Improving the performance and interpretation of cancer trials

The LSP is engaged in a wide-ranging project to understand the causes of success and failure in cancer clinical trials and develop improved computational methods for early-phase trials that can help predict trial success. 

Clinical trials are the most expensive and important stage of drug development and yet the determinants of success and failure remain poorly understood. In part, this is because it is difficult to access data on the outcomes of existing trials. To help, we are creating a database of survival curves and individual participant data (IPD) that can be used to test specific hypotheses about drug mechanism of action and improve how early phase trials are scored. We have already determined that mechanisms of combination cancer therapy rarely involve drug interaction (synergy) but are instead based on the mechanism of independent drug action in which each patient benefits from the single agent in the combination with the greatest activity. This approach is already being used by leading pharmaceutical companies to prioritize future immuno-oncology trials. In related work, we have developed methods to increase the statistical power associated with small clinical trials, including the basket trials increasingly used to identify responsive patient populations. Our data suggest that current therapeutic approaches would dramatically benefit from increasing precision based in part on new diagnostics involving next-generation digital histology.