Predicting the evolution of cancer, one patient at a time.
Our team focuses on deciphering the dynamics of cancer growth, progression and treatment resistance in each patient using mathematical and computational approaches applied to cancer multi-omics data, with the objective of predicting the future course of the disease.
We tackle cancer as a complex system, using rational tissue sampling and integrative genomics as the basis for data generation. We combine the data we generate in the lab with mathematical models of tumour evolution and machine learning methods, with the aim of formulating clinically-driven hypotheses and test predictions that will impact the way we treat cancer.
