AUG 21, 2013 10:00 AM PDT

Genomic Approaches to Discover Biomarkers of Drug Response in Cancer

  • Assistant Professor in the Department of Pathology, Research Scientist, Memorial Sloan-Kettering Cancer Center
      Michael Berger, PhD is an Assistant Attending in the Department of Pathology and an Affiliate Member of the Human Oncology and Pathogenesis Program, with expertise in cancer genomics and computational biology. His research focuses on the enumeration of the spectrum of genetic mutations in human tumors in order to identify biomarkers of cancer progression and drug response. Dr. Berger joined the MSKCC faculty in October 2010 after working as a research scientist and computational biologist in the Cancer Program at the Broad Institute of Harvard and MIT. At the Broad, he served as the project leader and primary data analyst for numerous efforts employing massively parallel "next generation" sequencing to characterize genetic mutations in a range of cancers. He now runs an independent research laboratory at MSKCC that is developing methods to reliably and accurately profile clinical specimens for cancer-related DNA mutations and copy number alterations. His laboratory is engaged in many collaborations with clinical and translational investigators to discover significant oncogenic mutations in rare or understudied tumor types and identify genomic biomarkers exhibiting correlations with clinical outcomes and therapeutic response. He is also working closely with the CLIA compliant Diagnostic Molecular Pathology Laboratory to build a robust profiling pipeline and analytical framework for use in real-time patient management. Dr. Berger received his Bachelor's Degree in Physics at Princeton University and his Ph.D. in Biophysics at Harvard University.


    Massively parallel sequencing technology has proven to enable the identification of driver genetic alterations in patients' tumors that may be suppressed by targeted therapies. Through retrospective analysis of clinical specimens, one can discover genomic biomarkers that predict outcomes and therapeutic response. Longitudinal profiling of multiple tumors in a single patient can reveal factors that influence tumor progression and drug resistance. Finally, prospective sequencing of patient specimens, when coupled with complementary radiology and histology based imaging, can enhance the clinical diagnosis and treatment of cancer patients. For increasingly lower costs, one can profile clinically relevant genes for mutations, copy number alterations, and structural rearrangements, with high detection sensitivity in low purity or multi-clonal tumor tissue. Advances in target capture, sample multiplexing, and profiling of formalin-fixed paraffin embedded (FFPE) specimens have further established the clinical utility of next generation sequencing. However, challenges remain in the application of these techniques to the analysis of clinical samples. In addition to the technical challenge of analyzing scant amounts of FFPE tissue, one must overcome the biological challenges of aneuploidy and heterogeneity inherent to the genetics of cancer. I will discuss different strategies for sequencing clinical samples, including different sequencing platforms, capture methods, and breadth of testing (i.e. targeted versus comprehensive approaches). I will describe examples in which our group has performed massively parallel sequencing on clinically annotated tumor specimens to identify genomic biomarkers of drug response and resistance. Finally, I will describe additional challenges in prospectively applying these techniques for clinical diagnosis involving bioinformatics, clinical interpretation, regulatory compliance, and ethics.

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