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Genetic Regulatory Complexity: Lessons from yeast to cancer Dana Pe’er Dept. of Biological Sciences C2B2 Center for Computational Biology Columbia University . How does sequence variation affect the function of molecular networks? Genetic Variation and Regulation: ? ? ? ? ? ? ? Variation perturbations to regulatory network “Genetics Genomics” Data … Lab (BY) profile gene expression Wine (RM) determine genome segregation correlate genotype with transcript abundance Modularity Module - a set of biological entities that act collectively to perform an identifiable and distinct function activator Target Activator Genotype RM BY Target … Target Power of modules: multiple co-regulated genes provide statistical power for linkage. Enables combinatorial regulation 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 Causality verses Co-regulation Cause Share Common Cause Effect 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 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 Network Learning Engine Genotype Data Expression Data Candidate regulators ~600 candidate regulators ~500 genotype regions :: clustering Gene partition Regulation Module program learning Geronimo: Gene reassignmen Networks Algorithm t to modules Functional modules Automatically identify modules of co-regulated genes & their regulatory program Puf3 Module Dhh1: Part of P-body complex that stimulates mRNA decapping, coordinates distinct steps in mRNA function and decay. Mitochondria 139/153 genes – p < 10-92 Puf3 (3’UTR) P<5.8X10-131 Puf3 Hypothesis: Puf3 “marks” mRNAs by binding, then Dhh1 p-body degrades them? expression genotype Prediction Validated ΔDhh1 leads to over expression of puf3 targets in a similar magnitude to ΔPuf3 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 Linkage Analysis Result ChrXIV 500 genes linked to ChrXIV locus Linkage analysis [Brem & Kruglyak] Large set of genes linked to ChrXIV region Highly heterogeneous No hypothesis suggested for linkage Identifying the Causal SNP Region contains 33 genes Use gene expression with Bayesian Prior to identify gene Chromosome XIV region Dhh1 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 The Full Ribosome Module Hundreds of ribosomal genes have clear coexpression 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… Can this approach scale to human? Challenges Network complexity Multiple tissues 100x genotypes No breeding No perturbation 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. 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 GISTIC: Significantly recurring changes AMPLOTYPE: Integrating SNPs Conexic: Module Network Algorithm prioritize a smaller set of potential causative genotypes: (GISTIC, Preliminary step, Gene Expression Copy Number 2008 PNAS) Integrating genotype to expression Who are the Conexic: driving mutations? Module Algorithm What genesNetwork and processes do they effect? How do they interact together? Capturing Regulation 3p14 -MITF TF which regulates the differentiation and development of melanocytes retinal pigment epithelium and is also responsible for pigment cell-specific transcription of the melanogenesis enzyme genes Module enriched for pigment metabolism and creation A Key Melanoma Oncogene Ras Raf Mek MapK MITF 3p14 MITF Pigmentation BCL2 SILV Pax6 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 Beyond Correlation Is simple correlation enough? Chromosome 13 Correlation alone does not identify any candidate gene in deleted region on chromosome 13. Perhaps not real driver mutation? Combinatorial Regualtion 13q12.11 - TBCD14 13q12.11 - EDNRB 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 Conexic picked two distinct genes in the same via the AKT pathway. deleted segment, which Dramatically different gene of genes in the combinatorially influence expression same deleted region a set of apoptosis genes Discovering Additional Driver Mutations Problem: Many known oncogenes are missed by GISTIC, high statistical burden. Solution: Lower the threshold. 3q21.3 - RAB7A 15q21 - RAB27A 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. Targeting the same pathway Rab27a and Rab7a regulate melanosome maturation. A region containing either of these genes was amplified in 23 samples 3q21.3 - RAB7A 15q21 - RAB27A Our approach successfully scales to better understand driving mutations in cancer Rab7a amp Rab27a amp How does gene expression effect growth? Complex Phenotype: Cell Growth Does the cell care that ~4000 gene expressions significantly change? How? Growth under 40 physiologically relevant conditions: by rm Carbon source (8 sugars), environment stress (e.g osmolarity, heat), starvation (nitrogen, phosphate) Robust highly quantitative protocol OD by sampled every 10 minutes rm spearman correlation 97-99% by rm Can we explain our growth phenotypes? by rm by rm by rm 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. Does regulation effect growth rates? Mean Squared Error Test MSE 0.8 0.5 Single Genotype S RegNet G RegNet RG 0.2 1 6 11 16 conditions Conclusion: Genetic variation in the regulatory network has a significant effect on growth. Growth Under Oxidative Stress Regressors Chr13:227254 Chr13:245457 0.31 0.21 -0.291 50 100 50 100 -0.41 1 1 1 Prediction 50 SUT2 DHH1 100 50 100 1 50 100 1 1 50 50 100 100 R+G G (3 markers) 1 Growth 50 100 1 50 100 1 50 100 H2O2 MSE: 10.50, compared with 0.81 of using only 50 100 genotype Integrating Genotype and Gene Expression Aids in Interpreting Growth Phenotypes Intermediate regulator DHH1 growth (H2O2) Oxidative stress causes pbodies to increase P-bodies degrade mitochondrial ribosome, critical under oxidative stress Zooming In the Region YAP1 M13 locus growth (H2O2) Yap1p activates transcription of genes in response to oxidative stress 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 What Next: Understanding Drug Resistance in Cancer 101006_plate1 1 ? OD [60 0nm] ? D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 0.1 0.01 -5 0 5 10 15 20 25 Ti me[h rs] 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 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 Acknowledgements Geronemo Su-in Lee Stanford Daphne Koller Stanford Yeast Bo-Juen Chen Pe’er lab Cancer Oren Litvin Pe’er lab Noel Goddard Hunter College Uri-David Akavia Oren Litvin Pe’er lab Pe’er lab Levi Garraway Dana Farber Funding Positions available contact: [email protected]