The CRC has recently welcomed three new researchers, whose expertise will enrich our community. The CRC extends a warm welcome to them and looks forward to collaborating with them on exciting new advances in cancer research.
Mathias Cavaillé is a clinical assistant professor in the Department of Pediatrics at the Faculty of Medicine of Université Laval. He is also a researcher in the Oncology Division of the CHU de Québec-Université Laval Research Center. Dr. Cavaillé completed a dual M.D., Ph.D. program in oncogenetics. From 2022 to 2023, he assumed responsibility for oncogenetic consultations and was appointed scientific director in oncogenetics at the Centre Jean Perrin in France. Concurrently, he held the positions of deputy director and director of the Biological Resources Center. His involvement played a crucial role in the development of teleconsultations in Auvergne, as well as in the establishment and coordination of genomic analyses in oncogenetics, as part of the France-Medicine-Genomics 2025 Plan. His research focuses on the identification of new hereditary cancer risks. This includes the search for new candidate genes through the analysis of gene panels, exome, or genome, as well as the integration of polygenic risk scores in the management of patients with a hereditary risk of cancer. His research program is divided into three main areas: 1) the integration of polygenic risk scores in the management of patients at risk of hereditary breast cancer; 2) the molecular and clinical profile in oncogenetics of the diverse population of Quebec, using diagnostic genetic analyses conducted in the province; and 3) the identification of new genes of hereditary predisposition to cancer in patients with a severe cancer phenotype, without mutations identified by usual gene panel analyses.
Louis Gagnon is a clinical assistant professor in the Department of Radiology and Nuclear Medicine at the Faculty of Medicine of Université Laval. He is a researcher at the CERVO Centre. Dr. Gagnon completed a bachelor's degree in physics engineering and a first master's degree in biomedical engineering at École Polytechnique de Montréal before moving to Boston, where he completed a second master's degree in electrical engineering and computer science at MIT and a doctorate in physics and biomedical engineering jointly at Harvard and MIT. He then returned to Quebec to complete his medicine and residency in radiology at Université Laval, and finally completed his fellowship in Magnetic Resonance Imaging (MRI) at the University of California, San Diego. His research focuses on the development of new biomarkers in brain MRI for use in neuro-oncology. The specific objectives of his research program are: 1) the detection and segmentation of brain tumors using artificial intelligence and diffusion MRI; and 2) the development of a new first-principles model of diffusion MRI to better delineate brain tumors.
Venkata Manem is assistant professor at the Department of Molecular Biology, Medical Biochemistry, and Pathology at the Faculty of Medicine of Université Laval. He began his career as a researcher at the IUCPQ and an adjunct research professor at UQTR before joining the Oncology Division of the CHU de Québec-Université Laval Research Center. Prof. Manem holds a master's degree in mathematics from Memorial University, Newfoundland, and another in mathematics from Sri Sathya Sai Higher Learning Institute, India. He completed his Ph.D. in Applied Mathematics at the University of Waterloo, Ontario. He worked as a research fellow in the Department of Radiation Oncology at Massachusetts General Hospital and Harvard Medical School in Boston and completed a postdoctoral fellowship at the Princess Margaret Cancer Centre in Toronto. His research program integrates diverse multimodal datasets to develop diagnostic, prognostic, and predictive biomarkers for cancer patients using Bioinformatics and AI-based methods. This program is structured around distinct axes, each addressing a major challenge in precision oncology and aligning with the clinical trajectory of patients: 1) Diagnostic biomarkers - focusing on the development of risk prediction tools for lung and breast cancer using medical imaging data; 2) Predictive biomarkers - aimed at constructing robust multimodal biomarkers for predicting response to immunotherapy and radiotherapy by leveraging OMICS, medical images, pathological slides, and clinical data; and 3) Prognostic biomarkers - targeting the development of multimodal biomarkers for relapse and survival prediction in cancer patients using OMICS, medical imaging, and clinical data.