Integrative Omics for The Systems Biology of Complex Phenotypes

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1 Integrative Omics for The Systems Biology of Complex Phenotypes Mehmet Koyutürk Case Western Reserve University (1) Electrical Engineering and Computer Science (2) Center for Proteomics and Bioinformatics

2 Joint Work With Salim Chowdhury CMU CS Sinan Erten CWRU EECS Vishal Patel CWRU Genetics/MSTP Rod Nibbe CWRU Pharmacology Mark Chance CWRU Proteomics Giri Gokulrangan (CWRU Proteomics), Jill Barnholtz-Sloan (CWRU Oncology) Andrew Sloan (CWRU Neurological Surgery), Yanwen Chen (Case Comprehensive Cancer Center)

3 Complex Diseases 3 Many diseases are based on complex interactions between multiple genes. Cancer, diabetes, obesity, Alzheimer s disease, hearth disease, etc. It is possible to interrogate affected samples at multiple levels Systems-level insights into disease mechanisms

4 omics Genomics Transcriptomics Proteomics Interactomics DNA mrna Protein Functional Protein Transcriptional Control Translational Control Post-Translational Control 4

5 Transcriptomics Differential mrna expression Dysregulated genes Diagnosis Prognosis Response to treatment Individual differential expression DNA microarrays Classification RNAseq

6 Systems-Level Analysis Collective dysregulation of (potentially) functionally associated genes Pathways Established biological knowledge Enrichment-based analyses Statistical tests (Protein Interaction) Networks Large-scale Noisy, incomplete Novel hypotheses Computational algorithms

7 Pathway-Based Analysis Gene Set Enrichment Analysis (GSEA) Subramanian et al., PNAS, 2005

8 Network-Based Algorithms First aggregate gene expression levels for each sample Then quantify dysregulation using mutual information Chuang et al., Mol Sys Bio, 2007

9 Identifying Dysregulated Subnetworks Synergy Watkinson et al., BMC Sys Bio, 2008 Biclustering Dao et al., ECCB, 2010 Combinatorial Dysregulation Chowdhury et al., RECOMB, 2010 Set Cover Ulitsky et al., RECOMB, 2008 Chowdhury & Koyutürk, PSB, 2010 Discriminative Subnetworks Dao et al., ISMB, 2011 Network-Guided Forests Dutkowski & Ideker., PLoS CB, 2011

10 Combinatorial Coordinate Dysregulation Quantize gene expression levels Compute the mutual information between the expression state of the subnetwork and the phenotype random variable

11 State Functions

12 CRANE Algorithm Exhaustive, yet efficient search for subnetworks and associated state functions that are informative of phenotype Branch-and-bound algorithm based on provable bounds on J- value Network further confines the search space

13 Validation via Cross-Classification

14 Predicting Colorectal Cancer Metastasis

15 Generating Novel Hypotheses Subnetwork composed of membrane-bound proteins ITGAV (Integrin alpha chain) is not differentially expressed at the mrna level, but is involved in cell adhesion Post-translational dysregulation of integrins?

16 Classification of Brain Tumors Brain tumors account for ~1-2% of all cancers Majority of malignant BTs are GLIOMAS, majority of benign BTs are MENINGIOMAS: Incidence and survival vary greatly by histological type Median survival in Gliablastoma (GBM) 12 months (Surgery + Chemotherapy + Radiation) Can we find markers of survival?

17 Predicting Survival Time Transcriptomic Profiling (TCGA) CRANE Top-Scoring Subnetworks Proteomic Markers of Survival Targeted LC-MS/MS 43 (25%) ShortTerm Survivors (< 225 days) Candidate Proteomic Markers 43 (25%) LongTerm Survivors (> 635 days) PPI Network (HPRD) 18 Clinical Samples (Ohio Brain Tumor Study)

18 Top 5 Subnetwork Markers of Survival in GBM

19 Cross-Validation on An Independent Dataset Top 5 CRANE subnetworks were used for binary classification of an independent dataset (GSE13041) obtained from the Gene Expression Omnibus

20 Proteomic Validation Question: How does protein expression vary as a function of survival? Hypothesis: The expression of proteins that are coordinately dysregulated at the mrna-level is more likely to serve as a marker for survival, as compared to that of proteins that exhibit individual mrna-level differential expression Sample: Ohio Brain Tumor Study 18 patients (10 short term vs. 6 long term survivors) 2 technical replicates for each patient

21 Differential Protein Expression

22 Conclusions Network algorithms can find combinations of genes that can distinguish phenotypes more accurately Useful classifiers Network markers provide insights into the regulatory logic of disease mechanisms Gene expression + protein-protein interactions => Dysregulated proteins at the functional level Computational algorithms can drive targeted proteomics

23 Acknowledgments Salim Chowdhury CMU CS Sinan Erten CWRU EECS Vishal Patel CWRU Genetics/MSTP Rod Nibbe CWRU Pharmacology Mark Chance CWRU Proteomics Giri Gokulrangan (CWRU Proteomics), Jill Barnholtz-Sloan (CWRU Oncology) Andrew Sloan (CWRU Neurological Surgery), Yanwen Chen (Case Comprehensive Cancer Center)

24 Acknowledgments Ananth Grama Purdue CS Tom LaFramboise CWRU Genetics Matthew Ruffalo CWRU EECS Gurkan Bebek CWRU Proteomics