Institut de recherche du Centre universitaire de santé McGill (CUSM)
Domaine : Cancer
The epidemic of cancer in Canada and Quebec is an alarming reality that demands fundamentally new insights into the therapeutic strategies being designed. Almost half of all cancer patients receive radiotherapy as part of their treatment and it is the main option at the advanced stages. However, radiation hypersensitivities that manifest as a host of normal tissue toxicities and inflammatory responses are major limiting factors to treatment success in many cancer sites and the main obstacle to applying new promising radiation modalities. A key goal of modern radiotherapy research is to predict at the time of treatment planning, the risks of radiation-induced toxicities for each patient. Because of the inherent complexity and variability of the biological response to radiation therapy, current physical or biological models taken separately are insufficient to accurately predict this risk. Therefore, we are investigating a novel system-based approach that integrates relevant biological information while controlling for physical variations in the same population using artificial intelligence and bioinformatics techniques. We plan to conduct retrospective and prospective studies in patients who receive radiotherapy at MUHC. The data collected and the developed methodology would provide new insights into radiation-induced toxicities signaling mechanisms and allow for better predictive models of toxicity risk. The findings could be used clinically to personalize patients' treatment plans, reduce the risk of complications or provide therapy that is more intensive for those patients likely to benefit.