"Advances in high-throughput genomic assay technologies and the computational techniques necessary for their productive use have created unique opportunities—for pursuing personalized approaches to cancer therapy and for better understanding the complex molecular basis of the disease."
In 2006, I obtained a Ph.D. in Statistics at the University of California, Berkeley, under the mentorship of Terry Speed, and I was one of the early participants in a new, cross-disciplinary designated emphasis program in computational biology. I went on to join the European Bioinformatics Institute as a staff scientist, investigating technical aspects of microarray and next-generation sequencing data analysis with Wolfgang Huber, and applying these technologies to study fundamental aspects of meiotic recombination and inheritance, in collaboration with Lars Steinmetz at the European Molecular Biology Laboratory.
I joined Genentech in 2010, applying my technical background to the study of prognostic and predictive cancer biomarkers. My initial work was in pre-clinical model systems; however, recent methodological advances have enabled application of the same technologies to clinical trial sample collections, and I now collaborate closely with scientists in both Research Molecular Oncology and Oncology Biomarker Development.
Advances in high-throughput data generation technologies have lead to a dramatic increase in the demand for computational scientists in molecular biology research. For individuals who are highly skilled in the development or use of specialized analytic techniques, Genentech provides an outstanding opportunity to apply these techniques to practical problems in drug target and biomarker discovery. However, quantitative sophistication is not sufficient for success in this space; it also requires a strong understanding of the relevant biological principles and a focus on the practical impact of methodological advances. I am interested in recruiting two types of recent graduates to post-doctoral positions in my group: (i) computational specialists who seek to deepen the biological and/or clinical relevance of their work, and (ii) experimental scientists who are transitioning to data-heavy research approaches and want to significantly increase their computational abilities.
Nature. 2018 Feb 22;554(7693):544-548.
I and my team at Genentech pursue research in three major areas: (i) statistical and bioinformatics aspects of genomic data analysis, (ii) identification and application of genomic-assay biomarkers in the context of oncology clinical trials, and (iii) general characterization of the mechanisms of oncogenesis and drug resistance. Recently, we have put increased emphasis on problems in cancer immunology and immunotherapy, including molecular characterization of the tumor microenvironment, and the potential use of tumor-specific neoantigens as biomarkers or therapeutic targets.
One of the most exciting aspects of working at Genentech is the interplay between traditional cancer research in model systems and our emerging ability to apply genomic technologies to human tumor samples obtained from our clinical trial participants. Pre-clinical and clinical data each present unique challenges to the researcher, but also unique opportunities. To fully enable therapeutic — and immunotherapeutic — benefit for patients, we will need to make the most of both of these rich resources.