揭示药物反应的决定因素：对数百种肿瘤治疗药物的 PRISM 活力分析揭示选择性和机制的介导因素

Incomplete understanding of drug mechanism, selectivity, and polypharmacology can contribute to failed clinical trials. Drug candidates are generally characterized with limited pre-clinical tools and a narrow indication focus based on expected target biology. Using approaches such as large-scale PRISM profiling of 900 cell lines that have been genomically profiled in the Dependency Map coupled with machine learning analysis can provide a more thorough and agnostic understanding of oncology drugs. Here, we demonstrate that systematic characterization of an Oncology Reference library of over 250 oncology drugs, encompassing biologics (e.g., cytokines, antibody-drug conjugates), targeted protein degraders, and small molecules, of which two-thirds have never been tested with large-scale cell line profiling, enables better understanding of on-target and off-target effects. We developed models to quantify selectivity, on-target activity, and polypharmacology. Surprisingly, in some instances, these measures of cellular specificity can anticipate differential outcomes in clinical trials. Among unexpected findings, we identified a recurrent viability signature predictive of compound lysosomal accumulation, a phenomenon associated with phospholipidosis. In addition, CDK4-selective inhibition emerged as a potential therapeutic vulnerability across a diverse range of cancer types, including CDK6-deficient or CDK4-altered cancer. Our findings establish the Oncology Reference dataset and PRISM as a benchmark for evaluating current and emerging cancer therapies: combining large-scale systematic cell line profiling, genomic characterization, and machine learning analysis enables comprehensive characterization of oncology drugs with potential to inform clinical development.