Precision Combination Therapies Based on Recurrent Oncogenic Coalterations.

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 coalterations 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 multiomic data, the method maps recurrent coalteration 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 impr

SUBMITTER: Li X 

PROVIDER: S-EPMC9524464 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature