The document outlines a project aimed at utilizing predictive networks in genomics to enhance precision medicine, addressing significant challenges in handling big biomedical data. The project intends to develop methodologies for predicting therapy outcomes, identifying disease mutation effects, and discovering drug responses through patient similarity networks and molecular interaction networks. A key deliverable will be a bioinformatic framework that integrates patient data with biological networks to facilitate clinically relevant diagnoses and predictions.

1. TRD 2: PREDICTIVE NETWORKS – PROJECT SUMMARY
A major promise of genomics is to enable precision medicine, through use of a patient's genome and
physiological state to improve treatment efficacy and outcome. Routine use of genomics data in
clinical research still faces major barriers, however, including some very important challenges posed
by ‘Big Data’. First, modern genomic datasets are typically so large and complex that most biomedical
researchers or clinicians have neither the computational infrastructure nor data mining expertise to
cope with them. Handling even a few hundred patients requires the ability to store, access, process,
and analyze petabytes (1012
bytes) of genomic data. Second, although mainstream computer
scientists and information technology companies are becoming very astute at data mining,
understanding big biomedical data is likely to require a depth of understanding in physiology,
biotechnology, and cellular and molecular mechanism that mainstream data analysts simply do not
have. Rather, encouraging preliminary results from the NRNB and several other groups indicate that
an effective way to address the challenge of Big Biomedical Data is to integrate and interpret these
big data sets against appropriate representations of biological, physiological, and clinical knowledge.
Biological network models at multiple scales are increasingly recognized as a natural way to represent
and visualize knowledge about biological mechanisms and relationships. While the field of Network
Biology has focused mostly on descriptive models of network structure, there is increasing evidence
that network knowledge can also be used to guide biological and clinical predictions. In some cases,
these network-guided approaches have yielded predictions of higher accuracy and / or robustness
with less input or training data than traditional ‘black-box’ machine learning methods. In this TRD
project, we will pursue novel methodology for using biological network information to predict the
outcome of therapy in a given patient, to identify which networks and pathways are affected by
mutations conferring risk of a disease, and to predict drug response and identify novel drug targets.
The major deliverable is a bioinformatic framework to integrate patient molecular and clinical data with
biological network information, with the goal of making clinically-relevant diagnoses and predictions
about an individual based on their genomic information.

2. TRD 2: PREDICTIVE NETWORKS – PROJECT NARRATIVE
Analysis of personalized human genomic data promises major medical advances. However, the
routine use of genomics data in medical research is in its infancy, due mainly to the challenges of
working with highly complex ‘Big Data’. Use of biological networks promises to help organize, analyze
and integrate these data into models that can be used to make clinically relevant diagnoses and
predictions about an individual.

3. TRD 2: PREDICTIVE NETWORKS – SPECIFIC AIMS
Network Biology has traditionally focused on defining descriptive models of biological systems, for
instance mapping all known protein-protein interactions for an organism. These networks have been
very useful for identifying modular structures and for improving our understanding of gene function. As
network models become more complete and detailed, and as genomics continues to accumulate
mountains of data relevant to human physiology and disease, we are poised to transition Network
Biology from a descriptive discipline towards a paradigm based largely on predictive outcomes.
Through the Specific Aims of this TRD, we explore and further develop two fundamental and
complementary means by which networks can impact predictions of patient status, based on networks
of patient similarities (Aim 1) and networks of molecular interactions (Aim 2), respectively. We also
invest in efforts to make these and other methods generally accessible, through software
infrastructure for processing and analyzing genetic variants using networks (Aim 3). By embedding
the available collection of network knowledge in routine analysis of patient data, we will seek to
enable medically-relevant prognosis and prediction tasks such as which cancer subtype a patient
belongs to, whether their tumor will metastasize or not, and what drug should be chosen to treat them.
Aim 1: Develop patient similarity networks as a major resource for predicting clinical outcome.
We will develop patient similarity networks as a major platform for predicting patient outcome (e.g.,
breast cancer risk, response to therapy). Using patient similarity networks, we will first develop
supervised methods to predict patient outcome using and, second, we will pursue unsupervised
analysis to stratify patient populations into more homogeneous groups with better correlation to
outcome. We will also develop technology to visually superimpose a patient’s genomic profiles on
these patient networks, highlighting features that are informative for patient classification and outcome
prediction, while simultaneously relating patient history and treatment response from other patients
with meaningfully similar profiles.
Aim 2: Supervised learning of patient response using multi-modal data integration on
molecular networks. As exemplified by The Cancer Genome Atlas, many large patient ‘omics
projects are now underway that span not just one but many layers of information, including genomes
and single nucleotide variants, copy number changes, genome rearrangements, epigenetic changes,
and RNA and protein abundances and modifications. Effective use of such multimodal datasets
remains an important challenge that may benefit significantly from the use of network-encoded prior
knowledge. We will address this challenge by extending existing methodology to operate in this
regime, leveraging gene and protein networks in multiple ways. First, networks will be used as a
means of extracting/generating robust network features. Second, we will extend regularization
methods to consider directional or weighted networks (e.g. that capture stimulatory versus inhibitory
interactions). Third, we will use networks to detect changes in correlation structure across data layers.
This aim strongly leverages databases (e.g. GeneMANIA, cBioPortal) and methods (Network-Based
Stratification) previously developed by NRNB personnel.
Aim 3: Develop Cytoscape Apps for accessible network analysis of genetic variant data. We
will develop software tools for accessing, modeling and annotating variant data, and for performing
network analysis of the processed variants. Focusing on approaches that best leverage NRNB
expertise and serve our DBPs, we will implement the following analytical methods as Cytoscape
Apps: 1) Pathway analysis of Genome-Wide Association Studies (GWAS); 2) Network-Based
Stratification (NBS); and 3) Detection of Mutual Exclusivity Modules in Cancer (MEMo). We will build
the data structures and services needed to support variant data analysis in Cytoscape, including user
data modeling, dynamic mapping and annotation services. This work will streamline access,
annotation and analytics for variant data in the context of networks. The goal is to put these tools in
the hands of diverse researchers and thereby enable broad exploratory and confirmatory analysis.

