CanDrivR-CS: cancer-specific machine learning to separate recurrent from rare missense variants
Overview
Cancer genomes contain huge numbers of mutations, but only a subset are functionally important. One simple clue is recurrence: if the same missense variant shows up repeatedly across patients with the same cancer type, that can suggest positive selection for growth advantage. At the same time, rare variants can still matter (for example, if they emerge under treatment as resistance mechanisms).
In work led by Amy Francis, we introduce CanDrivR-CS, a framework that trains cancer-type-specific machine-learning models to distinguish recurrent from rare somatic missense variants. It’s a useful reminder that “one-size-fits-all” predictors can miss disease-context signals, and that relatively interpretable models can still surface mechanistic hypotheses.