University of California Santa Cruz Sample

UNIVERSITY OF CALIFORNIA SANTA CRUZ

SAMPLE-SPECIFIC CANCER PATHWAY ANALYSIS USING PARADIGM A dissertation submitted in partial satisfaction of the requirements for the degree of

DOCTOR OF PHILOSOPHY

BIOMOLECULAR ENGINEERING AND BIOINFORMATICS

Stephen C. Benz

June 2012

The Dissertation of Stephen C. Benz is approved:

Professor David Haussler, Chair

Professor Joshua Stuart

Professor Nader Pourmand

Dean Tyrus Miller Vice Provost and Dean of Graduate Studies Copyright c by

Stephen C. Benz

2012 Table of Contents

List of Figures v

List of Tables xi

Abstract xii

Dedication xiv

Acknowledgments xv

1 Introduction 1 1.1 Identifying Genomic Alterations ...... 2 1.2 Pathway Analysis ...... 5

2 Methods to Integrate Cancer Genomics Data 10 2.1 UCSC Cancer Genomics Browser ...... 11 2.2 BioIntegrator ...... 16

3 Pathway Analysis Using PARADIGM 20 3.1 Method ...... 21 3.2 Comparisons ...... 26 3.2.1 Distinguishing True Networks From Decoys ...... 27 3.2.2 Tumor versus Normal - Pathways associated with Ovarian Cancer 29 3.2.3 Diﬀerentially Regulated Pathways in ER+ve vs ER-ve breast cancers ...... 36 3.2.4 Therapy response prediction using pathways (Platinum Free In- terval in Ovarian Cancer) ...... 38 3.3 Unsupervised Stratiﬁcation of Cancer Patients by Pathway Activities . . 42

4 SuperPathway - A Global Pathway Model for Cancer 51 4.1 SuperPathway in Ovarian Cancer ...... 55 4.2 SuperPathway in Breast Cancer ...... 61

iii 4.2.1 Chin-Naderi Cohort ...... 61 4.2.2 TCGA Breast Cancer ...... 63 4.3 Cross-Cancer SuperPathway ...... 67

5 Pathway Analysis of Drug Eﬀects 74 5.1 SuperPathway on Breast Cell Lines ...... 75 5.2 Culling Targets Using Machine Learning ...... 83 5.3 Simulating Knockdowns in PARADIGM ...... 83 5.4 Breast Cell Line Simulated Knockdowns ...... 89

6 Conclusion 94 6.1 Conclusions ...... 94 6.2 Future Expansions ...... 95 6.3 Final Thoughts ...... 100

iv List of Figures

1.1 TCGA GBM samples show overall alterations across GBM pathway, but changes to specific genes are limited. Part A) shows an OncoPrint profile of the 122 GBMs with complete genomic information. Part B) illustrates the underlying pathway connections that explain why alterations within any single node can deactivate TP53 function. OncoPrint generated from The cBio Cancer Genomics Portal (http://www.cbioportal.org/). . . . . 3 1.2 NCI Pathway interactions in TCGA GBM data. For all (n=462) pairs where A was found to be an upstream activator of gene B in NCI-Nature Pathway Database, the Pearson correlation (x-axis) computed from the TCGA GBM data was calculated in two different ways. The histogram plots the correlations between the A’s copy number and B’s expression (C2E, solid red) and between A’s expression and B’s expression (E2E, blue). A histogram of correlations between randomly paired genes is shown for C2E (dashed red) and E2E (dashed blue). Arrows point to the enrichment of positive correlations found for the C2E (red) and E2E (blue) correlation...... 6

2.1 UCSC Cancer Genomics Browser proportions view of chromosome 9 across three copy number variation datasets. The yellow box represents the genomic region that is commonly deleted across all three datasets and contains the tumor suppressor CDKN2A/B...... 13

v 2.2 Integrative visualization of TCGA glioblastoma multiforme (GBM) genomic data across four separate pathways. (a,b) Histogram of non-silent mutations (e.g. missense, insertion, deletions, etc.) per sample per base in GBM somatic and germ line tissues, respectively. (c) Copy number variation (CNV) in GBM tumor and normal samples. Red or blue color represents amplified or deleted genes. Note that the clinical data to the right of (c) are sorted by tissue type: tumor (black) vs. normal (yellow). The vast majority of copy number alterations seen in the heatmap occur in the tumor samples. (d) A Bonferroni-corrected t-test comparing the distribution of CNV in tumor versus normal samples. A green bar represents a significantly deleted gene (p < 0.05, after Bonferroni correction) and a red bar represents a significantly amplified gene in the tumor samples. 15 2.3 BioIntegrator web interface, designed to allow users to access both raw and analytical results in an exploratory fashion. Part A highlights the ability to flexibly search for genes and clinical features, as well as drag and drop reordering. Part B shows the dynamic nature of the sorting, allowing discrete clinical features to be placed at top with the data summarized under each label, allowing intuitive visualization of differences...... 16 2.4 Genes were scored independent of sample labels using both gene expression and copy number data from a melanoma cohort [47] in the BioInte- grator. Samples were split 80/20 into training and test sets and feature selection was performed on the training data thresholding on correlation coefficient [15]. Samples were run through SVM-light [38] using default settings, and the top 20 models (minimum 91% accuracy) were selected for downstream analysis. Part A shows the top 35 features selected by the SVMs, with both SVM weight and frequency plotted. When those top 35 features were run through NCI’s Pathway enrichment analysis, both Caspase Cascade in Apoptosis (p < 4e-6, Part B) and Fas Signal- ing Pathway (CD95) (p < 0.001) were found to be significantly enriched. The color of nodes in Part B illustrate the average enrichment towards the mutated class (red) or non-mutated class (blue) as calculated by the BioIntegrator gene level perturbation score. Nodes seen in both Part A and Part B are denoted with an orange asterisk...... 18 3.1 Overview of the PARADIGM method. PARADIGM uses a pathway schematic with functional genomic data to infer genetic activities that can be used for further downstream analysis...... 20

vi 3.2 Conversion of a genetic pathway diagram into a PARADIGM model. A. Data on a single patient is integrated for a single gene using a set of four different biological entities for the gene describing the DNA copy number, mRNA and protein levels, and activity of the protein. B. PARADIGM models various types of interactions across genes including transcription factors to transcript and protein targets (upper-left), protein subunits aggregating in a complex (upper-right), protein post-translational modi- fication (lower-left), and sets of gene products in a family performing redundant functions (lower-right). C. Toy example of a small sub-pathway involving P53, its inhibitor MDM2, and the high level P53 determined cell process, apoptosis as represented in the model...... 25 3.3 Distinguishing decoy from real pathways with PARADIGM and SPIA. Decoy pathways were created by assigning a new gene name to each gene in a pathway. PARADIGM and SPIA were then used to compute the perturbation of every pathway. Each line shows the receiver-operator characteristic (ROC) for distinguishing real from decoy pathways using the perturbation ranking. In breast cancer, the areas under the curve (AUCs) are 0.669 and 0.602 for PARADIGM and SPIA, respectively. In GBM the AUCs are 0.642 and 0.604 respectively...... 28 3.4 Summary of FOXM1 integrated pathway activities (IPAs) across all samples. The arithmetic mean of IPAs across tumor samples for each entity in the FOXM1 transcription factor network is shown in red, with heavier red shading indicating two standard deviations. Gray line and shading indicates the mean and two standard deviations for IPAs derived from permuted data...... 30 3.5 Summary of IPAs of FOXM1 and all tested transcription factors. His- tograms of each sample’s IPA in FOXM1 or in any other transcription factor from the PID. A. Histogram of IPAs, with IPAs of 0 removed. By a Kolmogorov-Smirnov test, FOXM1 shows a different distribution with a p-value < 10−225. B. Histogram IPAs including 0 valued IPAs. By a KS test, FOXM1 has a different distribution of IPAs with a p-value < 10−236...... 30 3.6 Expression of FOXM1 Transcription Factor Network genes in high grade vs. low grade carcinoma. Using data from Etemadmoghadam et al (2009) we plotted expression of genes in the FOXM1 transcription network in either low grade (I) or high grade (II/III) ovarian carcinomas. A student’s t-test was used to assess the significance of differential expression between high grade and low grade, with the p-value below each pair of boxplots. 31 3.7 The PathOlogist approach shows significant activation of the FOXM1 pathway in tumor samples. ( c 2012 IEEE) ...... 34 3.8 PathOlogist reveals that patients with higher activity levels in the Down- stream signaling in naive cd8+t cells pathway are associated with improved response to platinum therapy. ( c 2012 IEEE) ...... 40

vii 3.9 Clustering analysis of ovarian samples using Paradigm shows that that patients with lower IPA’s for genes in the E-cadherin adherens junction pathway are associated with better response to platinum therapy (p=0.003). ( c 2012 IEEE) ...... 41 3.10 Clustering of IPAs for TCGA GBM. Each column corresponds to a single sample, and each row to a biomolecular entity. Color bars beneath the hierarchical clustering tree denote clusters used for Figure 3.12. . . . . 43 3.11 Clustering of IPAs for TCGA OV. Each column corresponds to a single sample, and each row to a biomolecular entity. Color bars above the heatmap denote clusters used for Figure 3.13...... 44 3.12 Kaplan-Meier survival plots for the GBM clusters from Figure 3.10. . . 46 3.13 Kaplan-Meier survival plots for the OV purple cluster versus the rest from Figure 3.11...... 47 3.14 Clustering glioblastoma multiform (GBM) datasets with HOPACH did not reveal any obvious patient sub-types when using either gene-level copy number or expression data. Patient samples (columns) were clustered according to gene-level copy number or expression (rows). Clustering may be dominated by the nearly patient-wide ampliﬁcations and deletions (and correlated high and low expression) for a subset of the genes in the genome (large blue and red swaths in the heatmaps). A. Copy number estimates from competitive genome hybridization for 17,508 probes across 267 tumor (left) and 170 normal (right). B. Microarray gene expression data for 11,240 probes across 243 tumor (left) and 10 normal (right) samples...... 50

4.1 Topographical visualization of the SuperPathway...... 53 4.2 Number of subnets found for a variety of IPA cutoffs. The red line represents actual samples, with the black line representing the permuted samples. The max difference of 156 subnets between these two populations was found at IPA cutoff of 3.3...... 58 4.3 Paradigm reveals the regulatory network underlying estrogen receptor positivity in the Chin-Naderi Cohort...... 62 4.4 Paradigm concept hubs separating Luminal and Basal in the TCGA Breast Cancer Cohort. In order for a link to appear in the subnetwork, the parent node needed at minimum 10 children nodes in the overall network. In addition, the child node needed at minimum 5 children nodes in the overall network. Concepts are colored orange if their activity is higher in Basal samples, and cyan if their activity is higher in Luminal samples...... 64

viii 4.5 Mutations in different protein domains show differential pathway activity. A: Mutational comparison of TCGA breast cancer samples versus mutations seen in the COSMIC mutation database. B: Pathway hubs that show differential activity between kinase and non-kinase mutations, colored and sized by significance...... 66 4.6 PARADIGM activities computed across 1382 TCGA samples from 8 tissues of origin with matched copy-number and median-centered gene expression data...... 68 4.7 Receiver operating characteristics of SVM-Light classifications done across tissue types. The Cyan ROC indicates a linear SVM that was trained on lung squamous versus adenocarcinoma and given breast samples labelled basal or luminal for classification (AUC=0.971751). The orange ROC indicates the performance of a linear SVM that was trained on breast basal versus luminal and tested on lung squamous versus adenocarcinoma (AUC=0.894994)...... 70 4.8 Paradigm concept hubs found to have high SVM weights when trained on Lung Adenocarcinoma versus Lung Squamous. In order for a link to appear in the subnetwork, the parent node needed at minimum 10 children nodes in the overall network. In addition, the child node needed at minimum 5 children nodes in the overall network. Concepts are colored orange if their SVM weight is positive, meaning they contribute to the resulting classification, and cyan if they have a negative SVM weight, meaning they offer no discriminative power...... 72

5.1 Clustering of TCGA samples with breast cancer cell lines...... 76 5.2 Uncentered Pearson correlation of pathway concepts show samples and cell lines from the same subtype are more correlated, regardless of the origin of the sample...... 77 5.3 Cell-line subtypes have unique SuperPathway network features. In all panels, each node represents a pathway concept corresponding to a protein (circle), a multimeric complex (hexagon), or an abstract cellular process (square). Node sizes are drawn in proportion to the DA score; larger nodes correspond to concepts more correlated with a particular subtype than with all other subtypes. Color indicates whether the concept is correlated positively (red) or negatively (blue) with the subtype of interest. Lines represent interactions, including proteinprotein interactions (dashed lines) and transcriptional interactions (solid lines). Interactions are included if they connect concepts whose absolute level of DA is higher than the mean absolute level. Labels on some nodes are omitted for clarity. (A) An ERK1/2 subnet preferentially activated in basal breast cancer cell lines. (B) A MYC/ MAX network activated in claudin-low cell lines. (C) A FOXA1/FOXA2 network up-regulated in the luminal subtype. (D) A CTNNB1 subnet down-regulated in the ERBB2AMP subtype. . 79

ix 5.4 Pathway diagrams can be used to predict response to therapies. (A) (Left) Basal breast cancer cell lines respond preferentially to the DNA- damaging agent cisplatin. Each boxplot represents the distribution of drug response data for basal (right) and non-basal (left) cell lines. (Right) Basal cell lines show enhanced pathway levels in a subnetwork associated with the DNA-damage response, providing a possible mechanism by which cisplatin acts in these cell lines. (B) (Left) ERBB2AMP cell lines are sensitive to the HSP90 inhibitor geldanamycin. (Right) The ERBB2HSP90 network is up-regulated in ERBBP2AMP cell lines. Conventions are as in Figure 5.3...... 81 5.5 Schematic indicating how knockdown is performed within the factor graph central dogma. Part A represents the standard factor graph dogma model provided by PARADIGM. Part B represents the modified dogma used for the proteins that are being knocked out, with an example of how signal might propagate post KO. Of note is the disconnection of considering the mRNA evidence node and transcriptional regulation, with the resulting signal propagation to the protein node and any interactions that may occur with the knocked down node...... 84 5.6 Visualization of IPLs for the region around GNAI3 for the best Knock- down effectiveness from Table 5.1: A) GFPc of GFP scrCvsHR, B) GF- Pssi of GFP scrCvsHR and C) siRNAt of GNAI3 06 1vsHR ...... 87 5.7 Classification of response to QNZ (an NF-KB inhibitor) in the breast cell lines, before and after simulated knockdown experiments to E2F3. Tail of the arrow represents the predicted resistance score using the pathway activities, and the head represents the prediction after simulated knockdown was performed. Blue arrows indicate cell lines that are labelled sensitive and red arrows indicate resistant cell lines...... 91 5.8 Classification of response to Oxaliplatin in the breast cell lines, before and after simulated knockdown of MAPK1...... 91

6.1 An example expanded PARADIGM dogma model that accounts for pro- moter states and allows attachment of Methylation data. ( c IEEE 2012) 95 6.2 Allele and Isoform Speciﬁc Factor Graph Representation. An example of how we might represent a gene (“GENE1”) with allele-speciﬁc data and two isoforms...... 98

x List of Tables

3.1 The FOXM1 transcription factor network shows up as being upregulated in ovarian tumors compared to normal consistently across pathway methodologies...... 33 3.2 FOXA1 transcription factor network is upregulated in ER+ve tumors whereas the Hif-1-alpha transcription network and Syndecan-1 mediated signaling events are down-regulated...... 37 3.3 Pathways associated with platinum-free interval in ovarian carcinoma. . 39

4.1 Features and Interactions in SuperPathway (July 2011 version) . . . . . 53 4.2 Top TCGA Ovarian Concepts from SuperPathway ...... 56 4.3 Top pathway concepts consistently found in subnets across the OV samples using IPA cutoﬀ 3.3. Only HSP90AA1 has more null samples than real, indicating a likely false positive...... 59

5.1 Correlations between simulated knockdowns and true siRNA knockdowns for a series of genes in a colon cancer cell line...... 86 5.2 Resitance shift and p-values for the top 20 simulated KO and drug com- binations ranked by shift score...... 92

xi Abstract

Sample-speciﬁc cancer pathway analysis using PARADIGM

Stephen C. Benz

Identifying key somatic alterations in cancer is a critical step in understanding the mechanisms that ultimately determine a patient’s treatment outcome. High-throughput data are providing a comprehensive view of these molecular changes for individual samples, and new technologies allow for the simultaneous genome-wide assay of genome copy number variations, gene expression, DNA methylation, and epigenetics of patient tumor samples and established cancer cell lines. Analyses of current datasets find that genetic alterations between tumors can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients. This work presents a novel method called PARADIGM for inferring tumor-specific genetic pathway activities incorporating curated gene-pathway interactions. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of itself and its upstream and downstream products, allowing the incorporation of many types of -omic data as evidence. The method predicts the degree to which a given pathway’s activities

(e.g. internal gene states, interactions, or high-level outputs) are altered in the tumor sample using probabilistic inference. Compared to a competing pathway activity inference approaches, PARADIGM identiﬁes altered activities in cancer-related pathways

xii with fewer false-positives, as shown in glioblastoma multiform (GBM), ovarian (OV) and breast cancer datasets. PARADIGM also identified consistent pathway-level activities for subsets of the GBM and ovarian serous cystadenocarcinoma patients that are overlooked when genes are considered in isolation. Furthermore, grouping GBM and OV patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. Further analysis was done using an integrated pathway termed the SuperPathway that gives a more consistent global view of biology, illustrated through the analysis of ovarian, breast, and cross-cancer analysis. Finally, PARADIGM was used to simulated knockdowns in a pathway model as an approach to understand drug effects and provide a rational approach towards combination therapies. These findings suggest that thera- peutics might be personalized and chosen to hit target genes at critical points in the commonly perturbed cancer pathway(s) of specific patients using this model.

xiii To my parents,

Christopher and Constance Benz, and my best friend and love of my life, Danielle.

xiv Acknowledgments

I’d like to thank my advisor David Haussler for his support and encouragement of this work, and my committee chairman Josh Stuart for the advice and guidance he’s given on PARADIGM. I thank Nader Pourmand for his guidance on my committee as we move towards sequencing data, and Scott Lokey for taking time out to be a part of my advancement committee. Finally I’d like to thank the other members of the UCSC

Cancer Genomics team, without whom this work could not have been done: Zack

Sanborn, Charlie Vaske, Jing Zhu, Chris Szeto, Larry Meyer, Fan Hsu, and Ting Wang.

This work has been supported by a NIH Training Grant, the I-SPY INSTINCT project

(QB3), TCGA Pilot and U24 funding sources, and the Stand Up To Cancer (SU2C) project.

xv Chapter 1

Introduction

There is no doubt about the importance of cancer - the World Health Orga- nization has identiﬁed it as the leading cause of death worldwide (13% in 2004) [84].

Although all cancers are deadly, individual cancer frequencies and mortality rates vary widely. Lung cancer is currently the most deadly, responsible for an estimate 1.3 million deaths a year, with stomach, colorectal, liver and breast cancer close behind. It is pro- jected that approximately 30% of life-threatening tumors are preventable and caused by avoidable risk factors [16], with early detection potentially capable of preventing another third. However, even after detection, standard therapies are only capable of successfully curing a minor proportion of all malignancies.

Cancer is fundamentally a disease of the genome, often deﬁned by three main traits: uncontrolled growth, invasiveness, and potential for metastatic dissemination.

Although genetically inherited germline alterations have been shown to be responsible for a subset of cancers, the majority of new cancers develop through somatic alterations

1 within a tissue. These alterations can occur as a series of spontaneous mutations during cell division or, alternatively, from the eﬀects of DNA carcinogens such as chemicals

(exogenous or endogenous), tobacco smoke or viral infection. In addition, resulting genomic changes can eﬀect two main classes of genes (oncogenes and tumor suppressors), with alterations often being required in both in order for a cell to undergo full tumor- genesis. It has become increasingly clear that in order to successfully treat tumors, identiﬁcation of these genomic alterations is required so more directed therapies can be applied.

1.1 Identifying Genomic Alterations

While several high-throughput technologies have been available for identifying these alterations within each cancer, only a handful of successes have been achieved based on these advances. For example, 25% of breast cancer patients presenting with a particular ampliﬁcation or overexpression of the ERBB2 growth factor receptor ty- rosine kinase can now be treated with trastuzumab, a monoclonal antibody targeting the receptor [82]. However, even this success story is complicated by the fact that fewer than 50% of patients with ERBB2-positive breast cancers actually achieve any therapeutic beneﬁt from trastuzumab, emphasizing our incomplete understanding of this well-studied oncogenic pathway and the many therapeutic-resistant mechanisms intrinsic to ERBB2-positive breast cancers [55]. This overall failure to translate mod- ern advances in basic cancer biology is in part due to our inability to comprehensively

2 A. TCGA Glioblastoma (122 Tumors), TP53 Pathway (82% of samples altered))

B. TP53 Pathway Structure

CDKN2A

MDM2

MDM4

TP53

Figure 1.1: TCGA GBM samples show overall alterations across GBM pathway, but changes to specific genes are limited. Part A) shows an OncoPrint profile of the 122 GBMs with complete genomic information. Part B) illustrates the underlying pathway connections that explain why alterations within any single node can deactivate TP53 function. OncoPrint generated from The cBio Cancer Genomics Portal (http://www.cbioportal.org/). organize and integrate all of the omic features now technically acquirable on virtually any cancer sample. Despite overwhelming evidence that histologically similar cancers are in reality a composite of many molecular subtypes, each with significantly different clinical behavior, this knowledge is rarely applied in practice due to the lack of robust molecular signatures that correlate well with prognosis and treatment options.

High-throughput functional genomics have made tremendous progress in the

3 past decade towards understanding the alterations that lead to disregulation of cellular function [31, 1, 79]. However, the challenges of integrating multiple data sources to identify reproducible and interpretable molecular signatures of tumorigenesis and tumor behavior remain elusive. Recent pilot studies by TCGA and others [56, 74] make it clear that a pathway-level understanding of genomic perturbations is needed to understand the functional changes observed in cancer cells. These findings demonstrate that even when patients harbor genomic alterations or aberrant expression in different genes, these genes often participate in a common pathway (Figure 1.1). In addition, and even more striking, is that the alterations observed (e.g. deletions versus amplifications) often alter the pathway output in the same direction, either all increasing or all decreasing the pathway activation.

Approaches for interpreting genome-wide cancer data have focused on identifying gene expression proﬁles that are highly correlated with a particular phenotype or disease state, and have led to promising results [78, 20, 2]. Methods using analysis of variance [41], false-discovery [69] and non-parametric methods [77] have been proposed.

However, these methods often result in sets of genes that are diﬃcult to generalize between studies and have weak associations with known cellular processes.

Several pathway-level approaches use statistical tests based on overrepresentation of genesets to detect whether a pathway is perturbed in a disease condition. In these approaches, genes are ranked based on their degree of differential activity, for example as detected by either differential expression or copy number alteration. A probability score is then assigned reflecting the degree to which a pathway’s genes rank near the extreme

4 ends of the sorted list, such as is used in Gene Set Enrichment Analysis (GSEA) [70].

Other approaches include using a hypergeometric test-based method to identify Gene

Ontology [4] or MIPS Mammalian Protein-Protein Interaction [54] categories enriched in diﬀerentially expressed genes [71].

Overrepresentation analyses are limited in their eﬃcacy because they do not incorporate known interdependencies among genes in a pathway that can increase the detection signal for pathway relevance. In addition, they treat all gene alterations as equal, which is not expected to be valid for many biological systems. Because of these factors, overrepresentation analyses often miss functionally-relevant pathways whose genes have borderline diﬀerential activity. They can also produce many false positives when only a single gene is highly altered in a small pathway.

1.2 Pathway Analysis

Our collective knowledge about the detailed interactions between genes and their phenotypic consequences is growing rapidly. While the knowledge was tradition- ally scattered throughout the literature and hard to access systematically, new eﬀorts are cataloging pathway knowledge into publicly available databases. Some of the databases that include pathway topology are Reactome [39], KEGG [53], and the NCI Pathway

Interaction Database [62]. Updates to these databases are expected to improve our understanding of biological systems by explicitly encoding how genes regulate and com- municate with one another. A key hypothesis is that the interaction topology of these

5 C2E random C2E E2E 5 random E2E 4 3 2 Proportion of Interactions 1 0 −1.0 −0.5 0.0 0.5 1.0

Correlation

Figure 1.2: NCI Pathway interactions in TCGA GBM data. For all (n=462) pairs where A was found to be an upstream activator of gene B in NCI-Nature Pathway Database, the Pearson correlation (x-axis) computed from the TCGA GBM data was calculated in two diﬀerent ways. The histogram plots the correlations between the A’s copy number and B’s expression (C2E, solid red) and between A’s expression and B’s expression (E2E, blue). A histogram of correlations between randomly paired genes is shown for C2E (dashed red) and E2E (dashed blue). Arrows point to the enrichment of positive correlations found for the C2E (red) and E2E (blue) correlation. pathways can be exploited for the purpose of interpreting high-throughput datasets.

The hypothesis of pathway-based approaches is that the genetic interactions found in pathway databases carry information for interpreting correlations between gene expression changes detected in cancer. For example, if a cancer-related pathway includes a link from a transcriptional activator A to a target gene T, we expect the expression of A to be positively correlated with the expression of T (E2E correlation). Likewise, we also expect a positive correlation between A’s copy number and T’s expression (C2E correlation). Further, we expect C2E correlation to be weaker than E2E correlation

6 because ampliﬁcation in A does not necessarily imply A is expressed at higher levels, which in turn is necessary to upregulate B. In this way, each link in a pathway provides an expectation about the data; pathways with many consistent links may be relevant for further consideration. We tested these assumptions and found that the NCI pathways contain many interactions predictive of the recent TCGA GBM data [52] (Figure 1.2).

