Genetic Regulatory Complexity: Lessons from yeast to cancer

1. Genetic Regulatory Complexity: Lessons from yeast to cancer Dana Pe’er Dept. of Biological Sciences C2B2 Center for Computational Biology Columbia University .

2. How does sequence variation affect the function of molecular networks?

3. Genetic Variation and Regulation: ? ? ? ? ? ? ? Variation  perturbations to regulatory network

4. … Lab (BY) Wine (RM) profile gene expression determine genome segregation correlate genotype with transcript abundance “Genetics Genomics” Data

5. Activator Genotype RM BY Modularity • Module - a set of biological entities that act collectively to perform an identifiable and distinct function activator … Target Target Target • Power of modules: multiple co-regulated genes provide statistical power for linkage. • Enables combinatorial regulation

6. Utilizing Gene Expression DNA variation can change abundance of a regulator which in turn changes expression of many targets • Can “explain" the linkage • Can uncover novel regulatory mechanisms

7. Causality verses Co-regulation The key is to go beyond pair-wise correlation and test multivariate statistical dependencies Statistical test: Is regulator gene expression is significantly more predictive of trait than genotype? Permutation testing fixing genotype Share Common Cause Cause Effect

8. Zooming Into the Linked Region • A Bayesian score that integrates gene expression and SNP structure to help identify causal gene • Prioritizing genes within a region • Increasing confidence in a weak linkage

9. Expression Data Candidateregulators Genotype Data clustering clustering Geronimo: Module Networks Algorithm Gene partition Gene reassignment to modules : : Functional modules Network Learning Engine • ~600 candidate regulators • ~500 genotype regions Regulationprogramlearning Automatically identify modules of co-regulated genes & their regulatory program

10. expression genotype Puf3 Puf3 Module Dhh1: Part of P-body complex that stimulates mRNA decapping, coordinates distinct steps in mRNA function and decay. Hypothesis: Puf3 “marks” mRNAs by binding, then Dhh1 p-body degrades them? Mitochondria 139/153 genes – p < 10-92 Puf3 (3’UTR) P<5.8X10-131

11. ΔDhh1 leads to over expression of puf3 targets in a similar magnitude to ΔPuf3 Prediction Validated • Approach discovered a novel regulatory mechanism which we validated • Required using gene expression as an intermediary • Treating the module as an entity aided interpretation Our detected gene expression regulator is causal

12. Linkage Analysis Result • Large set of genes linked to ChrXIV region • Highly heterogeneous • No hypothesis suggested for linkage ChrXIV 500 genes linked to ChrXIV locus Linkage analysis [Brem & Kruglyak]

13. Dhh1 Identifying the Causal SNP Region contains 33 genes Chromosome XIV region Use gene expression with Bayesian Prior to identify gene • Binds at 3’ UTR of mRNAs • Regulates translation of Puf5-dependent mRNA (HO) • Significant SNP in highly conserved residue Our gene expression regulator aids in identifying causal SNP

14. The Full Ribosome Module • Hundreds of ribosomal genes have clear co-expression pattern, but only 4 link to primary locus with any significance, no loci associated with others, even as we lower p-value threshold. • When we use modularity to include interacting loci…

15. Can this approach scale to human? Challenges • Network complexity • Multiple tissues • 100x genotypes • No breeding • No perturbation

16. Genetic Genomics of Cancer Coordinated genomics study of tumor samples: Copy number, LOH, SNPs + Gene Expresion ? ? Melanoma • Problem! • Yeast: 500 genotypes and 600 regulators • Cancer: Tens of thousands of genotypes and regulators.

17. Limiting to only those regions with copy number change • Almost every region of the genome is altered in at least one tumor Cancer • Which are the drivers? Solution: Use evolutionary principles Beroukhim et.al PNAS 2007

18. GISTIC: Significantly recurring changes AMPLOTYPE: Integrating SNPs

19. Preliminary step,prioritize a smaller set ofpotential causative genotypes: (GISTIC, 2008 PNAS) Conexic: Module Network Algorithm Conexic: Module Network Algorithm Gene Expression Copy Number • Integrating genotype to expression • Who are the driving mutations? • What genes and processes do they effect? • How do they interact together?

