Computer-assisted functional precision medicine in cancer

Biological mechanisms provide a link between anti cancer compounds and therapy responses. Insight to biological mechanisms is central for tomorrow’s clinical decision support systems and personalised cancer therapy. I will present the NTNU DrugLogics software that combines cancer signalling prior knowledge and data measurements to provide models that can predict therapy responses. We combine in silico-generated predictions with in vitro observations in the PRESORT project where patient-derived cancer cultures are subjected to drugs and drug combinations predicted for the given patient, to appreciate the efficacy for individual patients. Together with Institut Curie, Charité, BSC, Uppsala University and ProtAtOnce, we test integrated computational and experimental pipelines on historic cohorts of patients treated at the molecular tumour boards at Institut Curie and Charité. Current precision medicine trials, including the nation-wide trial IMPRESS-Norway, typically rely on individual and static biomarkers like DNA mutations for prediction of drug responses. For future iterations of clinical decision support, computer simulations taking in a number of data points and analyzed together, combined with validation in patient-derived tumour samples, will be paramount.

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