As expected, C2E correlations were moderate, but had a striking enrichment for positive correlations among activating interactions than expected by chance. E2E correlations were even stronger and similarly enriched. Thus, even in this example of a cancer that has eluded characterization, a signiﬁcant subset of pathway interactions connect genomic alterations to modulations in gene expression, supporting the idea that a pathway-level approach is worth pursuing.

Until recently, few computational approaches were available for incorporating pathway knowledge to interpret high-throughput datasets. However, several newer approaches have been proposed that incorporate pathway topology [21] . One approach, called Signaling Pathway Impact Analysis (SPIA) [73], uses a method analogous to

Google’s PageRank to determine the influence of a gene in a pathway. In SPIA, more influence is placed on genes that link out to many other genes. SPIA was successfully applied to different cancer datasets (lung adenocarcinoma and breast cancer) and shown to outperform overrepresentation analysis and Gene Set Enrichment Analysis for identifying pathways known to be involved in these cancers. While SPIA represents a major step forward in interpreting cancer datasets using pathway topology, it is limited to using only a single type of genome-wide data. Newer computational approaches are

7 needed to connect multiple genomic alterations such as copy number variation, DNA methylation, somatic mutations, mRNA and microRNA expression. Integrated pathway analysis is expected to increase the precision and sensitivity of causal interpretations for large sets of observations since no single data source is likely to provide a complete picture on its own.

In the past several years, approaches in probabilistic graphical models (PGMs) have been developed for learning causal networks compatible with multiple levels of observations. Eﬃcient algorithms are available to learn pathways automatically from data [25, 51] and are well adapted to problems in genetic network inference [24]. As an example, graphical models have been used to identify sets of genes that form “modules” in cancer biology [63]. They have also been applied to elucidate the relationship between tumor genotype and expression phenotypes [45], and infer protein signaling networks

[60] and combinatorial gene regulatory codes [8].

More recently, a generalization of many existing graphical models (bayesian networks, markov random ﬁelds, etc.) called “factor graphs” has become popular in the ﬁeld [43]. In particular, factor graphs have been used to model expression data

[27, 28, 26]. A factor graph is a bipartite graph representing a set of factors, or functions, whose domain is a set of variables and range is the real numbers. By representing pathways as factor graphs, we avoid many issues often seen with bayesian networks

(the problem of cycles in particular), and although we have chosen to use probability distributions in this work, we are able to use any function to represent the factors.

Very eﬃcient exact and approximate inference methods have been developed that allow

8 graphs of thousands of nodes to be run quickly, making this solution attractive for analyzing thousands of samples in parallel. Armed with this approach, it is possible to determine the critical pathways altered in individual tumor samples and across cancer patient cohorts. In the following chapters, I will describe initial eﬀorts to interpret integrated genomics data and a novel pathway approach that exploits the multiple measurements to provide a powerful tool for understanding misregulated networks in cancer.

9 Chapter 2

Methods to Integrate Cancer Genomics

Data

Prior to 2008, very few tools were available to visualize and analyze cancer genomics data, and the few tools that did exist (Oncomap, Ingenuity) required expensive subscriptions and only handled individual types of data. Given the rich history of UCSC and the success of the genome browser for viewing annotations across genomes, we wanted to establish if a similar experience could be provided for cancer genomics data that would give users access to compare and contrast multiple high throughput genomic datasets. Without the appropriate tools, researchers are typically required to pass data back and forth via Excel or other text-based formats, which makes interpretation diﬃcult. State of the art visualization techniques at the time included clustering and heat map analysis, so we wanted to replicate views that would be familiar to users, and add on top the ability to group by clinical information and compute genome-wide

10 statistics at the push of a button. This chapter describes the creation of the UCSC

Cancer Genomics Browser and subsequent analysis pipeline coined the BioIntegrator for interpretation of multiple genomic data points across a set of samples.

2.1 UCSC Cancer Genomics Browser

The UCSC Cancer Genomics Browser is a tool designed to allow hypothesis generating exploration in an intuitive and real-time fashion. The browser was originally designed as an extension to the widely popular UCSC Genome Browser and currently beneﬁts from much of the underlying technology developed for the genome browser.

The Cancer Genomics Browser was conceived in collaboration with I-SPY lead investi- gator Laura Esserman, a breast cancer surgeon at UCSF. The browser was designed to display the multidimensional I-SPY data and allow other researchers in the consortium to manipulate and explore the data through a web browser. In the summer of 2008, the browser was completely overhauled and the interface was re-written as a “Web 2.0” application utilizing asynchronous javascript (AJAX) to dynamically load only the parts of the browser that changed. This overhaul was written as a programming collaboration between myself and Zack Sanborn, with later assistance from Chris Szeto, Jing

Zhu, and Larry Meyer. Zack primarily designed and wrote the C-based backend and drawing code, while I designed the interface and wrote the client-side Javascript. The

AJAX-powered browser allows for a much more ﬂuid user experience while providing scalability capable of handling hundreds of datasets viewed simultaneously. The browser

11 was published in April of 2008 in Nature Methods [86], but I will discuss many of the features in more detail in the follow sections.

The default view of the UCSC Cancer Genomics browser is the “chromosome view,” which enables researchers to view high-throughput genomic data in the form of a heatmap across a set of patients for the entire genome. The X-axis represents the chromosomal position with the Y-axis representing the set of patients. Thus, a single patient’s set of tumor data across the genome is a single pixel slice of the heatmap as you move from the left to the right at a single Y value. Alongside the genomic heatmap is a clinical heatmap, visually representing key clinical data across the entire patient cohort. These data are generally colored according to the relative values, with bright yellow representing the highest value and black representing the lowest. The sample order is conserved between the genomic and clinical heatmaps in order to facilitate comparison between the two. The browser supports click-to-zoom, as well as click-to- sort functionality allowing researchers to reorder and focus on regions of interest in the genomic heatmap and visually identify clinical attributes that may correlate with the genomic observations. In addition, data for an entire cohort can be visualized in a summary view allowing the user to ﬁnd regions of conserved copy number or expression data.

The true power in the cancer browser is the ability to simultaneously visualize multiple datasets allowing users to ﬁnd global patterns that apply between cancers.

When we look across three diﬀerent cancer types (Figure 2.1) at chromosome 9 in the summary view, we see a large blue peak in the middle of the p arm. This peak

12 Pancreatic Cancer (Jones et al. 2008)

Glioblastoma multiforme (TCGA 2008)

Melanoma (Lin et al. 2008)

CDKN2A/B

Figure 2.1: UCSC Cancer Genomics Browser proportions view of chromosome 9 across three copy number variation datasets. The yellow box represents the genomic region that is commonly deleted across all three datasets and contains the tumor suppressor CDKN2A/B.

13 represents concurrent copy number loss across all three datasets and is centered on the gene CDKN2A/B, a well documented tumor suppressor that has been shown to often be deleted in cancer [42]. By visualizing the data across the three datasets, it is possible to identify other regions where there is concurrent loss or gain across a large subset of samples across multiple cancers.

While the chromosome view provides a means to visualize cancer genomics data at the whole-genome or chromosomal level, it is becoming increasingly clear that functional groups of genes (such as those found in a pathway) offer a more useful view into the development of tumorigenesis. Copy number alterations or expression changes in any of several genes in the same pathway can cause equivalent disturbance of a cellular process. Pathways, therefore, provide a more robust and biologically meaningful way to summarize genomic data by grouping genes that may act in a similar fashion. The cancer browser supports a flexible geneset view that allows researchers to visualize differences within and between these pathways across multiple datasets. When we begin exploring the TCGA GBM data using this tool (Figure 2.2) we can easily recognize the systematic alterations to three main pathways across the entire cohort. Indeed, alterations in these three pathways were identified by the TCGA Research Network as being obligatory in most, if not all, glioblastomas. This type of visualization is powerful in helping researchers understand the complex relationships that occur in cancer and disease in general.

14 p53 Brain tumor somatic non-silent mutations a RB1

Brain tumor germline non-silent mutations

CDK4 b

Brain tumor amplified / deleted regions

EGFR PTEN

d Normal/T Dead/Aliv Male/F emale p53 RB RTK/Ras/PI3K umor e Control pathway pathway pathway

Figure 2.2: Integrative visualization of TCGA glioblastoma multiforme (GBM) genomic data across four separate pathways. (a,b) Histogram of non-silent mutations (e.g. missense, insertion, deletions, etc.) per sample per base in GBM somatic and germ line tissues, respectively. (c) Copy number variation (CNV) in GBM tumor and normal samples. Red or blue color represents amplified or deleted genes. Note that the clinical data to the right of (c) are sorted by tissue type: tumor (black) vs. normal (yellow). The vast majority of copy number alterations seen in the heatmap occur in the tumor samples. (d) A Bonferroni-corrected t-test comparing the distribution of CNV in tumor versus normal samples. A green bar represents a significantly deleted gene (p < 0.05, after Bonferroni correction) and a red bar represents a significantly amplified gene in the tumor samples.

15 ABSTRACT 2462 UCSC BioIntegrator: Multi-Platform Analysis Pipeline J. Zachary Sanborn1, Stephen Benz1, Charlie Vaske1, Jingchun Zhu1, Christopher Szeto1, Ting Wang1, David Haussler1,2 1 Center for Biomolecular Science and Engineering, UC Santa Cruz, 2 Howard Hughes Medical Institute

UCSC BioIntegratorUCSC BioIntegrator INTRODUCTION BIOINTEGRATOR WEBUCSC INTERFACEBioIntegratorUCSC BioIntegrator TCGA GLIOBLASTOMA RESULTS ABSTRACT 2462 UCSC BioIntegrator: Multi-Platform Analysis Pipeline ! Here we present a new analysis pipeline named BioIntegrator that works1 in 1 1 1 Samples 1 1 1,2 J. Zachary Sanborn , Stephen BenzCDKN2A, Charliecyclin-dependent Vaske kinase,CDKN2A Jingchun inhibitorcyclin-dependent 2Akinase inhibitor 2AZhuinfoinfo , Christopher... Szeto , Ting Wang... , David Haussler Geneset Meta-Analysis CDKN2A cyclin-dependent kinaseCDKN2A inhibitorcyclin-dependent 2Akinase inhibitor 2A infoinfo ...... three stages. In the first stage, gene-level perturbations are calculated by integrating MTAPCDKN2A 5'-methylthioadenosinecyclin-dependent kinaseMTAPCDKN2A phosphorylase 5'-methylthioadenosineinhibitorcyclin-dependent 2Akinase phosphorylase inhibitorinfo 2A infoinfo info ...... Age < 40 yrs 1 MTAP 5'-methylthioadenosineMTAP phosphorylase5'-methylthioadenosine phosphorylaseinfoinfoinfoinfo 2 Age at Death Center for Biomolecular ScienceEGFR andepidermal Engineering, growth factorEGFR receptorepidermal growth factor UC receptor infoinfo Santa Cruz, Howard Hughes Medical Institute Age > 40 yrs data from several platforms (copy number, expression, etc.) using a variety of meth- EGFR epidermal growth factorEGFR receptorepidermal growth factor receptor infoinfoinfoinfo EGFR/HE... EGFR/HER2_SignalingEGFR/HE... EGFR/HER2_Signaling ods. These gene-level perturbations and fed into the second stage, where they are EGFR/HE... EGFR/HER2_SignalingEGFR/HE... EGFR/HER2_Signaling MDM2MDM2 mousemouse double double minute minuteMDM2MDM2 2 2homolog mousehomologmouse double double minute minute 2 homolog 2 homologinfoinfoinfoinfo infoinfo combined with the perturbations values of other genes in genesets or structured h_ranbp...h_ranbp... h_ranbp2Pathwayh_ranbp2Pathwayh_ranbp...h_ranbp... h_ranbp2Pathwayh_ranbp2Pathway infoinfoinfo info h_ranbp... h_ranbp2Pathwayh_ranbp... h_ranbp2Pathway infoinfo UCSC BioIntegratorUCSC BioIntegrator pathways. Informative gene- and set-level perturbations are then used as features in Input Feature Name: Ag InputInputInput Feature InputFeatureFeatureInput InputName: Name:Name:UCSC MDM Feature MDM FeatureFeature BioIntegrator Name: Name:Name:UCSC MDM MDM BioIntegrator INTRODUCTION BIOINTEGRATORAge WEBClinical UCSC INTERFACEBioIntegratorUCSC BioIntegrator TCGA GLIOBLASTOMA RESULTS MDM2: Top Correlated MDM2: Top Correlated EGFR the final stage to predict sample characteristics (e.g. outcome, response, etc.) using A AGK Gene MDM2:MDM2: Top TopCorrelated Correlated MDM2:MDM2: Top Top Correlated Correlated GO:0017163, r=1 (click to load) GO:0017163, r=1 (click to load) GO:0017163,GO:0017163, r=1 r=1(click (click to load) to load)GO:0017163,GO:0017163, r=1 r=1 (click (click to to load) load) ! Here we present a new analysis pipeline named BioIntegrator that works in GO:0042176, r=0.98 (click to load)GO:0042176,Samples r=0.98 (click to load) various machine learning techniques. CDKN2A cyclin-dependent kinaseCDKN2A inhibitorcyclin-dependent 2Akinase inhibitor 2A infoinfo GO:0042176,GO:0042176, r=0.98 r=0.98 (click (click to load) to GO:0042176,load)GO:0042176,... r=0.98 r=0.98 (click (click to to load) load) ... Geneset Meta-Analysis CDKN2A cyclin-dependent kinaseCDKN2A inhibitorcyclin-dependent 2Akinase inhibitor 2A infoinfo ...... BioIntegrator’sCDKN2A cyclin-dependentweb-based kinase userCDKN2A inhibitor cyclin-dependentinterface 2Akinase inhibitor 2A info infoSumoylation_by_RanBP2_Regulates_Transcriptional_Repression,Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression,Sample order is r=0.91 determined(click... to load) dynamically r=0.91 (click... to load) Age < 40 yrs three stages. In the first stage, gene-level perturbations are calculated by integrating MTAP 5'-methylthioadenosineMTAP phosphorylase5'-methylthioadenosine phosphorylaseinfoinfoSumoylation_by_RanBP2_Regulates_Transcriptional_Repression,Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression, r=0.91 (click to load) r=0.91 (click to load) Age at Death MTAP 5'-methylthioadenosineMTAP phosphorylase5'-methylthioadenosine phosphorylaseinfoinfoinfoinfo h_arfPathway, r=0.87 (click to load)h_arfPathway, r=0.87 (click to load) Ras-PI3K KEY POINTS providesEGFR quick epidermal & interactive growth factorEGFR receptorepidermal access growth factor receptor toinfo info by theh_arfPathway, values r=0.87 (click to load)h_arfPathway, of the clinicalr=0.87 (click or to load)genomic Age > 40 yrs data from several platforms (copy number, expression, etc.) using a variety of meth- EGFR epidermal growth factorEGFR receptorepidermal growth factor receptor infoinfoinfoinfo EGFR/HE... EGFR/HER2_SignalingEGFR/HE... EGFR/HER2_Signaling Hypoxia Flexible pipeline for performing gene- and pathway-level both EGFR/HE... raw and analyticalEGFR/HER2_Signaling EGFR/HE... resultsEGFR/HER2_Signaling that feature located at the top of the table. ods. These gene-level perturbations and fed into the second stage, where they are MDM2 mouse double minuteMDM2 2 homologmouse double minute 2 homolog infoinfo allows MDM2researchersmouse to double explore minuteMDM2 2 theirhomologmouse double minute data 2 homolog infoinfo infoinfo When categorical clinical features are ECM combinedanalysis with the on perturbations whole genome values data of other genes in genesets or structured h_ranbp...h_ranbp... h_ranbp2Pathwayh_ranbp2Pathwayh_ranbp...h_ranbp... h_ranbp2Pathwayh_ranbp2Pathway infoinfoinfoinfo infoinfo pathways. Informative gene- and set-level perturbations are then used as features in intuitively. Clinical and genomicInput Feature Name: data Ag can InputplacedInputInput Feature InputFeatureFeatureInput InputName: Name:Name: MDM Feature MDM FeatureFeature at Name: the Name:Name: MDMtop, MDM all data below are Immune Age Clinical Web-based tool for exploratory analysis of BioIntegrator’s MDM2: Top Correlated MDM2: Top Correlated EGFR the final stage to predict sample characteristics (e.g. outcome, response, etc.) using be added in a variety of ways, includingAGK summarizedGene MDM2:MDM2: Top TopCorrelated Correlated appropriatelyMDM2:MDM2: Top Top Correlated Correlated within each GO:0017163, r=1 (click to load) GO:0017163, r=1 (click to load) results alongside clinical data UCSCGO:0017163,GO:0017163, BioIntegrator r=1 r=1(click (click to load) to UCSCload)GO:0017163,GO:0017163, BioIntegrator r=1 r=1 (click (click to to load) load) free-text search and correlation. category.GO:0042176, r=0.98 (click to load)GO:0042176, r=0.98 (click to load) various machine learning techniques. GO:0042176,GO:0042176, r=0.98 r=0.98 (click (click to load) to GO:0042176,load)GO:0042176, r=0.98 r=0.98 (click (click to to load) load) BioIntegrator’s web-based user interface Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression,Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression,Sample order is r=0.91 determined(click to load) dynamically r=0.91 (click to load) p53 Integrated machine learning algorithms to predict complex Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression,Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression, r=0.91 (click to load) r=0.91 (click to load) B h_arfPathway, r=0.87 (click to load)h_arfPathway, r=0.87 (click to load) Ras-PI3K KEY POINTS provides quick & interactive access to by theh_arfPathway, values r=0.87 (click to load)h_arfPathway, of the clinicalr=0.87 (click or to load)genomic chr12 sample characteristics Her2 ERBB2 Receptor Her2PositivityERBB2 Receptor Positivity Positive+ + Negative- - both raw and analytical results that feature located at the top of the table. ampliconHypoxia Flexible pipeline for performing gene- and pathway-level ERBB2 erbB-2ERBB2 erbB-2 infoinfo RB analysis on whole genome data allowsHER2 researchers am... to HER2explore ampliconHER2 am... theirHER2 amplicondata When categorical clinical features are ECM 221 TCGA GBM Samples DDX49 DEAD (Asp-Glu-Ala-Asp) boxDDX49 polypeptideDEAD (Asp-Glu-Ala-Asp) box polypeptide49 info 49 info 0 -log10(p) 3 intuitively.ESD Clinicalesterase and D/formylglutathione genomicESD esterase datahydrolase D/formylglutathione can hydrolaseinfo info placed at the top, all data below are Immune Web-based tool for exploratory analysis of BioIntegrator’s Method: BIOINTEGRATOR ANALYSIS PIPELINE be added in a variety of ways, including summarizedInput FeatureInput Name: Feature Name: appropriately within each results alongside clinical data free-text search and correlation. category.UCSC BioIntegrator UCSC BioIntegrator Gene expression (Affy. U133A) and copy number data (Agilent 244K) for 221 TCGA GBM samples Integrated machine learning algorithms to predict complex Figure 2.3: BioIntegrator web interface, designed to allow users to access both raw and were combined to calculate a meta-gene score, which were combined to calculate meta-genesetp53 scores for chr127,325 genesets using Fisher’s Method. Unsupervised hierarchical clustering of the top 35 Various high-throughput Semi-automated pipeline that can analytical results in an exploratory fashion. Part A highlights the ability to flexibly sample characteristics PREDICTINGHer2 ERBB2 Receptor BRAF Her2PositivityERBB2 Receptor MUTANTS Positivity Positive+ IN MELANOMA+ Negative- genesetsamplicon ranked- according to overall geneset meta-analysis scores resulted in 3 distinct clusters measurements from a common process any data available on the search for genesERBB2 and clinicalerbB-2 features,ERBB2 erbB-2 asinfo wellinfo as drag and drop reordering. Part B similar to 5 GBM profiles described by Liang et al. and noted by the colored bars on the RBleft above. patient cohort Method:HER2 am... HER2 ampliconHER2 am... HER2 amplicon Top 35 Genes 221 TCGA GBM Samples U C S C C a n c e r G e n o m i c s showsGenes the dynamicwereDDX49 scoredDEAD nature (Asp-Glu-Ala-Asp)independent of the boxDDX49of sorting, samplepolypeptideDEAD (Asp-Glu-Ala-Asp) boxlabels polypeptide49 allowinginfo 49 infousing both discrete gene clinical features to be placed 0 -log10(p) 3 ESD esterase D/formylglutathioneESD esterase hydrolase D/formylglutathione hydrolaseinfoinfo Avg SVM Weight Genesets defined by genes found in the TCGA pathways — RAS-PI3K, p53, and RB Browser (Abstract #2469) at topexpression with the and data copy summarized number data in under the BioIntegrator. each label, Samples allowing intuitive0.01 visualization0.04 of Method: BIOINTEGRATOR ANALYSIS PIPELINE Input FeatureInput Name: Feature Name: SND1 pathways (TCGA 2008) — score very highly by this method and are noted above. differences.were split 80/20 into training and test sets and feature selection SAT1 Gene expression (Affy. U133A) and copy number data (Agilent 244K) for 221 TCGA GBM samples Flexible analysis framework is was performed on the training data thresholding on correlation AS3MT were combined to calculate a meta-gene score, which were combined to calculate meta-geneset Gene Perturbation Detectors coefficient (Chai et al. 2004). Samples were run through SVM-light SLK designed to incorporate external OBFC1 scoresA statistically for 7,325 genesetssignificant using association Fisher’s Method. between Unsupervised samples hierarchical from older clustering patients of the (Age top 35> Various high-throughput Semi-automated pipeline that can (JoachimsPREDICTING 1999) using default BRAF settings, and MUTANTS the top 20 models IN CALHM3MELANOMA genesets40 yrs, green ranked bars) according and tohigh overall levels geneset of perturbations meta-analysis scoresin the resulted cluster in of 3 distinctEGFR-related clusters measurements from a common analytical algorithms (GISTIC, 2.2 BioIntegrator FHOD3 Gene Level Gene Level process any data available on the (minimum 91% accuracy) were selected for downstream analysis. similar to 5 GBM profiles described by Liang et al. and noted by the colored bars on the left above. Meta Analpatientysis cohort Factor Graphs SPIA, etc.) at any stage in the Method: MNAT1 genesets was observed (p < 0.0002, Fisher’s exact test), supporting an observed U C S C C a n c e r G e n o m i c s CYP19A1 Top 35 Genes pipeline. GenesDIABLO were scored independent of sample labels using both gene CRKRS relationship between age and EGFR expression (Stark et al. 2003). Caspase6 LMMB2 SAV1 Avg SVM Weight Browser (Abstract #2469) expression and copy number data in the BioIntegrator. Samples 0.01 0.04 Genesets defined by genes found in the TCGA pathways — RAS-PI3K, p53, and RB While the UCSC Cancer Genomics browser is a user-friendlyCNOT4SND1 tool for visualiza- were split 80/20 into trainingCaspase3 and test sets and feature selection pathways (TCGA 2008) — score very highly by this method and are noted above. 1000’s of genesets and pathways ! SORL1SAT1 Flexible analysis framework is was performed on the training data thresholding on correlation AS3MTATP5J2 are collected from MSigDB, SLK Gene Perturbation Detectors tion andcoefficient exploratory (Chai et al. analysis, 2004). Samples large were scale run genomic through SVM-light datasets needSERPING1 a more comprehensive Pathway Perturbation Detectors designed to incorporate external RUNX3OBFC1 A statistically significant association between samples from older patients (Age > BioCarta, KEGG, NCI-Nature, (Joachims 1999) using default settings, and thePos. top Regulation 20 models ACKNOWLEDGMENTS / REFERENCES analytical algorithms (GISTIC, MAP3K1 SRBEF1 SLK CAD CALHM3SLITRK5 40 yrs, green bars) and high levels of perturbations in the cluster of EGFR-related Gene Level WeightGeneed LeGenesevel t (minimum 91% accuracy) were selected for downstreamApoptosis analysis. C10orf26FHOD3 Geneset Meta Analysis and Gene Ontology. MNAT1TRRAP Meta Analysis FactAnalor Grysisaphs SPIA, etc.) at any stage in the analysis process in order to discover complex alterations that drive tumorigenesis. The Wegenesets would was like observedto thank Josh(p < Stuart 0.0002, & StuartFisher’s Lab exact for providingtest), supporting helpful insights an observed and CYP19A1PTEN Erichrelationship Weiler between& CBSE cluster age and administrators EGFR expression for their(Stark tireless et al. 2003). efforts in maintaining pipeline.Analytical results from each stage DIABLO Caspase6 LMMB2 DIABLOCRKRS Pathway Factor Graphs BioIntegrator is a semi-automated pipeline written in conjunctionSEMA3DSAV1 with Zack Sanborn our many computer systems. are stored in a MySQL relational Caspase3 Subset of CNOT4P2RY13 1000’s of genesets and pathways JNK Apoptosis! Stress DNA LUC7L2SORL1 Chai, H. et al. (2004). “An Evaluation of Gene Selection Methods for Multi-class Microarray Data Classification.” Proceedings of database that enables rapid access ATP5J2 Caspase GRIA2 the Second European Workshop on Data Mining and Text Mining in Bioinformatics. Pisa, Italy. are collected from MSigDB, Cascade Fiber Fragment. SERPING1FBXO34 to any slice of patient data. that can process any data available on the UCSC Cancer Genomic Browser and func- Joachims, T. (1999). “Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning.” B. Pathway Perturbation Detectors Cascade RUNX3ARL3 ACKNOWLEDGMENTS / REFERENCES BioCarta, KEGG, NCI-Nature, Pos. Regulation SLITRK5HERC2 Schölkopf and C. Burges and A. Smola (ed.), MIT-Press. MAP3K1 SRBEF1 SLK CAD Liang et al. (2005). "Gene expression profiling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme." WeightMacedhine Genese Learningt Results are passed to a series of Enrichment analysis performed on full listApoptosis of genes in C10orf26CSMD1 Geneset Meta Analysis and Gene Ontology. tions in three main stages. In the first stage, gene-level perturbationsYME1L1TRRAP are calculated by Proc Natl Acad Sci U S A 102(16): 5814-9. Web Application AlgorAnalysisithms for We would like to thank Josh Stuart & Stuart Lab for providing helpful insights and proven machine learning top 20 SVM models using NCI-Nature Pathway Interac- CSGALNACT1PTEN Lin et al. (2008). “Modeling genomic diversity and tumor dependence in malignant melanoma.” Cancer Res 68(3): 664-73 for Exploratory Automated Feature Erich Weiler & CBSE cluster administrators for their tireless efforts in maintaining Analytical results from each stage tion Database: DIABLOERICH1 Stark, A. M., H. H. Hugo, et al. (2003). "Age-related expression of p53, Mdm2, EGFR and Msh2 in glioblastoma multiforme." Zen- AnalysisPathway Factor GraphsSelection and techniques for training and integrating data from several platforms (copy number, expression,SEMA3DHYAL4 etc.) using a variety ourtralbl many Neurochir computer 64(1): 30-6. systems. Classification are stored in a MySQL relational P2RY13BRAF classification. Caspase Cascade in Apoptosis (p < 4e-6)Subset of The TCGA Research Network (2008). "Comprehensive genomic characterization defines human glioblastoma genes and core JNK Apoptosis Stress CUEDC2LUC7L2 Chai, H. et al. (2004). “An Evaluation of Gene Selection Methods for Multi-class Microarray Data Classification.” Proceedings of database that enables rapid access DNA 0 # of Models 20 pathways." Nature 455(7216): 1061-8. Fas Signaling Pathway (CD95) (p < 0.001)Caspase GRIA2 the Second European Workshop on Data Mining and Text Mining in Bioinformatics. Pisa, Italy. of methods.Cascade These gene-levelFiber perturbationsFragment. are fed into the secondFBXO34 stage where they are Pathway Interaction Database — http://pid.nci.nih.gov/search/batch_query.shtml to any slice of patient data. Cascade ARL3 Joachims, T. (1999). “Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning.” B. HERC2 Schölkopf and C. Burges and A. Smola (ed.), MIT-Press. Liang et al. (2005). "Gene expression profiling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme." Machine Learning Results are passed to a series of Enrichment analysis performed on full list of genes in CSMD1 combined with the perturbations values of other genes in genesetsYME1L1 or structured path- Proc Natl Acad Sci U S A 102(16): 5814-9. Web Application Algorithms for proven machine learning top 20 SVM models using NCI-Nature Pathway Interac- CSGALNACT1 Lin et al. (2008). “Modeling genomic diversity and tumor dependence in malignant melanoma.” Cancer Res 68(3): 664-73 for Exploratory Automated Feature tion Database: ERICH1 Stark, A. M., H. H. Hugo, et al. (2003). "Age-related expression of p53, Mdm2, EGFR and Msh2 in glioblastoma multiforme." Zen- Analysis Selection and techniques for training and HYAL4 tralbl Neurochir 64(1): 30-6. Classification Caspase Cascade in Apoptosis (p < 4e-6) BRAF The TCGA Research Network (2008). "Comprehensive genomic characterization defines human glioblastoma genes and core classification. CUEDC2 0 # of Models 20 pathways." Nature 455(7216): 1061-8. Fas Signaling Pathway (CD95) (p < 160.001) Pathway Interaction Database — http://pid.nci.nih.gov/search/batch_query.shtml ways. Informative gene- and set-level perturbations are then used as features in the

final stage to predict sample characteristics (e.g. outcome, response, etc.) using various machine learning techniques. The pipeline is a flexible analysis framework designed to incorporate external analytical algorithms (GISTIC, SPIA, etc.) at any stage in the pipeline, and geneset level analyses are run over thousands of genesets and pathways collected from MSigDB [70], BioCarta, KEGG [53], NCI-Nature [62], and Gene Ontol- ogy [4]. Analytical results from each stage are stored in a MySQL relational database that enables rapid access to any slice of patient data, with results being passed to a series of proven machine learning techniques for training and classification. On top of this powerful analytical pipeline, the BioIntegrator offers its own unique web-based visualization allowing researchers to view the results of each stage of the pipeline. The

