Predicting personalised treatment combinations with in silico CRISPR screens

Abstract

Targeted therapies can be very effective in the short term, but often are stymied by resistance in the long term. We explore two approaches to overcome this using computational modelling. First, we explore the potential for combination treatments to target different sub-types of triple negative breast cancer. Second we demonstrate how computational modelling can predict and overcome different resistance mehcanisms that emerge under PARP inhibitor treatment.