4. TRD 2: PREDICTIVE NETWORKS – RESEARCH STRATEGY
Overview. Advances in genomics are allowing for an ever-finer resolution mapping of complex
cellular state, holding great promise for major medical advances. In particular, genomics is enabling
precision medicine, the use of a patient’s genomic, cellular, and physiological state to improve
therapeutic efficacy and outcome1
. For instance, DNA sequencing technology has quadrupled the rate
of identification of rare disease-causing genes in the past two years2
which enables earlier and more
accurate diagnosis. In a second illustrative example, a pediatric neurologist, over the span of a mere
two years, used genomics to discover the causative mutation of a rare brain disease, affecting
multiple children in the same family, and based on this mutation found an existing drug to effectively
treat the patients3
.
The big data problem. While compelling anecdotes of precision medicine abound, the systematic
use of genomics data in medical research still faces major barriers, including some very important
challenges posed by ‘Big Data’. Collectively, these data are heterogeneous, requiring an
understanding of many aspects of experimental biology and medicine to correctly process and
interpret; noisy, both with systematic artifacts that must be removed and with random noise requiring
statistical validation of results; and complex with non-linear signal patterns and interrelationships that
must be considered to maximize the signal-to-noise ratio. As an example of heterogeneity, a modern
‘omics dataset might include data on whole genome/exome sequences, gene transcript and protein
expression, splicing, chromatin structure, molecular interaction, transcription factor, methylation and
metabolite profiles as well as rich patient phenotypes (e.g. imaging, biopsy, blood tests). Data size is
also an issue, as individual researchers may need to handle petabytes of information (e.g. whole
genome data from a few hundred patients), which is challenging to store and analyze locally and
prohibitive to transmit using standard internet protocols. These challenges, coupled with the
observation that sequencing capacity outgrows ‘Moore’s law’, implies that advances in computing will
not resolve these challenges in the foreseeable future. Inevitably as sequencing cost is reduced, most
people who see a doctor will likely have their germ-line and somatic genomes sequenced, perhaps
starting at birth4
. Thus, a fundamental challenge facing the field of genomics is to organize, analyze
and integrate these data into models that can be used to make clinically relevant diagnoses and
predictions about an individual, while maintaining patients’ rights to privacy and confidentiality.
Towards this challenge, encouraging preliminary results from NRNB and several other groups indicate
that an effective way to address the challenge of Big Biomedical Data is to integrate and interpret
these big data sets against appropriate representations of biological, physiological, and clinical
knowledge5,6
.
Using networks to make clinically important predictions. Network models at multiple scales are
increasingly recognized as a natural way to integrate patient clinical and molecular data, as well as
prior knowledge about patient and molecule relationships, in a common framework for use in sub-
categorizing disease, diagnosis, prognosis and drug target prediction. A number of examples have
shown that using a network-based approach is more accurate and functions well with smaller
amounts of input or training data compared to traditional approaches. For instance, we and others
have previously advanced the concept of ‘network biomarkers’, connected subnetworks of gene and
protein interactions whose genes show genetic variation or differential expression that can explain
and predict phenotypes or clinical outcomes7-17
. Such network biomarkers are often more robust
readouts than individual genes or proteins, and networks derived from prior knowledge of molecular
mechanism and pathways are more biologically meaningful than arbitrary sets of genes. In a
complementary example, the recently developed Similarity Network Fusion (SNF) method18
integrates
diverse genomic and clinical data using a very different type of network – patient-patient similarity
networks. It shows that patient similarity networks can identify clinically-relevant patient subtypes with
impressive survival prediction across five different cancer types.
In this project, we will explore the use of network information of both types (patient similarity and