BioIntegrator web-based user interface (Figure 2.3) provides quick and interactive access to both raw and analytical results that allows researchers to explore their data intuitively. Both clinical and genomic data can be explored in a variety of ways, including free-text search and correlation analysis.

By using this BioIntegrator on a set of gene expression and copy number data in melanoma [47], it is possible to identify a set of genes that are modulated in response to a mutation of BRAF in patient samples. Although BRAF mutation status can be determined trivially using sequencing, by asking a machine learning algorithm to try and determine the set of features responsible for splitting those samples we can ﬁnd a useful set of features associated with BRAF mutation status that may help interpret the underlying biology of BRAF activation. Because BRAF is an important predictive

17 - p53 RB Ras-PI3K Age < 40 yrs Age > 40 yrs 3 ) p

UCSC-log10( BioIntegrator: Multi-Platform Analysis Pipeline ABSTRACT 2462 1 1 . 2003). 1 1 1 1 1,2 J. Zachary Sanborn0 , Stephen Benz , Charlie Vaske , Jingchun Zhu , Christopher Szeto , Ting Wang , David Haussler 1 Center for Biomolecular Scienceet al and Engineering, UC Santa Cruz, 2 Howard Hughes Medical Institute

UCSC BioIntegratorUCSC BioIntegrator INTRODUCTION BIOINTEGRATOR WEBUCSC INTERFACEBioIntegratorUCSC BioIntegrator TCGA GLIOBLASTOMA RESULTS and noted by the colored bars on the left above. ! Here we present a new analysis pipeline named BioIntegrator that works in Samples et al. CDKN2A cyclin-dependent kinaseCDKN2A inhibitorcyclin-dependent 2Akinase inhibitor 2A infoinfo ...... Geneset Meta-Analysis CDKN2A cyclin-dependent kinaseCDKN2A inhibitorcyclin-dependent 2Akinase inhibitor 2A infoinfo ...... three stages. In the ﬁrst stage, gene-level perturbations are calculated by integrating MTAPCDKN2A 5'-methylthioadenosinecyclin-dependent kinaseMTAPCDKN2A phosphorylase 5'-methylthioadenosineinhibitorcyclin-dependent 2Akinase phosphorylase inhibitorinfo 2A infoinfo info ...... Age < 40 yrs MTAP 5'-methylthioadenosineMTAP phosphorylase5'-methylthioadenosine phosphorylaseinfoinfoinfoinfo Age at Death EGFR epidermal growth factorEGFR receptorepidermal growth factor receptor infoinfo Age > 40 yrs data from several platforms (copy number, expression, etc.) using a variety of meth- EGFR epidermal growth factorEGFR receptorepidermal growth factor receptor infoinfoinfoinfo EGFR/HE... EGFR/HER2_SignalingEGFR/HE... EGFR/HER2_Signaling ods. These gene-level perturbations and fed into the second stage, where they are EGFR/HE... EGFR/HER2_SignalingEGFR/HE... EGFR/HER2_Signaling MDM2MDM2 mousemouse double double minute minuteMDM2MDM2 2 2homolog mousehomologmouse double double minute minute 2 homolog 2 homologinfoinfoinfo info

Geneset Meta-Analysis MDM2 mouse double minuteMDM2 2 homologmouse double minute 2 homolog infoinfo h_ranbp...h_ranbp... h_ranbp2Pathwayh_ranbp2Pathwayh_ranbp...h_ranbp... h_ranbp2Pathwayh_ranbp2Pathway infoinfoinfo info combined with the perturbations values of other genes in genesets or structured h_ranbp... observed an supporting test), exact Fisher’s 0.0002, < h_ranbp2Pathwayh_ranbp... h_ranbp2Pathway infoinfo p Input Feature Name: Ag Input FeatureInput Name: MDMFeature Name: MDM pathways. Informative gene- and set-level perturbations are then used221 TCGA GBM Samples as features in InputInput Feature InputFeature InputName: Name: MDM Feature MDM Feature Name: Name: MDM MDM Age Clinical MDM2: Top Correlated MDM2: Top Correlated EGFR

AGK Gene MDM2:MDM2: Top TopCorrelated Correlated MDM2:http://pid.nci.nih.gov/search/batch_query.shtml MDM2: Top Top Correlated Correlated the ﬁnal stage to predict sample characteristics (e.g. outcome, response, etc.) using (16): 5814-9. GO:0017163, r=1 (click to load) GO:0017163, r=1 (click to load) GO:0017163,GO:0017163, r=1 r=1(click (click to load) to load)GO:0017163,GO:0017163, r=1 r=1 (click (click to to load) load)

102 GO:0042176, r=0.98 (click to load)GO:0042176, r=0.98 (click to load) various machine learning techniques. GO:0042176,GO:0042176, r=0.98 r=0.98 (click (click to load) to GO:0042176,load)GO:0042176, r=0.98 r=0.98 (click (click to to load) load) BioIntegrator’s web-based user interface Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression,Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression,Sample order (7216): 1061-8. is r=0.91 determined(click to load) dynamically r=0.91 (click to load) Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression,Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression, r=0.91 (click to load) r=0.91 (click to load) h_arfPathway, r=0.87 (click to load)h_arfPathway, r=0.87 (click to load) TCGA GLIOBLASTOMA RESULTS h_arfPathway, r=0.87(1): 30-6. (click to load)h_arfPathway, r=0.87 (click to load)

455 Ras-PI3K KEY POINTS provides quick & interactive access to by theh_arfPathway, values r=0.87 (click to load)h_arfPathway, of the clinicalr=0.87 (click or to load)genomic 64 1,2 Flexible pipeline for performing gene- and pathway-level- both raw and analytical results that feature located at the top of the table. Hypoxia ECM chr12 EGFR allows researchers to exploreACKNOWLEDGMENTS / REFERENCES their data When categorical clinical features are ECM analysis on whole genome data (2004). “An Evaluation of Gene Selection Methods for Multi-class Microarray Data Classification.” Proceedings of Immune Hypoxia (2005). "Gene expression profiling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme." amplicon intuitively. Clinical and genomic data can placed at the top, all data below are et al. Web-based tool for exploratory analysis of BioIntegrator’s (2008). “Modeling genomic diversity and tumor dependence in malignant melanoma.” Cancer Res 68(3): 664-73 Immune Age at Death be added in a variety of ways, including summarizedet al. appropriately within each the Second European Workshop on Data Mining and Text Mining in Bioinformatics. Pisa, Italy. Schölkopf and C. Burges and A. Smola (ed.), MIT-Press. Proc Natl Acad Sci U S A tralbl Neurochir pathways." Nature results alongside clinical data UCSCet al. BioIntegratorUCSC BioIntegrator eecmie ocluae eagn soe hc wr obndt aclt meta-geneset (Affy.expression Gene 221 for 244K) calculate (Agilent samples copydata GBM number and TCGA U133A) to combined were which score, meta-gene a calculate to combined were clusters distinct 35 top Fisher’sthe 3 using of genesets Method. clustering hierarchical 7,325 forscores Unsupervised in resulted scores meta-analysis geneset overall to according ranked genesets similar to 5 GBM profiles described by Liang foundtheby genes in defined Genesets pathwaysTCGA RAS-PI3K, — p53, RB and pathways (TCGA 2008) — score highly by very this method and are noted above. > (Age patients older from samples between association significant statistically A EGFR-related of cluster the in perturbations of levels high and yrs, bars) green40 ( observed was genesets relationship between age and EGFR expression (Stark Method: free-text search and correlation. category. We would like Lab for providing & Stuart to thank Josh Stuart helpful insights and Erich Weiler & CBSE cluster administrators for their tireless in maintaining efforts our many computer systems. Chai, H. Joachims, T. (1999). “Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning.” B. Liang Lin Stark, A. M., H. H. Hugo, et al. (2003). "Age-related expression of p53, Mdm2, Zen EGFR and Msh2 in glioblastoma multiforme." The TCGA Research Network (2008). "Comprehensive genomic characterization defines human glioblastoma genes and core Pathway Interaction Database — Integrated machine learning algorithms to predict complex p53 chr12 sample characteristics Her2 ERBB2 Receptor Her2PositivityERBB2 Receptor Positivity Positive+ + Negative- - ERBB2 erbB-2ERBB2 erbB-2 infoinfo amplicon , David Haussler A RB 1 HER2 am... HER2 ampliconHER2 am... HER2 amplicon 221 TCGA GBM Samples ...... DDX49 DEAD (Asp-Glu-Ala-Asp) boxDDX49 polypeptideDEAD (Asp-Glu-Ala-Asp) box polypeptide49 info 49 info ...

20 0 -log10(p) 3 0.04 ESD esterase D/formylglutathioneESD esterase hydrolase D/formylglutathione hydrolaseinfoinfo Method: BIOINTEGRATOR ANALYSIS PIPELINE Input FeatureInput Name: Feature Name: Gene expression (Affy. U133A) and copy number data (Agilent 244K) for 221 TCGA GBM samples were combined to calculate a meta-gene score, which were combined to calculate meta-geneset scores for 7,325 genesets using Fisher’s Method. Unsupervised hierarchical clustering of the top 35

, Ting Wang Various high-throughput Semi-automated pipeline that can

PREDICTING BRAF MUTANTS IN# of Models MELANOMA genesets ranked according to overall geneset meta-analysis scores resulted in 3 distinct clusters 1 measurements from a common Avg SVM Weight - process any data available on the Top 35 Genes similar to 5 GBM proﬁles described by Liang et al. and noted by the colored bars on the left above. patient cohort Method: Top 35 Genes Negative

U C S C C a n c e r G e n o m i c s 0 0.01 Genes were scored independent of sample labels using both gene

+ Avg SVM Weight Browser (Abstract #2469) expression and copy number data in the BioIntegrator. Samples 0.01 0.04 Genesets deﬁned by genes found in the TCGA pathways — RAS-PI3K, p53, and RB SLK SND1 SAT1 SAV1 ARL3 BRAF PTEN SND1 GRIA2 SORL1 HYAL4

were split 80/20 into trainingATP5J2 and test sets and feature selection pathways (TCGA 2008) — score very highly by this method and are noted above. ...

* TRRAP AS3MT ... CRKRS HERC2 OBFC1 P2RY13

CSMD1 SAT1 MNAT1 RUNX3 FHOD3 ERICH1 CNOT4 YME1L1 LUC7L2 MDM MDM DIABLO SLITRK5 FBXO34 MDM2: Top Correlated MDM2: SEMA3D ... MDM2: Top Correlated MDM2: CUEDC2 CALHM3 C10orf26 CYP19A1 MDM Howard Hughes Medical Institute MDM2: Top Correlated MDM2: Flexible analysis framework is was performed on the trainingSERPING1 data thresholding on correlation AS3MT GO:0017163, r=1 (click to load) to (click r=1 GO:0017163, GO:0017163, r=1 (click to load) to (click r=1 GO:0017163, 2

Samples * GO:0042176, r=0.98 (click to load) to (click r=0.98 GO:0042176, GO:0017163, r=1 (click to load) to (click r=1 GO:0017163, GO:0042176, r=0.98 (click to load) to (click r=0.98 GO:0042176, SLK Gene Perturbation Detectors load) to (click r=0.87 h_arfPathway, h_arfPathway, r=0.87 (click to load) to (click r=0.87 h_arfPathway, UCSC BioIntegrator UCSC UCSC BioIntegrator UCSC

GO:0042176, r=0.98 (click to load) to (click r=0.98 GO:0042176, coefﬁcient (Chai et al. 2004). Samples were run through SVM-light h_arfPathway, r=0.87 (click to load) to (click r=0.87 h_arfPathway, CSGALNACT1 UCSC BioIntegrator UCSC UCSC BioIntegrator UCSC designed to incorporate external OBFC1 A statistically signiﬁcant association between samples from older patients (Age > (Joachims 1999) using default settings, and the top 20 models CALHM3 - 40 yrs, green bars) and high levels of perturbations in the cluster of EGFR-related Gene Level Gene Level analytical algorithms (GISTIC, FHOD3 + (minimum 91% accuracy) were selected for downstream analysis. MNAT1 MDM MDM MDM2: Top Correlated MDM2: Meta Analysis Factor Graphs Top Correlated MDM2: B genesets was observed (p < 0.0002, Fisher’s exact test), supporting an observed GO:0017163, r=1 (click to load) to (click r=1 GO:0017163, GO:0017163, r=1 (click to load) to (click r=1 GO:0017163, SPIA, etc.) at any stage in the MDM MDM2: Top Correlated MDM2: GO:0042176, r=0.98 (click to load) to (click r=0.98 GO:0042176, GO:0042176, r=0.98 (click to load) to (click r=0.98 GO:0042176, h_arfPathway, r=0.87 (click to load) to (click r=0.87 h_arfPathway, h_arfPathway, r=0.87 (click to load) to (click r=0.87 h_arfPathway, Positive UCSC BioIntegrator UCSC

UCSC BioIntegrator UCSC CYP19A1 GO:0017163, r=1 (click to load) to (click r=1 GO:0017163, UCSC BioIntegrator UCSC GO:0042176, r=0.98 (click to load) to (click r=0.98 GO:0042176, h_arfPathway, r=0.87 (click to load) to (click r=0.87 h_arfPathway, UCSC BioIntegrator UCSC relationship between age and EGFR expression (Stark et al. 2003). Input Feature Name: Name: Feature Input CRKRS Input Feature Name: Name: Feature Input LMMB2 pipeline. Input Feature Name: * DIABLO Input Feature Name: Name: Feature Input , Christopher Szeto Caspase6 LMMB2 SAV1 Caspase Cascade 1 Input Feature Name: Name: Feature Input Input Feature Name: Name: Feature Input Input Feature Name: Gene Subset of Input Feature Name: Name: Feature Input Clinical Sample order is determined dynamically determined is order Sample genomic or clinical the of values the by table. the of top the at are located feature features are clinical below categorical data When all top, the at placed each within appropriately summarized category. Caspase3 CNOT4 Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression, r=0.91 (click to load) to (click r=0.91 Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression, Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression, r=0.91 (click to load) to (click r=0.91 Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression, Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression, r=0.91 (click to load) to (click r=0.91 Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression, Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression, r=0.91 (click to load) to (click r=0.91 Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression,

info info info info info ! info info info info info

info info info SORL1 Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression, r=0.91 (click to load) to (click r=0.91 Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression,

Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression, r=0.91 (click to load) to (click r=0.91 Sumoylation_by_RanBP2_Regulates_Transcriptional_Repression, 1000’s of genesets and pathways Apoptosis info info info info info info info info info info info info info info info

info info info ATP5J2 info info info info info are collected from MSigDB, Pos. Regulation SERPING1 K < 4e-6) e < 0.001)

p RUNX3 Ag AG Caspase6

Pathway Perturbation Detectors p Ag ACKNOWLEDGMENTS / REFERENCES

BioCarta, KEGG, erbB-2 NCI-Nature, Pos. Regulation SLITRK5 HER2 amplicon HER2 h_ranbp2Pathway h_ranbp2Pathway MAP3K1 SRBEF1 * SLK CAD EGFR/HER2_Signaling EGFR/HER2_Signaling C10orf26 ERBB2 ReceptorERBB2 Positivity Weighted Geneseh_ranbp2Pathway t

EGFR/HER2_Signaling and Gene Ontology. Apoptosis DNA epidermal growth factor receptor factor growth epidermal mouse double minute 2 homolog 2 minute double mouse epidermal growth factor receptor factor growth epidermal mouse double minute 2 homolog 2 minute double mouse Geneset Meta Analysis CAD

cyclin-dependent kinase inhibitor 2A inhibitor kinase cyclin-dependent TRRAP cyclin-dependent kinase inhibitor 2A inhibitor kinase cyclin-dependent esterase D/formylglutathione hydrolase 5'-methylthioadenosine phosphorylase 5'-methylthioadenosine 5'-methylthioadenosine phosphorylase 5'-methylthioadenosine

epidermal growth factor receptor factor growth epidermal We would like to thank Josh Stuart & Stuart Lab for providing helpful insights and mouse double minute 2 homolog 2 minute double mouse

Analysis (Asp-Glu-Ala-Asp)DEAD box polypeptide 49 cyclin-dependent kinase inhibitor 2A inhibitor kinase cyclin-dependent 5'-methylthioadenosine phosphorylase 5'-methylthioadenosine

Fragment. PTEN ESD Her2 Erich Weiler & CBSE cluster administrators for their tireless efforts in maintaining ERBB2 DDX49 DIABLO MTAP EGFR MTAP EGFR MDM2 MDM2 HER2 am... HER2 CDKN2A Analytical results from each stage CDKN2A h_ranbp... h_ranbp... EGFR/HE... EGFR/HE... MTAP EGFR MDM2 CDKN2A

, Jingchun Zhu SEMA3D Pathway Factor Graphs h_ranbp... EGFR/HE... our many computer systems. 1 are stored in a MySQL relational P2RY13 Input Feature Name: erbB-2 Subset of LUC7L2 SLK

JNK Apoptosis Stress DNA Fiber Chai, H. et al. (2004). “An Evaluation of Gene Selection Methods for Multi-class Microarray Data Classiﬁcation.” Proceedings of Stress HER2 amplicon HER2

h_ranbp2Pathway database that enables rapid access h_ranbp2Pathway Caspase GRIA2 Caspase3 h_ranbp2Pathway the Second European Workshop on Data Mining and Text Mining in Bioinformatics. Pisa, Italy. EGFR/HER2_Signaling EGFR/HER2_Signaling Cascade Fiber Fragment. FBXO34 EGFR/HER2_Signaling 2004). Samples were run through SVM-light through run were 2004). Samples to any slice of patient ReceptorERBB2 Positivity data. Cascade ARL3 Joachims, T. (1999). “Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning.” B. epidermal growth factor receptor factor growth epidermal epidermal growth factor receptor factor growth epidermal mouse double minute 2 homolog 2 minute double mouse mouse double minute 2 homolog 2 minute double mouse HERC2 Schölkopf and C. Burges and A. Smola (ed.), MIT-Press. epidermal growth factor receptor factor growth epidermal mouse double minute 2 homolog 2 minute double mouse cyclin-dependent kinase inhibitor 2A inhibitor kinase cyclin-dependent cyclin-dependent kinase inhibitor 2A inhibitor kinase cyclin-dependent 5'-methylthioadenosine phosphorylase 5'-methylthioadenosine ! 5'-methylthioadenosine phosphorylase 5'-methylthioadenosine cyclin-dependent kinase inhibitor 2A inhibitor kinase cyclin-dependent Liang et al. (2005). "Gene expression proﬁling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme."

esterase D/formylglutathione hydrolase CSMD1 et al. al. et 5'-methylthioadenosine phosphorylase 5'-methylthioadenosine Machine Learning Results are passed to a series of Enrichment analysis performed on full list of genes in

DEAD (Asp-Glu-Ala-Asp)DEAD box polypeptide 49 YME1L1 Proc Natl Acad Sci U S A 102(16): 5814-9.

Web Application SRBEF1 BIOINTEGRATOR WEB INTERFACE Algorithms for Figure 2.4: Genes were scored independent of sample labels using both gene expression proven machine learning top 20 SVM models using NCI-Nature PathwayApoptosis Interac- CSGALNACT1 Lin et al. (2008). “Modeling genomic diversity and tumor dependence in malignant melanoma.” Cancer Res 68(3): 664-73 for Exploratory Automated Feature and copy number data from a melanoma cohort [47] in the BioIntegrator. SamplesERICH1 were Stark, A. M., H. H. Hugo, et al. (2003). "Age-related expression of p53, Mdm2, EGFR and Msh2 in glioblastoma multiforme." Zen- Selection and techniques for training and tion Database: HYAL4 MTAP EGFR ESD MTAP EGFR Her2 MDM2

Analysis MDM2 tralbl Neurochir 64(1): 30-6. ERBB2 , Charlie Vaske DDX49

CDKN2A split 80/20 into training and test sets and feature selection was performed on the training Caspase Cascade in Apoptosis ( Fas Signaling Pathway (CD95) ( MTAP EGFR CDKN2A MDM2 h_ranbp... BRAF

Classiﬁcationh_ranbp... EGFR/HE... EGFR/HE... 1 CDKN2A

HER2 am... HER2 Caspase Cascade in Apoptosis (p < 4e-6) The TCGA Research Network (2008). "Comprehensive genomic characterization defines human glioblastoma genes and core h_ranbp... EGFR/HE... classification. data thresholding on correlation coefficient [15]. Samples were run through SVM-lightCUEDC2 PREDICTING BRAF MUTANTS IN MELANOMA 0 # of Models 20 pathways." Nature 455(7216): 1061-8. JNK [38] using default settings,Fas Signaling andDIABLO the Pathway top 20 models (CD95) (minimum (p < 0.001) 91% accuracy) were selected Pathway Interaction Database — http://pid.nci.nih.gov/search/batch_query.shtml BioIntegrator’sweb-based that to interface user access interactive results & quick analytical provides and raw both data their exploreresearchers to allows can data intuitively.genomic and Clinical ways, including of variety a in added be free-text search and correlation. Cascade MAP3K1 Genes were scored independent of sample labels using both gene both using labels sample Samples BioIntegrator.of independent scored were the Genes in data number copy and selection featureexpression and sets test and training into correlation 80/20 split on were thresholding data training the on performed models was 20 top the and (Chai coefficient settings, default using 1999) (Joachims (minimum 91% accuracy) were selected for downstream analysis. in genes of list full on performed analysis Enrichment Interac PathwayNCI-Nature using models SVM 20 top tion Database: for downstreamMethod: analysis. Part A shows the top 35 features selected by the SVMs, with both SVM weight and frequency plotted. When those top 35 features were run through NCI’s Pathway enrichment analysis, both Caspase Cascade in Apoptosis (p < 4e-6, Part B) and Fas Signaling Pathway (CD95) (p < 0.001) were found to be significantly enriched. The color of nodes in Part B illustrate the average enrichment towards the -

, Stephen Benz mutated class (red) or non-mutated class (blue) as calculated by the BioIntegrator gene 1 level perturbation score. Nodes seen in both Part A and Part B are denoted with an orange asterisk. 18 Center for Biomolecular Science and Engineering, UC Santa Cruz, 1 UCSC BioIntegrator: Multi-Platform Analysis Pipeline UCSC BioIntegrator: J. Sanborn Zachary (Abstract #2469)

Cancer s c i m o n e can that G pipeline Semi-automated r e the c on available n data any a process C C S C U is Browser framework analysis Flexible external (GISTIC, incorporate to designed the algorithms in stage analytical any at etc.) SPIA, pipeline. MSigDB, pathways and from genesets of 1000’s collected are NCI-Nature, KEGG, BioCarta, and Gene Ontology. stage each from results Analytical relational MySQL a in stored are rapidaccess enablesthat database g n i n r a e to any slice of patient data. l e n of i series h and a c to a passed m are Results n training e v ro for p techniques classification. e t or ning eatur el KEY POINTS v aphs ors cation hms f ed F ors it ysis ect t ed Genese hine Lear or Gr INTRODUCTION ect t omat Anal Classifi Selection and Algor Gene Le 2462 act ut aphs F Mac eight A t W oughput or Gr hr om a common act turbation De y F ysis a turbation De y er el patient cohor ysis hw er v or ements fr ious high-t at y P at P ar a Anal a t V ysis a Anal t hw BIOINTEGRATOR ANALYSIS PIPELINE measur lxbe ieie o promn gn- n pathway-level and gene- performing for pipeline Flexible analysis on whole genome data BioIntegrator’s of analysis exploratory for tool Web-based results alongside clinical data complex predict to algorithms learning machine Integrated sample characteristics t Me Gene Le Gene P at Me Anal P eb Application or Explor f Genese W ee e rsn a e aayi ppln nmd iItgao ta wrs in works that BioIntegrator named pipeline analysis new a present we Here ! integrating by stages. stage, threecalculated are first the gene-levelperturbations In from(copyseveral number,platforms data meth of expression, variety a using etc.) are they stage, where second the into fed and ods. structuredperturbations gene-level or These genesets in genes other of values perturbations the with combined featuresin as pathways. used thenare set-levelperturbations and Informative gene- (e.g. outcome, using characteristics response, sampleetc.) predict to stage final the various machine learning techniques. ABSTRACT marker for a class of targeted therapies in melanoma, understanding associated genes may provide a set of features that are functionally equivalent as well as reveal the underlying tumorigenic pathways responsible for sensitivity to the therapy. In fact, when the melanoma samples were run through SVM-light [38] and the corresponding features with the highest weights were analyzed, there was a clear functional enrichment for the caspase cascade in apoptosis (p < 4e-6) and Fas signaling pathway (p < 0.001) (Fig- ure 2.4). This result illustrates the added pathway information that the BioIntegrator is capable of providing in an automated fashion.

