Short bio:
Dr. Benjamin Haibe-Kains is a Senior Scientist at the Princess Margaret Cancer Centre (PM), University Health Network, and Professor in the Medical Biophysics Department of the University of Toronto. Dr. Haibe-Kains earned his PhD in Bioinformatics at the Université Libre de Bruxelles (Belgium). Supported by a Fulbright Award, he did his postdoctoral fellowship at the Dana-Farber Cancer Institute and Harvard School of Public Health (USA). He is now the Canada Research Chair in Computational Pharmacogenomics and the Scientific Director of the Cancer Digital Intelligence Program at PM. Dr. Haibe-Kains’ research focuses on the integration of high-throughput data from various sources to simultaneously analyze multiple facets of cancer progression and therapy response using machine learning and artificial intelligence methods. Dr. Haibe-Kains’ team analyzes large-scale radiological and (pharmaco)genomic datasets to develop new prognostic and predictive models to improve cancer care.
Abstract:
The application of machine learning (ML) and artificial intelligence (AI) is transforming predictive oncology, yet clinical adoption remains limited due to the lack of standardized methodologies. In this talk, I introduce the seven hallmarks of predictive oncology, a framework ensuring that Ml/AI models for drug response prediction are scientifically and clinically relevant. By establishing rigorous benchmarks, we can bridge the gap between research and real-world applications, accelerating the integration of AI-driven predictors in oncology.
In the second part, I present PMATCH, a clinical trial matching platform developed at Princess Margaret Cancer Centre. PMATCH automates patient-trial matching by leveraging clinical and genomic data, optimizing trial selection based on predictive biomarkers. This system aims to enhance accessibility, reduce clinician workload, and ensure more equitable enrollment—especially for patients in remote areas. Together, these advances in predictive modeling and automated trial matching could pave the way for more personalized, efficient, and scalable cancer treatment strategies.