5. molecular networks) to improve the accuracy with which we can predict the outcome of therapy in a
given patient, identify which networks and pathways are affected by mutations conferring risk of a
disease, as well as predict drug response and identify novel drug targets. We acknowledge that
clinical use of the computational methods we develop has many requirements that will not likely be
met during the course of this grant19
. However, our activities will nonetheless progress significantly
towards this goal and have immediate benefits for medical research, as we will work with leading
clinician-scientist collaborators within Driving Biomedical Projects. We will also investigate application
of our methods in collaboration with James Fowler to use social network information to predict
behavioral and disease outcomes and to guide selection of novel intervention mechanisms, i.e. which
individuals should be targeted for weight loss or smoking cessation.
2.1 DEVELOP PATIENT SIMILARITY NETWORKS AS A MAJOR RESOURCE FOR
PREDICTING CLINICAL OUTCOME
Project Leader: Gary Bader (University of Toronto)
Overview. In this project, we will develop patient similarity networks as a major platform for predicting
patient outcomes such as breast cancer risk or response to therapy. Recently, work has shown that
analyzing patient networks, wherein nodes represent patients and edges some measure of patient
similarity, is useful for identifying predictive patient subgroups18
. Using patient similarity networks, we
will first develop supervised methods to predict patient outcome using and, second, we will pursue
unsupervised analysis to stratify patient populations into more homogeneous groups with better
correlation to outcome (a well tested phenomenon in multiple disease areas like cancer20
). We will
also develop technology to visually superimpose a patient’s genomic profiles on these patient
networks, highlighting features that are informative for patient classification and outcome prediction,
while simultaneously relating patient history and treatment response from other patients with
meaningfully similar profiles. In our preliminary work to predict the response to methotrexate therapy
in rheumatoid arthritis patients with collaborators at Sage (DBP) and Dr. Kathy Siminovitch (CSP), we
have found that this type of approach increases prediction accuracy. The new technology developed
in this TRD will be applied to clinical data sets made available as community challenges by
SAGE/DREAM and studied by our collaborators (e.g. rheumatoid arthritis and breast cancer risk).
Background and Significance. Subdividing patients into subgroups homogeneous with respect to
biology, disease progression and response to treatment enables “precision medicine”. Although it is a
new term, precision medicine is simply traditional medicine that considers more detailed patient data.
In the next few years, these new data will include much more detailed patient genotypes, such as
whole genome sequences, and phenotypes, such as concentrations of thousands of molecules (RNA,
proteins, metabolites) in blood and other bodily fluids. Historically, improvements in the depth of
patient data have led to a substantial improvement in patient care – before autism spectrum disorder
was defined in 1971 based on improved phenotyping, it was considered the same disease as
schizophrenia21
– and new large-scale phenotype data are already leading to more precise care:
breast cancer is now treated by subtype (e.g. ER+, HER2+, triple negative) using targeted drugs that
outperform the older one-size-fits-all therapies22
. Recent precision medicine successes have required
computational methodology to analyze large data sets. Generalizing to even more detailed data and
diseases not yet studied using genomics will require substantial, concerted, and expert effort in
algorithm development to automatically organize the genotype-phenotype landscape6
and relate the
results to patient outcomes.
Motivating Collaborations. The following three DBP / CSP projects provide ample motivation for
pursuing this technology development:
Rheumatoid arthritis treatment prediction (Sage Bionetworks DBP, Siminovitch CSP). Rheumatoid
arthritis is a common immune disorder that often causes painful swelling in joints. An important
challenge in RA treatment is choosing effective therapy. The current standard approach

6. (methotrexate) yields no or poor response in ~35% of cases, at which point alternate therapy (e.g.
anti-TNF) is prescribed, which are effective in ~60% of these cases. Pharmacogenetic research
implicates genetic polymorphisms in treatment response23
. The challenge is to predict which
treatment will be effective based on genomic and clinical information collected upon initial
presentation to the clinic (Task 1 below). Sage Bionetworks ran a public challenge in this area to
crowd-source solutions (https://www.synapse.org/#!Synapse:syn1734172). The input is SNP and
WES mutations and clinical data; the output will be prediction of response to methotrexate or anti-TNF
therapy. We are also collaborating with Kathy Siminovitch, MD, at Mount Sinai Hospital in Toronto
who has SNP genotype data on ~1000 RA cases and 500 controls with measured methotrexate
response.
Pediatric brain cancer outcome prediction (ICGC DBP, Goldenberg CSP). Michael Taylor, MD, PhD,
at The Hospital for Sick Children in Toronto, is leading a study of pediatric medulloblastoma (a rare
and serious brain tumor) as part of the International Cancer Genomics Consortium (ICGC) led by Tom
Hudson (DBP collaborator). The Taylor lab has collected WES, SNP chips, DNA methyl chips,
miRNA-Seq and RNA-Seq for >1000 samples and WGS for 300 samples. A major challenge is to
molecularly dissect medulloblastoma to identify subtype-specific diagnostic and therapeutic targets20
.
Already, four subtypes with distinct outcomes have been defined based on gene expression data and
these will likely lead to improved treatment protocols for patients. For instance, the WNT signaling
enriched subtype has a substantially better prognosis than other subtypes, thus it may be possible to
reduce brain damaging radiation and chemotherapeutic treatment in these individuals. The Taylor lab
has developed a gene expression-based signature aimed at the clinic to subtype tumors. Recent
evidence has shown that other genomics data, such as DNA methylation, may be more accurate at
subtype classification. It is hypothesized that using all available genomic data will lead to the best
achievable classifier. In collaboration with SNF developer Anna Goldenberg (CSP), we will develop
the SNF Cytoscape app to analyze this genomic data to improve accuracy of medulloblastoma
subtyping, as defined by survival outcome (Task 2 below).
Breast cancer genetic risk prediction (Simard CSP). Breast cancer is the most common cancer in
women in the US and over 40,000 people die per year from the disease. The lifetime risk of
developing breast cancer in women is over 10%. There is wide variation in breast cancer
susceptibility, due to genetic and other identifiable factors. Mammographic surveillance of all women
aged 50-74 is recommended, but early detection and prevention of breast cancer can be improved by
stratification of women according to risk, targeting of screening on the basis of risk and use of
additional preventative strategies (e.g. MRI screening) for higher risk women. At present, only a third
of the overall genetic variation in breast cancer risk is accounted for, the identification of the missing
inherited susceptibility is a critical step to improve the predictive power of risk models. Our
collaborator Jacques Simard (University of Laval, Quebec City) is leading a multi-investigator $10M
project to genotype 78,000 breast cancer patients and 200,000 controls on a 530k custom single
nucleotide polymorphism chip as part of the NIH NCI funded OncoArray Consortium and the
international Breast Cancer Association Consortium. Our role in the project is to use network and
pathway information to help interpret SNPs associated with breast cancer and to help predict breast
cancer risk from genotype (Task 1 below).
Preliminary Results and Progress Report. The use of patient similarity networks, especially those
that consider multiple types of genomic, phenotype and clinical data is very recent. In fact, we are
aware of only a few papers on the subject18,24
. However, we have been actively working in this area
and have developed an initial version of our GeneMANIA based predictive patient similarity network
method (Task 1 below). Preliminary results using this tool for the Siminovitch rheumatoid arthritis data
set described above show that we can predict good and poor methotrexate response from genotypes
and clinical data. Genes and pathways relating to immunological function and metabolism of
methotrexate were most informative in our model and enabled us reach up to 90% accuracy.
Methods. Our aim is to develop methods for using patient similarity networks to improve prediction
accuracy of clinical outcome in a range of disease areas. We have factored this aim into three tasks:

7. 1. Predict outcome (e.g. breast cancer risk, response to therapy) based on supervised machine
learning analysis of patient similarity information based on genotype, phenotype and clinical
information. Patients that are similar in certain, potentially hidden features will be more likely to be
similar in their outcome. Machine learning methods will be designed to identify the informative
features and use them for prediction. This task is useful to answer directed clinical questions, such
as “Will a patient respond to treatment or not?”
2. Develop methods for unsupervised clustering of patient networks to stratify patient populations,
based on molecular network and pathway features. It is expected that patient clusters that are
more homogeneous will better predict clinical features, such as disease outcome or survival. This
task is useful for exploratory analysis of new large and diverse data sets to identify possible
subgroups.
3. Develop patient similarity network visualization technology, simultaneously showing both patients
and informative features for patient classification and outcome prediction. This task supports
interpretation of the results of the first two tasks and will be useful to communicate the results to
non-specialists in computational biology, such as clinical collaborators.
Task 1: Predict outcome based on supervised machine learning analysis of patient similarity
information. We will develop a novel decision support system for precision medicine, implemented
using powerful computational and statistical analysis methods we previously developed, which will
enable clinician-researchers to mine large amounts of clinical and genomic data to identify patterns
that are predictive of outcome. We will define “patient phenotype similarity” based on all patient
genetic variants and any associated clinical and molecular data. To enable clinical decision support,
we will adapt a machine learning method previously developed for the GeneMANIA gene function
prediction system we created and implemented in Cytoscape25,26
. Instead of working with genes and
their functional similarities, the system will work with patients and their clinical-genomic similarities.
The system will identify types of similarities among patients (e.g. gene variants) that uniquely define
their subgroup, such as ‘treatment responders’. It will then use these features to predict additional
patients likely to be in that subgroup (scored using label propagation27,28
). Clinical and genomic data
will be converted to patient similarity networks, in which nodes represent patients and links (or edges)
represent patient similarities. Each clinical or genomic variable under study, or dimensionality reduced
versions, or relevant combinations, will be used to create a separate network or feature, such that
each represents similarity in a particular aspect such as age, smoking status or genotype (possibly
broken down into common, disease-associated, or rare variants). We will also explore ontology,
clinical database and electronic medical record-based similarity measures as available. For instance,
we will use the Human Phenotype Ontology29
to formally describe clinical symptoms and compute
patient phenotypic similarities30
.
Our approach has numerous advantages: it is fast; has built in redundancy reduction (regularization)
to simplify input data and reduce chances for over-fitting; handles missing data (omits a given edge
between patients); and has an intuitive representation of the data used to make a prediction, mirroring
the process of diagnosis by similarity to historical cases that many clinicians implicitly use. Exposing a
human readable representation of the data is likely to be far more interpretable by clinician users than
black box machine learning approaches. The major work involved will be to define and optimize
patient similarity measures and indices, to evaluate the system on multiple DBP data sets, and
explore feature-combining functions, to see if modeling non-linear interactions improves performance.
We will also explore ways to integrate pathway information and the millions of functional gene-gene
interactions from GeneMANIA to improve sensitivity for patient data that may not be very informative
alone, but would become so when they are combined based on prior information (e.g. mutations
within the same pathway or in genes co-expressed in disease-relevant tissues). We will accomplish
this by relating patients based on affected pathways and gene interaction neighborhoods, and
modifying the label propagation algorithm to boost patient similarity based on prior data. This may
require implementation of newer, but established methods31
to speed prediction calculations. The