19 Inferred Pathway Levels

p53 pathway FoxM1 network Angiopoietin receptor Tie2-mediated signaling PARADIGM

Copy Number AlterationsCopy Number Gene Measurements GeneAlterationsCopy Number ExpressionGeneAlterations ExpressionGene Sample 1 Expression Sample 2 Sample 3 etc.) Entities (Genes, Complexes, Pathway Samples

Figure 3.1: Overview of the PARADIGM method. PARADIGM uses a pathway schematic with functional genomic data to infer genetic activities that can be used for further downstream analysis.

Chapter 3

Pathway Analysis Using PARADIGM

In collaboration with Charles Vaske under the advisement of Joshua Stuart, we have developed a sample-speciﬁc probabilistic graphical model (PGM) based on factor

20 graphs that we refer to as PARADIGM (PAthway Recognition Algorithm using Data

Integration on Genomic Models). This algorithm allows us to infer the activities of genetic pathways from integrated genomic patient data, giving us a more in-depth view of pathways than was capable with tools like the BioIntegrator. Figure 3.1 illustrates the overview of the approach. Multiple genome-scale measurements on a single patient sample are combined to infer the activities of genes, products, and abstract process inputs and outputs for a single NCI pathway. PARADIGM produces a matrix of integrated pathway activities (IPAs) A where Aij represents the inferred activity of entity i

in patient sample j. The matrix A can then be used in place of the original constituent datasets to perform downstream analysis, including unsupervised clustering paired with survival analysis[81]. I will discuss aspects of the algorithm in more detail and some of the analysis that has been done using this method in the next subsections.

3.1 Method

The correlations seen in Figure 1.2 clearly show that useful information is encoded in the pathway structures provided by NCI’s pathway database, and so we began by convertering each NCI pathway into a distinct probabilistic model that can be used by PARADIGM. A toy example of a small fragment of the p53 apoptosis pathway is shown in Figure 3.2 where we have a pathway diagram from NCI that was converted into a factor graph, including both hidden and observed states. The factor graph integrates observations on gene- and biological process-related state information with a structure

21 describing known interactions among the entities.

To represent a biological pathway with a factor graph, we use variables to describe the states of entities in a cell, such as a particular mRNA or complex, and use factors to represent the interactions and information flow between these entities. These variables represent the differential state of each entity in comparison to a “control” or normal level rather than the direct concentrations of the molecular entities. This representation allows us to model many types of high-throughput datasets, such as gene expression detected with DNA microarrays, that often either directly measure the differential state of a gene or convert direct measurements to measurements relative to matched controls. It also allows for many types of regulatory relationships among genes. For example, the interaction describing MDM2 mediating ubiquitin-dependent degradation of p53 can be modeled as activated MDM2 inhibiting p53’s protein level.

The factor graph encodes the state of a cell using a random variable for each entity X = {x1, x2, . . . , xn} and a set of m non-negative functions, or factors, that con-

strain the entities to take on biologically meaningful values as functions of one another.

th The j factor φj deﬁnes a probability distribution over a subset of entities Xj ⊂ X.

The entire graph of entities and factors encodes the joint probability distribution over

all of the entities as:

m 1 Y P (X) = φ (X ) , (3.1) Z j j j=1 where Z = Q P φ (S) is a normalization constant and S < X denotes that S is a j S < Xj j

“setting” of the variables in X.

22 Each entity can take on one of three states corresponding to activated, nominal, or deactivated relative to a control level (e.g. as measured in normal tissue) and encoded as 1, 0, or -1 respectively. The states may be interpreted diﬀerently depending on the type of entity (e.g. gene, protein, etc). For example, an activated mRNA entity represents overexpression, while an activated genomic copy entity represents more than two copies are present in the genome. Figure 3.2 shows the conceptual model of the factor graph for a single protein-coding gene. For each protein-coding gene G in the pathway, entities are introduced to represent the copy number of the genome (GDNA), mRNA expression (GmRNA), protein level (Gprotein), and protein activity (Gactive) (ovals labeled “DNA”, “mRNA”, “protein”, and “active” in Figure 3.2). For every compound, protein complex, gene family, and abstract process in the pathway, we include a single variable with molecular type “active.” While the example in Figure 3.2 shows only one process (“Apoptosis”), in reality many pathways have multiple such processes that represent everything from outputs (e.g. “Apoptosis” and “Senescence”) to inputs (e.g.

“DNA damage”) of gene activity.

In order to simplify the construction of factors, we first convert the pathway into a directed graph, with each edge in the graph labeled with either positive or negative influence. First, for every protein coding gene G, we add edges with a label “positive” from GDNA to GmRNA, from GmRNA to Gprotein, and from Gprotein to Gactive to reflect the expression of the gene from its number of copies to the presence of an activated form of its protein product. Every interaction in the pathway is converted to a single edge in the directed graph.

23 Using this directed graph, we then construct a list of factors to specify the factor graph. For every variable xi, we add a single factor φ(Xi), where Xi = {xi} ∪

{Parents(xi)} and Parents(xi) refers to all the parents of xi in the directed graph. The value of the factor for a setting of all values is dependent on whether xi is in agreement

with it’s expected value due to the settings of Parents(xi). For this study, the expected

value was set to the majority vote of the parent variables. If a parent is connected by a

positive edge it contributes a vote of +1 times its own state to the value of the factor.

Conversely, if the parent is connected by a negative edge, then the variable votes -1

times its own state. The variables connected to xi by an edge labeled “minimum” get a single vote, and that vote’s value is the minimum value of these variables, creating an AND-like connection. Similarly the variables connected to xi by an edge labeled

“maximum” get a single vote, and that vote’s value is the maximum value of these variables, creating an OR-like connection. Votes of zero are treated as abstained votes.

If there are no votes the expected state is zero. Otherwise, the majority vote is the expected state, and a tie between 1 and -1 results in an expected state of -1 to give more importance to repressors and deletions.

Given this deﬁnition of expected state, φi(xi, Parents(xi)) is speciﬁed as:    1 − xi is the expected state from Parents(xi) φi(xi, Parents(xi)) =   2 otherwise.

For the results shown here, was set to 0.001, but orders of magnitude differences in the choice of epsilon did not significantly affect results.

Finally, we add observation variables and factors to the factor graph to com-

24 Transcriptional Translational Intracellular and A Regulation Regulation, Extracellular Protein Degradation Signaling

GeneCopy Expression Protein Protein Number State Level Activity

Variable Array CGH, Transcriptomics SNP chips Factor - interaction term

B Transcriptional Formation of mRNARegulation ComplexProtein Protein Protein DNA Protein Activity Activity Activity mRNA Activity AND-like connection

Protein Complex

Protein ActivationActivity Gene Family mRNA Protein mRNA Protein Protein

Protein Activity Protein Activity Activity Activity OR-like connection Activity Gene C Family MDM2 DNA mRNA Protein

MDM2 Active Protein

TP53 TP53 DNA mRNA Protein Active Apoptosis Protein

Apoptosis

Figure 3.2: Conversion of a genetic pathway diagram into a PARADIGM model. A. Data on a single patient is integrated for a single gene using a set of four diﬀerent biological entities for the gene describing the DNA copy number, mRNA and protein levels, and activity of the protein. B. PARADIGM models various types of interactions across genes including transcription factors to transcript and protein targets (upper- left), protein subunits aggregating in a complex (upper-right), protein post-translational modiﬁcation (lower-left), and sets of gene products in a family performing redundant functions (lower-right). C. Toy example of a small sub-pathway involving P53, its inhibitor MDM2, and the high level P53 determined cell process, apoptosis as represented in the model.

25 plete the integration of pathway and multi-dimensional functional genomics data (Fig- ure 3.2). Each discretized functional genomics dataset is associated with one of the molecular types of a protein-coding gene. Array CGH/SNP estimates of copy number alteration are associated with the “genome” type. Gene expression data is associated with the “mRNA” type. Though not presented in the results here, future expansion will include DNA methylation data with the “mRNA” type, and proteomics and gene- resequencing data with the “protein” and “active” types. Each observation variable is also ternary valued. The factors associated with each observed type of data are shared across all entities and the associated parameters are learned from the data using a standard Expectation Maximization (EM) procedure[19].

3.2 Comparisons

In order to assess the viability of the integrated factor graph approach, PARADIGM was compared to three competing methods across three biological scenarios, done in collaboration with Vinay Varadan, Prateek Mittal, and Charles Vaske[80]. An initial, more detailed comparison between SPIA and PARADIGM was done using two datasets spiked with decoys, designed as a more conservative comparison technique. The first two biological scenarios focus on the identification of pathways that differentiate between two pre-defined cohorts, while the third scenario identifies pathways that can best stratify patient survival. For these applications, we considered two datasets corresponding to ovarian and breast carcinoma. Copy number data (Agilent 244K CGH

26 platform), gene expression data (Aﬀymetrix U133A platform), and associated patient clinical information was obtained from the high-grade serous ovarian carcinoma dataset at TCGA. Overall, we used 423 ovarian cancer samples and 8 normal samples. The breast cancer data for a total of 113 patients was obtained from two public sources - copy number data derived from the publication by Chin et al. (GEO accession GPL5737) and the gene expression data for the same samples from the publication by Naderi et al. (MIAMIExpress accession E-UCon-1). Pathway information was derived from the

NCI-PID database. The spiked datasets were generated from the same breast cancer data and the glioblastoma multiforme data available from TCGA. The implementations of GSEA and PathOlogist were obtained from their respective authors’ websites. All of these implementations ship with inbuilt support for NCI-PID pathways. The oﬃcial

SPIA implementation works only with KEGG pathways, so we used a modiﬁed implementation developed by Dent Earl that supports NCI-PID pathways in order to provide a fair comparison between these approaches.

3.2.1 Distinguishing True Networks From Decoys

Because of the similarities in approach between SPIA and PARADIGM, we

ﬁrst asked whether the integrated activities could be obtained from arbitrary genes connected in the same way as the genes in the NCI pathways in each method. To do this, we estimated the false discovery rate and compared it to SPIA in the breast cancer and TCGA GBM cohorts. Because many genetic networks have been found to be implicated in cancer, we chose to use simulated ”decoy” pathways as a set of negative

27 1.0 0.8 0.6 0.4

PARADIGM - Breast Fraction of Real Pathways

0.2 SPIA - Breast PARADIGM - GBM SPIA - GBM 0.0

0.0 0.2 0.4 0.6 0.8 1.0

Fraction of Total Pathways

Figure 3.3: Distinguishing decoy from real pathways with PARADIGM and SPIA. De- coy pathways were created by assigning a new gene name to each gene in a pathway. PARADIGM and SPIA were then used to compute the perturbation of every pathway. Each line shows the receiver-operator characteristic (ROC) for distinguishing real from decoy pathways using the perturbation ranking. In breast cancer, the areas under the curve (AUCs) are 0.669 and 0.602 for PARADIGM and SPIA, respectively. In GBM the AUCs are 0.642 and 0.604 respectively.

28 controls. For each NCI pathway, we constructed a decoy pathway by connecting random genes in the genome together using the same network structure as the NCI pathway. We then ran PARADIGM and SPIA to derive IPAs for both the NCI and decoy pathways.

For PARADIGM, we ranked each pathway by the number of IPAs found to be signiﬁcant across the patients after normalizing by the pathway size. For SPIA, pathways were ranked according to their computed impact factor.

We found that PARADIGM excludes more decoy pathways from the top-most activated pathways compared to SPIA (Figure 3.3). For example, in breast cancer,

PARADIGM ranks 1 decoy in the top 10, 2 in the top 30, and 4 in the top 50. In comparison, SPIA ranks 3 decoys in the top 10, 12 in the top 30, and 22 in the top 50.

The overall distribution of ranks for NCI IPAs are higher in PARADIGM than in SPIA, observed by plotting the cumulative distribution of the ranks (P < 0.009, K-S test).

3.2.2 Tumor versus Normal - Pathways associated with Ovarian Can-

cer

One of the ﬁrst applications of PARADIGM to a large-scale cohort with adjacent normal tissue was in collaboration with The Cancer Genome Atlas during the analysis of high-grade serous adenocarcinoma. In serous ovarian cancer, the FOXM1 transcription factor network was found to be diﬀerentially altered in the 330 tumor samples compared to the normal controls in the highest proportion of the patient samples (Figure 3.4). Pathways with recurrently high IPAs reveal fundamental mechanisms dysregulated in serous ovarian cancer, particularly interesting because of the aggressive

29 Figure 3.4: Summary of FOXM1 integrated pathway activities (IPAs) across all samples. The arithmetic mean of IPAs across tumor samples for each entity in the FOXM1 transcription factor network is shown in red, with heavier red shading indicating two standard deviations. Gray line and shading indicates the mean and two standard deviations for IPAs derived from permuted data.

Figure 3.5: Summary of IPAs of FOXM1 and all tested transcription factors. Histograms of each sample’s IPA in FOXM1 or in any other transcription factor from the PID. A. Histogram of IPAs, with IPAs of 0 removed. By a Kolmogorov-Smirnov test, FOXM1 shows a diﬀerent distribution with a p-value < 10−225. B. Histogram IPAs including 0 valued IPAs. By a KS test, FOXM1 has a diﬀerent distribution of IPAs with a p-value < 10−236.

30 Figure 3.6: Expression of FOXM1 Transcription Factor Network genes in high grade vs. low grade carcinoma. Using data from Etemadmoghadam et al (2009) we plotted expression of genes in the FOXM1 transcription network in either low grade (I) or high grade (II/III) ovarian carcinomas. A student’s t-test was used to assess the significance of differential expression between high grade and low grade, with the p-value below each pair of boxplots. nature of the disease. In order to validate the highly activated nature of FOXM1, we asked whether the IPAs of the FOXM1 transcription factor were more highly altered than other transcription factors. We compared the FOXM1 level of activity to all of the other 202 transcription factors in the NCI-PID to validate the signature was not an artifact of the highly-connected nature of transcription factors in general. Even compared to other transcription factors in the NCI set, the FOXM1 transcription factor had significantly higher levels of activity (p < 0.0001) suggesting further that it may be a important signature (Figure 3.5).

31 Because the entire cohort for the TCGA ovarian were high-grade serous, we asked whether the FOXM1 signature was specific to high-grade serous or a more general marker of both low- and high-grade. To determine whether the signature is associated with serous ovarian cancer, we obtained the expression of FOXM1 and several of its targets from the dataset of [22] in which both low- and high-grade ovarian tumors had been transcriptionally profiled. This independent data confirmed that FOXM1 and several of its targets are significantly up-regulated in serous ovarian, even relative to low-grade ovarian cancers (Figure 3.6).

We next wanted to understand how this result compares with competing methods using the TCGA ovarian cancer dataset[13]. Using a larger set of 423 ovarian cancer samples against 8 normal samples, we attempted to identify pathways that were dysregulated in ovarian cancer using GSEA, PathOlogist, SPIA and Paradigm. We used a FDR cutoﬀ of 0.25 to capture only the statistically signiﬁcant pathways. Table 3.1 presents the top pathways that were found to be upregulated in ovarian cancer samples when compared to adjacent normal ovarian tissue. The FOXM1 transcription factor network from the NCI-PID is consistently found to be upregulated by all four methodologies. Figure 3.7 captures the level of activity in this pathway using PathOlogist’s activity metric across all the ovarian samples, while all the normal samples have low activity levels, a majority of the ovarian cancer samples show high activity levels.

FOXM1’s role in many diﬀerent cancers, including breast and lung, has been well documented but its role in ovarian cancer has not been investigated. FOXM1 is a multifunctional transcription factor with 3 known dominant splice forms, each regulating

32 Table 3.1: The FOXM1 transcription factor network shows up as being upregulated in ovarian tumors compared to normal consistently across pathway methodologies. GSEA Pathologist SPIA PARADIGM Activity Consistency Validated tar- Signaling by Au- Aurora A signal- FOXM1 tran- Inﬂuence of Ras gets of C-MYC rora Kinases ing scription fac- and Rho pro- transcriptional tor network teins on G1 to S activation transition E2F transcrip- FOXM1 tran- Signaling events PLK1 signaling IL2-Mediated tion factor scription fac- mediated by events signaling events network tor network PRL FOXM1 tran- PLK1 signaling PRC2 complex AuroraB Signal- IL2 signaling scription fac- events sets long-term ing events mediated tor network gene silencing by PI3K FOXO family AuroraB signal- C-MYB tran- E2F Transcrip- signaling ing scription factor tion factor net- network work AuroraB signal- BARD1 signal- Regulation of c-MYC Pathway ing ing events cytoplasmic and nuclear SMAD2/3 signaling PLK1 signaling Class I PI3K sig- Signaling events BCR signaling events naling events mediated by pathway VEGFR1 and VEGFR2 Signaling Events mTOR signaling Signaling events Mediated by pathway mediated by VEGFR1and PRL VEGFR2 A6B1 and A6B4 A6B1 and A6B4 Signaling events integrin signal- integrin signal- mediated by ing ing TCPTP IGF1 pathway FOXM1 transcription factor network

33 [TABLE 1] THE FOXM1 TRANSCRIPTION FACTOR NETWORK SHOWS UP AS BEING UPREGULATED IN OVARIAN TUMORS COMPARED TO NORMAL CONSISTENTLY ACROSS PATHWAY METHODOLOGIES.

PATHOLOGIST GSEA ACTIVITY CONSISTENCY SPIA PARADIGM VALIDATED TARGETS OF SIGNALING BY AURORA AURORA A SIGNALING INFLUENCE OF RAS AND RHO C-MYC TRANSCRIPTIONAL KINASES PROTEINS ON G1 TO S TRANSITION ACTIVATION SIGNALING EVENTS FOXM1 TRANSCRIPTION FOXM1 TRANSCRIPTION MEDIATED BY PRL FACTOR NETWORK IL2-MEDIATED SIGNALING E2F TRANSCRIPTION FACTOR NETWORK EVENTS FACTOR NETWORK PRC2 COMPLEX SETS PLK1 SIGNALING EVENTS PLK1 SIGNALING EVENTS LONG-TERM GENE SILENCING IL2 SIGNALING EVENTS MEDIATED FOXM1 AURORAB SIGNALING BY PI3K TRANSCRIPTION FACTOR AURORAB SIGNALING C-MYB TRANSCRIPTION NETWORK FACTOR NETWORK E2F TRANSCRIPTION FACTOR BARD1 SIGNALING EVENTS NETWORK REGULATION OF CYTOPLASMIC FOXO FAMILY SIGNALING CLASS I PI3K SIGNALING AND NUCLEAR SMAD2/3 VALIDATED TARGETS OF C-MYC EVENTS SIGNALING TRANSCRIPTIONAL ACTIVATION AURORAB SIGNALING SIGNALING EVENTS SIGNALING EVENTS MEDIATED C-MYC PATHWAY PLK1 SIGNALING EVENTS MEDIATED BY VEGFR1AND BY VEGFR1 AND VEGFR2 VEGFR2 BCR SIGNALING PATHWAY MTOR SIGNALING PATHWAY A6B1 AND A6B4 INTEGRIN SIGNALING EVENTS MEDIATED SIGNALING A6B1 AND A6B4 INTEGRIN BY PRL SIGNALING SIGNALING EVENTS MEDIATED IGF1 PATHWAY BY TCPTP

FOXM1 TRANSCRIPTION FACTOR NETWORK

integrated pathway level (IPL): the signed log-likelihood ratio for between two predefined cohorts, while the third scenario identi- each pathway elements activity. The totality of IPLs forms a fies pathways that can best stratify patient survival. For these matrix with samples along one axis, and pathway components on applications, we considered two data sets corresponding to ovari- another. This matrix is amenable to the same analyses per- an and breast carcinoma. We obtained copy number data (Agilent formed for expression or copy number alone but incorporates 244K CGH platform), gene expression data (Affymetrix U133A multiple data types for each sample, as well as pathway signaling platform), and associated patient clinical information from the information. high-grade serous ovarian carcinoma data set at TCGA [25]. Overall, we used 423 ovarian cancer samples and eight normal APPLICATIONS OF PATHWAY-BASED APPROACHES samples. The breast cancer data for a total of 113 patients was We now show the utility of the pathway-based approaches by obtained from two public sources—copy number data derived applying them to three scenarios in cancer research. The first two from the publication by Chin et al. [26] (GEO accession GPL5737) scenarios focus on the identification of pathways that differentiate and the gene expression data for the same samples from the publication by Naderi et al. [27] (MIAMIExpress accession E-UCon-1). Pathway information was derived from the NCI-PID database. 0.7 The implementations of GSEA, PathOlogist, and Paradigm were obtained from their respective authors’ Web sites. All of 0.6 these implementations ship with built-in support for NCI-PID pathways. The official SPIA implementation works only with 0.5 KEGG pathways, so we used a modified implementation that 0.4 supports NCI-PID pathways to provide a fair comparison between these approaches [24]. 0.3 TUMOR VERSUS NORMAL PATHWAYS Pathway Activity 0.2 ASSOCIATED WITH OVARIAN CANCER 0.1 Tumor Our first application involves the identification of pathways asso- Normal ciated with high-grade serous ovarian carcinoma. Using the 0 0 50 100 150 200 250 300 350 400 450 TCGA ovarian cancer data set, we compared the 423 ovarian can- Samples cer samples against eight normal samples to identify pathways that were dysregulated in ovarian cancer using GSEA, Figure 3.7: The PathOlogist approach shows signiﬁcant activation of the FOXM1 path- PathOlogist, SPIA, and Paradigm. We used a FDR cutoff of 0.25 [FIG8]way in The tumor PathOlogist samples. ( c 2012 approach IEEE) shows significant activation of the FOXM1 pathway in tumor samples. to capture only the statistically significant pathways.

IEEE SIGNAL PROCESSING MAGAZINE [44] JANUARY 2012

34 distinct subsets of genes with a variety of roles in cell proliferation and DNA repair. The

FOXM1c isoform directly regulates several targets with known roles in cell proliferation including AUKB, PLK1, CDC25, and BIRC5 [48]. On the other hand, the FOXM1b isoform regulates a completely diﬀerent subset of genes that include the DNA repair genes BRCA2 and XRCC1 [72]. CHEK2, which is under indirect control of ATM, directly regulates FOXM1’s expression level. In addition to increased expression of

FOXM1 in most of the ovarian patients, a small subset also have increase copy number ampliﬁcations detected by GISTIC (< 5%). Thus the alternative splicing regulation of

FOXM1 may be involved in the control switch between DNA repair and cell proliferation.

Furthermore, recent evidence has shown FOXM1 is a target of p53-mediated repression

[6], which is consistent with P53 mutational rates close to 100% in these samples. The observation that PARADIGM detected the highest level of altered activity centered on this transcription factor suggests that FOXM1 resides at a critical regulatory point in the cell, and the mutational rates of P53 may oﬀer an explanation for the consistency of the signal across all the samples.

It is interesting to note here that PLK1 signaling events and AuroraB signaling were also identiﬁed as upregulated in ovarian cancers by the GSEA and PathOlogist methodologies. Since FOXM1 is known to positively regulate both PLK1 and Aurora

B, this is consistent with the FOXM1 network being upregulated[57]. Another pathway consistent with the hypothesis of increased cell proliferation, the c-Myc pathway was found by both GSEA and Paradigm. Ampliﬁcation of c-Myc is a common event in

HGSOCs and a recent study has shown that c-Myc transformation is suﬃcient to induce

35 oncogenicity in normal fallopian tube tissue[40]. Similarly Signaling events mediated by

VEGFR1 and VEGFR2 and mTOR signaling was identiﬁed by PathOlogist, but not by

GSEA. This is again consistent with prior studies that have reported that the VEGF pathway via VEGFR2 stimulates the AKT/mTOR pathway in ovarian cancer[76].

