Cancer arises through the accumulation of genetic and epigenetic alterations that promote the altered phenotypes that result in a heterogeneous collection of disease states. By elucidating the molecular mechanisms that drive these cancer phenotypes, we acquire a more detailed understanding of the tumorigenic process and in turn, better insight into how cancer can be treated.

My research utilizes functional genomics approaches to uncover and characterize mechanisms of tumorigenesis and tumor maintenance, with the ultimate goal of identifying potential therapeutic targets for cancer. To this end, I use high-throughput technologies coupled with RNAi and open-readingframe (ORF) expression libraries to perform functional characterization in a systematic and comprehensive manner. Current projects include use of loss-of-function RNAi screening approaches to identify genes essential for cancer cell proliferation and survival, as they provide points of vulnerability of the cancer and may translate directly to drug targets. Moreover, by performing synthetic-lethal screens, we can identify those genes that are essential only in specific contexts, for example in the presence of specific oncogenic mutations or aberrantly activated signaling pathways. This approach may be of particular clinical relevance, as it provides both a specific drug target and a biomarker to identify patients most likely to respond, facilitating the development of personalized, molecularly targeted therapy. Other projects currently ongoing include gain-of-function ORF screens to identify (1) modifiers of chemotherapeutic drug response and (2) regulators of signaling pathways commonly activated during tumorigenesis.

One strategy that has been particularly productive in recent years in the identification of novel cancer-relevant genes and mechanisms is the integration of high throughput functional approaches with large-scale efforts to enumerate all the structural alterations recurrently found in cancer genomes. By comparing these two complementary sets of data, we can rapidly identify which of the structural alterations actually contribute directly to the oncogenically transformed state, i.e. driver mutations. This information is of clinical importance, as previous experience has shown that many tumors are oncogene-addicted and some of the most effective cancer therapies are those that target an oncogenic driver, such as the BCR/ABL translocation in CML, ERBB2 amplification in breast cancer or EGFR mutation in lung cancer. My research aims to continue leveraging both functional and structural information of the cancer genome, which is currently being generated at a tremendous pace, to gain new mechanistic insights into cancer.