Benjamin Haley: Mapping the Tumor Before It Resists

Discover Benjamin Haley, IVADO professor and full professor in the Department of Ophthalmology at Université de Montréal.

Every year, patients with lung cancer begin targeted treatment with promising initial results. Benjamin Haley knows what comes next: almost inevitably, the tumor develops resistance and the drug loses its efficacy. That is the moment he is trying to get ahead of, so that clinicians have the upper hand. 

Holder of the FRQ Chair in Genomic Engineering at Université de Montréal and researcher at the Centre de recherche de l’Hôpital Maisonneuve-Rosemont, he leads a laboratory specializing in large-scale genomic screens. His team uses CRISPR technology to simultaneously perturb millions of genetic sequences in cancer cells and observe what happens, in particular which mutations will render a drug ineffective before the patient even develops resistance. 

The concrete objective: produce a predictive map of mutational signatures associated with drugs targeting the KRAS gene, implicated in lung and pancreatic cancers. These signatures could then be detected in a patient’s blood to signal the emergence of resistance in real time and guide a change in treatment. 

This work would have been technically possible twenty years ago, but not at this scale, nor at this speed. 

Benjamin Haley spent more than thirteen years at Genentech before joining Université de Montréal in 2023. In industry, projects are evaluated every three or four months: they move forward or they are shut down. This pace eliminates precisely the open-ended projects, those where the invalidated hypothesis leads to something unexpected. It is in those detours, he says, that the most lasting discoveries are found. 

His appointment in the ophthalmology department, which is clinician-dominant, gives him direct access to physicians who treat patients, some with drugs he himself worked on at Genentech. This contact with clinical experience, which he never had in industry, now shapes his research choices. It is in this context that data analysis and machine learning take on their full meaning. 

Screens generate thousands of results that existing databases, designed for older technologies, can no longer interpret. He sees machine learning as a tool to cross-reference multiple sources, prioritize relevant literature, and design more efficient experiments, with the ambition of multiplying tenfold the number of screens conducted annually. 

The next step is to transpose this work from flat cell cultures to organoids, three-dimensional structures that reproduce in the laboratory the architecture of a lung, an intestine, or an eye. The closer the models are to biological reality, the more likely the predictive map is to be accurate when a patient develops resistance.

The stakes remain the same: getting ahead of resistance, before it becomes irreversible.

Holding a doctorate in biomedical sciences from the University of Massachusetts and a postdoctoral fellowship from the University of California, Berkeley, Benjamin Haley spent more than thirteen years at Genentech leading research teams in oncology, immunology, and neurobiology, before joining Université de Montréal in 2023 as a full professor in the Department of Ophthalmology.

It was in industry, working on mRNA-based cancer vaccine programs and T cell therapies, that he came to measure the distance between a fundamental discovery and a treatment that reaches a patient. A distance he is now determined to close, from the lab.