This kind of consistency and interpretability of the gene expression changes in tumor samples compared to normal tissues across multiple pathways highlights the explanatory power of the pathway-based methodologies. However, the fact that some pathways listed in Table 3.1 occur only in one of the methodologies illustrates the complex nature of this problem. It is an open question whether all of these pathways are indeed biologically relevant to ovarian cancer, or whether some of these pathways were false positives, identiﬁed only as a result of some assumptions underlying the speciﬁc the computational methodology.

3.2.3 Diﬀerentially Regulated Pathways in ER+ve vs ER-ve breast

cancers

Our second application involves identifying pathways associated with specific subtypes of breast cancer. Estrogen receptor positivity is a major facet of luminal breast cancers and is typically associated with better prognosis and responsive to tamoxifen treatment. Understanding the molecular mechanisms that differentiate estrogen receptor positive (ER+ve) breast cancers from other subtypes could help identify potential modulators of cancer aggressiveness and novel therapeutic targets. We looked at pathways that were differentially regulated in 74 ER+ve samples when compared to the

36 Table 3.2: FOXA1 transcription factor network is upregulated in ER+ve tumors whereas the Hif-1-alpha transcription network and Syndecan-1 mediated signaling events are down-regulated. GSEA Pathologist SPIA PARADIGM Activity Consistency FOXA1 tran- C-MYB tran- EPO signaling Cellular roles of FOXA1 transcription factor scription factor pathway Anthrax toxin scription factor network network network Validated tar- EPO signaling C-MYB tran- Glucocorticoid Syndecan- gets of C-myc pathway scription factor receptor regula- 1-mediated transcriptional network tory network signaling events activation Hiv-1 nef: neg- CXCR3- IL6-mediated E2F transcrip- C-MYB tran- ative eﬀector of mediated signal- signaling events tion factor scription factor fas and tnf ing events network network AuroraB signal- IFN-gamma ARF6 traﬃck- Hif-1-alpha Erbb recep- ing pathway ing events transcription tor signaling factor network network Hif-1-alpha Syndecan-1 me- PLK3 signaling IL4 mediated Hif-1-alpha transcription diated signaling events signaling events transcription factor network events factor network PLK1 signaling FAS signaling ErbB1 down- events pathway (cd95) stream signaling Class I Pi3K sig- FOXO family naling events signaling Caspase cascade FOXA1 tran- in apoptosis scription factor network TCR signaling Proteogylcan in nave CD4+ T Syndecan- cells mediated signaling events

37 39 ER-ve tumor samples in the Chin-Naderi cohort. Table 3.2 depicts the comparative analysis of the pathways identified using the four pathway-based methodologies. We can see from Table 3.2 that FOXA1 transcription factor network is identified as upregulated in ER+ve tumors by the GSEA, PathOlogist and Paradigm methodologies. FoxA1 is a key determinant of estrogen receptor function, and has been shown to influence differential interactions between ER and chromatin, thus mediating the transcriptional activity of ER and the action of tamoxifen, which is the frontline targeted therapy for

ER+ve breast cancer patients[36]. FoxA1 expression is also correlated with the lumina

A subtype of breast cancer[5], and among the ER-positive subgroup treated with tamoxifen, FOXA1 was found to be an independent prognostic marker whose expression was associated with low risk of recurrence[49].

3.2.4 Therapy response prediction using pathways (Platinum Free In-

terval in Ovarian Cancer)

In our third application, we demonstrate an example of how pathway based methodologies can be used to predict patient survival outcome. For this analysis, we use

PathOlogist and Paradigm on the ovarian cancer dataset. We ﬁrst compute pathway activity/consistency levels and gene IPAs for all tumor samples using PathOlogist and

Paradigm respectively, and then use clustering algorithms on these metrics for each pathway to group patients. In case of PathOlogist, for each pathway, we use the k-means clustering algorithm on the patient pathway activity/consistency levels to cluster patient samples into two groups. This approach works best for the one-dimensional nature of

38 Table 3.3: Pathways associated with platinum-free interval in ovarian carcinoma. Pathologist PARADIGM Activity Consistency Pathway P-value Pathway P-value Pathway P-value (FDR) (FDR) (FDR) Downstream sig- 0.018 Alk1 signaling 0.023 Stablization and 0.003 naling in naive (0.78) events (0.79) expansion of (0.21) cd8+ t-cells E-cadherin adherens junction Alk1 signaling 0.023 Hif-2-alpha 0.036 Arf6 signaling 0.020 events (0.78) transcription (0.79) events (0.72) factor

PathOlogist Activity scores, which only provides a single score value per pathway. In case of Paradigm, for each pathway, we use hierarchical clustering with average linkage on all gene IPAs belonging to that pathway to cluster patients into two groups. This approach better accounts for the two-dimensional nature of the Paradigm results which provide a score per feature per sample within each pathway. Platinum free survival data was available for a total of 113 samples in the ovarian dataset and was associated with the pathway-based clustering of the patients. Pathways that led to clusters with less than 30 samples were eliminated from consideration for survival analysis. Kaplan-

Meier curves were estimated for each cluster of the selected pathways and the diﬀerences between survival curves were estimated using the Mantel-Haenszel test.

Stratifying ovarian cancer patients into responders or non-responders using genomic features is known to be an extremely difficult problem, with no reliable predictor currently available in the clinic. This is reflected even in the pathway analysis, as can be seen from Table 3.3. PathOlogist did not find any significant pathways after cor- recting for multiple testing, while Paradigm found just one pathway that had marginal

39 Figure 3.8: PathOlogist reveals that patients with higher activity levels in the Down- stream signaling in naive cd8+t cells pathway are associated with improved response to platinum therapy. ( c 2012 IEEE) stratifying capability in terms of diﬀerences in the platinum free interval. Figure 3.8 captures PathOlogist’s top pathway activity level across all patients, and the corresponding k-means clustering of these samples into two clusters. Figure 3.8B depicts the

Kaplan-Meier survival curves associated with these clusters. The relative diﬀerences in platinum free interval is quite small, as already highlighted in Table 3.3.

Paradigm successfully found a single pathway as being signiﬁcant after multiple testing to identify patient clusters with diﬀerent response outcomes to platinum therapy - the E-cadherin adherens junction pathway. Figure 3.9 depicts the dendrogram corresponding to the hierarchical clustering analysis and Figure 3.9 shows the Kaplan-

Meijer curves for these clusters. We ﬁnd that patients with lower IPAs in the genes associated with the E-cadherins adherens junction pathway have a better response to

40 Figure 3.9: Clustering analysis of ovarian samples using Paradigm shows that that patients with lower IPA’s for genes in the E-cadherin adherens junction pathway are associated with better response to platinum therapy (p=0.003). ( c 2012 IEEE) platinum therapy (p= 0.003). E-cadherin has been rather controversial as a prognostic factor in serous ovarian cancer with some studies identifying it as being only marginally associated with prognosis[83], while other studies pointing to a deeper role suggestive of a novel subtype of serous ovarian carcinoma harboring a mesenchymal phenotype[75].

This particular subtype of serous ovarian carcinoma was associated with slightly improved relapse-free survival, which would correspond to our ﬁnding of slightly better response to platinum therapy. These results suggest that a pathway-level framework is likely to provide deeper insights on mechanisms underlying clinically-relevant subtypes when compared to evaluating the expression levels of just one or more genes, even if they were chosen from within the same pathway.

41 3.3 Unsupervised Stratiﬁcation of Cancer Patients by Path-

way Activities

While finding consistently altered pathways across sample cohorts can provide clues to globally misregulated processes within known cancer subtypes, it does not help us find new pathways that might successfully stratify patients into more useful prognostic subgroups based on survival or treatment response. Gene expression data has been used successfully to define molecular subtypes for various cancers, and cancer subtypes have been found that correlate with different clinical outcomes such as drug sensitivity and overall survival. We asked whether we could identify new, informative subtypes for GBM and Serous Cystadenocarcinoma using PARADIGM IPAs rather than the raw expression data. The advantage of using IPAs is they provide a summarization of copy number, expression, and known interactions among the genes and may therefore provide more robust signatures for elucidating meaningful patient subgroups.

We ﬁrst determined all IPAs that were at least moderately recurrently activated across the GBM samples and found that 1,755 entities had IPAs of 0.25 in at least 75 of the 229 samples. We collected all of the IPAs for these entities in an activity matrix. The GBM samples and entities were then clustered using hierarchical clustering with uncentered Pearson correlation and centroid linkage (Figure 3.10). Visual inspection revealed four obvious subtypes based on the IPAs with the fourth subtype clearly distinct from the ﬁrst three. The Serous Cystadenocarcinoma samples contained less obvious clusters and thus were determined by running HOPACH with uncentered

42 1 2 3 4 Cluster Assignment

GATA/IL

E2F

EGFR

HIF-1- alpha

Figure 3.10: Clustering of IPAs for TCGA GBM. Each column corresponds to a single sample, and each row to a biomolecular entity. Color bars beneath the hierarchical clustering tree denote clusters used for Figure 3.12.

43 HIF-1-alpha

PIK3CA As) FOXM1 Activities (IP y athwa

HDAC III Integrated P

PIK3R1 IGF1

Samples

Figure 3.11: Clustering of IPAs for TCGA OV. Each column corresponds to a single sample, and each row to a biomolecular entity. Color bars above the heatmap denote clusters used for Figure 3.13.

44 Pearson correlation, ﬁnding ﬁve consistent clusters across the samples (Figure 3.11).

The fourth cluster in GBM exhibits clear downregulation of HIF-1-alpha transcription factor network as well as overexpression of the E2F transcription factor network (also shared by cluster 3). HIF-1-alpha is a master transcription factor involved in regulation of the response to hypoxic conditions. In contrast, two of the ﬁrst three clusters have elevated EGFR signatures and an inactive MAP kinase cascade involving the GATA interleukin transcriptional cascade. Interestingly, mutations and ampliﬁca- tions in EGFR have been associated with high grade gliomas as well as glioblastomas

[44]. Ampliﬁcations and certain mutations can create a constitutively active EGFR either through self stimulation of the dimer or through ligand-independent activation.

The constitutive activation of EGFR may promote oncogenesis and progression of solid tumors. Geﬁtinib, a molecule known to target EGFR, is currently being investigated for its eﬃcacy in other EGFR-driven cancers.

In OV, it is clear the red cluster is deﬁned by high PIK3CA levels, an upstream kinase of AKT2 for which therapies are currently being explored in ovarian cancer [68].

The yellow cluster shows high HIF-1-alpha related activity, for which there is increasing evidence that high levels of HIF-1-alpha is prognostic for lower overall survival [17].

Overall, the purple cluster was mostly deﬁned by lower activities in histone deacetylase class III (NAD-dependent) related proteins, in particular EP300, a protein which has previously been shown to be mutated in a subset of epithelial cancers [30]. In light of these well deﬁned clusters, qualitatively they appeared to be honing in on biologically meaningful themes that can stratify patients.

45 TCGA GBM Patients (censored at 4 years)

logrank p−value: 2.09e−03 1.0 cox p−value: 7.81e−02 cluster 1 (n = 44) cluster 2 (n = 31) cluster 3 (n = 76)

0.8 cluster 4 (n = 23) 0.6 0.4 proportion surviving 0.2 0.0

0 20 40 60 80 100 120

survival (in days)

Figure 3.12: Kaplan-Meier survival plots for the GBM clusters from Figure 3.10.

46 K−M curves for OV data Survival data censored at 48 months 1.0 0.8 0.6 proportion alive 0.4 0.2 log−rank p: 4.26e−03 cox: −0.72; p: 5.18e−03 purple cluster (n = 71) all others (n = 315) 0.0

0 10 20 30 40

time until death Figure 3.13: Kaplan-Meier survival plots for the OV purple cluster versus the rest from Figure 3.11.

47 To quantify these observations, we asked whether the different GBM and OV subtypes identified by PARADIGM coincided with different survival profiles. We calculated Kaplan-Meier curves for each of the sets of cluster clusters by plotting the proportion of patients surviving versus the number of months after initial diagnosis.

We plotted Kaplan-Meier survival curves for each of the sets of clusters to see if any cluster associated with a distinct IPA signature was predictive of survival outcome (Fig- ure 3.12). The fourth cluster in GBM is signiﬁcantly diﬀerent from the other clusters

(P < 2.11 × 10−5; Cox proportional hazards test). Half of the patients in the ﬁrst three clusters survive past 18 months; the survival is signiﬁcantly increased for cluster 4 patients where half survive past 30 months. In addition, over the range of 20 to 40 months, patients in cluster 4 are twice as likely to survive as patients in the other clusters. In

OV, when the purple cluster is compared to the remaining samples, we see a signiﬁ- cantly better overall survival curve even when corrected for all possible single-cluster comparisons (p < 0.0213 with bonferroni correction). On average, approximately 20% more patients are alive in the purple cluster at any one time-point during the four years following diagnosis.

The survival analysis in GBM revealed that the patients in cluster 4 have a signiﬁcantly better survival proﬁle. Cluster 4 was associated with an inactivity of the

HIF-1-alpha transcription factor. The inactivity in the fourth cluster may be a marker that the tumors are more oxygenated, suggesting that they may be smaller, newer, or simply better vascularized. These vascularized tumors with increased E2F may have allowed chemotherapy to more eﬀectively reduce the aerobic state, suppressing the tu-

48 mor’s high E2F and proliferation, effectively slowing its growth and allowing for longer patient survival. In Ovarian, as previously mentioned, the purple cluster is defined by low HDAC-III signaling-related activities, in particular EP300. EP300 has been shown to have truncating mutations in many epithelial cancers, including ovarian, and is a known tumor suppressor. Because these ovarian patients were given platinum-based treatments, having reduced tumor suppressor levels (and higher proliferation levels) may have resulted in increased effectiveness of the treatment. It is clear that PARADIGM

IPAs are pointing to novel and clinically meaninfgul pathways that can now be experi- mentally evaluated for their relation to treatment response and/or patient outcome.

To confirm the novelty of pathways identified by PARADIGM, we also attempted to cluster the patients in GBM using only expression data or CNA data to derive patient subtypes. No obvious groups were found from clustering using either of these data sources, consistent with the findings in the original TCGA analysis of this dataset [74] (Figure 3.14). This suggests that the interactions among genes and resulting combinatorial outputs of individual gene expression may provide a better predictor of such a complex phenotype as patient outcome.

49 Figure 3.14: Clustering glioblastoma multiform (GBM) datasets with HOPACH did not reveal any obvious patient sub-types when using either gene-level copy number or expression data. Patient samples (columns) were clustered according to gene-level copy number or expression (rows). Clustering may be dominated by the nearly patient-wide ampliﬁcations and deletions (and correlated high and low expression) for a subset of the genes in the genome (large blue and red swaths in the heatmaps). A. Copy number estimates from competitive genome hybridization for 17,508 probes across 267 tumor (left) and 170 normal (right). B. Microarray gene expression data for 11,240 probes across 243 tumor (left) and 10 normal (right) samples.

50 Chapter 4

SuperPathway - A Global Pathway

Model for Cancer

The original implementation of PARADIGM is an important tool for interpreting multi-dimensional datasets at a single-sample resolution. However, that algorithm heavily relied on a single pathway source (NCI PID) and two input datatypes (genomic copy number variations and transcriptome data). In order to more accurately reﬂect the underlying biology in the system, additional pathway representations are critical.

Currently NCI PID contains pathways central to the mechanisms of cancer, including functional processes such as DNA repair, cell cycle regulation, and angiogenesis. There are large classes of pathways that are missing from this database that we must also model, such as metabolic and cellular diﬀerentiation processes. Although NCI PID is one of the highest quality pathway databases available, many other pathway databases that represent additional cellular mechanisms could be utilized by PARADIGM to suc-

51 cessfully infer activities and stratify patient populations. In particular, Biocarta and

Reactome [39] oﬀer large databases of interactions from both curation and experimental assays.

This raises an important question about the fundamental information being modeled in PARADIGM. In it’s original form, each pathway was modeled as an independent collection representing a set of cellular entities related to each other in a particular process. Because of this independence, a particular protein or complex with multiple functions may be represented separately in each pathway. This results in the model inferring multiple values for certain proteins that appear in more than one pathway in the database. While modeling these pathways independently allows us to deconvolute diﬀerentially activated processes within cancer, multifunctional proteins are intrinsically tied to all their roles within the cell, and each pathway is not acting in its own compart- ment. This naturally raised the question of whether of not it is possible for PARADIGM to model a more “global” pathway model in the cell, representing thousands of genes and interactions in a singular context that would more accurately represent a snapshot of a cancer cell.

Other groups have successfully built databases designed to combine pathways sources, such as Pathway Commons. Pathways Commons is a collaborative database powered by cPath [14] and run by MSKCC and University of Toronto that consolidates nine pathway databases onto a single cellular network. As of May 2010, they represent 1,400 pathways containing 420,000 interaction and 88,500 cellular entities across

440 organisms. Unfortunately, the approach taken by Pathway Commons is focused

52 Table 4.1: Features and Interactions in SuperPathway (July 2011 version) Concepts Interactions Protein 6906 Protein Activation 7269 Complex 7345 Protein Inhibition 1005 Family 1449 Transcriptional Activation 1963 Abstract 582 Transcriptional Inhibition 386 miRNA 15 Complex Formation 23132 RNA 55 Family Membership 6559 Total 16352 Total 40314

Figure 4.1: Topographical visualization of the SuperPathway. primary on integration, and thus their representation of the cellular network contains only undirected interactions. Because of this limitation, their database is unsuitable for a method that relies on direction and sign of interactions to propagate information.

Furthermore, there are huge computational implications in representing and modeling thousands of interactions and entities simultaneously.

53 Because of the incomplete nature of Pathway Commons, I built a superimposed version of the collection of pathways we have termed the SuperPathway. 1321 Pathways were obtained on July 25th, 2011 in BioPax Level 2 format from NCI-PID, Reactome and BioCarta. Genes, complexes, families and abstract processes (e.g. ”cell cycle” and ”apoptosis”) were uniﬁed by Uniprot ID (genes) or name across the three sources, created a deﬁnitive list of what we call ”pathway concepts.” Across the three sources, this list contained 16,352 total concepts, including 6,906 genes, 7,345 complexes, 1,449 families and 582 processes and 40,314 interactions as outlined in Table 4.1. Links were combined if they consisted of the same parent, child and interaction type, and the overall topology of the network can be see in Figure 4.1.

Across the initial set of pathways, an additional concept type of small molecule was present and represented over 800 unique concepts. The small molecule type is reserved for biochemical products that are byproducts of enzymatic or kinase activities, including molecules such as H2O and ATP/ADP. While maintaining these elements in the pathway might increase our ability to understand the metabolic flux that is occurring in the tumor, these elements have a disproportionately higher degree than other concepts in the pathway. As a result, these concepts tend to connect aspects of the network that would normally be several links away, causing inappropriate propagation of belief to nodes. Although future versions of PARADIGM may attempt to model these reactions using techniques such as flux-balance analysis [23], that work is outside the scope of the original intention of PARADIGM and so these nodes were removed from the final

SuperPathway. This results in a loss of just over 1500 concepts across all the categories

54 due to them only being connected in the pathway through the small molecules that were removed. The net eﬀect resulted in a much more computationally tractable pathway that forms the basis for the studies in this chapter.

In order to efficiently compute activities across this network, a number of optimization techniques were required beyond the removal of small molecules. Of particular interest, exact inference was no longer possible due to a number of cycles and contrac- tions within the network. Instead, loopy belief propagation can be used to compute probabilities with a tolerance of 1e-9 and a maximum of 10,000 iterations. Because this method can be run on cycles and with contradictory information, all interactions in the network were considered and no attempt to resolve them was made. The final Su- perPathway was built by performing a breadth-first traversal starting from the concept with the highest number of interactions, which resulted in capturing a majority of the concepts, effectively capturing the largest possible connected network.

4.1 SuperPathway in Ovarian Cancer

Our previous studies had shown the importance of FOXM1 in the TCGA

Ovarian Cancer cohort, so as a ﬁrst test of the SuperPathway I asked if we could recapture this information given the new overall pathway structure. Using both the

SNP 6.0 copy number data and Agilent 244k mRNA expression data from the 316 high grade serous cystadenocarcinoma samples found in the TCGA publication, I computed pathway IPAs across the SuperPathway. Because of the reduced I/O and number of

55 Table 4.2: Top TCGA Ovarian Concepts from SuperPathway Concept OV Avg OV StdDev NA Avg NA StdDev -Log(TTest) FOXM1 3.7861 1.0077 0.3327 0.9395 209.5321 SKP2 3.2782 0.9818 0.5409 0.8146 169.2913 DSP 3.2318 1.9157 1.0678 2.0729 52.5983 MAPK 3.2180 1.9049 1.0429 2.0814 53.3704 E2F3 3.1120 0.7703 0.5761 0.8801 211.2003 BCAT1 3.0954 0.8581 0.5739 0.8689 184.4879 CKS1B 3.0590 0.7361 0.4401 0.6965 217.3218 CENPF 2.9807 0.7310 0.4242 0.6916 214.0751 NEK2 2.9700 0.7703 0.4466 0.6898 199.4847 MINA 2.7870 1.2480 0.5661 0.8791 102.9586 ... PIK3C2A -1.3275 0.4235 -0.3690 0.6195 137.2294 chromatin re- -1.3311 0.5115 -0.4604 0.6970 93.0525 modeling Alpha v beta -1.3901 0.4417 -0.3998 0.6296 136.1474 3 : Vitronectin complex Alpha v beta -1.3901 0.4417 -0.3998 0.6296 136.1474 5 : Vitronectin complex Alpha v beta -1.3901 0.4417 -0.3998 0.6296 136.1474 8 : Vitronectin complex WNT7B / -1.3950 0.7056 -0.4736 0.7383 66.4470 FZD10 / LRP5 FOXA1 -1.4200 0.8062 -0.2365 0.8047 79.5173 WNT5A / -1.4550 0.7294 -0.4981 1.1383 57.9418 FZD4 / LRP5 WNT5A / -1.4550 0.7294 -0.4981 1.1383 57.9418 ROR2 VTN -1.5199 0.5141 -0.3677 0.7677 134.8298

56 processes required, the overall run time was more eﬃcient at approximately 5 minutes per sample versus the approximately 1 minute per pathway per sample across 1300 pathways. Due to there only being a single pathway, the traditional analysis methods of comparing individual pathways no longer apply in an analogous fashion. Instead, features can be compared with a more traditional Student’s T-Test for the real samples versus the cohort of permuted samples (generated as outlined earlier).

Table 4.2 lists the top features found in the SuperPathway versus the null model, where activity is ranked according to the average IPA value across the real samples. Again, FOXM1 appears as the top entry, conﬁrming that despite large scale struc- tural changes of the underlying pathway data, PARADIGM can identify consistently overactive features across a cohort. SKP2, the second highest activity across all the samples, has recently been indicated as a prognostic factor in ovarian adenocarcinoma[64].

The appearance of WNT signaling towards the bottom of the activity list is interesting, given there are increasing numbers of papers implicating WNT signaling and the diﬀer- ences between normal ovarian and cancerous tissue, despite the lack of mutations[29].

PIK3C2A, a critical subunit of the PI3K Complex has been clearly implicated in ovarian cancer, however as a oncogene[29] - a feature that appears to contradict the appearance towards the bottom of the activity list. However, primary alteration of PI3K is through mutational alterations, a feature that was not provided to PARADIGM in this context. The discrepancy may be explained as a result of parent nodes attempted to downregulate PIK3C2A activity as the cell detects overactive PI3K levels.

Although looking at top concepts across the patient cohort can oﬀer insight into

57 # of Subnets

IPA Cutoff

Figure 4.2: Number of subnets found for a variety of IPA cutoffs. The red line represents actual samples, with the black line representing the permuted samples. The max difference of 156 subnets between these two populations was found at IPA cutoff of 3.3.

58 Table 4.3: Top pathway concepts consistently found in subnets across the OV samples using IPA cutoﬀ 3.3. Only HSP90AA1 has more null samples than real, indicating a likely false positive.

Concept NA Sum OV Sum FOXM1 9 157 SKP2 8 154 MYC/Max 13 150 CKS1B 6 148 NEK2 7 138 CENPF 5 137 BCAT1 2 116 E2F3 6 116 MINA 7 115 DDX18 7 107 PFKM 8 105 PEG10 5 93 BMI1 4 92 CCNB2 4 85 MYC/Max/RPL11 6 82 MTDH 5 81 TAF4B 7 72 HSP90AA1 93 57 PRDX3 7 52 BRCA2 3 49

59 highly over- or under-active pathway processes, this approach doesn’t take advantage of the connected nature of the resulting pathway concepts. One alternative approach is to reconstruct small subnetworks within the larger pathway context, retaining the information regarding interactions and taking advantage of the existence of concepts that span cellular contexts to obtain a better understanding of the interconnected nature of the cell. As an initial approach to a cohort-level subnet analysis, I looked for regions of the network above a certain IPA score continuing to expand as long as connected components met the cutoﬀ. Subnets were only retained if they contained at least 5 concepts. Figure 4.2 shows the number of subnets found for each IPA cutoﬀ possible, both for real samples and permuted samples.

At the optimal cutoﬀ of abs(IPA) >= 3.3, there were 156 more subnets in the real samples versus the permuted samples. Every concept was then ranked across all the samples to compute the most prevalent features within the subnets, the results of which appear in Table 4.3. Again, FOXM1 appears as the most consistent concept across approximately half of the ovarian samples, showing that the high IPA of this concept is supported by data from interactions in at least half of the cases. Reassuringly, many of the same concepts from this subnet analysis are also seen in the previous, single concept analysis. However, some of the concepts that appeared to have high overall pathway activity do not appear, such as DSP and MAPK. These concepts also have lower t-test p-values than the other members in the list, indicating they are less likely to be pathway speciﬁc drivers and instead may be due to technical artifacts from the arrays that measured them. From this study it is clear the SuperPathway provides an

60 exciting new view of oncogenic pathway activities while still recapitulating previously established biology, and presents a framework that allows us to build new analysis methods to understand diﬀerential activities in a global scope.