20. 3p14 -MITF Capturing Regulation • Module enriched for pigment metabolism and creation TF which regulates the differentiation and development ofmelanocytes retinal pigment epithelium and is also responsible forpigment cell-specific transcription of the melanogenesis enzyme genes

21. 3p14 -MITF Ras Raf Mek MapK MITF BCL2 Pigmentation SILV Pax6 A Key Melanoma Oncogene • Anti-proliferation factor that is a crucial event for the progression of melanomas that harbor oncogenic B-RAF. • MITF chosen as key regulator for 14 modules (different combinatorial regulation) • All known MITF targets detected

22. Beyond Correlation Is simple correlation enough? • Correlation alone does not identify any candidate gene in deleted region on chromosome 13. • Perhaps not real driver mutation? Chromosome 13

23. 13q12.11 - TBCD14 13q12.11 - EDNRB Combinatorial Regualtion • Module significantly enriched for apoptosis and AKT • EDNRB is needed for melanocyte proliferation. Inhibiting its action in melanoma leads to apoptosis. • TBC1D4 connected to EDNRB via the AKT pathway. • Dramatically different gene expression of genes in the same deleted region Conexic picked two distinct genes in the same deleted segment, which combinatorially influence a set of apoptosis genes

24. 3q21.3 - RAB7A 15q21 - RAB27A Discovering Additional Driver Mutations Problem: Many known oncogenes are missed by GISTIC, high statistical burden. Solution: Lower the threshold. Significantly recurring copy number change coinciding with its ability to predict the expression patterns varying across tumors, strengthens the evidence of its causative role in cancer.

25. 3q21.3 - RAB7A 15q21 - RAB27A Rab7a amp Rab27a amp Targeting the same pathway • Rab27a and Rab7a regulate melanosome maturation. • A region containing either of these genes was amplified in 23 samples Our approach successfully scales to better understand driving mutations in cancer

26. How does gene expression effect growth?

27. Complex Phenotype: Cell Growth • Does the cell care that ~4000 gene expressions significantly change? How? • Growth under 40 physiologically relevant conditions: • Carbon source (8 sugars), environment stress (e.g osmolarity, heat), starvation (nitrogen, phosphate) • Robust highly quantitative protocol • OD sampled every 10 minutes • spearman correlation 97-99%

28. Can we explain our growth phenotypes? Same as before, try to use gene expression as an intermediary to explain cellular phenotype Note: gene expression measured in Glucose and growth measured in many other conditions.

29. Does regulation effect growth rates? Conclusion: Genetic variation in the regulatory network has a significant effect on growth.

30. Chr13:227254 Chr13:245457 Regressors SUT2 DHH1 Prediction R+G G (3 markers) Growth H2O2 Growth Under Oxidative Stress • MSE: 0.50, compared with 0.81 of using only genotype

31. YAP1 • Oxidative stress causes p-bodies to increase • P-bodies degrade mitochondrial ribosome, critical under oxidative stress M13 locus growth (H2O2) DHH1 growth (H2O2) Integrating Genotype and Gene Expression Aids in Interpreting Growth Phenotypes Intermediate regulator Zooming In the Region • Yap1p activates transcription of genes in response to oxidative stress

32. Summary ? ? ? ? • Combining genotype and gene expression: • Helps better explain observed variation • Uncovers regulatory network • Approach scales to discovering driver genes in cancer and the pathways they alter • Towards a complex phenotype, using the network to understand growth in different biological conditions

33. What Next: Understanding Drug Resistance in Cancer 600 cell lines, derived from different cancers Affy 500K SNP chip Gene expression Growth under 100 drugs, 3 doses each What mutations drive drug resistance and how? ? ? With Jeff Settleman, MGH, Harvard

34. What Next:How is Signal Processing Altered in Cancer? • 70 Melanoma samples • SNP chip and Gene expression • Reverse Phase Protein Array, 300 antibodies • Growth and response under Mek inhibitor With Levi Garraway, Dana Farber

35. Acknowledgements Geronemo Yeast Cancer Su-in Lee Stanford Bo-Juen Chen Pe’er lab Oren Litvin Pe’er lab Oren Litvin Pe’er lab Uri-David Akavia Pe’er lab Daphne Koller Stanford Noel Goddard Hunter College Levi Garraway Dana Farber Funding Positions available contact: dpeer@biology.columbia.edu