Events Calendar

Alice Berger (Fred Hutch): Discovery of oncogene and non-oncogene dependencies to inform lung cancer precision medicine
Tuesday 06 December 2022, 12:00pm - 01:00pm

Abstract: Dr. Berger will present two different complementary approaches for target discovery in cancer. First, she will discuss the discovery of RIT1 as a new lung cancer oncogene and genome-wide CRISPR approaches for finding RIT1 dependencies. Next, she will discuss the limitations of current CRISPR screening approaches and her lab’s approaches to overcome these. Finally, she will show how this new approach enables identification of synthetic lethal therapies beyond PARP inhibitors in BRCA-mutant cancer. She will show how paralog families are a rich source of synthetic lethal interactions and strategies for understanding and exploiting paralog synthetic lethality in cancer.

About the speaker: Alice Berger, Ph.D., is an Associate Professor and the Innovators Network Endowed Chair at Fred Hutchinson Cancer Center in Seattle, WA. The Berger laboratory is focused on improving the outcomes of lung cancer patients through discovery of the genomic determinants of lung cancer initiation and therapeutic response.

Dr. Berger earned a B.S. in Chemistry at the University of Virginia and a Ph.D. in Biochemistry and Molecular Biology from Cornell University’s Weill Graduate School of Medical Sciences.  She then trained as a postdoctoral fellow in Matthew Meyerson’s lab at the Broad Institute of MIT and Harvard and Dana-Farber Cancer Institute. In her work with The Cancer Genome Atlas, she discovered and described cancer-associated mutations in lung cancer such as RIT1 and MET exon 14 skipping. She is a co-developer of eVIP, a technology for rapid assessment of the functional impact of genetic variants. Dr. Berger is the recipient of the Devereaux Outstanding Young Investigator award by the Prevent Cancer Foundation for her work on the genomics of lung cancer in women and an NCI MERIT award on her work on RIT1 driven lung cancer.

 

Location : Virtual