in TNBC?

• Illustrates the importance of Embryonic Epithelial AND Stromal Expression Pathways in adult tumorigenesis/progression

fMaSC-ii overexpressed fSTR-iv repressed

fMaSC-ii (23 genes) Embryonic morphogenesis

fSTR-iv (25 genes) Wounding

Benjamin Spike

Presenter

Presentation Notes

Left-Univariate Kaplan-Meier All others—all pts without fMRU overexp or fSTR repressed Right—Multivariate analysis---individual sig tested against 3 diff models in multivariate—this is discussed in poster LN and size for all Only things shown are what remains sig Chart shows plotted Log for visualization—enables magnification of data for easier visualization Log2 centered on 0 so have equal dist of + and neg hazard ratios Red—increased haz ratio, poorer prog--=lines are confidence intervals==95% Middle—does not include ER—so here, with these sig, worse prog indep of –if DO include ER, no longer become sig Several subsig have sig hHR in model including all 4; In multivariate analyses, some of gene sets additional value over commonly used metrics

Developing Tumor

Stem Cell Frequency

Adult E12.5 E13.5 – E15.5 E16.5 E18.5

Stem Cells

Stem Cell Precursor

Stromal Cell

Fetal Mammary Development

Well known and lesser studied pathways in development and cancer

Summary and Perspective

Presenter

Presentation Notes

The same richness is evident when we look at curated pathways as opposed to ontological categories.

Moving from Bench to Bedside

Well known and lesser studied pathways in development and cancer

Developing Tumor

Stem Cell Frequency

Resurrection of fetal programs in

cancer

Adult E12.5 E13.5 – E15.5 E16.5 E18.5

Stem Cells Stem

Cell Precurso

r Stromal Cell

Fetal Mammary Development

P53 Signalling

PPAR Signalling MAPK Signalling

RTK Signalling

Focal Adhesion

DNA Repair

Wnt and HH Signaling

Inhibition of differentiation

Pathways in Cancer (Development)

DNA Repair

Cellular adhesion Metabolism

LDHA

PDK1

PKM1/2

Glycolytic shift

Presenter

Presentation Notes

In this case it is not important that you see the gene names (though these are to be found in the manuscript) but that one notices the many red stars representing fetally expressed genes in pathways such as this one called pathways in cancer which may as well be called pathways in development. And we also find, among others, pathways one might expect from the stem cell literature such as DNA repair pathways, cell adhesion pathways, or metabolic pathways. though these later pathways pathways may be expected by many but which are really under appreciated at present in terms of how they function in either development or cancer such as cell adhesion and metabolism and in this regard we are particularly interested in pathways contributing to glycolytic shift under hypoxic or even normoxic conditions. The infamous warburg effect.

Tetranectin Vitronectin

Amphiregulin

Syndecan-2

TSH receptor Fibronectin Lysyl Oxidase

CTFG

Alpha6/Beta4 Integrin

CXCR4 SDF-1 Adiponectin

ErbB2

ErbB4

TGF-alpha

CD44

E-selectin

Versican

EGFR

EBP50 PLAT(TPA)

PAI1

IGF-2 Transferrin

Trophinin

ClCa1 Laminin1

ErbB3

EGF

Neuregulin 1 Neuregulin 2

P85(Pik3r1)

HGF

Neurophillin-1

VEGFR-1

Semaphorin 3A

Semaphorin 3B

L1CAM Neurocan

NCAM1 PTPR-zeta

GFRalpha1

ITGA5

Met

fMaSC Population fStromal Population

Part of interactome Not differentially exp.

