The Network Effect: Integrative

Systems Approaches to Modeling

Biological Processes

John Quackenbush

AMATA

October 14, 2013

Essentially, all models are wrong,

but some are useful.

– George E. Box

The purpose of models is not to fit the

data but to sharpen the questions.

–Samuel Karlin

Every revolution in science—from

Copernican heliocentric model to the

rise of statistical and quantum

mechanics, from Darwin’s theory of

evolution and natural selection to the

theory of the gene—has been driven

by one and only one thing: access to

data.

–John Quackenbush

Treatment

Options

Quality

Of Life

Genetic

Risk

Early

Detection

Patient

Stratification

Disease

Staging

Outcomes

Natural History of Disease Clinical Care

Environment

+ Lifestyle

Birth Treatment Death

Disease Progression and

Personalized Care

Biomarkers

Networks

Why we care about networks

Biological processes are driven not by genes

but by networks

We want to understand causal relationships in

biological systems wherever possible

Correlations in gene expression can be

considered to be the result of network

interactions

We want to find networks using available

genomic data (largely expression data)

What we are not talking about:

Metabolic pathways – KEGG

Signal transduction pathways – BioCarta

Biochemical Pathways – Roche (Bohringer)

Transcription Factor Networks

etc.

Networks

When we say “Networks” we mean…

Genes are represented as “nodes”

Interactions are represented by

“edges”

Edges can be directed to show

“causal” interactions

Edges are not necessarily direct

interactions

Networks as Models

Phenomenology and Models Ultimately, we look to develop a theory that describes the interactions that drive biological systems

The embodiment of the resulting theory should be a model describing the interactions we are seeking to understand

Phenomenology, or phenomenological models, describe a body of knowledge that relates empirical observations of phenomena to each other, in a way which is consistent with fundamental theory, but is not directly derived from theory

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

Subtypes in Ovarian Cancer

2004 Estimated US

Cancer Deaths*

ONS=Other nervous system.

Source: American Cancer Society, 2004.

Men

290,890

Women

272,810 25% Lung & bronchus

15% Breast

10% Colon & rectum

6% Ovary

6% Pancreas

4% Leukemia

3% Non-Hodgkin lymphoma

3% Uterine corpus

2% Multiple myeloma

2% Brain/ONS

24% All other sites

Lung & bronchus 32%

Prostate 10%

Colon & rectum 10%

Pancreas 5%

Leukemia 5%

Non-Hodgkin 4% lymphoma

Esophagus 4%

Liver & intrahepatic 3% bile duct

Urinary bladder 3%

Kidney 3%

All other sites 21%

A new subtype of ovarian cancer

mRNA/miRNA and DNA were extracted from 132

well-annotated FFPE samples and profiled on arrays

We used a technique called ISIS to find robust bi-partitions in

the data

A major, robust subtype was associated with expression of

angiogenesis genes

We curated all published gene expression data to validate the

split and signature

Identifying modules using ISIS*

Module: Set of genes supporting a bi-partition

*ISIS: Identifying splits of clear separation (von Heydebreck et al., Bioinformatics 2001)

ISIS searches for stratifications of samples into two groups that maximize a DLD score.

Angiogenic Subtype

Survival and Validation

1606 published

ovarian tumors

1090 high grade,

late stage

serous tumors

mir202 is under-expressed in the poor prognosis set,

correlating with up-regulation of its putative targets

miRNA expression supports the subtypes

Beyond Bayesian Networks

21

Normal Tissue

Network

Chemosensitive

Tumor

Chemoresistant

Tumor

What can we learn from networks?

Regulation of Transcription

Specific transcription factors

promoter regulatory

sequences

22

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

.

