SynergyMiner

1. SynergyMiner :  find  better  drug   combinations  to  treat  cancer Li  Wang

2. Why  drug  combinations?

3. From  Mono  therapy  to  pairwise  combination Drug  target  genes Genetic  context

4. From  Mono  therapy  to  pairwise  combination Drug  target  genes 85  cell  lines,  119  drugs     0.6M  testable   conditions,  0.3%  tested Genetic  context

5. Integrating  in  vitro  and  in  silico data  sources Systematic Drug  screen Drug  target   gene  network Cell  line   genetics IC50,   IC50,   %  of  cell  killed,   %  of  cell  killed,   Max  conc.,  etc. Max  conc.,  etc. Gene  expression Gene  expression (17,000  genes) (17,000  genes) 77  signaling  pathways 77  signaling  pathways 88,996   88,996   protein protein-­‐ -­‐protein interactions interactions DNA  mutations DNA  mutations (75,000  IDs) (75,000  IDs) protein

6. Synergy:  Combined  effect   >  Σ individual  effects   Gene   Network Genetic  Data Synergy  Scores Top  20% Positive Negative Graph-­‐based Feature Engineering Univariate Feature Selection Mono   Therapy Binary   Classification

7. Model  performance  and  feature  analysis Feature  Importance Drug  Screen Gene  Network Gene  Expression Gene  Pathway 4-­‐fold  CV DNA  Mutation Tissue  of  Origin 0 0.005 0.01 0.015 0.02

8. About  me: About  me: Li  Wang Li  Wang • PhD  in  Molecular  Biology • M.S  in  Applied  Math