fStromal/fMaSC fMaSC/fStromal

Microarray Analysis Reveals ErbB Signaling in fMaSC’s

Benjamin Spike

Presenter

Presentation Notes

Pink = lower statistics; Laminin 5 in the adult overexpressed, not fMaSC Blue--up reg in f Orange --up reg in non-masc Pink --part of interactome but not diff exp-- Nrg3---very low exp for 1 probe-- Interactome defined by Genego Take all genes up or down plus ones that don’t show up--put into genego==ask to tell rec and lig and it draws arrows--then remove everything without arrows and left with everything else--these are sig genes rep rec and lig that interact--probably other ec in sig but did not have lig in the sig so are not represented---THESE ARE THE ONES THAT INTERACT PINK--look thu interaction network--ErbB family network is prominent--because of this, used archived gene spec pathways from GeneGo and pulled up 2 ErbB2 related pathways--ErbB in Dev and ErbB in adenocarc--from these pathways added Pink genes--kept in ones that just missed stat cutoff in exp--by the MEANS, the exp levels are different but when do multiple testing correction doesn’t make it into top 1300 Do single T test, is certain prob are wrong--Mult testing weights the P values so that it excludes those most likely to be wrong--False discovery rate-- Line up all P values, rank genes by P values, give rank number, 1st gene gets mult by 1, 2nd gene P val miult by 2, 3rd gene by 3---so 1000th gene, if significant, would have had to had VERY low P value--repeat pairwise T test over and over, make lots of mistakes--this minimizes mistakes-- PINK--supervised additions based on archived pathways and genego BUT still have differential exp, though it did not quite make the cutoff--PLACED in chart based on diff expt that is ErbB3 higher in fMASc Laminin 5---does not make grade for either f or Non--but when did gene addition, had adult MaSC we profiled and it was up reg in adult ---so this might be switch between adult and embryo--Lam 5 is VERY low in the Embryo, and very HIGH in the adult. We used Nimblegen micro arrays which has 60mers.The arrays were normalized by RMA at the UCSD core.Normalized intensities were log2 converted and median centered probe-wise.This is a standard procedure and will return high and low expressers as long as they are differentially expressed. However, it is much harder to get a significantly different low expressor as the contribution of noise will more strongly affect stdev.To identify differentially expressed genes we utilized significance analysis of microarrays (SAM) with FDR10%. Some people would use a 5%FDR but the looser FDR returns results more consistent with published reports using another algorithm, LIMMA. As one form of validation, when I used the SAM algorithm and statistics on visvader痴 data I got 92-96% overlap with signatures reported by them using Limma at 5%FDR, this relative liberalism of LIMMA compared to SAM is known. 10%FDR also permits retention of largegene sets upon downstream conversion to human orthologues and a 10% FDR in LIMMA (quite a bit more liberal than our stats in this case) was used by visvader to identify conserved pathways mouse to human.The genes significantly up regulated in the fMaSC and the genes significantly down regulated in the fMaSC(i.e. up regulated in fNonMaSC)were analysed for receptor ligand pairs using currated data in the GeneGodatabase/software. And then parsed into fMaSC or fNon-MaSC based on whether they were up or down in the differential expression analysis.

0% 1% 2% 3% 4% 5% 6% 7% 8% 9%

10%

Sphe

re F

orm

ing

Effic

ienc

y (%

)

<50um 50 - 100um >100um

Growth factor dependence of the fMaSC population

0

50

100

150

200

250

0 0.001 0.01 0.1 1

Spheres > 50um Total Spheres

Neratinib [µM]

fMaSCs are sensitive to ErbB Pathway Antagonists

Jennifer Lin

Spheres per 2000 cells from the

fMaSC-enriched population

?

0

50

100

150

0 0.01 0.1 1

Neratinib [µM] Cel

l Num

ber (

% o

f un

treat

ed) BT549

MCF10A /Her2

50um K14 K8

Presenter

Presentation Notes

Develop conditions under which single fMRU can generate multilineage spheres in vitro. The conditions are very important, and you should go to Jen’s poster to find out more about it. The important point that that the growth of the spheres is dependent on EGFR signaling, shown here by the sensitivity to the IREVERSIBLE Pan ERB-B kinase inhibitor Neratinib. Similar results were observed for Lapatinib. From Prognosis to Prediction

The Future: Solving the Heterogeneity Problem

Microarray Loss of Detail

K14+

K8+

K8+ K14+

K14+ K8+

K8+ K14+

Reality = Cell Mixture

K14-_ K8-

K8+

K14+

K14+

K8+

K14-_ K8-

FACS

Single Cells Into Single Wells

Single Cell Analyses Marker Discovery for Purification

Deconvolving heterogeneity to to identify the fMaSC: Fluidigm High-throughput single cell qRT-PCR

