From November 2022
As a group leader in Oncology Bioinformatics at Genentech, I am managing computational biologists who collaborate with experimentalists in Discovery Oncology to identify new therapeutic targets, develop novel drugs, and find biomarkers of sensitivity. My group and myself are bioinformatics leads for multiple molecule programs and I am also the biology lead for one of them. Beyond our contributions to advance drug pipeline projects, we are analyzing multimodal data to understand drug response. For my personal research, I am leveraging pre-clinical and clinical data including real-world data to study drug mechanisms of resistance in breast cancer. In the department of Oncology Bioinformatics, I am leading the implementation of a drug response software suite that includes the GR method and interactive visualization tools. I am also a member of the Roche-wide HR+ Breast Cancer Disease Area Community team that aims at identifying opportunities to advance treatment for Breast Cancer patients.
From November 2017
As a scientist in bioinformatics, I have multiple responsibilities in the department of translational oncology and the department of bioinformatics and computational biology. My main goal is to work jointly with experimentalists to identify new therapeutic targets, contribute to the development of new drugs aimed at these targets, and identify the patient populations that would most benefit from these drugs. I am the bioinformatics lead on different projects aimed at developing and characterizing new targeted therapies. In these projects, I optimize the target inhibition profile, identify in vitro models for testing the candidate molecules, and analyze the pre-clinical and clinical data for these projects. In the department of bioinformatics, I have been extensively involved in the implementation of the method for characterizing drug response that I developed during my postdoctoral work. I am also involved in the Genentech-Roche initiative for advanced analytics that aims at developing and implementing state-of-the-art machine learning algorithms across the organization.
January 2012 – November 2017
My postdoctoral research focused on developing computational methods integrating in vitro –omics data with patient data in order to understand the causes of the variable efficacy of cancer therapies. The goal of my research was to identify mechanisms of drug resistance, design therapeutic strategies that efficiently target them, and identify stratification biomarkers to best use these therapies. As lead computational biologist of the LINCS center at HMS (lincs.hms.harvard.edu), I applied statistical methods that cope with high-dimensional and heterogeneous data to identify biomarkers of drug sensitivity, growth factor responsiveness, and toxicity. In collaboration with the Broad Institute, I combined transcriptomic and proteomic data to better understand the phenotypic response of cancer cells to kinase inhibitors. In 2016, I made my most significant scientific contribution by defining novel drug sensitivity metrics based on growth rate inhibition (GR). These metrics are robust to variation in division rates and distinguish cytotoxic from cytostatic responses. My work shows that developing appropriate theoretical frameworks to analyze large datasets yields new biological insights and improves the interpretation of in vitro data.
May 2007 – September 2011
During my PhD, I developed novel computational approaches to model and quantify the phenotypes of biological systems ranging from protein aggregation, circadian cycles, the apoptotic pathway, and synthetic circuits regulating homeostasis.