predicting the impact of mutations using pathway- guided integrative genomics aacr annual meeting...

Predicting the impact of mutations using pathway- guided integrative genomics AACR Annual Meeting 2012 Major Symposium: Designing Rational Combination Therapy for Cancer Josh Stuart, UC Santa Cruz Apr 3, 2012

Disclosure SAB for Five3 Genomics

Overview of pathway-guided approach Integrate many data sources to gain accurate view of how genes are functioning in pathways Predict the functional consequences of mutations by quantifying the effect on the surrounding pathway Use pathway signatures to implicate mutations in novel genes to (re-)focus targeting Identify critical Achilles Heels in the pathways that distinguish a particular sub-type

Flood of Data Analysis Challenges Multiple, Possibly Conflicting Signals Multiple, Possibly Conflicting Signals This is What it Does to You This is What it Does to You Genomics, Functional Genomics, Metabolomics, Epigenomics =

Analysis of disease samples like automotive repair (or detective work or other sleuthing) Patient Sample 1 Patient Sample 2 Patient Sample 3Patient Sample N

Much Cell Machinery Known: Gene circuitry now available. Curated and/or Collected ReactomeKEGGBiocartaNCI-PID Pathway Commons

Integration key to interpret gene function Expression not always an indicator of activity Downstream effects often provide clues TF Inference: TF is OFF (high expression but inactive) TF Inference: TF is ON (low-expression but active ) TF Inference: TF is ON (expression reflects activity) Expression of 3 transcription factors: high low

Integration key to interpret gene function Need multiple data modalities to get it right. TF Expression -> TF ON BUT, targets are amplified Lowers our belief in active TF because explained away by cis evidence. Copy Number -> TF OFF

Probabilistic Graphical Models: A Language for Integrative Genomics Generalize HMMs, Kalman Filters, Regression, Boolean Nets, etc. Language of probability ties together multiple aspects of gene function & regulation Enable data-driven discovery of biological mechanisms Foundation: J. Pearl, D. Heckerman, E. Horvitz, G. Cooper, R. Schacter, D. Koller, N. Friedman, M. Jordan, Bioinformatics: D. Peer, A. Hartemink, E. Segal, E Schadt Nir Friedman, Science (2004) - Review

Integration Approach: Detailed models of gene expression and interaction MDM2 TP53

Integration Approach: Detailed models of expression and interaction MDM2 TP53 Two Parts: 1.Gene Level Model (central dogma) 2. Interaction Model (regulation)

PARDIGM Gene Model to Integrate Data Vaske et al. 2010. Bioinformatics 1. Central Dogma-Like Gene Model of Activity 2. Interactions that connect to specific points in gene regulation map Charlie Vaske Steve Benz

Integrated Pathway Analysis for Cancer Integrated dataset for downstream analysis Inferred activities reflect neighborhood of influencearound a gene. Can boost signal for survival analysis and mutationimpact Multimodal Data CNV mRNA meth Pathway Model of Cancer Cohort Inferred Activities

TCGA Ovarian Cancer Inferred Pathway Activities Patient Samples (247) Pathway Concepts (867) TCGA Network. 2011. Nature (lead by Paul Spellman)

Ovarian: FOXM1 pathway altered in majority of serous ovarian tumors FOXM1 Transcription Network Patient Samples (247) Pathway Concepts (867) TCGA Network. 2011. Nature (lead by Paul Spellman)

FOXM1 central to cross-talk between DNA repair and cell proliferation in Ovarian Cancer TCGA Network. 2011. Nature (lead by Paul Spellman)

PATHMARK: Identify Pathway-based markers that underlie sub-types Identify sub-pathways that distinguish patients sub-types (e.g. mutant vs. non-mutant, response to drug, etc) Predict mutation impact on pathway neighborhood Identify master control points for drug targeting. Predict outcomes with quantitative simulations. Insight from contrast Ted Goldstein Sam Ng

Pathway signatures of mutations to reveal therapeutic candidates Mutated genes are the focus of many targeted approaches. Some patients with right mutation dont respond. Why? Many cancers have one of several novel mutations. Can these be targeted with current approaches? Pathway-motivated approaches: Identify gain-of-function from loss-of-function. Compare novel signatures Sam Ng Poster # 2985

FG High Inferred Activity Low Inferred Activity Predicted Loss-Of-Function Predicted Gain-Of-Function PARADIGM-Shift: Pathway context of GOF and LOF events Sam Ng Use pathways to predict the impact of observed mutations in patient tumors

PARADIGM-Shift Predicting the Impact of Mutations On Genetic Pathways FG Inference using all neighbors FG Inference using downstream neighbors FG Inference using upstream neighbors Sam Ng High Inferred Activity Low Inferred Activity mutated gene

FG PARADIGM-Shift Calculation Overview Sam Ng

FG 1. Identify Local Neighbor- hood PARADIGM-Shift Calculation Overview Sam Ng

FG 1. Identify Local Neighbor- hood FG 2a. Regulators Run 2b. Targets Run PARADIGM-Shift Calculation Overview Sam Ng