4.2 SuperPathway in Breast Cancer

4.2.1 Chin-Naderi Cohort

The success of the SuperPathway to identify key pathway activities and subnets across a cohort of samples provides a useful framework for finding key differential activities within a subset of a cohort. To explore this idea, we asked if we could use the activities generated in the SuperPathway to identify key subnetworks that correspond with a particular clinical feature. We re-ran the previously analyzed Chin-Naderi breast cancer cohort on the SuperPathway, and used the clinical annotation of ER+ve and ER-ve, as provided in the publications. Using the non-parametric Wilcoxon rank sum test, we assigned each node in the network a differential score of the negative log10 p-value, signed by the group with the higher mean. We then removed any node with an absolute negative log10 value below 1.3 as a method to trim activities to only those found significant. This resulted in a series of subnets, the largest consisting of 141 nodes and 188 edges, which were visualized using Cytoscape[66].

Figure 4.3 captures the output of the SuperPathway subnet analysis on the

Chin-Naderi breast cancer, showing the regulatory networks of genes that are signiﬁ- cantly diﬀerentially expressed in the ER+ve compared to the ER-ve subgroups of breast

61 glucocorticoid/glucocorticoid Alpha v beta Alpha v beta receptor/glucocorticoid/glucocorticoid 5:Vitronectin PIK3C2A 8:Vitronectin receptor/hSWI/SNF/NF1 complex complex (complex) TAFs/TF2A/TBP/TF2F/TF2B/TF2H/TF2E/RNA (complex) (complex) POL II (complex) Alpha v beta RXRs/RARs/NRIP1/9cRA 3:Vitronectin (complex) bile salts and albumin:bile complex chromatin SCGB1A1 acids salt complex (complex) remodeling complexed (complex) VTN (abstract) with FABP6 IL-3R-alpha/Colony stimulating (complex) DHT/AR/FOXA1 APOB FABP6 CYP2C18 Cyclin factor 2 (complex) PRL D1 receptor albumin:cholate beta/IL-3 bile salt SFTPD NRIP1 (complex) complex ALB POU2F1 (complex) SERPINA1 NKX3-1 FOXA1 TFF1 IL-3/IL-3R-alpha/IL-3R-beta albumin:bile Probasin IL3 (complex) salt and acid (OATP-A) PISD complex SFTPA NCOA3 AP1B1 (complex) albumin:2-lysophosphatidylcholine Spermine DSCAM complex XBP1 E2/ERA (complex) binding IGF-1R-alpha/IGF-1R-alpha/IGF-1R-beta/IGF-1R-beta/IRS-1 protein (complex) (complex) NDUFV3 SOD1 SHH IRS1 heart ErbB2/ErbB3/neuregulin morphogenesis ERBB3 2 (complex) COL18A1 ATP5J Insulin/Insulin (abstract) Ra/Insulin Insulin/Insulin/Insulin Ra/Insulin MINA Ra/Insulin Rb/Insulin Rb DOCK7 ERBB2/ERBB3 MYC/Max/DNA Ra/Insulin (complex) (complex) replication Rb/Insulin Rb ErbB2/ErbB3/neuregulin preinitiation (complex) mammary 1 beta BCL6 MTDH complex MYC/Max/PML4 G-protein gland (complex) (complex) (complex) alpha i/o:GTP morphogenesis ERBB2 ARNT Signal Peptidase INS Complex (abstract) EIF4G1 (complex) ErbB2/ErbB2/HSP90 GAPDH Complex nervous (dimer) (complex) system (complex) MYC/Max DDX18 Proinsulin-Zinc-Calcium development (complex) Core SNARE Complex (abstract) Complex Syntaxin-1A-Unc18-1 (golgi) STEAP3 Complex PERP UBTF (complex) (complex) MSH2 TFRC (complex) PMAIP1 MYC/Max/p14ARF IRF5 (complex) MYC/Max/P-TEFb PCBP4 (complex) ENO1 C13orf15PTGFB TAP1 MIR34A ADFP HK2 Leptin/Leptin DYRK2 p53 CDCA7 NPM1 R (complex) NLRC4 tetramer NDRG1 EGLN3 PRKDC RCHY1 RNF144B RORA4 DNA damage MYC/Max/Cbp/p300 (cleaved)_0-0 PML (abstract) LEP EPHA2 (complex) NT5E PRKDC CCNK HIF1A/ARNT TRAILR4 maspin FURIN SESN1 (complex) CX3CL1 S100A2 EIF2S1 GLUT3 ARID3A EGLN1 CX3C FA complex DDIT4 CXCL12 receptor:fractalkine (complex) EDN2 (complex) PKM2 HK1 BRCA1/BACH1/BARD1/TopBP1 REDD1/14-3-3 ADM (complex) family CDC14B APOBEC3G (complex) BHLHE41 FECH ETA CP receptor/Endothelin-2 APC/C/HCDH1 (complex) (complex)

transcriptional activation transcriptional inhibition Signed log10 Wilcoxon p-value protein activation protein inhibition member of complex -4.0 0.0 4.0

Figure 4.3: Paradigm reveals the regulatory network underlying estrogen receptor positivity in the Chin-Naderi Cohort.

62 cancer. The FoxA1 network is clearly seen as highly diﬀerentially expressed, followed by the Hif-1-alpha transcription factor network, which was also identiﬁed by SPIA and

GSEA as being downregulated in ER+ve tumors. This is concordant with recently published evidence that that Hif-1-alpha represses the transcription of the estrogen receptor gene, ESR1, in breast cancer cell lines and thus could play a role in ER-ve breast cancers[59]. This is particularly important given that Hif-1-alpha is an important regulator of the cellular response to hypoxia and has been shown to be an independent prognostic factor in breast cancer[11].

4.2.2 TCGA Breast Cancer

Using the same approach as in the Chin-Naderi cohort, I wanted to validate these ﬁndings on an independent cohort using a more prognostic clinical feature, in hopes to understand the molecular basis of prognosis in breast cancers. Using 463 samples in the TCGA breast cancer dataset, PARADIGM was run on the SuperPathway utilizing the SNP 6.0 copy number and Agilent 244k gene expression platforms. Resulting features were ranked using the same approach as in the Chin-Naderi cohort, this time using the intrinsic expression subtypes (as previously calculated by the Perou group) of

Luminal A and Basal as the clinical feature of interest. Because of the large number of features found differential by this clinical attribute, the subnet creation was modified to a more stringent approach to ease with visualization. Links were only kept if the parent of the link had at least 10 children concepts in the SuperPathway and the child of the link had at least 5 children concepts in the SuperPathway. Effectively termed ”hubs”,

63 Axin:GSK3:CK1alpha:APC:PP2A complex (complex) PPP2CA TFIIH (complex) RASA1 PP2A (complex) Incision complex with 3'-incised ERBB4 Telomerase damaged DNA WWP1 (complex) (complex) G2/M transition HRAS of mitotic cell cycle (abstract) EIF4G1 HSP90AA1 ERBB2 WNT5A

PFKM NME2 response to Cyclin B/CDC2 stress (abstract) (complex) XPO1 PAK2 NPM1 E2F3

Cyclin B1/CDK1 (complex) CCNB1 EIF4A1 CYCS TSC2

MYC/Max NCOR2 (complex) LAMA4 MYC/Max/TRRAP/TIP60/TIP49A/TIP49B/BAF53 SKP2 (complex) MYC/Max/MIZ-1 (complex) MAP2K1 FOXM1 BIRC5 DKC1 MYC NFATC3

NEK2 HSPA1A E2F1-3/DP FOXM1-2 (complex) ORC1

Chromosomal G1 phase of passenger CENPA mitotic cell complex PLK1 cycle (abstract) (complex) E2F1-3 (family) AURKB Cyclin E (family)

FBXO5 ROCK2 mitosis SHC1 SHC1-2 (abstract) MYLK TPX2

Figure 4.4: Paradigm concept hubs separating Luminal and Basal in the TCGA Breast Cancer Cohort. In order for a link to appear in the subnetwork, the parent node needed at minimum 10 children nodes in the overall network. In addition, the child node needed at minimum 5 children nodes in the overall network. Concepts are colored orange if their activity is higher in Basal samples, and cyan if their activity is higher in Luminal samples.

64 these concepts are key features in the SuperPathway that are responsible for regulating a disproportionally high number of other nodes, given the average number of neighbors of 4.864 and network density of close to 0 across the entire pathway. Figure 4.4 shows these hubs colored by the class with the higher mean activity across the samples (orange for Basal, cyan for Luminal). Features of note include basal enrichment in the proliferative markers FOXM1, MYC/Max, AURKB and the mitosis abstract process.

Overall a much more limited set of concept hubs appear to have increased activity in the luminal samples, consistent with the known aggressiveness of basal tumors compared to luminals[12].

It is then interesting to ask if it is possible to use the same analysis to distinguish between mutations within a single protein that occur in different domains. There is increasing evidence of the importance of PI3K mutations in breast cancer, and recent studies have suggested that the effect of the mutation is highly dependent on the protein domain that is altered[85]. Using the same subnetwork hub analyses, TCGA breast cancer samples were split on kinase versus non-kinase domain mutants. The resulting subnetwork with the mutational visualization can be seen in Figure 4.5. Although less significant than the split done with the intrinsic subtypes, the kinase domain mutants appear to be enriched for proliferation-related activities, while non-kinase domain mutants appear to be enriched in proteins responsible for cellular migration (ACTN1) and adhesion (E-cadherin). Mutation distribution was independent of subtype and TP53 status, indicating these results are unlikely to be confounded by alternative factors. Al- though the clinical implications of these results are unclear, this methodology provides

65 A Homozygous Heterozygous  Missense

Silent

In-Frame

 Out-of- Frame

                





Protein Domains

B FOXM1 NEK2 NFATC3 Kinase G2/M transition of mitotic cell CCNB1 cycle (abstract) BIRC5 SKP2 MAP2K1 MAPK1 Other Cyclin B/CDC2 NME2 N-cadherin/Ca2+/beta (complex) EIF4A1 catenin-gamma MYC catenin/alpha MYC/Max catenin/p120 NPM1 (complex) catenin E2F3 (complex) CDKN1B

PIP5K1C TIRAP EIF4G1 FGFR1-FGF PFKM complex (family) PAK2 DKC1 Cyclin E/CDK2 (complex) HSP90AA1 E-cadherin/Ca2+/beta HMGB1/TLR4/MD2 catenin/alpha (dimer)/MYD88/TIRAP CTNNB1 ACTN1 catenin/p120 Telomerase (complex) (complex) TP73 catenin Viral RNA (complex) dependent RNA polymerase TNFSF10 FAK/Src-Yes-Fyn (complex) CD3D PTPN6 (complex) vRNA RBL1 (Genomic):NP E2F1/DP Complex (complex) TCR/CD3/MHC VEGFR2 (complex) I/CD8/LCK/ZAP-70 TRAIL (trimer) Src-Yes-Fyn-active (dimer)/VEGFA (complex) (complex) (dimer) (complex)

Figure 4.5: Mutations in different protein domains show differential pathway activity. A: Mutational comparison of TCGA breast cancer samples versus mutations seen in the COSMIC mutation database. B: Pathway hubs that show differential activity between kinase and non-kinase mutations, colored and sized by significance.

66 a useful mechanism for understanding even subtle sample diﬀerences that can eﬀect regions of the pathway, allowing researchers to focus on subnetworks that are more easily interpretable.

4.3 Cross-Cancer SuperPathway

Recurrent pathways that are commonly activated in subsets of tissue-speciﬁc cohorts are particularly interesting, as these may indicate shared molecular mechanisms for oncogenesis. If common pathways can be detected and correlated with response to known therapies, it may be an indication that the molecular proﬁle of those tissues is more important than the tissue of origin. Diseases such as breast cancer are good candidates for this type of analysis, as there are well documented molecular heterogeneity and survival-based subtypes describing this heterogeneity [67]. As the TCGA has promised to produce expression and copy-number data for over twenty thousand patients across more than twenty tissues of origin, that sample population represents a powerful dataset to explore the molecular origins of cancer. The consistency of datatypes and platforms that are used for measuring mRNA expression and copy-number alterations (Agilent

244K arrays and Affymetrix SNP 6.0 arrays, respectively) limits the effect of batch effects that can confound cross-cohort analyses. However, these samples have stringent purity and size requirements which may result in other unknown confounding effects.

In order to ask if molecular mechanisms resulting in the observed subtypes in breast cancer can be found in other tissues, I ﬁrst ran PARADIGM across the entire

67 1382 tumor samples: 377 OV 69 KIRC 251 GBM 339 BRCA 117 LUSC 21 LUAD 67 READ 141 COAD

Figure 4.6: PARADIGM activities computed across 1382 TCGA samples from 8 tissues of origin with matched copy-number and median-centered gene expression data.

68 TCGA cohort. Expression data was median centered across all tissues for each gene to assure a common reference when comparing samples, and copy-number data remained normalized to blood normal for each sample. Figure 4.6 visualizes the resulting activity heatmap for close to 1400 samples across the 8 available tissues of origin at the time of analysis. Hierarchical clustering for the samples was performed, resulting in a surprising mixture of tissues across the dataset. Of particular interest, the breast appears to cluster into two distinct groups, each having similarities with a lung subset.

Because of the similarity in features between the breast and lung cancer samples within TCGA, I decided to focus my analysis on comparing breast intrinsic subtypes with the two classes of lung cancer (squamous and adenocarcinoma). Intrinsic subtypes for breast cancer were obtained from the Perou lab using their previously published methods, and were the same as used in the previous breast cancer analysis. Only samples labelled Luminal A were considered as the luminal class, and all basal labelled cancers were used, resulting in 250 samples. The two lung subtypes contained a total of

138 samples. Based on conversations with Dr. Eric Collison of UCSF and other collaborators, I decided to focus this analysis by asking if a machine learner can distinguish subtypes regardless of tissue of origin.

Using SVM-Light[38], I trained a linear SVM using default parameters on the entire set of breast cancer samples, where Basal samples were labelled ”-1” and Luminal samples were labelled ”+1”. I then labelled lung squamous ”-1” and lung adenocarcinoma ”+1” and asked the SVM to classify these samples. Using 63 support vectors, the

SVM classiﬁed 130 out of 138 lung samples correctly, resulting in an accuracy of 94.2%,

69 ROC Classification 1.0 0.8 0.6 TPR 0.4 Train Lung, Predict Breast 0.2 Train Breast, Predict Lung 0.0

0.0 0.2 0.4 0.6 0.8 1.0

FPR

Figure 4.7: Receiver operating characteristics of SVM-Light classiﬁcations done across tissue types. The Cyan ROC indicates a linear SVM that was trained on lung squamous versus adenocarcinoma and given breast samples labelled basal or luminal for classiﬁ- cation (AUC=0.971751). The orange ROC indicates the performance of a linear SVM that was trained on breast basal versus luminal and tested on lung squamous versus adenocarcinoma (AUC=0.894994).

70 precision of 84.21% and recall of 76.19%. A plot of the resulting ROC can be seen in

Figure 4.7 and the resulting AUC was 0.894994.

I then reversed the experiment, asking if breast samples can be distinguished by training on lung. I passed all 138 lung samples to a linear SVM light trainer with default parameters, and asked it to classiﬁed the 250 breast samples. Using 53 support vectors, the SVM was only capable of classifying 173 of the 250 samples correctly, resulting in an accuracy of 69.2%, however with a precision of 100% and recall of 56.5%. A plot of the resulting ROC can also be seen in Figure 4.7 with a surprising AUC was 0.971751. The large AUC with corresponding low accuracy likely indicates a non-optimal cut point was chosen by the SVM, something that could likely be improved with additional training or parameters.

Given the classiﬁer was capable of achieving high AUCs in both classiﬁcation tasks, it is interesting to explore the features that were used by the SVM to help understand what might best distinguish the two subtypes. Figure 4.8 is a subnetwork analysis using SVM-weights assigned by training on the lung subtypes (the same weights that were used to classify on the breast samples). A concept was required to have a weight greater than 2 standard deviations from the mean of all weights in order to appear in the resulting network, in addition to the hub restrictions. A majority of concepts pulled out through this analysis have positive weights, as expected, with the most discriminative features being P53 (arguably the most famous tumor associated protein), HGF, VDR, SERPINE1 and HIF1alpha. The common basal / luminal markers of FOXA1, MYC/Max, and DNA Damage also appear. This analysis clearly shows that

71 CXCR4 PFKL ITGB2 alphaL/beta2 Integrin (complex) SHH VTN HIF1A/ARNT GSK3A ESR1 E2F3 FURIN (complex) LEP PROC AKT1 CXCL12 FOXA1 E2/ERA (complex) NBN SERPINE1 FOXA2 MYC/Max NPM1 (complex) JUN/FOS PFKM HGF Antigen/IgE/Fc (complex) APC epsilon PLK3 R1/LYN/SYK DKC1 (complex) VDR IL23/IL23R/JAK2/TYK2 CDKN2A Fra1/JUN (complex) EPHA2 p53 (tetramer) (complex)CXCL9 (complex) HIF2A/ARNT STAT4 (dimer) (complex) KAT5 SFN NFATC2 (complex) IL1A DNA damage IL12/IL12R/TYK2/JAK2 EFNA1 (abstract) SPP1 dNp63a (complex) (tetramer) VEGFA-2 TNFRSF10B STAT1 (dimer) (complex) NCOR2 BCL6 (complex) VEGFA-6 VEGFA TNFRSF10A MAPK14

ERK1-2-active VEGFA (dimer) (complex) JUN VEGFA-4

Figure 4.8: Paradigm concept hubs found to have high SVM weights when trained on Lung Adenocarcinoma versus Lung Squamous. In order for a link to appear in the subnetwork, the parent node needed at minimum 10 children nodes in the overall network. In addition, the child node needed at minimum 5 children nodes in the overall network. Concepts are colored orange if their SVM weight is positive, meaning they contribute to the resulting classiﬁcation, and cyan if they have a negative SVM weight, meaning they oﬀer no discriminative power.

72 there are corresponding molecular subtypes that can help explain origin of disease better than tissue of origin. As molecular classiﬁcation becomes more prevalent in the clinic, it will be increasingly important to understand treatment from the aspect of molecular features instead of tissue of origin, and PARADIGM provides a powerful framework for that type of analysis.

73 Chapter 5

Pathway Analysis of Drug Eﬀects

Detecting commonly perturbed pathways either across an entire cohort or that can successfully stratify patients is critical to understanding the underlying misregulation within a cancer type. However, we ultimately need to understand how these misregulated pathways can be ﬁxed in order to provide clinically actionable information to healthcare providers. By examining the pathway activities in conjunction with drug responses in a model system such as cell lines, it is possible to identify key pathway activities that correspond with treatment response. These key pathways and activities can be used for *in silico* knockout experiments to ﬁnd combinational therapies that will increase drug response within the subset of cell lines that did not respond.

PARADIGM can provide a unique insight into the possible functional implications of pathway alterations, and may provide the information necessary to model these combination therapies.

74 5.1 SuperPathway on Breast Cell Lines

In order to asses the ability of pathway activities to provide a novel mechanism to study drug effects, it is critical to show both that pathway activities accurately capture the molecular signatures of samples, and that these molecular signatures can also be mirrored in a model system allowing interrogation with therapeutic agents. Cell line studies done in collaboration with Dr. Joe Gray, Laura Heiser, Ted Goldstein, Sam Ng, and Dr. Josh Stuart allow us to establish the ability of PARADIGM to accurate model the molecular profile of breast cancer cells. Using combined copy-number profiles and exon expression measures, PARADIGM IPAs were calculated for 46 breast cancer cell lines, including lines from each intrinsic subtype. Using the PARADIGM IPAs, cell lines were clustered together with TCGA tumor samples to determine if cell lines were similar to tumor samples of the same subtype. Well-studied areas of the SuperPathway contain genes with many interactions (hubs) and large signaling chains of many intermediate complexes and abstract processes for which no direct data is available. In order to prevent noise introduced from the integration of multiple sources of arrays, pathway concepts with highly correlated vectors (Pearson correlation coefficient > 0.9) across both the cell line and tumor samples were unified into a single vector prior to clustering.

This uniﬁcation resulted in 2351 non-redundant vectors from the original 8768 pathway concepts.

Samples were clustered using the resulting set of non-redundant concepts. The matrix of inferred pathway activities for the 46 breast cancer cell lines and 183 TCGA

75 Figure S4

Figure 5.1: Clustering of TCGA samples with breast cancer cell lines.

76 Figure S5

A TCGA-Cell line subtype correlations 8 0 . 0

Same Subtype 6 y 0 t . i

s Different Subtype 0 n e D

n 4 o i 0 t . a l 0 e r r o C 2 0 . 0 0 0 . 0 -10 0 10 20 30

Pearson Correlation T-statistic

B Cell line subtype correlations 8 0 . 0 6 0 . y t i 0 s n e D

n 4 o 0 i . t a 0 l e r r o C 2 0 . 0 0 0 . 0 -10 0 10 20 30 40 50

Pearson Correlation T-statistic

C TCGA subtype correlations 8 0 . 0 6 y 0 t i . s 0 n e D

n o 4 i t 0 . a l 0 e r r o C 2 0 . 0 0 0 . 0 -10 0 10 20 30 40 Pearson Correlation T-statistic

Figure 5.2: Uncentered Pearson correlation of pathway concepts show samples and cell lines from the same subtype are more correlated, regardless of the origin of the sample.

77 tumor samples was clustered using complete linkage hierarchical agglomerative clustering implemented in the Eisen Cluster software package version 3.0(2). Uncentered

Pearson correlation was used as the metric for the pathway concepts and Euclidean distance was used for sample metric (Figure 5.1).

To quantify the degree to which cell lines clustered with tumor samples of the same subtype, we compared two distributions of t-statistics derived from Pearson correlations (Figure 5.2). Let Cs be the set of cell lines of subtype s. Similarly, let Ts be the set of TCGA tumor samples of subtype s. For example, Cbasal and Tbasal are the set of all basal cell lines and basal tumor samples respectively. The ﬁrst distribution was made up of t-statistics derived from the Pearson correlations between every possible pair containing a cell line and tumor sample of the same subtype; i.e. for all subtypes s, every pairwise correlation t- statistic was computed between a pair (c, t) such that c ∈

Cs and t ∈ Ts. The second distribution was made of correlation t-statistics between cell lines of diﬀerent subtypes; i.e. computed over pairs (c, c0) such that c ∈ Cs and c0 ∈ Cs0

and s 6= s0. We performed a Kolmogorov-Smirnov test to compare the distributions. We repeated this analysis using samples from the same source (cell line or tumor) to verify that cells of the same subtype have overall pathway activities that are more similar than cells of diﬀerent subtypes. As above, the ﬁrst distribution was made up of t-statistics between pairs of samples of the same subtype and the same origin (cell line or tumor).

The second distribution was made of correlation t-statistics between samples of diﬀerent subtypes again from the same origin.

