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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?
EpigeneticsProtein-protein
interactions
Protein-DNA
interactions
gene expression
Data- Specific
Regulatory Network
data
integration
Regulation of Transcription
Kimberly Glass, Network model - Ovarian Cancer, fged_seattle_2013
“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
genes
Potential Co-
Regulatory Events
Use Message Passing to find a
consensus among the networks
InitialNetwork
Information
Message
Passing
LearnedNetwork
Information
Message-Passing Networks: PANDA
(Passing Attributes between Networks for Data Assimilation)
PPI0 Expression0
Network1
Responsibility Availability
Network0
Motif Data
Expression1PPI1
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
Genes
Conditions
Expression data
(Angiogenic)
Genes
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 25912253
Genes Targeted in
Angiogenic Subnetwork
Genes Targeted in Non-
Angiogenic Subnetwork
Network Differences are Captured in Edges
Key Regulators of Angiogenesis in OvCa
TFTF
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 25912253
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 2828
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
TF2TF1
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
#genes
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/
Kimberly Glass, Network model - Ovarian Cancer, fged_seattle_2013

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Kimberly Glass, Network model - Ovarian Cancer, fged_seattle_2013

  • 1. A Network Model for Controlling and Potentially Reversing Angiogenic Progression in Ovarian Cancer Kimberly Glass Functional Genomics Data Society June 20, 2013
  • 2. • 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
  • 7. 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
  • 8. Passing Messages between Biological Networks Protein-protein interactions Protein-DNA interactions Genomic Data Gene Expression Network Representation Cooperation between TFs Potential Regulatory Events genes genes Potential Co- Regulatory Events Use Message Passing to find a consensus among the networks InitialNetwork Information Message Passing LearnedNetwork Information
  • 9. Message-Passing Networks: PANDA (Passing Attributes between Networks for Data Assimilation) PPI0 Expression0 Network1 Responsibility Availability Network0 Motif Data Expression1PPI1 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/
  • 10. Application of PANDA to Ovarian Cancer Subyptes
  • 11. A new subtype of ovarian cancer
  • 12. 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
  • 13. Genes Conditions Expression data (Angiogenic) Genes 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
  • 14. 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 25912253 Genes Targeted in Angiogenic Subnetwork Genes Targeted in Non- Angiogenic Subnetwork Network Differences are Captured in Edges
  • 15. Key Regulators of Angiogenesis in OvCa TFTF Edges from Regulator in Angiogenic Subnetwork Edges from Regulator in Non-Angiogenic Subnetwork Calculate an “Edge Enrichment” and corresponding significance
  • 16. 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
  • 17. 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.
  • 18. 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 25912253 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
  • 19. Both Activation and Repression of Pathways is Important in Angiogenesis
  • 20. 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
  • 21. 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 2828 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?
  • 22. • 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 TF2TF1 A+ ARID3A PRRX2 SOX5 N- AHR ARNT MZF1 ETS1
  • 23. 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 #genes 1) Download data 4) Compute summary statistic for Gene Group
  • 24. Other Disease Datasets Provide Validation The effects of the VEGF-inhibiting drug Sorafenib directly correspond to groups of network-identified genes.
  • 25. Other Disease Datasets Provide Validation Expression of Network-identified A+ genes decreases with proposed treatments.
  • 26. 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?”
  • 27. 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/