FG 1. Identify Local Neighbor- hood FG 2a. Regulators Run 2b. Targets Run P-Shift Score 3. Calculate Difference FG - (LOF) PARADIGM-Shift Calculation Overview Sam Ng

P-Shift Predicts RB1 Loss-of-Function in GBM Shift Score PARADIGM downstream PARADIGM upstream Expression Mutation RB1 Sam Ng

RB1 Network (GBM) Sam Ng Neighbor Gene Key Activity Expression RB1 Mutation Focus Gene Key P-Shift Expression Mutation T-Run R-Run

RB1 Discrepancy Scores distinguish mutated vs non-mutated samples Signal Score (t-statistic) = -5.78 Sam Ng

RB1 discrepancy distinction is significant Given the same network topology, how likely would we call a gain/loss of function Background model: permute gene labels in our dataset Compare observed signal score to signal scores (SS) obtained from background model Observed SS Background SS Sam Ng

TP53 Network Sam Ng

Gain-of-Function (LUSC) NFE2L2 P-Shift Score PARADIGM downstream PARADIGM upstream Expression Mutation Sam Ng

NFE2L2 Network (LUSC) Sam Ng Neighbor Gene Key Activity Expression RB1 Mutation Focus Gene Key P-Shift Expression Mutation T-Run R-Run NFE2L2

Discrepancy scores are sensitive Discrepancy Score Signal Score (t-statistic) = -5.78 Signal Score (t-statistic) = 4.985 RB1NFE2L2 Signal Score (t-statistic) = -10.94 TP53 Observed SS Background SS Sam Ng

Expect passenger mutations to lack shifts Is the discrepancy specific? Negative control: calculate scores for passenger mutations Passengers: insignificant by MutSig (p > 0.10) well-represented in our pathways Discrepancy of these neutral mutations should be close to whats expected by chance (from permuted) Sam Ng

Discrepancies of Passenger Mutations are NOT distinctive Sam Ng

PARADIGM-Shift gives orthogonal view of the importance of mutations in LUSC Enables probing into infrequent events Can detect non-coding mutation impact (pseudo FPs) Can detect presence of pathway compensation for those seemingly functional mutations (pseudo FPs) Extend beyond mutations Limited to genes w/ pathway representation NFE2L2 (29) CDKN2A (n=30) Pathway Discrepancy LUSC MET (n=7) (gefitinib resistance) HIF3A (n=7) TBC1D4 (n=9) (AKT signaling) MAP2K6 (n=5) EIF4G1 (n=20) GLI2 (n=10) (SHH signaling) AR (n=8) Sam Ng

Defining Pathway Signatures for Mutations and Sub-Types Build a signature for every mutation and tumor/clinical event. Correlate every signature to each other. Reveals common molecular similarities between different divisions of patient subgroups Mutations in novel genes may phenocopy mutations in known genes Ted Goldstein

PathMark: Differential Subnetworks from a SuperPathway Pathway Activities Ted Goldstein Sam Ng

PathMark: Differential Subnetworks from a SuperPathway SuperPathway Activities Pathway Signature Ted Goldstein Sam Ng

Traditional methods treat each gene as a separate feature Traditional methods treat each gene as a separate feature Use features reflecting overall pathway activity Use features reflecting overall pathway activity Smaller number of features are now fed to predictors Smaller number of features are now fed to predictors Predictor PIL: Pathway-informed Learning Artem Sokolov

Basal vs. Luminal Recursive feature elimination: we train an SVM, drop the least important half of features and recurse The number of times each feature survived the elimination across 100 random splits of data Artem Sokolov

Methotrexate Sensitivity Non sub-type specific drug Artem Sokolov Pathway involving the target of the drug.

One large highly-connected component (size and connectivity significant according to permutation analysis) Higher activity in ER- Lower activity in ER- Triple Negative Breast Pathway Markers Identified from 50 Cell Lines 980 pathway concepts 1048 interactions HIF1A/ARNT Characterized by several hubs IL23/JAK2/TYK2 Myc/Max P53 tetramer ER FOXA1 Sam Ng, Ted Goldstein

Identify master controllers using SPIA (signaling pathway impact analysis) Impact factor: Google PageRank for Networks Determines affect of a given pathway on each node Calculates perturbation factor for each node in the network Takes into account regulatory logic of interactions. Yulia Newton Googles PageRank

Slight Trick: Run SPIA in reverse Reverse edges in Super Pathway High scoring genes now those at the top of the pathway Yulia Newton

Master Controller Analysis on Breast Cell Lines Basal Luminal Yulia Newton

Master regulators predict response to drugs: PLK3 predicted as a target for basal breast Up Down DNA damage network is upregulated in basal breast cancers Basal breast cancers are sensitive to PLK inhibitors BasalClaudin-lowLuminal GSK-PLKi Heiser et al. 2011 PNAS Ng, Goldstein

HDAC Network is down- regulated in basal breast cancer cell lines Basal/CL breast cancers are resistant to HDAC inhibitors VORINOSTAT HDAC inhibitor HDAC inhibitors predicted for luminal breast Heiser et al. 2011 PNAS Ng, Goldstein

PARADIGM in TCGA patient BRCA tumors Christina Yau, Buck Inst

Christina Yau, Buck Inst.