8. system’s user interface will present evidence supporting its recommendation, illustrating how a new
patient is similar to previous patients, alongside relevant outcome data (see Task 3 below).
Task 2: Unsupervised clustering of patient networks to stratify patient populations, based on
molecular network and pathway features. As mentioned above, the recently developed Similarity
Network Fusion (SNF) method integrates diverse genomic and clinical data using patient similarity
networks and identifies clinically relevant patient subtypes with better survival prediction than other
widely used methods18
. Briefly, SNF uses network diffusion theory to combine diverse patient
networks into a single network that reflects patterns across all of the integrated data types and then
clusters the results to obtain patient subtypes. SNF is robust to various kinds of noise and is powerful
even on small sample sizes (e.g. less than 100 patients). We will work with SNF developer Anna
Goldenberg (CSP) to implement it as a Cytoscape App and apply it to pediatric medulloblastoma data
from Michael Taylor’s lab (DBP).
Task 3. Develop visualization methods for patient similarity networks, simultaneously showing both
patients and informative features for patient classification and prediction. Patient similarity networks
draw from patient similarity in clinical data (e.g. age, smoking status), molecular profile (e.g. similar
gene expression profile), genetic (e.g. similar SNPs), or cellular systems information (e.g. similar
molecular network or pathway alterations). These networks are naturally visualized in Cytoscape
using standard file formats and visualization options. However, to interpret the results of the predictive
and classification networks in Tasks 1 and 2, it is important to understand which of many patient
similarity features are most informative for prediction and classification performance. The machine
learning methods of Task 1 involve feature selection, which will identify which patient network types
and which aspects of those networks (e.g. which genes in a gene expression profile) contribute most
to the prediction performance. We will develop a visualization method in Cytoscape to show these
informative features in the context of patient networks. Each patient node will be graphically
represented as a chart showing the informative features associated with that patient. For instance,
instead of showing a 20,000 long gene expression vector for each patient, we will show a vector
including a few top genes that contribute most to the correlation. Similarly, for pathway correlation
vectors, we will show the genes that contribute most to performance. We will coordinate work with
multi-scale modeling and visualization technology development in TRD3 to share and re-use
technology as needed.
2.2 SUPERVISED LEARNING OF PATIENT RESPONSE USING MULTI-MODAL DATA
INTEGRATION ON MOLECULAR NETWORKS
Project Leader: Trey Ideker (UCSD)
Overview. Many large patient ‘omics projects are underway that span not just one but many layers of
information. Effective use of such multimodal datasets remains an important challenge that may
benefit significantly from the use of network-encoded prior knowledge. We will explore this idea by
extending existing methodology for regression and classification to leverage gene and protein
networks in multiple ways. First, networks will be used as a means of extracting/generating robust
network features. Second, we will extend regularization methods to account for directional or weighted
networks (e.g. that capture stimulatory versus inhibitory interactions). Third, we will use networks to
detect changes in correlation structure across data layers. This aim strongly leverages databases
(Pathway Commons) and methods (Network-Based Stratification) previously developed by NRNB.
Background and Significance. Predicting aspects of complex human traits, such as disease
progression and the response to pharmaceutical treatment, remains a major challenge in biology and
medicine. Presently a wealth of measurements is being generated using a variety of technologies and
tools. For example, a growing number of global consortia, such as The Cancer Genome Atlas32
, the
Cancer Cell Line Encyclopedia33
, and ENCODE34
, all produce datasets that span not just one but
many layers of ‘omics information, including genomes and single nucleotide variants, copy number

9. changes, genome rearrangements, epigenetic changes, and RNA and protein abundances and
modifications. These data are all too often analyzed separately, using simple approaches seeking to
connect single features with biological conditions of interest. This aim will further advance the use of
networks in integrated interpretation of heterogeneous biological data. Supervised machine learning
methods and molecular networks encoding prior biological knowledge are the key tools we propose to
use for making progress on this front.
Within the supervised learning field, regression analysis is perhaps the most common statistical
method for association of heterogeneous inputs with continuous phenotypic traits. Both the Cancer
Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer projects have successfully
applied a regularized regression analysis, known as elastic net, for the reconstruction of drug activity
profiles as a weighted sum of genomic features33,35,36
. Towards network-based regression, some work
has been conducted to develop regularization methodology that makes use of known interactions
among input features37-39
. These methods have been used to identify features relevant to glioblastoma
survival time and the identification of age-dependent molecular modules in human brains. One
limitation of this work has been its restriction to a relatively small number of features (<2000 genes).
The work proposed here will enable a marked expansion of features to be analyzed by using network
information and leveraging resources developed by the NRNB, including GeneMANIA and Pathway
Commons.
Motivating Collaborations. Several our Driving Biomedical Projects are excellent proving grounds
for technology development under this aim. We will be driven by three DBPs in particular – The
Cancer Genome Atlas (Stuart), the Colorectal Cancer Subtyping Consortium (Sage Bionetworks), and
the NCI drug response prediction project (Pommier) – all of which center on prediction of cancer
outcome from multi-modal ‘omics datasets. For example, Yves Pommier’s lab at the National Cancer
Institute has been systematically screening ~20,000 publicly available compound drug screening
library of the NCI-60 cancer cell lines made available by the National Cancer Institute as part of the
Development Therapeutics Program. One fundamental challenge is that off-target effects of many
drugs are not well understood and as a result, development of novel pharmaceuticals brought to
market has declined sharply in recent decades. Pommier’s interest is to use network biology to
increase the efficacy of drug compounds, while minimizing their side effects, by creating a greater
understanding for the factors involved in drug response. We will also investigate application of new
technology we develop in liposarcoma treatment (White, MIT) and the Wrensch Glioma DBP.
Preliminary Results and Progress Report. In prior work, NRNB investigators have developed a
number of network-based prediction methods relevant to this aim. In Chuang et al. we used protein-
protein interaction networks to detect modules of RNA expression that were predictive of CLL
progression14
. Using a greedy search heuristic, a protein interaction network was mined to identify
connected subnetworks for which the genes had an aggregate expression level predictive of CLL
progression. These subnetworks were validated in two other independent patient cohorts, illustrating
the robustness of using network extracted features. Later, this method was improved using the
technique of network-guided random forests17
. Random forests use sampling with an iterative tree
based classifier to result in robust and accurate classifiers. By adding a network constraint to this
method, we were able to identify complex ‘logic-circuit’ relationships among perturbations in gene
expression with implication to cancer outcome and tissue differentiation. This work showed that use of
a network constraint resulted in coherent biologically meaningful predictive functions (as opposed to
‘black-box’ results from standard random forest approaches, which are typically difficult to interpret).
Most recently we introduced the method of Network-Based Stratification (NBS), which integrates
somatic tumor genomes with gene networks using network propagation40
(Figure 1). Network
propagation is a feature transformation method that uses a random-walk-like procedure to integrate a
measurement (in this case gene mutations) with local network topology, generating a new feature
space of network activity profiles. We found this approach could be used in an unsupervised