Focusing on the collection of breast cancer cell lines, it was possible to detec-

78 Integration of Copy-Number and Transcription Measurements Iden- Basal Claudin-low fi A B ti es Biologically Relevant SuperPathways. We used the network DUSP6 CAPN2 analysis tool PARADIGM (17) to identify pathway-based RUNX2 POLR3D fi EIF4A1 BCAT1 mechanisms that underlie subtype-speci c responses. PARA- ZFP36 DIGM uses copy number and transcription data to calculate TP53 DUSP1 MTA1 E2F3 integrated pathway levels (IPLs) for 1,441 curated signal trans- ERK1-2 duction, transcriptional, and metabolic pathways (18). We MYC/MAX MSK1-2 DDX18 compared IPLs for cell lines and primary breast tumors using JUN CDCA7 data from The Cancer Genome Atlas (TCGA) project (http:// SMAD3 MSH2 cancergenome.nih.gov) and found a general concordance be- MAP2K1 SKP2 BIRC5 tween transcriptional subtype and pathway activity across the two PTGS2 cohorts (SI Appendix, Figs. S4 and S5 and Dataset S5). This CDK4 fi subtype-speci c pathway activity likely explains much of the ob- FOXM1 FOXM1 served subtype specific responses. Mechanistic interpretation of IPLs for 1,441 pathways is Luminal ERBB2AMP complicated by the overlapping elements in many of the curated C D MAPK9 pathways. We overcame this complication by merging the 1,441 SCGB1A1 SHH CAMK4 DLK1 PISD curated pathways into a single “SuperPathway” in which re- RANBP3 TCF1E/CTNNB1 dundant pathway elements are eliminated. This approach en- SOD1 LEF1 abled us to identify SuperPathway subnets that differed in FOXA2 BDH1 activity between transcriptional subtypes (SI Appendix, Fig. S6). FOXA1 MYC CTNNB1 APOB TCF4E/CTNNB1 As an example, comparison of subnet activities between basal GCG fi CTNNB1/PITX2 cell lines and all others in the collection identi ed a network TFF1 VTN Positive regulation of VCAN comprised of 1,104 nodes (e.g., proteins, protein complexes, or XBP1 CCND2 Wnt signaling cellular processes) connected by 1,242 edges (e.g., protein– KLK3 protein interactions) between these elements. Several subnetworks were up- or down-regulated in the SuperPathway networks. Fig. Fig. 2. Cell-line subtypes have unique SuperPathway network features. In 2A, for example, shows up-regulation of an ERK1/2 subnetworkFigureall 5.3: panels, Cell-line each subtypes node represents have unique a pathway SuperPathway“concept network” corresponding features. In to all pan- controlling cell cycle, adhesion, invasion, and macrophage acti-els, eacha protein node represents (circle), a a multimeric pathway concept complex corresponding (hexagon), or to an a proteinabstract (circle), cellular a mul- vation (19). The forkhead box M1 and DNA-damage subnet-timericprocess complex (square). (hexagon), Node sizes or an are abstract drawn in cellular proportion process to the (square). DA score; Node larger sizes are works also were up-regulated markedly in the basal cell lines.drawnnodes in proportion correspond to the to concepts DA score; more larger correlated nodes correspond with a particular to concepts subtype more corre- The claudin-low network showed up-regulation of many of thelatedthan with with a particular all other subtype subtypes. than Color with indicates all other whether subtypes. the Color concept indicates is corre- whether same subnetworks, as well as up-regulation of a MYC/Myc-as-the conceptlated positively is correlated (red) positively or negatively (red) (blue) or negatively with the subtype (blue) with of interest. the subtype Lines of in- sociated factor X (MAX) subnetwork (Fig. 2B) associated withterest.represent Lines represent interactions, interactions, including including protein– proteinproteinprotein interactions interactions (dashed (dashed lines) lines) metabolism, proliferation, angiogenesis, and oncogenesis (20).and transcriptionaland transcriptional interactions interactions (solid (solid lines). lines). Interactions Interactions are are included included if ifthey they connect Comparison of the luminal cell lines with all others showedconceptsconnect whose concepts absolute whose level absoluteof DA is higherlevel of than DA isthe higher mean than absolute the meanlevel. Labelsab- on down-regulation of an activating transcription factor 2 network,somesolute nodes level.are omitted Labels for on clarity. some nodes (A) An are ERK1/2 omitted subnet for clarity. preferentially (A)AnERK1/2 activated in which inhibits tumorigenicity in melanoma (21), as well as up-basalsubnet breast cancerpreferentially cell lines. activated (B) A MYC/ in basal MAX breast network cancer activated cell lines. in (B) claudin-low A MYC/ cell regulation of forkhead box A1 (FOXA1)/forkhead box A2lines.MAX (C) Anetwork FOXA1/FOXA2 activated in net- claudin-low work up-regulated cell lines.( inC)AFOXA1/FOXA2net- the luminal subtype. (D) A (FOXA2) networks that control transcription of estrogen re-CTNNB1work subnet up-regulated down-regulated in the luminal in the ERBB2AMP subtype. (D)ACTNNB1subnetdown- subtype. regulated in the ERBB2AMP subtype. APOB, apolipoprotein B [including Ag(x) ceptor-regulated genes (Fig. 2C) and are associated with good- AMP antigen]; BCAT1, branched chain amino-acid transaminase 1, cytosolic; prognosis luminal breast cancers (22, 23). ERBB2 subnet- BDH1, 3-hydroxybutyrate dehydrogenase, type 1; BIRC5, baculoviral IAP re- works were similar to those for luminal cells; this similarity is not 79 AMP fi peat containing 5; CAMK4, calcium/calmodulin-dependent protein kinase IV; surprising because most ERBB2 cells also can be classi ed as CAPN2, calpain 2, (m/II) large subunit; CCND2, cyclin D2; CDCA7, cell division luminal. However, Fig. 2D shows down-regulation of a β-catenin AMP cycle associated 7; DDX18, DEAD (Asp-Glu-Ala-Asp) box polypeptide 18; (CTNNB1) network in ERBB2 cell lines; up-regulation of DLK1, delta-like 1 homolog (Drosophila); DUSP1, dual specificity phospha- this network has been implicated in tumorigenesis and is asso- tase 1; DUSP6, dual specificity phosphatase 6; E2F3, E2F transcription factor ciated with poor prognosis (24, 25). 3; EIF4A1, eukaryotic translation initiation factor 4A1; ERK1-2, mitogen- SuperPathway analysis of differential drug response among activated protein kinase 3-1; GCG, glucagon; JUN, jun proto-oncogene; the cell lines also revealed subnet activities that provide in- KLK3, kallikrein-related peptidase 3; LEF1, lymphoid enhancer-binding fac- formation about mechanisms of response. For example, basal tor 1; MAP2K1, mitogen-activated protein kinase kinase 1; MAPK9, mitogen- cell line sensitivity to the DNA-damaging agent cisplatin was activated protein kinase 9; MSH2, mutS homolog 2, colon cancer, non- associated with up-regulation of a DNA-damage response sub- polyposis type 1 (E. coli); MSK1-2, ribosomal protein S6 kinase, 90kDa, network that includes ataxia telangiectasia mutated and check- polypeptide 5; MTA1, metastasis associated 1; PISD, phosphatidylserine point kinase 1 homolog, key genes associated with response to decarboxylase; PITX2, paired-like homeodomain 2; POLR3D, polymerase cisplatin (Fig. 3A) (26). Likewise, ERBB2AMP cell line sensitivity (RNA) III (DNA directed) polypeptide D, 44kDa; PTGS2, prostaglandin-endo- to geldanamycin [an inhibitor of heat-shock protein 90 (HSP90)] peroxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase); was associated with up-regulation of an ERBB2-HSP90 sub- RANBP3, RAN binding protein 3; RUNX2, runt-related transcription factor 2; network (Fig. 3B). This observation is consistent with the known SCGB1A1, secretoglobin, family 1A, member 1 (uteroglobin); SKP2, S-phase ERBB2 degradation induced by geldanamycin binding (27, 28). kinase-associated protein 2 (p45); SMAD3, SMAD family member 3; SOD1, superoxide dismutase 1, soluble; SSH, slingshot homolog; TCF1E, HNF ho- Discussion meobox A; TCF4E, transcription factor 4; TFF1, trefoil factor 1; TP53, tumor protein p53; VCAN, versican; VTN, vitronectin; XBP1, X-box binding protein Efforts to personalize breast cancer treatment are aimed at 1; ZFP36, zinc finger protein 36, C3H type, homolog (mouse). identifying subsets of patients most likely to benefit from treatment and avoiding treatment-associated morbidity and mortality in patients who are unlikely to respond. We have supported this mation about the observed subtype-specific responses. Compar- effort by testing 77 therapeutic compounds in an in vitro cell-line ative analysis of pathways between cell lines and tumors shows panel and have shown that approximately one third are prefer- that the majority of subtype-specific subnetworks are conserved entially effective in one or more transcriptional or genomic between cell lines and tumors. This similarity is important, given breast cancer subtypes. We also have shown that integration of the very different environments between a cell line growing in the transcriptional and genomic data for the cell lines reveals a standard 2D culture and a primary or metastatic tumor, and SuperPathway subnetworks that provide mechanistic infor- supports the clinical relevance of the in vitro studies.

4of6 | www.pnas.org/cgi/doi/10.1073/pnas.1018854108 Heiser et al. tion subnets that diﬀered in activity between transcriptional subtypes. As an example, comparison of subnet activities between basal cell lines and all others in the collection identiﬁed a network comprised of 965 nodes connected by 941 edges, where nodes represent proteins, protein complexes, or cellular processes and edges represent interactions, such as protein phosphorylation, between these elements, including several subnetworks that were up- or down-regulated. Figure 5.3A, for example, shows up-regulation of the

MYC/MAX subnetwork associated with metabolism, proliferation, angiogenesis, and oncogenesis[58]; and up-regulation of the ERK1/2 subnetwork controlling cell cycle, adhesion, invasion, and macrophage activation[37]. The FOXM1 and DNA damage subnetworks also were markedly up-regulated in the basal cell lines. The claudin-low network showed up-regulation of many of the same subnetworks as well as up-regulation of the beta-catenin (CTNNB1) network in Figure 5.3B, a network already implicated in tumorigenesis and associated with poor prognosis[9, 32]. Comparison of the luminal cell lines with all others showed down-regulation of an ATF2 network, which inhibits tumorigenicity in melanoma[5], and up-regulation of FOXA1/FOXA2 networks that control transcription of ER-regulated genes (Figure 5.3C) and are implicated in good prognosis luminal breast cancers[50, 46]. ERBB2AMP subnetworks were similar to those for luminal cells, which is not surprising because most ERBB2AMP cells also are classiﬁed as luminal. However, Figure 5.3D also shows down-regulation centered on RPS6KBP1 in ERBB2AMP cell lines.

SuperPathway analysis of diﬀerential drug response among the cell lines also revealed subnet activities that provide information about mechanisms of response. For

80 We anticipate that the power of this in vitro systems approach

A SPECIAL FEATURE TP63 CASP6 will increase as additional molecular features, including mutations, methylation, and alternative splicing, are included in the Cisplatin FAS analysis. In addition, expanding the cell-line panel will increase (DNA damage) BCL6 6 the power to identify low-frequency molecular patterns and to DNA DAMAGE develop robust predictive models. Most important, however, is CASP1 CHEK2 iterative reﬁnement of the in vitro assay system based on lessons 5 learned by comparing in vitro predictions with clinical reality. PLK3 TP53 FEATURE

-log10(GI50) Methods 4 Unless stated otherwise, analyses were performed in R (http://www.r-project.org). UV RADIATION ATM BREAST CANCER SPECIAL Not Basal Basal CHEK1 Cell Proliferation Assay and Growth Rate. The efficacy of 77 compounds in ∼50 breast cancer cell lines was assessed as described previously (29). Briefly, cells TAO family GADD45A were treated in triplicate for 72 h with nine doses of each compound in 1:5 serial dilution. Cell proliferation was estimated using the Cell Titer-Glo assay (Promega). DT was estimated from the ratio of 72 h to 0 h for untreated ERBB2/ERBB4/ B wells. A Gompertz curve was fit to the dose-response data using a nonlinear Geldanamycin EREG ERBB2/ least squares procedure with the following parameters: upper and lower HSP90 EREG (HSP90) asymptotes, slope, and inflection point. The fitted curve was transformed DOCK7 into a GI curve as described previously (30, 31). 8.5 ERBB2 Several quantitative response metrics were estimated including the GI50, 8.0 the concentration needed to inhibit proliferation completely (TGI), and the ERBB2/ERBB3/ concentration needed to reduce the population by 50% (LC50). When the 7.5 NRG1B ERBB2/ ERBB3 ERBB2/ underlying proliferation data were of high quality but the end point re- -log10(GI50) ERBB3/NRG2 7.0 sponse (GI50, TGI, or LC50) was not reached, the values were set to the highest concentration tested. The drug-response data were filtered so that Not ERBB2AMP MAMMARY ERBB3 (i) the median SD across the nine triplicate-treated data points was <0.20; ERBB2AMP GLAND MORPHOGENESIS (ii) the DT was within 2 SD of the median DT for each cell line; (iii) the slope of the fitted curve was >0.25; (iv) we identified assays with no response by requiring inhibition at the maximum concentration <50%. Approximately SYSTEMS BIOLOGY Figure 5.4:Fig. Pathway3. Pathway diagrams diagrams can can be be used used to to predict predict response response to to therapies. therapies. (A) (A)80% (Left) of all assays passed all filtering requirements. Basal breast(Left) cancer Basal breast cell lines cancer respond cell lines preferentially respond preferentially to the DNA-dam- to the DNA-damaging agent cis- aging agent cisplatin. Each boxplot represents the distribution of drug re- platin. Each boxplot represents the distribution of drug response data for basal (right)Compound and Cell-line Screening. Compounds were excluded from analysis if sponse data for basal (right) and non-basal (left) cell lines. (Right) Basal cell and non-basal (left) cell lines. (Right) Basal cell lines show enhanced pathway levels(i) more in than 40% of GI50 values were missing across the set of cell lines and/ lines show enhanced pathway levels in a subnetwork associated with the or (ii) the GI50 was not >1.5 times the median GI50 for a given drug (mGI50)or a subnetworkDNA-damage associated response, with providingthe DNA-damage a possible response, mechanism providing by which a cisplatin possible mecha- <0.5 mGI50 for least three cell lines. Nonmalignant cell lines were not in- nism byacts which in these cisplatin cell lines. acts (B in)(Left these)ERBB2 cellAMP lines.cell lines(B) (Left)are sensitive ERBB2AMP to the HSP90 cell lines are cluded in the molecular-response assessments. sensitiveinhibitor to the HSP90 geldanamycin. inhibitor (Right geldanamycin.)TheERBB2–HSP90 (Right) network The ERBB2HSP90 is up-regulated network is up-regulatedin ERBBP2 in ERBBP2AMPAMP cell lines. Conventions cell lines. Conventions are as in Fig. are 2. BCL6, as in B-cellFigure CLL/lym- 5.3. SNP Array and DNA Copy-Number Analysis. Genome copy number was phoma 6; CASP1, caspase 1, apoptosis-related cysteine peptidase (interleukin assessed using the Affymetrix Genome-Wide Human SNP Array 6.0 analysis 1, beta, convertase); CASP6, caspase 6, apoptosis-related cysteine peptidase; platform. Arrays were analyzed using aroma.affymetrix (http://aroma-pro- CHEK2, CHK2 checkpoint homolog (S. pombe); DOCK7, dedicator of cytoki- ject.org) (32), data were normalized as described (33), and DNA copy-num- nesis 7; ERBB3, v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 ber ratios at each locus were estimated relative to a set of 20 normal sample (avian); ERBB4, v-erb-a erythroblastic leukemia viral oncogene homolog 4 arrays. Data were segmented using circular binary segmentation from the (avian); EREG, epiregulin; FAS, Fas (TNF81 receptor superfamily, member 6); bioconductor package DNAcopy (34). Significant DNA copy-number changes GADD45A, growth arrest and DNA-damage-inducible, alpha; NRG1B, neu- were analyzed using MATLAB-based Genomic Identification of Significant regulin 1; NRG2, neuregulin 2; PLK3, polo-like kinase 3; TP53, tumor protein Targets in Cancer (GISTIC) (35). Nonmalignant cell lines were not included p53; TP63, tumor protein p63. in the GISTIC analysis. GISTIC scores for one member of each isogenic cell- line pair were used to infer genomic changes in the other (Dataset S1). fi Raw data are available in The European Genotype Archive (accession no. The potential clinical utility of these ndings is supported by EGAS00000000059). concordance of in vitro-derived molecular predictors of response to therapeutic compounds and clinical results. For example, Exon Array Analysis. Gene expression was assessed using the Affymetrix ERBB2-amplified cell lines are preferentially sensitive to GeneChip Human Gene 1.0 ST exon array platform. Gene-level summaries of ERBB2-targeted agents, and basal subtype cell lines are pref- expression were computed using aroma.affymetrix (33) with quantile nor- erentially sensitive to platinum salts, as observed clinically. That malization and a log-additive probe-level model based on the HuEx-1_0-st- said, additional work remains before the signatures reported in v2,DCCg,Spring2008 chip definition file (CDF). The raw data are available in this study can be used to select patients for clinical trials. Such ArrayExpress (E-MTAB-181). future work would include the development of robust and reli- Consensus Clustering. Cell-line subtypes were identified using hierarchical able molecular assays that can be applied to clinical samples, consensus clustering (36) of genes with an SD >1.0 on the log2 scale across all establishment of predictive algorithms with decision-making cell lines. Consensus was computed using 500 samplings of the cell lines, 80% thresholds optimized for clinical use, and validation of predictive of the cell lines per sample, agglomerative hierarchical clustering, Euclidean power in multiple independent studies. To initiate this process, distance metric, and average linkage. we suggest that the response-associated signatures identified in this study be developed into standardized assays that can be Associations of Subtypes and Response to Therapeutic Agents. Associations assessed for clinical predictive power in early-stage clinical trials between drug response and subtype were assessed for (i) luminal vs. basal vs. claudin-low; (ii) luminal vs. basal and claudin-low; and (iii)ERBB2AMP vs. and used to design trials that are properly powered to detect the AMP non–ERBB2 .Differencesbetween−log10(GI50) of the groups were com- responses in the clinical subsets predicted by the in vitro studies. pared with a nonparametric Kruskall–Wallis ANOVA. The P values for the Assays that show positive predictive power in early clinical trials three sets of tests were combined, and the Benjamini–Hochberg false dis- then can be “locked down” and tested for predictive power in covery rate (q-value) was used to correct for multiple testing. For the three- follow-on clinical trials. sample test, the most sensitive group was identified by performing a post

Heiser et al. PNAS Early Edition | 5of6 example, basal cell line sensitivity to the DNA damaging agent, cisplatin, was associated with up-regulation of a DNA-damage response subnetwork that includes ATM, CHEK1 and BRCA1, key genes associated with response to cisplatin[65] (Figure 5.4A). Likewise,

ERBB2AMP cell line sensitivity to geldanamycin (HSP90 inhibitor) was associated with up-regulation of an ERBB2-HSP90 subnetwork (Figure 5.4B). This is consistent with the known ERBB2 degradation induced by geldanamycin binding[10, 7].

The potential clinical utility of these findings is supported by concordance of in vitro-derived molecular predictors of response to therapeutic compounds and clinical results. For example, ERBB2-amplified cell lines are preferentially sensitive to ERBB2- targeted agents, and basal subtype cell lines are preferentially sensitive to platinum salts, as observed clinically. That said, additional work remains before the signatures reported in this study can be used to select patients for clinical trials. Such future work would include the development of robust and reliable molecular assays that can be applied to clinical samples, establishment of predictive algorithms with decision-making thresholds optimized for clinical use, and validation of predictive power in multiple independent studies. To initiate this process, we suggest that the response-associated signatures identified in this study be developed into standardized assays that can be assessed for clinical predictive power in early-stage clinical trials and used to design trials that are properly powered to detect the responses in the clinical subsets predicted by the in vitro studies. Assays that show positive predictive power in early clinical trials then can be locked down and tested for predictive power in follow-on clinical trials.

82 5.2 Culling Targets Using Machine Learning

Although the SuperPathway and subnet analysis allow us to identify potential networks that might correspond with response or sensitively, attempting to ﬁnd the optimal location for combinatorial interventions requires a more automated approach.

Supervised learning methods such as support vector machines (SVM) or non-negative matrix factorization (NMF) can be designed to identify these key activities, and would intrinsically provide a mechanism for assessing how well these activities map to response.

By attempting to predict response to each therapy in a cross validation setting, these machine learners will attempt to identify activities that can best separate the responders and non-responders, learning weights for each feature given. By examining the weights each machine learner has assigned, and evaluating each learning in a cross-validation setting using a receiver operating characteristic (ROC) curve and calculating an area under the curve (AUC), it is possible to assign a score both to how well the learner is modeling the data corresponding to drug response, and which features were optimal for separation beween the classes. The top features within the top classiﬁers are ones that oﬀer a clear separation between the two classes (responders and non-responders), and can be passed to PARADIGM in a series of knockout simulations.

5.3 Simulating Knockdowns in PARADIGM

Figure 5.5 shows the approach taken to simulate siRNA knockdowns within

PARADIGM. In brief, once EM has converged a ﬁnal run of paradigm is performed ﬁrst

83 Transcriptional Translational Intracellular and A Regulation Regulation, Extracellular Protein Degradation Signaling

GeneCopy Expression Protein Protein Number State Level Activity

Variable Array CGH, Transcriptomics SNP chips Factor - interaction term

Transcriptional Translational Intracellular and B Regulation Regulation, Extracellular Protein Degradation Signaling

GeneCopy Expression Protein Protein Number State Level Activity

Variable Clamped to -1 State Array CGH, Transcriptomics SNP chips

Figure 5.5: Schematic indicating how knockdown is performed within the factor graph central dogma. Part A represents the standard factor graph dogma model provided by PARADIGM. Part B represents the modiﬁed dogma used for the proteins that are being knocked out, with an example of how signal might propagate post KO. Of note is the disconnection of considering the mRNA evidence node and transcriptional regulation, with the resulting signal propagation to the protein node and any interactions that may occur with the knocked down node.

84 without the knockdown. The factor graph is then modified to clamp the mRNA node internal to the dogma structure for the specified protein node. This causes a fluctuation of probabilities in the vicinity of the node, and belief propagation is performed one final time. Although this falls short of a true causative model, the corresponding nodes in the network are sufficiently perturbed to assess the initial viability of this approach.

In order to assess if these changes correspond with actual siRNA knockdowns down in model systems, gene expression data from House et al [35] was obtained from the authors containing GFP-controls and post-knockdown expression for colon cancer cell lines treated with siRNAs covering 29 genes. The expression data was normalized for

PARADIGM as previously described, and all analysis used the SuperPathway model generated from BioPAX Level 3 pathways obtained on Feb 27, 2012 from NCI-PID,

BioCarta and Reactome pathway databases. Inference and EM learning were performed consistent with the previously described methods using the SuperPathway, and learning parameters converged in 5 iterations. GFP-control cell line paradigm values were re- computed using a simulated knockdown model for each of the ﬁve tier 3 gene knockdowns that had pathway context available. Samples were only kept if they had a .75 tau or larger correlation to the medoid of the other knockdowns for that same gene in the original expression data. The simulated knockdown model was computed by clamping the mRNA to the -1 state within the central dogma and regenerating inference and IPL values.

Integrated Pathway Levels (IPLs) were computed for each element in the pathway for GFP-control (GFPc), GFP-simulated-siRNA (GFPssi) and true-siRNA (siR-

85 Table 5.1: Correlations between simulated knockdowns and true siRNA knockdowns for a series of genes in a colon cancer cell line.

GFP KO KO Eﬀectiveness % Real Null Diﬀ GFP scrCvsHR GNAI3 06 1vsHR 97.61% 0.5544 0.5526 0.0018 GFP scrAvsHR GNAI3 06 1vsHR 95.02% 0.5475 0.5472 0.0003 GFP scrCvsHR GNAI3 05 2vsHR 97.13% 0.4330 0.4288 0.0043 GFP scrCvsHR UBE2L6 05 1vsHR 78.23% 0.4258 0.4258 0.0000 GFP scrAvsHR SEC24D 10 2vsHR 78.58% 0.4188 0.4188 0.0000 GFP scrCvsHR SEC24D 10 2vsHR 79.30% 0.4103 0.4103 0.0000 GFP scrAvsHR GNAI3 05 2vsHR 95.41% 0.4084 0.4058 0.0026 GFP scrAvsHR UBE2L6 05 1vsHR 77.94% 0.4058 0.4058 0.0000 GFP scrCvsHR SEC24D 12 1vsHR 77.94% 0.3744 0.3744 0.0000 GFP scrAvsHR GNAI3 06 2vsHR 90.69% 0.3744 0.3740 0.0003 GFP scrCvsHR GNAI3 06 2vsHR 93.53% 0.3703 0.3693 0.0009 GFP scrAvsHR SEC24D 12 1vsHR 76.97% 0.3698 0.3698 0.0000 GFP scrAvsHR EIF2AK2 11 1vsHR 94.73% 0.3611 0.3601 0.0010 GFP scrCvsHR SEC24D 12 2vsHR 74.53% 0.3425 0.3425 0.0000 GFP scrCvsHR EIF2AK2 11 1vsHR 93.35% 0.3242 0.3236 0.0005 GFP scrCvsHR SEC24D 10 1vsHR 73.88% 0.3104 0.3104 0.0000 GFP scrAvsHR SEC24D 12 2vsHR 75.51% 0.3093 0.3093 0.0000 GFP scrAvsHR SEC24D 10 1vsHR 75.31% 0.2721 0.2721 0.0000 GFP scrCvsHR UBE2L6 05 2vsHR 73.19% 0.1488 0.1488 0.0000 GFP scrAvsHR UBE2L6 06 1vsHR 73.43% 0.1361 0.1361 0.0000 GFP scrCvsHR SCN5A AvsHR 75.75% 0.1299 0.1299 0.0000 GFP scrAvsHR UBE2L6 05 2vsHR 73.28% 0.1291 0.1291 0.0000 GFP scrAvsHR SCN5A AvsHR 74.74% 0.1246 0.1246 0.0000 GFP scrCvsHR SCN5A BvsHR 76.75% 0.1128 0.1128 0.0000 GFP scrCvsHR UBE2L6 06 1vsHR 75.02% 0.1062 0.1062 0.0000 GFP scrAvsHR SCN5A BvsHR 75.92% 0.1051 0.1051 0.0000 GFP scrAvsHR EIF2AK2 11 2vsHR 92.97% 0.0823 0.0814 0.0009 GFP scrCvsHR EIF2AK2 11 2vsHR 91.15% 0.0807 0.0802 0.0005 GFP scrCvsHR SCN5A CvsHR 74.02% 0.0523 0.0523 0.0000 GFP scrAvsHR SCN5A CvsHR 74.79% 0.0365 0.0365 0.0000 GFP scrCvsHR UBE2L6 06 2vsHR 73.46% -0.0116 -0.0116 0.0000 GFP scrAvsHR UBE2L6 06 2vsHR 75.31% -0.0433 -0.0433 0.0000

