Precision combination therapies based on recurrent oncogenic co-alterations

Abstract Cancer cells depend on multiple driver alterations whose oncogenic effects can be suppressed by drug combinations. Here, we provide a comprehensive resource of precision combination therapies tailored to oncogenic co-alterations that are recurrent across patient cohorts. To generate the resource, we developed Recurrent Features Leveraged for Combination Therapy (REFLECT), which integrates machine learning and cancer informatics algorithms. Using multi-omic data, the method maps recurrent co-alteration signatures in patient cohorts to combination therapies. We validated the REFLECT pipeline using data from patient-derived xenografts, in vitro drug screens, and a combination therapy clinical trial. These validations demonstrate that REFLECT-selected combination therapies have significantly improved efficacy, synergy, and survival outcomes. In patient cohorts with immunotherapy response markers, DNA repair aberrations, and HER2 activation, we have identified therapeutically actionable and recurrent co-alteration signatures. REFLECT provides a resource and framework to design combination therapies tailored to tumor cohorts in data-driven clinical trials and pre-clinical studies.