Connect genomic alterations to downstream expression/activity What circuitry connects mutations to transcriptional changes? Mutations general (epi-) genomic perturbation Expression activity Mutation/perturbation and expression/activity treated as heat diffusing on a network HotNet, Vandin F, Upfal E, B.J. Raphael, 2008. HotNet used in ovarian to implicate Notch pathway Find subnetworks that link genetic to mRNA and protein-level changes. ? Evan Paull

HotLink Genomic Perturbations (Mutations, Methylation, Focal Copy Number) Gene Activity (Expression, RPPA, PARADIGM) ? Evan Paull

HotLink Genomic Perturbations (Mutations, Methylation, Focal Copy Number) Gene Activity (Expression, RPPA, PARADIGM) 1. Add heat2. Diffuse heat3. Cut out linkers Evan Paull

HotLink Genomic Perturbations (Mutations, Methylation, Focal Copy Number) Gene Activity (Expression, RPPA, PARADIGM) Linking Sub-Pathway Evan Paull

Basal-LumA HotLink Basal LumA TCGA Interlinking Network

Basal-LumA HotLink Map Basal LumA

AKT/PI3K

TP53, RB1 Basal LumA

MYC Neighborhood Basal LumA

AKT signaling Basal LumA

Mutation Association to Pathways What pathway activities is a mutations presence associated? Can we classify mutations based on these associations? Mutations PARADIGM Signatures Ted Goldstein

Mutation Association to Pathways What pathway activities is a mutations presence associated? Can we classify mutations based on these associations? (Note: CRC figure below; soon for BRCA) Mutations PARADIGM Signatures APC and other Wnt Ted Goldstein

Mutation Association to Pathways What pathway activities is a mutations presence associated? Can we classify mutations based on these associations? Mutations PARADIGM Signatures Ted Goldstein

Mutation Association to Pathways What pathway activities is a mutations presence associated? Can we classify mutations based on these associations? (Note: CRC figure below; soon for BRCA) Mutations PARADIGM Signatures TGFB Pathway mutations Ted Goldstein

Mutation Association to Pathways What pathway activities is a mutations presence associated? Can we classify mutations based on these associations? (Note: CRC figure below; soon for BRCA) Mutations PARADIGM Signatures PIK3CA, RTK pathway, KRAS Ted Goldstein

Mutation Association to Pathways What pathway activities is a mutations presence associated? Can we classify mutations based on these associations? (Note: CRC figure below; soon for BRCA) Mutations PARADIGM Signatures Ted Goldstein

Summary Modeling information flow on known pathways gives view of gene activity.Modeling information flow on known pathways gives view of gene activity. Patient stratification into pathway-based subtypesPatient stratification into pathway-based subtypes Sub-networks provide pathway-based signatures of sub- types and mutations.Sub-networks provide pathway-based signatures of sub- types and mutations. Loss- and gain-of-function predicted from pathway neighbors for even rare mutations.Loss- and gain-of-function predicted from pathway neighbors for even rare mutations. Identify interlinking genes associated with mutations to implicate additional targets even in LOF casesIdentify interlinking genes associated with mutations to implicate additional targets even in LOF cases E.g. Target MYC-related pathways in certain TP53- deficient cells?E.g. Target MYC-related pathways in certain TP53- deficient cells? Current work: Use pathways to now simulate the affects of specific knock-downs.Current work: Use pathways to now simulate the affects of specific knock-downs.

James Durbin Chris Szeto UCSC Integrative Genomics Group Sam Ng Ted Golstein Dan Carlin Evan Paull Artem Sokolov Chris Wong Marcos Woehrmann Yulia Newton

Acknowledgments UCSC Cancer Genomics Kyle Ellrott Brian Craft Chris Wilks Amie Radenbaugh Mia Grifford Sofie Salama Steve Benz UCSC Genome Browser Staff Mark Diekins Melissa Cline Jorge Garcia Erich Weiler Buck Institute for Aging Christina Yau Sean Mooney Janita Thusberg Collaborators Joe Gray, LBL Laura Heiser, LBL Eric Collisson, UCSF Nuria Lopez-Bigas, UPF Abel Gonzalez, UPF Funding Agencies NCI/NIH SU2C NHGRI AACR UCSF Comprehensive Cancer Center QB3 Jing Zhu David Haussler Chris Benz, Broad Institute Gaddy Getz Mike Noble Daniel DeCara

UCSC Cancer Browser genome-cancer.ucsc.edu Jing Zhu

predicting the impact of mutations using pathway- guided integrative genomics aacr annual meeting...

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