Marc Hafner, PhD

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Resume:

My research approach is to develop novel computational methods to quantify the phenotypes of biological systems. In brief, I propose to develop 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 is to identify mechanisms of drug resistance, design therapeutic strategies that efficiently target them, and identify stratification biomarkers to best use these therapies. I am currently developing and applying such methods as a scientist in translational oncology at Genentech.

One unexploited opportunity in current biomedical research is the integration of multi-omics data into interpretable and actionable computational models that surpass single gene biomarkers. Approaches routinely used in molecular biology fail to cope with the multidimensionality of high-throughput data, whereas routine statistical methods and machine learning algorithms cannot account for the complex interactions found in biological systems. These limitations are particularly tangible in translational cancer research because oncogenes are tangled in a network of signaling proteins, the number of genomic features greatly surpasses the number of samples, and most tumors have complex genetic profiles. Innovative computational methods that capture the nature of the data are necessary to exploit the richness of recent profiling efforts and available patient data and thus advance our understanding of cancer biology and guide treatment personalization.

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. 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. Recently, 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.

As a scientist at Genentech, I collaborate with experimentalists and oncologists focusing on translational oncology. I want to apply my expertise in numerical methods and signaling pathways to study drug resistance in patients and design therapeutic strategies that increase drug efficacy.

Curriculum Vitae