Marc Hafner
Senior Principal Scientist Bioinformatics, Oncology Bioinformatics, Discovery Oncology
"I am excited by the opportunity to drive drug development using data science and computational modeling."
In 2017, I joined the department of Oncology Bioinformatics at Genentech. Now, I lead the Discovery Oncology Bioinformatics team, composed of computational biologists focused on identifying potential therapeutic targets, developing novel drugs, and studying the mechanisms underlying drug response and resistance. The group plays a pivotal role in Genentech's drug pipeline, collaborating with other functions to move molecules towards the clinic and identify suitable patient populations and associated biomarkers for clinical development.
Prior to Genentech, I completed my PhD at the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland in 2011. I pursued my training as a Swiss National Science Foundation Fellow in Professor Sorger's laboratory at Harvard Medical School (HMS). During my postdoctoral research, I published a new method for normalizing drug response data to avoid artifacts and applied it to determine the impact of polypharmacology of CDK4/6 inhibitors on drug response.
Postdoctoral Mentor
The most exciting part of being a mentor is helping trainees develop their potential and guide them to become independent scientists.
Featured Publication
Real-world data reveal increased ESR1 mutation prevalence and altered genomic landscape in HR+/HER2- breast cancer tumors treated with CDK4/6 inhibitors.
npj Breast Cancer. 2024
N. Chaudhary, …, and M. Hafner.
An allosteric pan-TEAD inhibitor blocks oncogenic YAP/TAZ signaling and overcomes KRAS G12C inhibitor resistance.
Nature Cancer. 2023
Thijs Hagenbeek*; Jason R. Zbieg*; Marc Hafner*; et al.
Growth-rate model predicts in vivo tumor response from in vitro data.
CPT: Pharmacometrics & Systems Pharmacology. 2022
R. Diegmiller, L. Salphati, … , M. Hafner.
Multiomics Profiling Establishes the Polypharmacology of FDA-Approved CDK4/6 Inhibitors and the Potential for Differential Clinical Activity.
Cell Chem Biol. 2019
M. Hafner*, C.E. Mills*, K. Subramanian, et al.,
Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs.
Nat Methods. 2016
M. Hafner*, M. Niepel*, M. Chung, and P.K. Sorger.
My personal research is focused on analyzing pre-clinical and clinical data, including real-world data, to investigate drug resistance mechanisms in breast cancer and develop treatments that effectively target these mechanisms. I am also the biology lead on an internal program and the computational lead on a few others. Throughout those programs, I learnt how computational analyses can impact drug development.
- Ecole Polytechnique Fédérale de Lausanne (EPFL), PhD in Computational Systems Biology – 2011
- Ecole Polytechnique Fédérale de Lausanne (EPFL), M.S in Physics with minor in biophysics – 2007
- Ecole Polytechnique Fédérale de Lausanne (EPFL), B.S in Physics – 2005
- AACR Scholar-in-Training Award at AACR Annual Meeting – 2017
- Best Computational System Design Poster Award at the SB5.0 conference – 2011