A Network Model for Controlling and Potentially Reversing Angiogenic

Progression in Ovarian Cancer

Kimberly Glass Functional Genomics Data Society

June 20, 2013

• Biological processes are driven not by individual genes but by the networks linking those genes

• Ultimately, we look to develop models that describe the interactions driving different biological systems

• We want to find networks using available genomic data (largely expression data)

• Correlations in gene expression can be considered to be the result of network interactions

• The question is not “Is this model right?” Rather, the question is “Is the model useful?”

Why We Care About Networks

Normal Tissue Network

Chemosensitive Tumor

Chemoresistant Tumor

What can we learn from networks?

Epigenetics Protein-protein interactions

Protein-DNA interactions

gene expression Data- Specific

Regulatory Network

data integration

Regulation of Transcription

“data integration”

“genomic data”

“regulatory network”

Another Idea: Message Passing

Transcription Factor

Downstream Target

The TF is Responsible for communicating with its Target

The Target must be Available to respond to the TF

GC Yuan, Curtis Huttenhower, John Quackenbush

Passing Messages between

Biological Networks

Protein-protein interactions

Protein-DNA interactions

Genomic Data

Gene Expression

Network Representation

Cooperation between TFs

Potential Regulatory Events

genes

gen

es

Potential Co-Regulatory Events

Use Message Passing to find a consensus among the networks

Initial N

etw

ork

Info

rmatio

n

Message Passing

Learn

ed

Ne

two

rk In

form

ation

Message-Passing Networks: PANDA (Passing Attributes between Networks for Data Assimilation)

PPI0 Expression0

Network1

Responsibility Availability

Network0

Motif Data

Expression1 PPI1

Glass et. al. “Passing Messages Between Biological Networks to Refine Predicted Interactions.” PLoS One. 2013 May 31;8(5):e64832. Implementation available on sourceforge: http://sourceforge.net/projects/panda-net/

Application of PANDA to Ovarian Cancer Subyptes

A new subtype of ovarian cancer

A new subtype of ovarian cancer

• mRNA/miRNA and DNA were extracted from 132 well-annotated FFPE samples and profiled on arrays

• A technique called ISIS was used find robust splits in the data

• A major, robust split was associated with expression of angiogenesis genes

• Published gene expression data was curated and used to validate the split and signature

Ge

ne

s

Conditions

Expression data (Angiogenic)

Ge

ne

s

Conditions

Expression data (Non-angiogenic)

Application of PANDA to Ovarian Cancer

• Downloaded expression data from 510 OvCa patients from TCGA. Normalized data using fRMA and mapped probes to EnsEMBL IDs using BiomaRt

• Assigned subtypes using a Gaussian Mixture Model using Mclust: Identified 188 angiogenic, 322 non-angiogenic patient samples.

• Combined with TF motif and PPI data and used PANDA to map out networks.

Network for Angiogenic Subtype

Network for Non-angiogenic Subtype

Interaction data

Motif data Compare and Identify Differences

GC Yuan, Dimitrios Spentzos, John Quackenbush

12631 unique edges, Including 56 TFs Targeting 4081 genes

15735 unique edges, Including 49 TFs Targeting 4419 genes

Each point: TF→gene edge

An individual gene can actually be targeted in both subnetworks, although by different upstream transcription factors.

Gene Overlap

1828 2591 2253

Genes Targeted in Angiogenic Subnetwork

Genes Targeted in Non-Angiogenic Subnetwork

Network Differences are Captured in Edges

Key Regulators of Angiogenesis in OvCa

TF TF

Edges from Regulator in Angiogenic Subnetwork

Edges from Regulator in Non-Angiogenic Subnetwork

Calculate an “Edge Enrichment” and corresponding

significance

TF Potential Connection with Angiogenesis/Cancer Publication(s) PMID

NFKB1 important chromatin remodeler in angiogenesis 20203265

ARID3A required for hematopoetic development 21199920

SOX5 involved in prostate cancer progression, responsive to estrogen 19173284, 16636675

TFAP2A increases MMP2 expression and angiogenesis in melanoma 11423987

NKX2-5 regulates heart development 10021345

PRRX2 deletion cause vascular anomalies 10664157

AHR knock-out impairs angiogenesis 19617630

SPIB inhibits plasma cell differentiation 18552212

MZF1 represses MMP-2 in cervical cancer 22846578

BRCA1 inhibits VEGF and represses IGF1 in breast cancer 12400015, 22739988

Key Regulators of Angiogenesis in OvCa

TF differential Expression

Target differential Expression

TF differential Methylation

Target differential Methylation

Target genes’ availability to be regulated is made possible through

epigenetic modifications

Key Regulators of Angiogenesis in OvCa

Some TFs are acting as transcriptional repressors.