Application of PANDA to OvCa

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

188 angiogenic, 322 non-angiogenic

Anecdotal evidence suggests about 1/3 of patients treated

with angiogenesis inhibitors respond

Used PANDA to map out networks

Gen

es

Conditions

Expression data

(Angiogenic)

Gen

es

Conditions

Expression data

(Non-angiogenic)

Co

mp

are

/Iden

tify D

iffere

nces

Network for

Angiogenic Subtype

Network for

Non-angiogenic Subtype

PANDA: Integrative Network Models

Message-Passing Networks:

PANDA

PPI0 Expression0

Network1

Responsibility Availability

Network0

Motif Data

Expression1 PPI1

12631 unique edges,

Including 56 TFs

Targeting 4081 genes

15735 unique edges,

Including 49 TFs

Targeting 4419 genes

Network Differences are captured in

Edges

TF differential Expression

Target differential Expression

TF differential Methylation

Target differential Methylation

TF Potential Connection with Angiogenesis 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

Ten “Key” Transcription Factors

Complex Regulatory Patterns Emerge

"A+/A-" genes targeted and more highly/lowly expressed in angiogenic

subtype

"A+;N-" genes, or genes targeted in both subnetworks and more highly

expressed in angiogenic subtype

"N+;A-" genes, or genes 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

Inner ring: key TFs

Colored by Edge

Enrichment (A or N)

Outer ring: genes

Colored by Differential

Expression (A or N)

Interring Connections

Colored by

Subnetwork (A or N)

Ticks – genes

annotated to

“angiogenesis” in GO

TF1 TF2 sig. # Class

ARID3A PRRX2 1.16E-23 244 A+

ARID3A SOX5 1.01E-14 155 A+

PRRX2 SOX5 3.83E-12 157 A+

ARNT MZF1 5.83E-23 92 N-

AHR ARNT 6.13E-16 382 N-

ETS1 MZF1 9.08E-16 148 N-

Co

-reg

ula

tory

TF

Pa

irs

Complex Regulatory Patterns Emerge

Regulatory Patterns suggest Therapies

Sham Dex

Inhaled Corticosteroids in Asthma

Message-Passing Networks:

PANDA 2.0

PPI0 Expression0

Network1

Responsibility Availability

Network0

Motif Data

Expression1 PPI1

Methylation Genetics

miRNA targets

Metabolomics

Generalizing to Individual Patients

Edge probabilities for subtypes are

an average over individual patients

We can generalize this to a

weighted sum over patients

We can then define a matrix of

edge weights

And a matrix of observed edges

We can solve for the edges

for each patient/sample

We can write this as a matrix equation

Matthew Tung, Kimberly Glass

Yeast Cell Cycle Data: Edges Oscillate

Matthew Tung, Kimberly Glass

eQTL Networks: A simple idea

Fah Sathirapongsasuti

eQTLs should group together with core SNPs

regulating particular cellular functions

Perform a “standard eQTL” analysis:

Y = β0 + β1 ADD + ε

where Y is the quantitative trait and ADD is the

allele dosage of a genotype.

Create a bipartite graph where SNPs and genes

are nodes and significant eQTL associations are

edges.

Use “leading eigenvector” clustering to find

“communities” in the graph

eQTL Networks: A simple idea

Fah Sathirapongsasuti

Common QTL SNPs regulate common functions

The modularity of a network quantifies the extent to which

vertices cluster into community groups.

Bipartite network clustering was done uaing the leading

eigenvector method (Barber 2007 Physical Rev).

We assessed functional enrichment for each cluster using the

Bioconductor GOStats package which takes into account the

hierarchical structure of GO annotation.

eQTL Networks: A simple idea

Genomics is here to stay

Before I came here I was confused

about this subject.

After listening to your lecture,

I am still confused but at a higher level.

- Enrico Fermi, (1901-1954)

Gene Expression Team

Fieda Abderazzaq

Stefan Bentink

Aedin Culhane

Benjamin Haibe-Kains

Jessica Mar

Melissa Merritt

Megha Padi

Renee Rubio

University of Queensland

Christine Wells

Lizzy Mason

<johnq@jimmy.harvard.edu> Acknowledgments

http://compbio.dfci.harvard.edu

(Former) Stellar Students

Martin Aryee

Kaveh Maghsoudi

Jess Mar

Systems Support

Stas Alekseev, Sys Admin

Array Software Hit Team

Eleanor Howe

John Quackenbush

Dan Schlauch

Administrative Support

Joan Coraccio

Julianna Coraccio

Center for Cancer

Computational Biology

Mick Correll

Victor Chistyakov

Dustin Holloway

Lan Hui

Lev Kuznetsov

Niall O'Connor

Jerry Papenhausen

Yaoyu Wang

John Quackenbush

http://cccb.dfci.harvard.edu