title: Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data.
abstract: Large-scale pharmacogenomic screens of cancer cell lines have emerged as an attractive pre-clinical system for identifying tumor genetic subtypes with selective sensitivity to targeted therapeutic strategies. Application of modern machine learning approaches to pharmacogenomic datasets have demonstrated the ability to infer genomic predictors of compound sensitivity. Such modeling approaches entail many analytical design choices; however, a systematic study evaluating the relative performance attributable to each design choice is not yet available. In this work, we evaluated over 110,000 different models, based on a multifactorial experimental design testing systematic combinations of modeling factors within several categories of modeling choices, including: type of algorithm, type of molecular feature data, compound being predicted, method of summarizing compound sensitivity values, and whether predictions are based on discretized or continuous response values. Our results suggest that model input data (type of molecular features and choice of compound) are the primary factors explaining model performance, followed by choice of algorithm. Our results also provide a statistically principled set of recommended modeling guidelines, including: using elastic net or ridge regression with input features from all genomic profiling platforms, most importantly, gene expression features, to predict continuous-valued sensitivity scores summarized using the area under the dose response curve, with pathway targeted compounds most likely to yield the most accurate predictors. In addition, our study provides a publicly available resource of all modeling results, an open source code base, and experimental design for researchers throughout the community to build on our results and assess novel methodologies or applications in related predictive modeling problems.
authors.affiliation: Sage Bionetworks, 1100 Fairview Ave. N Seattle, WA 98109, USA. in.sock.jang@sagebase.org.
authors.forename: In Sock
authors.initials: IS
authors.lastname: Jang
authors.forename: Elias Chaibub
authors.initials: EC
authors.lastname: Neto
authors.forename: Juistin
authors.initials: J
authors.lastname: Guinney
authors.forename: Stephen H
authors.initials: SH
authors.lastname: Friend
authors.forename: Adam A
authors.initials: AA
authors.lastname: Margolin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D045744:Cell Line, Tumor; D019295:Computational Biology; D030541:Databases, Genetic / Q000706:statistics & numerical data; D019008:Drug Resistance, Neoplasm / Q000235:genetics; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000188:drug therapy* / Q000235:genetics*; D010597:Pharmacogenetics / Q000706:statistics & numerical data*; D012044:Regression Analysis