10. framework allowing for stratification of cancer into informative subtypes, by clustering together
patients with mutations in similar network regions. NBS has been demonstrated in ovarian, uterine,
and lung cancer40
and, more recently, thyroid cancer cohorts41
from The Cancer Genome Atlas. For
each tissue, NBS identifies clear subtypes that are predictive of clinical outcomes such as patient
survival or response to therapy, and it identifies network regions characteristic of each subtype.
The work proposed here will further develop these network-based approaches in combination with
interaction networks and other information available in GeneMANIA26,42
, Pathway Commons43
and
cBioPortal44
, three databases that were created by our team.
Methods. Our development of network-based supervised learning methods centers on three
synergistic tasks:
1. Methods for feature extraction spanning multiple data types. We will extend current methods for
network propagation and network influence to efficiently compute network level features
integrating measurements spanning multiple molecular level data types.
2. An integrated framework for network-guided regression across multiple data types. We will draw
on existing methods for network regularized regression to develop methods for network
regularized classification and regression of data features from multiple data types, considering
covariation and dependence patterns across the data layers.
3. Integration of molecular network directionality and context-specific network edge weighting. We
will extend the two above methodologies to benefit from directed networks, capturing gene
repression and activation relationships, and condition-specific reweighting of network links. We will
also explore methods for using genomic data to predict condition specific edge weighting and
directionality.
Task 1: Methods for feature extraction spanning multiple data types. We will extend methods for
feature transformation and extraction using a network propagation based approach. The key advance
over earlier work will be to effectively integrate information from both multiple molecular networks and
multiple types of molecular measurement data. Briefly, we will jointly perform network propagation of
perturbed measurements from different data sources, by considering this as a propagation task on a
Figure 1. Network based stratification
of somatic tumor mutations. (A) Co-
clustering matrices comparing NBS to
standard consensus clustering. (B)
Ovarian cancer: Association of NBS
subtypes with patient survival time. (C)
Kaplan-Meier survival plot for NBS
ovarian cancer subtypes (k = 4). (D)
Network view of genes with high network
smoothed mutation scores in subtype 1
relative to other subtypes. Subtype 1
has the lowest survival and highest
platinum resistance rates amongst the
four recovered subtypes. Node size
shows smoothed mutation scores. Node
color corresponds to functional classes
of interest. Thickened node outlines
indicate genes that are known cancer
genes included in the COSMIC cancer
gene census.

11. meta-network with nodes for every measured value in the input. The meta-network includes edges
between nodes representing values of the same type. Performing propagation using the standard
approach and this meta-network is equivalent to independent propagation of each data type. This
framework will be extended to capture complex structure among the data types, however. For
example, adding an edge between corresponding nodes in different data layers (e.g. a somatic
mutation in gene X and a copy number deletion in the same gene X), causes the propagation of
values in network regions across the different data types. Similarly, we will explore more complex
relationships among different data types, e.g. by adding to the meta-network an edge linking gene X
of one data type to a related gene Y in a different data type. These cross data layer relationships can
be mined from prior knowledge or separate experiments (e.g. trans eQTL relationships, TF-
downstream target) or these may be learned directly from the data using simple information theoretic
approaches as was recently demonstrated45
. The above approach can be extended to be jointly
performed on multiple input networks mitigating the effects of missing edges or noise in any single
network. Furthermore, we will also explore performing inference of missing edges across data types,
resulting in an expectation maximization-like procedure able to further highlight putative cross-data-
type network relationships.
Task 2: Develop an integrated framework for network guided classification and regression across
multiple data types. We are currently extending the use of a network-based regularized regression
analysis37
. In this framework, two penalty terms are combined, one introduced by a LASSO result in a
sparse regression function using a small number of features. The second added network
regularization term encourages solutions that ‘respect’ edges in the input network, penalizing large
differences between the regression coefficients of network-connected features. Here, we will use the
GeneMANIA gene interaction network database for this second regularization term, which has the
ability to weight gene interactions according to the strength of evidence for that interaction across
multiple data sources. The relative influence of each of these penalty terms will be balanced using an
additional parameter as in the elastic net regression method36
. These resulting predictors are
expected to be more biologically informative than classical ‘black box’ regression because they link to
interpretable biological concepts.
Several immediate challenges will need to be addressed as part of this work. First, as highlighted by
Li and Li46
, the above regularization scheme may not perform well when network-linked predictors
have opposite signs in their regression coefficients38
. Second, Li and Li developed their approach for
expression measurements only; the appropriate normalization and scaling of data types and the
specification of prior knowledge networks relating predictors of heterogeneous data is less clear.
Furthermore, biological network knowledge is incomplete, and may include errors. As part of our work
we will seek to understand the robustness of network-constrained regression methods in the face of
missing and/or erroneous data. Finally, the idea for cross data type integration using a global meta-
network presented above (Task 1) naturally extends to the regularization framework described here.
Task 3: Improve performance of biological system response prediction by integration and prediction of
network directionality and context specific network edge weighting. The methodology introduced
above (Tasks 1 and 2) may further benefit from explicit modeling of the directionality of molecular
interaction (e.g. repression vs. activation) and types of interaction beyond simple protein-protein
interactions (e.g. TF-target binding, kinase-signaling, metabolic process links). Furthermore, in the
same way the genome is not uniformly expressed, we have no reason to assume that molecular
networks operate uniformly across conditions. A trivial but illustrative example is to consider that any
gene not expressed in a certain condition or tissue will have no role in the interaction network for that
condition. Thus, developing advanced methods for inferring the condition-specific importance of
network links is central to the above discussed tasks. We propose to accomplish this task through
information theoretic approaches for capturing changes in the dependence and correlation of
measurements in different conditions (e.g., coherent links in the networks which become less
coherent should be scaled down, and vice versa).

