1. A quick introduction to Oncinfo Lab Dr. Habil Zare, PhD PI of Oncinfo Lab Department of Computer Science Texas State University 18 September 2015

2. Dr. Habil Zare, PhD The PI Computational Biologist Dr. Amir Forpushani, PhD Postdoc, Computational Biologist Rupesh Agrihari Grad student, Computer Science A highly collaborative team (Oncinfo Lab Members) 2 Vignesh Kuppa Grad student, Computer Science

3. Dr. Aly Karsan, MD Immunopathologist, British Columbia Cancer Agency Dr. Ron Walter Geneticist, Texas State University Dr. Kavitha Venkatesan, PhD Bioinformatician, Novartis A highly collaborative team (External collaborators) 3

4. Bioinformatics: Computational and statistical analysis of biological data Data Biologists Results Genotypes / Phenotypes 4

5. Hypothesis Because cancer is a heterogeneous disease, synergistic medications can treat it better than a single drug. 5

6. Treatment A Treatment B Relapse Rational 6

7. Treatment A+B Cured Rational 7

8. Challenge Which drug combination to use? Erlotinib Lapatinib Vandetanib AEW541... … Topotecan ~100 compounds known to have some effect on cancer 8

9. A+B Challenge Which drug combination to use? Erlotinib Lapatinib Vandetanib AEW541... … Topotecan ~100 compounds known to have some effect on cancer ~5000 combinations of 2 compounds 9

10. A+B Challenge Which drug combination to use? Erlotinib Lapatinib Vandetanib AEW541... … Topotecan ~100 compounds known to have some effect on cancer ~5000 combinations of 2 compounds A+B +C ~160,000 combinations of 3 compounds 10

11. Challenge Which drug combination to use? It is not feasible to try all possible combinations of compounds in vivo or in vitro. 11

12. Challenge Which drug combination to use? It is not feasible to try all possible combinations of compounds in vivo or in vitro. Can in silico experiments help? 12

13. Predicting the compound response In order for in in silico experiment to work, we need to develop a reasonable framework to model the underling biological phenomena. 13

14. Bayesian networks are useful in modeling genes interactions 14

15. Data was provided in collaboration with Novartis 15 Affymetrix expression levels of 41896 mRNAs in 1036 cell lines

16. CCLE provides expression data useful for learning the network 16

17. First application Biological interpretation Gene hubs, causal relationships, interaction between pathways, …. 17

18. Response to compounds can be incorporated to the model too. 18

19. Response to compounds can be incorporated to the model too. 1 Bernoulli variable per compound 19

20. Response to compounds can be incorporated to the model too. The response variable (e.g. activity area) 1 Bernoulli variable per compound 20

21. The dependencies can be learned from CCLE data 21

22. Third application In silico experiments can predict the best candidates In vitro experiments can be designed more efficiently. (Run on 100 compounds instead of 1000.) 22

23. Challenge Which drug combination to use? Erlotinib Lapatinib Vandetanib AEW541... … Topotecan ~100 compounds known to have some effect on cancer A+B ~5000 combinations of 2 compounds ~160,000 combinations of 3 compounds A+B +C 23

24. Synergistic treatment Identifying the best compound combinations in silico Unlike in vitro experiments, running thousands of in silico experiments is feasible. 24

25. Forth and most exciting application By inference on the Bayesian network, we can rank top compound combinations. 1.Erlotinib+Lapatinib 2.Vandetanib+Lapatinib 3.AEW541+Topotecan 4.17-AAG+AZD6244 5.Erlotinib+AZD6244 6.Erlotinib+LBW242 7.. 8... 9.… 100. Topoteca+Paclitaxel 25

26. Preliminary Results on CCLE data 26

27. Learning the Bayesian network 27 Because learning a Bayesian network with thousand of variables is difficult, our strategy is to first identify gene modules by WGCNA, learn a network for each module, and then combine the networks in a later step. We use Banjo and bnlearn packages for learning the networks.

28. We identified a “cancer module”. 28 ~Corrected P-value 0.001

29. Comparison with random selection 29 ~Corrected P-value 0.01 Cancer module Random selection of the same size (1309 genes)

30. Examples of learned networks by Banjo

31. Examples of learned networks by Banjo

32. Conclusion 32 Network analysis is useful and provides valuable information on the biology of diseases. The heterogeneity of a disease can be revealed by studying the interaction between genes. Network analysis is an unbiased method for studying thousands of genes to identify those tens or hundreds that are associated with cancer.

33. Supplementary slides 33 For references see http://oncinfo.org/Gene+networks+inference.http://oncinfo.org/Gene+networks+inference