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Detecting repeated cancer evolution in human tumours from multi-region sequencing data

posted Jun 29, 2017, 12:53 AM by Giulio Caravagna
My last effort to study cancer evolution is finally out as preprint (biorXiv). The problem is to infer regularities in the development of tumours (precisely, recurrent evolutionary trajectories), with clear implications for the detection of prognostic markers, and evolutionary subgroups. The problem is hard but we have observed that, since we see cancer evolution happening multiple times in several patients, we can use a cool type of Machine Learning called Transfer Learning to make better inferences. So we have developed REVOLVER (Repeated EVOLution in cancER), and applied to lung, breast and renal cancers. We found subgroups of tumours that are characterised by recurrent evolutionary trajectories, and that have prognostic value. Joint work with Ylenia, GuidoDanieleTrevor and Andrea!
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