12. 2.3 TECHNOLOGY FOR NETWORK ANALYSIS OF GENETIC VARIANT DATA
Project Leader: Alexander Pico (Gladstone Institutes)
Overview. Recently, we and others have shown that network information is useful for predicting
causal variants from the background of many variants potentially associated with disease5
. In this
project, we will focus on end-user software technology for network analysis of genetic variant data,
such as SNPs and somatic mutations, associated with human disease. We will develop a variant data
resource accessible from within Cytoscape that will facilitate frequent variant analysis workflows. This
technology development will be driven by an ongoing and long-standing DBP with the Wrensch lab at
UCSF on identifying causal variants in glioma, a devastating brain cancer. With an emphasis on
making a suite of tools that are accessible to the broader research community, we will also coordinate
with the DREAM Challenges DBP with Sage Bionetworks and Gustavo Stolovitzky. Co-organizing a
challenge around cancer subtyping, for example, would drive network methodology development in
the field. And the winning methods would help prioritize which methods–beyond those listed below–
we should include in our variant analysis technology suite.
Objectives. The major tasks of this aim are to develop software tools for: 1) performing network
analysis on variant data; and 2) accessing, modeling and annotating variant data. Together, these
tasks will produce technologies to enable geneticists, epidemiologists, and clinicians to work with and
visualize variant data in new and powerful ways.
The first task will focus on both common and recent methodologies not yet broadly available for
variant data visualization and analysis using biological networks. In many cases, these methods have
only been implemented in a localized computer environment to produce the proof-of-concept needed
to publish and have not been more widely distributed in any form. In other cases, they are distributed
for only specialized users and environments, e.g., command-line. By supporting these methods in
Cytoscape, we will produce tools that can be more widely distributed and adopted, leveraging the
generic and powerful Cytoscape platform as well as its associated ecosystem.
Given the potentially large size and scope of gene-disease association study datasets, the second
task addresses a non-trivial data management challenge. Network-based variant analysis will
necessitate a query-and-selective-data-import approach, rather than a single import into in-memory
data structures. Developed for just this reason, the ADAM format and processing patterns are useful
for interactively working with these genetic variant data47
. In terms of annotating variant data, there
exist dozens of resources providing a wide range of annotations from structural to functional and from
reference to predictive. Given our prior work in this area with the SNPLogic project48
, and the
preliminary work on MyVariant.info, we aim to greatly expand the accessibility and improve the
maintenance of a JSON-based web service dedicated to variant annotations. We will then develop a
Cytoscape app to access this general service, bringing up-to-date variant annotations to user variant
datasets.
Background and Significance. As yet, most inherited variation influencing complex diseases is likely
still unknown, including the causative mutations of over 3500 rare monogenic diseases2
. A key
distinguishing feature of disease-causing, versus other less severe mutations, is that they disrupt
normal cellular and biochemical mechanisms. Current mutation assessment tools, such as
Polyphen249
, SIFT50
and MutationAssessor51
, generally focus on structured protein coding regions
and evaluating damage to protein function based on amino acid conservation, physicochemical
properties and splice sites. However, a reasonable hypothesis is that knowledge of biochemical
mechanisms and networks will substantially improve our ability to interpret mutations across the
genome6
.
Over the past 24 years, Dr. Wrensch and colleagues have studied cohorts of UCSF patients with
devastating brain cancers, including adult glioma, focused on etiology and genetic variant

13. associations (see Wrensch DBP). We have worked with the Wrensch lab since 2008 to analyze and
interpret data she has collected48,52-57
. A persistent challenge in this work relates to identifying the set
of causal variants that are responsible for heritable variation for a given disease risk. These variants
are informative for mechanistic models, diagnostics and drug development programs. In some cases,
the causal variants may be a subset of associated variants or perhaps be in linkage disequilibrium
(LD) with them. Modern imputation methods and expanded sequencing coverage can help in these
cases. But, in other cases the causal variants may simply be too rare in frequency to be detected with
significance. This is where molecular networks and pathways provide a biologically meaningful
context for working with variant data, as well as for integrating expression, proteomics, environmental
or practically any other data types that can be mapped to genes, proteins and interactions.
Combinations of variants in a given individual are responsible for conferring a risk threshold or for
bypassing protective redundancies in biological processes for complex diseases. Different
combinations of variants can be responsible for the same disease, affecting convergent processes,
while simultaneously conferring different
subtypes, survival rates and treatment
efficacy rates. Ultimately, it is the direct and
indirect interactions among a set of variants
that produces phenotype. This perspective of
variant interactions is not new, but a recent
and impressive surge in network analytical
methods applicable to variants is promising
new approaches and insights. Over the past
18 months, there have been an
unprecedented number of papers relevant to
this topic, spanning pathway curation and
association, random forest and inference
approaches, network-based stratification and
a variety of integrative analyses40,58-81
. There
has also been a recent burst of published
activity on the combined topic of network
analysis and GWAS (genome-wide
association studies), independent of the
publishing trends for either topic alone
(Figure 2).
Preliminary Results and Progress Report. We have previously developed variant data
management and annotation tools relevant to this aim and the challenges faced by our Wrensch DBP.
The Variation app for Cytoscape was a prototype many years in the making that Dr. Pico contributed
through NRNB and GSoC collaborations (http://apps.cytoscape.org/apps/variation). In this work, we
identified the initial requirements for a variant data import app and solved some of the major initial
roadblocks regarding file formats and access via ADAM formats and patterns47
. This prototype has
allowed us to enable basic VCF file import and visualization in a robust way starting from any network
annotated with gene or protein identifiers. The work proposed below will build upon this foundation
and greatly expand the access, analysis and visualization potential. Another relevant prototype project
is MyVariant.info (http://myvariant.info/). Arising from a recent Network of Biothings hackathon
attended by the Pico group, this resource is modeled off the MyGene.info resource developed by
Andrew Su’s group at Scripps82
. MyVariant.info is currently a proof of concept with a handful of
annotation types, but the foundation exists for developing the most up-to-date and programmatically-
friendly variant annotation resource available, as described in the Methods below.
This aim also leverages the work of Drs. Sander, Ideker and Bader as co-investigators of the NRNB.
Dr. Sander's work on MEMo generates network models that characterize tumor subtypes83
, while Dr.
Figure 2. Normalized publication counts per year for
PubMed search terms. The number of results for the
search ["network analysis" gwas] more than doubled in
2013 relative to 2012 (from 14 to 31), shown in red. This
burst is not seen in either ["network analysis"] search
results, which are fairly linear, nor [gwas] results, which
doubled in 2008 and 2009 and have since plateaued,
nor in their additive relative results (gray dashed line).
0
0.2
0.4
0.6
0.8
1
2013
2012
2011
2010
2009
2008
2007
2006
["network
analysis"]
+
[gwas]
["network
analysis"
gwas]

