Computational prediction of therapeutic response and cancer outcomes
Griffiths, M.; Kubeyev, A.; Laurie, J.; Giorni, A.; Zillmann da Silva, L. A.; Sivasubramaniam, P.; Foster, M. T.; Biankin, A. V.; Asghar, U. S.
Show abstract
Oncology therapeutic development continues to be plagued by high failure rates leading to substantial costs with only incremental improvements in overall benefit and survival. Advances in technology including the molecular characterisation of cancer and computational power provide the opportunity to better model therapeutic response and resistance. Here we use a novel approach which utilises Bayesian statistical principles used by astrophysicists to measure the mass of dark matter to predict therapeutic response. We construct "Digital Twins" of individual cancer patients and predict response for cancer treatments. We validate the approach by predicting the results of clinical trials. Better prediction of therapeutic response would improve current clinical decision-making and oncology therapeutic development.
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