Cancers arise through the accumulation of genetic and epigenetic alterations that often target signal transduction pathways, leading to dysregulation of downstream transcriptional effectors and widespread gene expression changes. Since many targeted therapies are small molecule inhibitors of signal transduction proteins or monoclonal antibodies against growth factor receptors, deciphering the signaling pathways that are deregulated in a given tumor in order to personalize therapy is a major goal of cancer genomics.
The aim of this project is to develop algorithmic approaches linking signaling to transcriptional response for precision medicine. During the K99 phase of the award, I will develop statistical modeling approaches to integrate publicly available transcriptomic, proteomic and genomic data across tumor types with epigenomic data in appropriate cell lines in order to study altered transcriptional programs and signaling pathways in cancer. With these methods in hand, during the R00 phase I will study the impact of common and cancer-specific transcription factor and signaling regulators on clinical outcome and drug response. We expect that our results will lead to new insights in cancer biology and furthermore assist in the design of clinical trials that match actionable oncogenic signatures with personalized therapies. I propose a training plan under the mentorship of a broad, interdisciplinary team of clinicians, scientists, and computational biologists with extensive combined experience in all aspects of the proposed research project. This focused research mentorship, together with frequent presentation of results and informal interactions, will help me develop the communication and leadership skills vital for my transition to independence. In the long term, this training will prepare me to lead a laboratory that centers on developing statistical and computational approaches for precision medicine to bridge the gap between basic science and the clinic.
The algorithms developed in this proposal will leverage massive cancer genomics data sets to gain new insights in cancer biology and also to assist in the rationale design of clinical trials that match actionable oncogenic signatures with personalized therapies.
