Recent advances in high-throughput technologies have facilitated the profiling of large panels of cancer cell lines with responses measured for thousands of drugs. The computational challenge is now to realize the potential of these data in predicting patients responses to these drugs in the clinic. We address this issue by examining the spectrum of prediction models of patient response: models predicting directly from cell lines, those predicting directly from patients, and those trained on cell lines and patients at the same time. We tested 21 classification models on four drugs (bortezomib, erlotinib, docetaxel and epirubicin) for which clinical trial data were available. Our integrative models consistently outperform cell line-based predictors, indicating that there are limitations to the predictive potential of in vitro data alone. Furthermore, these integrative models achieve better predictive accuracy and require substantially fewer patients than would be the case if only patient data were available. Altogether our results support the relevance of preclinical data for therapy prediction in clinical trials, enabling more efficient and cost-effective trial design.
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