Molecular Analysis and Precision Medicine in Renal Cell Carcinoma

Johns Hopkins University, Baltimore MD

Investigators

Linked publications & trials

Abstract

? DESCRIPTION (provided by applicant) My proposed Mentored Career Development Award in Biomedical Big Data Science (K01) will focus an important but largely unmet need within the field of oncology. Renal cell carcinoma is the 8th most common cancer and the most lethal of the urologic cancers. Systemic medical therapy is required for the 25% of patients who initially present with metastatic disease and 30% of patients who recur following surgery. The selection of which medical treatment to use is not based on biomolecular characteristics of the tumor, despite large variability in the efficacy of th treatments. Only recently, with improvements in sequencing technologies, have these molecular analyses been possible. The research goals proposed within this application combine bioinformatics, biostatistics, and molecular biology to establish an informatics toolkit for investigating the molecular markers of renal cell carcinoma as they relate to treatment success. We hypothesize that building a toolkit into an existing, well-annotated clinical database and tissue repository will allow us to (1) create the first multi-institutional registry of renal cell carcinoma patients with clinical, genomic, and outcomes data, (2) identify molecular predictors of treatment success for existing cancer therapies, and (3) investigate personalized subtypes of patients that correlate with treatment response to immunotherapy. An understanding of molecular predictors of treatment success can have a major impact on decision-making within renal cell carcinoma and establish hypotheses for future clinical trials. Over the course of this Mentored Career Development Award in Biomedical Big Data Science, my goal is to acquire the expertise from my mentors that is required to succeed as an independent investigator, advancing Precision Medicine in renal cell carcinoma through the use of biomedical data science. The 5 years allotted for this project will provide ample time to truly establish the skill necessary for independent research. At least 50% of my time will be devoted solely to research, which will be further supplemented by clinical work involving care for patients with renal cell carcinoma. I have a multi-institutional and multi-disciplinary panel of experts who will guide me through this research project and use the resultant tools. The education that I will receive will b essential in cultivating my ability to use biomedical big data towards improving care in renal cell carcinoma patients. My long-term career goals include pursing additional NIH funding as an independent investigator and lead a multi-disciplinary team in advancing data-driven medicine. In doing so, I hope to inspire future scientists to pursue challenges in biomedical data science and guide them in the same tradition of mentorship that is being given to me.