14. Ideker's work on network-based stratification (NBS) classifies tumors based molecular markers in the
context of networks40
. Dr. Bader's patient network visualization work in aim 1 of this TRD will also be
used here to help visually identify important variants in a large network context. Preliminary work in
this regard includes the Circos-styled custom graphics support added to Cytoscape core84
by another
NRNB staff member, Dr. Morris and work on general node charts by the Bader group.
Methods. To enable broad adoption of network methods in the analysis of variant data, this aim
focuses on two main tasks: making new variant analysis technologies more accessible, and
streamlining the import and annotation of variant data.
Task 1: Accessible tools for variant analysis with associated visualization. This task will enable a
powerful genetic variant pathway analysis workflow that leverages NRNB technology and expertise
and serves our DBP and broader research community needs:
Pathway analysis of GWAS identified genes. We will port two recently-published approaches to
Cytoscape apps as part of a set of GWAS-specific pathway analysis tools. First, Evangelou et al.
presented novel hierarchical-based pathway analysis methods optimized for genome-wide association
studies66
. These methods are available in an R package and we will make them accessible to
Cytoscape via the existing bridge85
. Second, Bakir-Gungor et al. developed PANOGA, a web server
for pathway analysis of GWAS data68
. We will develop a Cytoscape app to interface with this web
service and make the analysis available in Cytoscape. The interaction network and pathway-based
results from both tools are ideally suited for visualization in Cytoscape. Combined with access to richly
annotated variant data, described in the next aim, these tools will enable GWAS-specific pathway
analysis workflows needed by our DBP in Cytoscape.
Network based patient population stratification. The recent availability of molecular profile data has
improved accuracy of cancer subtype classification into clinically-relevant groups, compared to using
traditional features, such as from histology. Identification of these groups helps improve diagnosis,
prognosis and treatment planning. Also, accurate groupings help characterize the mechanisms
underlying the etiology and progression of a tumor. As an NRNB-supported project, Dr. Ideker's group
developed a novel cancer classification method using networks and a topology-based smoothing
algorithm to address issues with otherwise sparse and indirectly associated molecular markers40
.
Although the student code for the project is available, this network-based stratification (NBS) method
has yet to be developed as a robust tool for community use. Dr. Pico will collaborate with Dr. Ideker's
group to port this functionality into the Cytoscape environment. This aspect of the work will focus on
making the method accessible to Cytoscape users and specifically for use cases relevant to Dr.
Wrensch's DBP.
Integrative analysis with MEMo. Complementary to the NBS approach, Dr. Sander's group developed
a method to infer gene network modules that exclusively characterize tumor subtypes based on
molecular markers and reference pathways and networks83
. This method, called Mutual Exclusivity
Modules (MEMo), is currently available as a distributed Java-based command line tool. Dr. Pico will
collaborate with Dr. Sander's team to make MEMo analysis available to variant data from within
Cytoscape. This effort will put this technology in the hands of our DBP and other researchers, making
it a routine matter to apply to available data.
Task 2: Tools for accessing variant data and annotations. To enable the analytical and visualization
approaches described above, variant data must be properly modeled and annotated in Cytoscape.
Import and annotation of variants into Cytoscape will also facilitate the development of variant
analysis workflows by the Cytoscape app development community (over 200 apps by 460 app
developers to-date; see Dissemination Core). Many tools exist to perform gene-to-variant identifier
mapping, including scandb.org and our own SNPLogic.org, and a prototype of this feature is available
in the Cytoscape Variation app. However, these tools are limited to considering a few of the useful

15. methods to map variants to genes, including considering proximity outside of a gene and variants that
predict gene expression (eQTLs). Thus, we will expand this functionality by developing intelligent
mappings that will consider the proximity of neighboring genes, regions of conservation, regulatory
sites and haplotype blocks. Once a set of variants are loaded into Cytoscape, we will facilitate the
query and import of relevant variant annotations. These span a wide range, including general
annotations (e.g., major allele frequency), characteristics (e.g., exonic or intronic), and predictions
(e.g., affects protein structure or binding sites). We will port our SNPLogic48
sources for variant
annotations over to the prototype MyVariant.info platform, which is more amenable to programmatic
access. We will then be able to develop a relatively simple Cytoscape app to retrieve annotations from
this extensible resource. Finally, we will facilitate the adoption of ADAM formatting to enable
programmatic query and import of standardized variant data for individuals and large cohorts. We will
store user-provided sample data distinct from background population data within the Cytoscape data
model and also support the labeling of samples as patient or control. Support for distinct sets of
variant data (i.e., background, control, and per patient) will make possible several analytical and
visualization options described in the previous aim.

16. TRD 2: PREDICTIVE NETWORKS –
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