9,216 reactions per dynamic array

96 wells for Assays

96 wells for Single Cells

Presenter

Presentation Notes

This methodology utilizes a microfluidic chip to ascertain expression levels of 96 different genes within 96 different single cells. Each chip can perform over 9,000 reactions simultaneously, which can be used to segregate your population into its constituent cell types

Hierarchical Clustering of Single Cells by Gene Expression

Dannielle Engle, Justin La

Kera

tin-

Kera

tin +

Presenter

Presentation Notes

Upon completion of this analysis, you can see that every single cell is represented as an individual row, and each column is a different gene. First, I would like to draw your attention to CD24 expression. Although these cells were isolated based on their level of CD24 protein expression, this population of cells has downregulated expression of CD24 mRNA. In addition, these cells are not expressing epithelial associated genes such as EpCam or the previously mentioned keratins. In addition, within these non-epithelial cells, we detected expression of several neuronally associated genes such as neural cell adhesion molecule that correlated with this cell type bi-furcation. In order to examine the contribution of this cell type to the fMRU enriched population, I first wanted to determine whether this non-epithelial cell type represented stromal contamination.

8.2%

0.0% 0.0% 0.0%

0.9% 0.8%

0%

2%

4%

6%

8%

10%

12%

NCAM- fMaSC NCAM+ fMaSC fStromal

Perc

enta

ge

Sphere Forming Efficiency 2% Matrigel

No Matrigel

fMaSCs are in the NCAM- Keratin Expressing Population

K14 K8 DAPI

Dannielle Engle, Justin La

Transplantation: +++ + -

Presenter

Presentation Notes

Next we wanted to determine if NCAM expression correlated with stem cell activity in any way, first using in vitro sphere generation. We were only able to detect sphere formation from the NCAM negative fraction in the presence of Matrigel. Upon removal of matrigel from the culture media, we were able to detect spheres from both the Ncam positive and fstromal populations. The Ncam negative spheres had a smooth, round morphology and expressed markers of both epithelial lineages. The ncam negative spheres however had a grapelike morphology that is often indicative of aggregation. At this point, it was unclear what the ncam positive cells were doing within the fMRU enriched population. However,

Parsing the problem of Heterogeneity

• Idenitify processes and markers specific to each cell type

• Functional Stem Cell tests

• Examine cellular heterogeneity in the system

• Examine developmental plasticity and the flexibility of the stem cell state

• Identify requisite stem cell biology for application in developmental biology, regenerative medicine and Cancer therapy.

1 2 3 4 5 6 7 C

Single Cell Microfluidic RT-PCR (Fluidigm)

Single Cell RNA-Seq (SOLiD) Dannie Engle Justin La Ben Spike Roger Lasken (JCVI) Rashel Grindberg Joyclyn Yee

cell

type

sp

ecifi

c ge

ne s

ets

and

mar

kers

K14+K8+ (1-7) fSTR

Model : Origins of Stem-like Cells in Cancer ( probably not Cancer Stem Cells)

(“Just because it’s a model doesn’t mean it’s wrong!” B. Weinberg)

Differentiation

Mizuno, Spike, Wahl, Levine (2010) PNAS

p53 Inactivation Other Mutations

“Tumor Reprogramming”

Ben Porath et al (2009) Nature

aMaSC fMaSCL- Ca

fMaSC

Presenter

Presentation Notes

How does het arise-many mech, but I will focus on one particular model due to the potential impact it could have for patient diagnosis and treatment. Adopts a stem cell phenotype in that it is able to….

Breast Cancer Research Program, Breast Cancer Research Foundation, Susan G. Komen for the Cure, G. Harold and Leila Y. Mathers Charitable Foundation, NCI

Acknowledgements Dannielle Engle Jennifer Lin Benjamin Spike Samantha Cheung Justin La Jennifer Green TaNeashia Morrell Rose Rodewald Leo Li

Collaborators Carl Hansen

Roger Lasken Rashel Grindberg

Joyclyn Yee Charles Perou

Aleix Prat

Thiery Bonnefoix Gerald Cunha

Dan Medina Barbara Vonderhaar

Positions Open!

Stem Cells, Cancer, And Cancer Stem Cells-Lorne Cancer Conference 2012

Documents

Transcript of Stem Cells, Cancer, And Cancer Stem Cells-Lorne Cancer Conference 2012