A+ A- A+;N- N+;A- N- N+

yes yes yes yes no no

no no yes yes yes yes

yes no yes no yes no

no yes no yes no yes

927 1326 624 1204 982 1609

Gene Group Nickname

Gene targeted in Angiogenic Subnetwork

Gene targeted in non-Angiogenic Subnetwork

Gene’s expression increases in Angiogenic tumors

Gene’s expression increases in non-Angiogenic tumors

Number of Genes in Group

1828 2591 2253

Genes Targeted in Angiogenic Subnetwork

Genes Targeted in Non-Angiogenic Subnetwork

Both Activation and Repression of Pathways is Important in Angiogenesis

• "A+/A-" genes targeted and more highly/lowly

expressed in angiogenic subtype

• "A+;N-" genes are targeted in both

subnetworks and more highly expressed in

angiogenic subtype

• "N+;A-" genes are targeted in both

subnetworks and more highly expressed in

non-angiogenic subtype

• "N-/N+" genes targeted in the non-angiogenic

subnetwork but are more highly/lowly

expressed in angiogenic subtype

Both Activation and Repression of Pathways is Important in Angiogenesis

Transcription Factors

Genes (expression higher in angiogenic subtype)

Edges unique to Angiogenic Subnetwork

Edges unique to Non-Angiogenic

Subnetwork

Genes (expression higher in

non-angiogenic subtype)

A- A+;N-

N-

A+

N+;A-

N+

A Network Model of

Angiogenesis

Complex Regulatory Patterns Emerge

Key TF Co-TF P-value #

ARID3A PRRX2 1.16E-23 244

ARID3A SOX5 1.01E-14 155

PRRX2 SOX5 3.83E-12 157

ARID3A

PRRX2 SOX5

129

115 26

48

162 28 28

A+

ARID3A PRRX2 SOX5

Top Combinatorial Pairs: A+

46

AHR ARNT

ETS1 MZF1

273

10

1

82

1 1 53

33

3

60

17

68

27

50

N-

AHR ARNT MZF1 ETS1

Top Combinatorial Pairs: N- Key-TF Co-TF P-value #

MZF1 ARNT 5.83E-23 92

AHR ARNT 6.13E-16 382

MZF1 ETS1 9.08E-16 148

TF1 TF2 P-value #

ARNT ETS1 2.19E-23 149

AHR ETS1 9.08E-16 101

AHR MZF1 2.68E-7 58

What is the role of ETS1 and ARNT in angiogenesis?

• ARNT and ETS1 dimerization with HIF1a and HIF2a, respectively, play a role VEGF production. However, AHR can inhibit this by competing as an alternate dimerzation partner.

There is a small molecule ligand that dimerizes with HIF2a and which may block angiogenesis

AHR agonists may also help to prevent activation of the angiogenic pathway

• ARID3A, SOX5 and PRRX2 activate many genes through CpG-poor promoters

Therapies altering methylation may alter some of these transcriptional programs

Regulatory Patterns Suggest Therapies

X

X

VEGF production and angiogenesis

HIF1a ARNT

HIF2a ETS1

HIF1a ARNT

HIF2a ETS1 AHR

AHR AHR

AHR

AHR

(1) Prevent ARNT/HIF1a and ETS1/HIF2a dimerization

(2) Promote ARNT/AHR and ETS1/AHR dimerization

TREATMENT MODEL

ANGIOGENIC BEHAVIOR

(3) Decrease genome-wide methylation

ARID3A

SOX5

PRRX2

High levels of CpG methylation

TF2 TF1

A+

ARID3A PRRX2 SOX5

N-

AHR ARNT MZF1 ETS1

Other Disease Datasets Provide Validation

Standard platinum-based therapies may actually be priming the cellular network to towards angiogenesis.

GEO

treatment conditions

control conditions

2) RMA-normalize

3) Compute differential expression (T-statistic)

T-statistic

# ge

ne

s

1) Download data

4) Compute summary statistic for Gene Group

Other Disease Datasets Provide Validation

The effects of the VEGF-inhibiting drug Sorafenib directly correspond to groups of

network-identified genes.

Other Disease Datasets Provide Validation

Expression of Network-identified A+ genes decreases with proposed treatments.

What’s Next?

• Represent other genomic data in the network model

• Investigate networks underlying other cancers/diseases

• Continue to think about how biological mechanisms are represented in network models

• Use network predictions to hypothesize on treatments

• The question is not “Is this model right?” Rather, the question is “Is the model useful?”

Acknowledgements

PANDA Development Guo-Cheng Yuan John Quackenbush Curtis Huttenhower Angiogenic Subtyping Benjamin Haibe-Kains John Quackenbush Ursula Matulonis Ovarian Network Analysis Guo-Cheng Yuan John Quackenbush Dimitrios Spentzos

Others Zhen Shao Jeremy Bellay Michelle Girvan Cristian Tomasetti Emanuele Mazzola Luca Pinello Eugenio Marco-Rubio Matthew Tung Funding: NIH R01HL111759 PANDA Availability: sourceforge.net/projects/panda-net/