86 VAV1 Rho/Rac CDKN2A SMAD2-3/SMAD4/SP1 G protein-GTP MYC (rna) effectors:GTP (complex) (complex) (complex) G protein-GDP VAV1/CBL/Ubiquitin Rac GEFs (family) complex CREB1 MEF2/MYOD (complex) A (complex) G protein (complex) alpha:GTP SP1 FRS2 FRS2 G protein complex S1P/S1P2/Gi family/SHP2/CRK family/SHP2/CRK VAV1 alpha:GDP (complex) (complex) family/C3G/GAB2 family complex (complex) DNA mediated MAPK14 RAP1/GTP (complex) transformation (complex) FGFR3-FGF (complex) (abstract) KLRG1/SHP2 FRS2 complex/Pyk2 PDGF/PDGFRA Heterotrimeric MAP3K2 (complex) family/SHP2 (complex) (complex) G-protein (complex) (complex) G-protein alpha RET9/GFRalpha1/GDNF/FRS2/SHP2/Grb2 (i/o/z/t) (complex) afadin/SHP2 SHC/GRB2/SOS1 subunit (family) Phospho-CBL:GRB2 (complex) (complex) Gi ARAP1-2 ARAP1 PTPN11 (complex)pFAK:Grb2 family/GTP/RGS1 GNAI3/GDP SHP2:SFKs:p-KIT:sSCF (complex) RET9/GFRalpha1/GDNF/SHC/GAB1/Grb2 (complex) (complex) dimer:p-KIT SHC 2-3/GRB2 (complex) p210 Adenylyl (complex) (complex) GRB2/GAB2 Gi(beta/gamma) bcr-abl/Grb2 Cyclase (family) (complex) (complex) Arap2 (family) (complex) EGFR/EGFR/EGF/EGF/GNAI1/GNAI3/GAB1 SCF/KIT Gi family (family) GRB2/PAK1 (complex) (complex) CBL:GRB2 (complex) S1P/S1P4/Gi HGF(dimer)/MET(dimer)/GRB2/GAB1/SHP2 (complex) (complex) GNAI3 (complex) GRB2 SHP2/GAB1/GRB2/FRS2/FGFR/FGF/Syndecan-2 GRB2/SOS2 S1P/S1P5/Gi Gi family/GTP (complex) (complex)GRB2:pY-GAB2 (complex) (complex) PRLR GRB2:Phospho-GAB1 (complex) ligands:p(Y611)-PRLR:p(Y1007)-JAK2 (complex) p210 Gi dimer:SHP2 G-protein alpha bcr-abl/PTP1B family/GNB1/GNG2/GDP (complex) (i) (family) RET51/GFRalpha1/GDNF/SHC/GAB1/Grb2 Ephrin (complex) GRB2:GAB2 S1P/S1P3/Gi (complex) (complex) B/EPHB1/Src/p52 (complex) (complex) SHC/GRB2 PDGF-BB/PDGFRB RET9/GFRalpha1/GDNF/Shank3/Grb2(complex) (dimer)/SHP2 GRB2/GAB1 (complex) PAG1 G-protein alpha (complex) (complex) G-protein alpha STAT3 CXCR4 (dimer) CXCR4 (i): GTP (i):GDP RET51/GFRalpha1/GDNF/FRS2/SHP2/Grb2 (complex) (complex) GRB2:Sos1 (complex) (complex) (complex) EGFR/EGFR/EGF/EGF/GNAI1/GNAI3/GAB1/RasGAP GRB2:GAB1 (complex) (complex) STAT3-2 Heterotrimeric HGF(dimer)/MET(dimer)/GAB1/SHP2 CD4/CD4/CXCR4 G-protein Gi G-protein alpha (complex) GRB2:Phospho-GAB1(partially (complex) (inactive) (i):GTP:Adenylate Heterotrimeric dephosphorylated) p(Y705)-STAT3 HIV-1/VIF/gag-pol/env (complex) cyclase G-protein Gi (complex) STAT3 (dimerdimer (complex) (complex) (complex) (active) alpha4/beta1 non-phopshorylated) Ligand:GPCR (complex) Integrin HES1 GRB2:Sos1:P-Erbb2mut (complex) complexes that (complex) (complex) HIV-1 (rna) activate (Gi alpha1:GTP:Adenylate Gi:Heterotrimeric ADP:P2Y GAB family cyclase):(G GAB1 G-protein Gi purinoceptor (family) (inactive) alpha-olf:GTP) 12:G-protein Gi JAM-L (dimer) JUN/ATF2 (complex) (complex) (inactive) (complex) (complex) (complex)

VAV1 Rho/Rac CDKN2A G protein-GTP SMAD2-3/SMAD4/SP1 effectors:GTP B C MYC (rna) (complex) VAV1 Rho/Rac (complex) (complex) CDKN2A SMAD2-3/SMAD4/SP1 G protein-GDP VAV1/CBL/Ubiquitin G protein-GTP effectors:GTP CREB1 Rac GEFs (family) MYC (rna) (complex) complex MEF2/MYOD (complex) (complex) (complex) G protein VAV1/CBL/Ubiquitin (complex) (complex) G protein-GDP Rac GEFs (family) alpha:GTP SP1 FRS2 complex CREB1 MEF2/MYOD (complex) FRS2 G protein complex S1P/S1P2/Gi family/SHP2/CRK VAV1 (complex) G protein (complex) family/SHP2/CRK alpha:GTP SP1 FRS2 alpha:GDP (complex) (complex) family/C3G/GAB2 FRS2 family DNA mediated G protein complex S1P/S1P2/Gi family/SHP2/CRK VAV1 complex (complex) family/SHP2/CRK MAPK14 RAP1/GTP (complex) transformation alpha:GDP (complex) (complex) family/C3G/GAB2 (complex) FGFR3-FGF family (complex) (abstract) complex (complex) DNA mediated KLRG1/SHP2 FRS2 complex/Pyk2 PDGF/PDGFRA MAPK14 RAP1/GTP (complex) transformation (complex) FGFR3-FGF Heterotrimeric MAP3K2 (complex) family/SHP2 (complex) (complex) (complex) (abstract) KLRG1/SHP2 FRS2 complex/Pyk2 PDGF/PDGFRA G-protein (complex) G-protein alpha RET9/GFRalpha1/GDNF/FRS2/SHP2/Grb2 Heterotrimeric MAP3K2 (complex) family/SHP2 (complex) (complex) (complex) (i/o/z/t) (complex) G-protein (complex) afadin/SHP2 SHC/GRB2/SOS1 subunit (family) Phospho-CBL:GRB2 (complex) G-protein alpha RET9/GFRalpha1/GDNF/FRS2/SHP2/Grb2 (complex) (complex) (i/o/z/t) (complex) Gi ARAP1-2 ARAP1 PTPN11 (complex)pFAK:Grb2 afadin/SHP2 SHC/GRB2/SOS1 Phospho-CBL:GRB2 subunit (family) family/GTP/RGS1 GNAI3/GDP SHP2:SFKs:p-KIT:sSCF (complex) ARAP1-2 ARAP1 (complex) (complex) (complex) RET9/GFRalpha1/GDNF/SHC/GAB1/Grb2 Gi PTPN11 pFAK:Grb2 (complex) (complex) dimer:p-KIT SHC 2-3/GRB2 GNAI3/GDP (complex) p210 family/GTP/RGS1 SHP2:SFKs:p-KIT:sSCF (complex) Adenylyl (complex) (complex) GRB2/GAB2 (complex) (complex) RET9/GFRalpha1/GDNF/SHC/GAB1/Grb2 Gi(beta/gamma) bcr-abl/Grb2 dimer:p-KIT SHC 2-3/GRB2 Cyclase (family) (complex) (complex) p210 (complex) (complex) Adenylyl (complex) (complex) GRB2/GAB2 Arap2 (family) Gi(beta/gamma) bcr-abl/Grb2 Cyclase (family) (complex) EGFR/EGFR/EGF/EGF/GNAI1/GNAI3/GAB1 SCF/KIT (complex) Arap2 (family) (complex) Gi family (family) GRB2/PAK1 (complex) (complex) CBL:GRB2 (complex) EGFR/EGFR/EGF/EGF/GNAI1/GNAI3/GAB1 SCF/KIT HGF(dimer)/MET(dimer)/GRB2/GAB1/SHP2 Gi family (family) GRB2/PAK1 S1P/S1P4/Gi (complex) (complex) (complex) CBL:GRB2 GNAI3 (complex) (complex) (complex) S1P/S1P4/Gi HGF(dimer)/MET(dimer)/GRB2/GAB1/SHP2 (complex) GRB2 SHP2/GAB1/GRB2/FRS2/FGFR/FGF/Syndecan-2 (complex) GNAI3 (complex) GRB2/SOS2 GRB2 S1P/S1P5/Gi Gi family/GTP (complex) (complex)GRB2:pY-GAB2 SHP2/GAB1/GRB2/FRS2/FGFR/FGF/Syndecan-2 GRB2/SOS2 (complex) (complex) PRLR GRB2:Phospho-GAB1 (complex) S1P/S1P5/Gi Gi family/GTP (complex) (complex)GRB2:pY-GAB2 ligands:p(Y611)-PRLR:p(Y1007)-JAK2 (complex) GRB2:Phospho-GAB1 p210 (complex) (complex) PRLR (complex) Gi dimer:SHP2 ligands:p(Y611)-PRLR:p(Y1007)-JAK2 (complex) G-protein alpha bcr-abl/PTP1B p210 family/GNB1/GNG2/GDP (complex) Ephrin Gi dimer:SHP2 (i) (family) RET51/GFRalpha1/GDNF/SHC/GAB1/Grb2 (complex) GRB2:GAB2 G-protein alpha bcr-abl/PTP1B S1P/S1P3/Gi (complex) B/EPHB1/Src/p52 family/GNB1/GNG2/GDP (complex) (complex) (complex) (i) (family) RET51/GFRalpha1/GDNF/SHC/GAB1/Grb2 Ephrin (complex) GRB2:GAB2 (complex) SHC/GRB2 S1P/S1P3/Gi (complex) B/EPHB1/Src/p52 (complex) (complex) PDGF-BB/PDGFRB RET9/GFRalpha1/GDNF/Shank3/Grb2(complex) (complex) SHC/GRB2 (dimer)/SHP2 GRB2/GAB1 (complex) PDGF-BB/PDGFRB RET9/GFRalpha1/GDNF/Shank3/Grb2(complex) PAG1 (complex) (dimer)/SHP2 G-protein alpha (complex) GRB2/GAB1 (complex) G-protein alpha STAT3 PAG1 (complex) (complex) CXCR4 (dimer) CXCR4 (i): GTP G-protein alpha (i):GDP RET51/GFRalpha1/GDNF/FRS2/SHP2/Grb2 G-protein alpha STAT3 (complex) (complex) GRB2:Sos1 CXCR4 (dimer) CXCR4 (i): GTP (complex) (complex) (i):GDP RET51/GFRalpha1/GDNF/FRS2/SHP2/Grb2 (complex) (complex) (complex) GRB2:Sos1 EGFR/EGFR/EGF/EGF/GNAI1/GNAI3/GAB1/RasGAP GRB2:GAB1 (complex) (complex) (complex) (complex) (complex) EGFR/EGFR/EGF/EGF/GNAI1/GNAI3/GAB1/RasGAP GRB2:GAB1 STAT3-2 (complex) (complex) STAT3-2 Heterotrimeric HGF(dimer)/MET(dimer)/GAB1/SHP2 CD4/CD4/CXCR4 G-protein Gi G-protein alpha GRB2:Phospho-GAB1(partially Heterotrimeric HGF(dimer)/MET(dimer)/GAB1/SHP2 (complex) (complex) (inactive) (i):GTP:Adenylate dephosphorylated) CD4/CD4/CXCR4 G-protein Gi G-protein alpha (complex) GRB2:Phospho-GAB1(partially Heterotrimeric p(Y705)-STAT3 HIV-1/VIF/gag-pol/env (complex) cyclase (complex) STAT3 (dimer (complex) (inactive) (i):GTP:Adenylate Heterotrimeric dephosphorylated) p(Y705)-STAT3 G-protein Gi dimer (complex) (complex) (complex) non-phopshorylated) HIV-1/VIF/gag-pol/env (complex) cyclase G-protein Gi (complex) STAT3 (dimerdimer (complex) (active) alpha4/beta1 Ligand:GPCR (complex) (complex) (complex) (active) alpha4/beta1 non-phopshorylated) (complex) Integrin HES1 GRB2:Sos1:P-Erbb2mut Ligand:GPCR complexes that (complex) (complex) Integrin HES1 GRB2:Sos1:P-Erbb2mut (complex) HIV-1 (rna) (Gi (complex) complexes that activate (complex) (complex) alpha1:GTP:Adenylate HIV-1 (rna) activate (Gi Gi:Heterotrimeric ADP:P2Y GAB family cyclase):(G GAB1 Gi:Heterotrimeric alpha1:GTP:Adenylate G-protein Gi purinoceptor (family) ADP:P2Y GAB family GAB1 alpha-olf:GTP) G-protein Gi cyclase):(G (inactive) 12:G-protein Gi JUN/ATF2 purinoceptor (family) (complex) JAM-L (dimer) (inactive) alpha-olf:GTP) (complex) (inactive) 12:G-protein Gi JAM-L (dimer) JUN/ATF2 (complex) (complex) (complex) (complex) (inactive) (complex) (complex) (complex) (complex)

Figure 5.6: Visualization of IPLs for the region around GNAI3 for the best Knockdown eﬀectiveness from Table 5.1: A) GFPc of GFP scrCvsHR, B) GFPssi of GFP scrCvsHR and C) siRNAt of GNAI3 06 1vsHR

87 NAt) samples. Using a Kendall Correlation of the top 2500 variable genes across both

GFPc and siRNAt samples, it was possible to assess the knockdown models ability to simulate IPLs that more closely resemble the IPLs in the true knockdowns (siRNAt).

All pairwise-correlations were calculated between siRNAt and GFPc IPL vectors and directly compared to the corresponding pairwise-correlations between siRNAt and GFPssi

IPL vectors, the result of which can be seen in Table 5.1. A one-sided pairwise t-test between the two populations of correlations was computed to assess the eﬀectiveness of the simulated knockdown, which resulted in a p-value of 0.008133. Knockdown effectiveness (KE) for each simulated knockdown is calculated by simulating knockdowns for all possible genes in the pathway and measuring the change in correlation for each.

The correlation changes for the simulated knockdowns were ranked among the true gene correlation change and the eﬀectiveness is calculated as the percentage of genes resulting in worse correlations than the intended target.

Many of the knockdowns resulted in no detectable change to the resulting correlations, however for many of these the KE was still relatively high, indicating that for many genes a simulated knockdown reduces the correlation to the true siRNA activities. The top result as ranked by KE is visualized as subnetworks in ﬁgure 5.6.

From the visualization, it appears as though the PTPN11 down regulation that’s seen in the siRNAt sample for GNAI3 accounts for a large part of the positive correlation shift. This result illustrates that the knockdowns are able to capture a small result of the total overall changes between the networks in the immediate area of the target, highlighting the need for accurate interaction information to successfully model the total

88 effect. Another explanation for the lack of larger correlation increases may be due to insufficient down regulation signaling by key regulatory paths, which might benefit from the implementation of interaction strengths providing more accurate signal propagation.

Overall this method warrants testing on additional data and should be able to prioritize the order of targets for testing in a laboratory setting.

5.4 Breast Cell Line Simulated Knockdowns

In a series of initial experiments preparing for validation, paradigm activities were generated for the breast cell line panel using the previously discussed copy number, gene expression data, and drug sensitivity measure for 58 drugs. Four feature selection methods, 10 classiﬁcation algorithms, and 2 methods for subgrouping samples into binary classes (responders vs. non-responders), were combinatorially used to build a maximum of 80 fully-trained models for each drug response prediction problem. Due to limited sampling in some drugs there was insuﬃcient data to build the full compli- ment of 80 models, so in these cases only a subset of these models were assessed. All models were assessed using average accuracy in 5x5 fold cross-validation.

To create ranked lists of top candidates from these models we extracted feature weights from the most accurate linear-kernel models in TopModel, a machine learning system developed by Christopher Szeto that evaluates multiple machine learning algorithms to ﬁnd the best for a particular prediction problem. These included support- vector machines [38], squential minimal optimization models [33] and NMF-based mod-

89 els. We justify using only linear models by showing that, overall, these linear-kernel models perform as well as higher-order models in our cross-validation experiments, and are more easily interpreted biologically. For each drug, we selected the 200 most highly weighted features (or fewer where applicable) from the most accurate linear-kernel models as candidates for likely optimal intervention points.

PARADIGM produced a new set of pathway activities that correspond to the estimated levels if the protein of each knocked down feature. To establish the effectiveness of these simulations, the vectors of resulting IPLs are applied back to the classifier for sensitivity predictions. The shift in the classification value (resistance score) is calculated; giving a score we refer to as Resistance Shift. The significance of this shift is calculated by building a background distribution from classifying the IPLs derived from knockdowns of genes picked for other drug classifications and not selected for the drug being assessed.

The top results of the classiﬁcation tasks and knockdowns can be found in Ta- ble 5.2. Figure 5.8 highlights the top prediction score shift from resistant to sensitive, found for the combination of QNZ (an NF-KB inhibitor) and E2F3. Previous studies have shown that NF-KB may activate E2F family transcription factors [3], indicating a mechanism by which the cells may have developed resistance to an NF-KB therapy.

Additionally, it appears as though treatments such as bortezomib, which function partially by E2F3 downregulation, act independently of NF-KB activity [61] indicating that this pair of targets may oﬀer eﬀectiveness in certain settings. It appears as though all resistant lines may show some shift towards sensitivity with a knockdown like this,

90 Figure 5.7: Classiﬁcation of response to QNZ (an NF-KB inhibitor) in the breast cell lines, before and after simulated knockdown experiments to E2F3. Tail of the arrow represents the predicted resistance score using the pathway activities, and the head represents the prediction after simulated knockdown was performed. Blue arrows indicate cell lines that are labelled sensitive and red arrows indicate resistant cell lines.

Figure 5.8: Classiﬁcation of response to Oxaliplatin in the breast cell lines, before and after simulated knockdown of MAPK1.

91 Table 5.2: Resitance shift and p-values for the top 20 simulated KO and drug combina- tions ranked by shift score.

Drug KO Sample PreScore PostScore Shift P-Value QNZ E2F3 MDAMB468 1.3335 -0.327 1.6604 9.17E-144 Oxaliplatin MAPK3 HCC1419 1 -0.5927 1.5927 1.72E-132 Oxaliplatin FOXM1 CAMA1 1.745 0.2027 1.5423 2.01E-124 Oxaliplatin MAPK1 BT483 0.6869 -0.8431 1.53 1.76E-122 Oxaliplatin GREB1 ZR75B 2.1429 0.6866 1.4563 3.13E-111 Oxaliplatin MAPK3 HCC1428 1.3238 -0.0655 1.3893 1.78E-101 Oxaliplatin MAPK1 MDAMB361 0.5662 -0.6577 1.2239 2.41E-79 Oxaliplatin FOXM1 HCC2185 0.2991 -0.8675 1.1667 2.48E-72 Oxaliplatin E2F3 MDAMB468 1.2147 0.1002 1.1146 3.12E-66 Baicalein CITED2 LY2 1 -0.0792 1.0792 2.98E-62 Oxaliplatin FOXA1 BT474 1 -0.0738 1.0738 1.19E-61 QNZ E2F3 HCC1500 0.9999 -0.0672 1.067 1.10E-25 QNZ GAPDH ZR75B 1.0292 -0.0292 1.0584 5.73E-60 Oxaliplatin MAPK1 MCF7 -0.0621 -1.0766 1.0144 2.73E-55 Baicalein GREB1 HCC1419 2.0221 1.0433 0.9788 1.22E-51 Oxaliplatin MAPK1 MDAMB415 1.1615 0.1834 0.9781 1.44E-51 QNZ E2F3 CAMA1 0.8331 -0.1257 0.9588 7.07E-05 Oxaliplatin MYC HBL100 1 0.0662 0.9338 3.22E-47 JH175 GAPDH ZR75B 0.29 -0.6367 0.9267 1.55E-46 Oxaliplatin AP1B1 LY2 -0.0936 -0.9751 0.8814 2.52E-42

92 however MDAMB468 shows a shift of 1.66, the largest across the entire experiment.

When compared to a null model of other knockdowns and a p-value is computed, this shift is highly signiﬁcant (9.18e-144).

Many of the top hits across the cell lines and treatments are speciﬁc to treatment with Oxaliplatin, a DNA damage agent. Literature searches indicate the importance of the MAPK cascade in sensitivity to platinum-based treatment across multiple tissues[34, 18], a relationship illustrated in the simulated knockdowns for 6 of the top

20 that were ranked. In addition, some of the common markers of pathway activities that diﬀerentiate basal and luminal subtypes in breast cancer appear with Oxaliplatin

(FOXM1, FOXA1, etc), a result that is not surprising given the preferred sensitivity of platinum-based therapies towards basal tumors. Overall this top list of targets illustrates a good place to start experimental validation of combinational therapies, and should assist in searching an extremely large number of drug pairs in a more systematic fashion.

93 Chapter 6

Conclusion

6.1 Conclusions

As technological advances have increased the amount of data produced by single experiments to thousands of gigabytes, it’s becoming increasingly clear the bot- tleneck in making clinically relevant discoveries is distilling this information to under- standable levels. The overall approach of PARADIGM aims to provide an intuitive and scalable framework to understand these data in biologically relevant contexts in a patient-speciﬁc manner. Combining the PARADIGM engine with the power of the Su- perPathway’s representation of the cellular interactome oﬀers an unprecedented ability to understand the heterogeneity that exists within cancer. This method also moves us away from preconceived curated pathway models that contain large amounts of study bias and towards models that can accommodate the complexities of cellular signaling and transcription networks. This is particularly critical in cancer where much of the

94 Figure 6.1: An example expanded PARADIGM dogma model that accounts for pro- moter states and allows attachment of Methylation data. ( c IEEE 2012) misregulation occurs by bypassing standard cellular protective mechanisms, the understanding of which is necessary for understanding which therapies may be eﬀective.

The PARADIGM simulated knockdown model will aid our thinking about combinational therapies in help reﬁne pathway models as functional testing begins. Overall

PARADIGM’s greatest strength is the extremely powerful and highly extensible engine that is provided to model biology in intuitive ways that reduce the time it takes to test biological phenomenon in a computational framework.

6.2 Future Expansions

Despite all of the work done to date, there are many enhancements to PARADIGM that should increase our modeling resolution to provide more accurate results. The incorporation of the additional measurements currently taken about a particular cancer

95 is still necessary to move to a more accurate model within PARADIGM. These include

DNA Methylation from the Illumina methylation arrays, mutational information from sequencing, microRNA levels, and proteomics assays. Each of these datatypes presents unique challenges to modeling that must be overcome, and one example of how we might model methylation data can be seen in Figure 6.1.

For example, “driver” mutations within cancer are typically viewed as hitting two types of proteins, tumor suppressors and oncogenes. If a mutation hits a tumor suppressor, the typical result is to inactivate the protein so that it can no longer perform its function. When we go to model these types of interactions in PARADIGM, we will want to assume mutations to tumor suppressors are large negatively activating observations. However, when mutations hit oncogenes, they typically hit a consistent domain across multiple patients and tumors, and generally cause the protein to be constitutively activated. This would require modeling a large positively activation observation for the protein that contains the mutation. In addition, there are a vast number of “passenger” mutations that will be carried clonally in the tumor genome with the small number of

“driver” mutations. It may be possible to distinguish these two classes of mutations using PARADIGM by sorting passengers into irrelevant pathways, with drivers only appearing in cancer-dependent pathways.

MicroRNAs (miRNAs) present another technical challenge to modeling within

PARADIGM. It is clear that a single miRNA has the potential to modulate the transcript abundance of hundreds of diﬀerent proteins. Simply attempting to add miRNA levels to the model will likely result in highly linked networks that are no longer com-

96 putationally tractable, and it may be necessary to ﬁnd functional subsets of miRNA targets that can be grouped together.

With the recent advances in sequencing technology, it’s rapidly becoming af- fordable to sequence the entire genome and transcriptome of a cancer sample. Al- though pathway modeling is generally not restricted to a particular type of data used for observational variables, all of the work to this point has been done on array-based technologies. These technologies provide measurements of RNA and DNA abundance in relative light intensities, allowing researchers to compare these between samples after standard normalization techniques. Moving away from array-based measurements into sequencing-based measurements is going to require modifying these normalization techniques, but will ultimately provide a higher resolution of the biology and should be capable of better informing the model. More importantly, sequencing-based measurements allow modeling of gene fusions, splice variants, and allele-speciﬁc alterations which may modify the underlying pathway structure. For example, despite our ability to identify FOXM1 as a critical transcription factor in ovarian cancer, it is clear that we will never be able to fully elucidate the mechanism of misregulation without isoform-speciﬁc transcript levels provided by RNA-seq.

Sequencing data also may enable the ability to model allele-speciﬁc variations and potential gene fusions that result in dysregulated pathways. Linking the results of sequence analysis pipelines like BamBam, which can be used to detect allele speciﬁc variations, to the pathway model may provide interesting evidence that helps proba- bilistically explain discrepancies in the pathway. In order to model each allele, it will

97 GENE1

Gene Copy Expression Protein Protein Allele 1 Number State Level Activity

Allele 1 CGH Allele 1, GENE1a GENE1a

Gene Copy Expression Protein Protein Allele 2 Number State Level Activity

Allele 2 CGH Allele 2, GENE1a

Expression Protein Protein State Level Activity

Allele 1, GENE1b GENE1b

Expression Protein Protein State Level Activity

Allele 2, GENE1b

Figure 6.2: Allele and Isoform Speciﬁc Factor Graph Representation. An example of how we might represent a gene (“GENE1”) with allele-speciﬁc data and two isoforms.

98 be necessary to expand our current “central dogma” representation to an allele-speciﬁc dogma. This new model will have genome, mRNA, and protein nodes speciﬁc to each allele represented. An example of what this representation might look like is shown in

figure 6.2. If BamBam identifies an allele specific mutation, PARADIGM can dynamically expand the standard dogma to this new variant, allowing inference on each allele and eventually trying to consolidate those alleles at the active protein node. By linking this information with functional mutation algorithms being developed by collaborators,

PARADIGM can further expand this to remove or create direct interactions depending on the mutational type. For example, if a mutation is detected that might disable a substrate binding site, PARADIGM could disconnect the appropriate interactions in an attempt to consolidate the data and increase the likelihood of the model.

Beyond allele-specific variations, transcriptome sequencing data will allow PARADIGM to identify gene fusions and splice-specific isoforms. As a simplistic initial analysis, we can take the genes fused and form a new network with the union of the two pathways in which each gene appears. This new pathway will represent the potential chimeric function of this new protein, and we can assess the validity of this pathway compared to each of the individual pathways by comparing the likelihood of the data given the pathway structure. In addition, since splice variants are already annotated in our pathway models, having splice-specific data we can attach will allow us to more accurately represent the observational nodes for those isoforms. The combination of these new types of data will hopefully build on PARADIGM’s ability to identify critical pathways within each tumor.

99 6.3 Final Thoughts

As we begin to unravel the molecular origin of each cancer, it becomes clear that despite their shared hallmarks, no two cancers are misregulated using the same combination of genes. Establishing molecular ﬁngerprints for each tumor from pathway models such as Paradigm is the only way to sanely approach treatment as we move towards the future of personalized medicine. Despite the early stage, having the computational power to simulate response to therapy on an individual basis should prove to vastly increase the speed at which therapies are developed and tested. This excitement of a new era where discovery and treatment are more closely coupled may prove to be the method by which the war on cancer is ﬁnally won.

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