Development of predictive models to identify pathways relevant to compound sensitivity in

cancer.

James R. Ortega Florida International University

Mentor: Amrita Basu, PhD Clemons Group

Broad Institute SRPG

Importance

•  Aim: To characterize the biological determinants of compound sensitivity in cancer. •  Personalization and improved therapeutics.

9/2/11 2 Schreiber et al. (2010). Nat Biotechnol 28(9):904-906

Methodology

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Methodology

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The NCI-60 cancer cell lines

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NCI-60 diversity leukemia

NSC-lung carcinoma

colon

CNS

melanoma

ovarian

renal

prostate

breast

Monks et al. (1991) . J Natl Cancer Inst. 83(11):757-66

17-allylamino-geldanamycin(17-AAG)

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•  Being investigated in many cancers, including phase II clinical trials for breast cancer

•  Inhibitor of Hsp90

Modi et al. (2011) Clin Cancer Res [Epub ahead of print] Schulte & Neckers (1998) Cancer Chemother Pharmacol 42(4):273-9

Data pipeline

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Methodology

9/2/11 8 Zou & Hastie (2005) Appl Stat J Roy St-C 67(2):301-320

Elastic net regression

•  Allows for •  The analysis of high-dimension data sets •  Quantitative predictions •  Simple model interpretation

•  Limitation •  Sensitive to the number of input features

•  Implementation •  Ranking and scheduling algorithms for feature

selection

9/2/11 9 Zou & Hastie (2005) Appl Stat J Roy St-C 67(2):301-320

Methodology

9/2/11 10 Zou & Hastie (2005) Appl Stat J Roy St-C 67(2):301-320

pre-filter feature list

Data pipeline

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Scheduling algorithm results

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204 input gene expression features

Log2 number of top features selected

Elastic net predictions

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•  o Actual sensitivity •  x Prediction using all 60 cell lines •  + Prediction using leave-one-out

cross-validation

Methodology

9/2/11 14 Zou & Hastie (2005) Appl Stat J Roy St-C 67(2):301-320

16K

204

72

pre-!ltered feature list

Methodology

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pre-!ltered feature list

16K

204

72

Subramanian et al. (2005) PNAS 102(43):15545-15550

GSEA MSigDB network analysis results

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22

50

GSEA MSigDB breast cancer overlaps

Breast cancer gene set overlap

No breast cancer gene set overlap

Network Enrichment Analysis results

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= breast cancer gene-set overlap

Conclusion

•  Our method has provided a framework for future analyses of the genetic basis for compound sensitivity in cancer.

•  In addition, our method of analysis could be useful for the study of other diseases and biological contexts.

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Acknowledgements

•  Computational Chemical Biology •  Amrita Basu •  Nicole Bodycombe •  Hyman Carrinski •  Paul Clemons •  Vladimir Dancik •  Joshua Gilbert •  Taner Kaya •  Sandrine Muller

•  Diversity Initiative •  Bruce Birren •  Nicole Edmonds •  Eboney Smith

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Potential applications

•  Identifying pathways relevant to stem cell differentiation.

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Potential applications

•  Identifying pathways relevant to compound sensitivity in infectious diseases

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Potential applications

•  Identifying small molecule probes relevant to the genetics of a given cellular context.

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