My prior training and long-term career goals make this Translational Scholar Award in Pharmacogenomics and Personalized Medicine (K23) the ideal opportunity to help me become an independent investigator. Within the last three years, I completed joint fellowships in Hematology/Oncology and Clinical Pharmacology and Pharmacogenomics at the University of Chicago. My fellowship research introduced me to the field of pharmacometrics, which is the science of quantifying drug, disease and trial characteristics. Specifically, I became interested i using pharmacometrics to guide the development and optimize the use of cancer therapeutics. I furthered this interest as a Paul Calabresi K12 scholar over the last two years, during which I earned a Master's in Pharmacometrics from one of the only programs in pharmacometrics in this country at the University of Maryland - Baltimore. At the same time, I have gained significant experience writing and conducting clinical trials with major pharmacogenomic components. To advance my career as an independent investigator, I now ask whether pharmacometrics can improve the ability to translate genomic data into clinical decision-making. There have been many pharmacogenomic discoveries describing associations between germline genetic variation and drug toxicity or efficacy. Further discoveries regarding drug efficacy may be enhanced in two ways: first, by identifying better phenotypes of drug efficacy; and second, by utilizing large numbers of common variants in prediction tools rather than relying on a small number of variants. In my fellowship research, we developed a disease progression model of renal cell carcinoma (RCC) that can estimate the treatment effect of drug in the population and in individual patients. This model-estimated treatment effect is an intriguing potential phenotype, as it takes into consideration the full set of longitudinal data regarding tumor size, in contrast to more conventional phenotypes such as objective response (decrease in tumor size by ? 30%), progression-free survival and overall survival. Collaborators have shown that semi-automated measurements of tumor volumes, in contrast to the longest diameters on cross-sectional images, might further enhance these phenotypes by providing a more precise assessment of tumor burden. Finally, colleagues of mine at the University of Chicago have developed a method (called OmicKriging) for using all common variants identified during whole genome interrogation (and potentially other -omic data) to make predictions about a phenotype. The COMPARZ trial was the largest ever conducted randomized phase III trial in metastatic RCC, with 1,110 patients randomized to pazopanib versus sunitinib in North America, Europe, and Asia. Approximately two-thirds of these patients provided germline DNA for genome-wide genotyping, which has already been completed. As part of an ongoing collaboration with GlaxoSmithKline Pharmaceuticals (GSK), who sponsored the trial, we have access to clinical data, images from computed tomography scans, and genome-wide genotyping data for these patients. These data offer a unique opportunity to revise our previous disease progression model of RCC using two new therapies and a new phenotype (longitudinal tumor volume), and to explore how common variants can be used to predict both model-estimated treatment effect and conventional phenotypes such as objective response, PFS and OS. The hypothesis is that phenotypes estimated by disease progression models will lead to better genomic prediction tools than conventional phenotypes. These tools could predict which patients are more or less likely to benefit from therapy with tyrosine kinase inhibitors in metastatic RCC. Additionally, these tools could be improved by adding other data elements (such as tumor genotype) and could serve as a blueprint for similar tools using model-based phenotypes in other cancers and other complex diseases. In the research plan, I describe four steps (aims) that logically take us from the raw data to validated genomic prediction tools for both model-based and conventional phenotypes. The first step is to capture the phenotype of model-estimated treatment effect for each patient by measuring tumor volumes and revising our previous disease progression model of RCC. The second step is to estimate the heritability of this phenotype and the conventional ones, in order to understand the upper limit of interpatient variability that might be accounted for by genomic data. The third step is to develop the genomic prediction tools using the OmicKriging approach, and the fourth (and final) step is to validate these tools in a prospective clinical trial. In order to be successful in developing genomic prediction tools and prospectively validating them in clinical trials with cancer therapeutics, I need additional training and experience in the areas of genomics and statistical genetics. I have identified two mentors with expertise in these areas to oversee my career development plan. With their guidance, I have planned a comprehensive curriculum of courses and meetings for advanced training in genomics and statistical genetics to supplement my advanced degree in pharmacometrics. With this K23 award, I will acquire the skills to independently develop genomic prediction tools with phenotypes derived from disease progression models and prospectively validate these tools in clinical trials. In future work, I wil demonstrate how these tools can be used to personalize the treatment plan for cancer patients and ultimately improve their outcomes.
Physicians have always observed that different patients treated with the same medication often respond differently, and recent research has shown that at least some of these differences in the response to medications may be explained by genetic variation between people. The ability to use someone's genetic information to predict how well they might respond to a medication could profoundly change the way we treat complex diseases such as cancer. This work has the potential to help patients with all kinds of diseases live longer and better by treating them with medications that are more likely to work and avoiding those that are less likely to work for each individual.
| Thirman, Michael J (2017) Paradoxical Effects of MLL Paralogs in MLL-Rearranged Leukemia. Cancer Cell 31:729-731 |
| Thorn, Caroline F; Sharma, Manish R; Altman, Russ B et al. (2017) PharmGKB summary: pazopanib pathway, pharmacokinetics. Pharmacogenet Genomics 27:307-312 |
| Mehrotra, Shailly; Sharma, Manish R; Gray, Elizabeth et al. (2017) Kinetic-Pharmacodynamic Model of Chemotherapy-Induced Peripheral Neuropathy in Patients with Metastatic Breast Cancer Treated with Paclitaxel, Nab-Paclitaxel, or Ixabepilone: CALGB 40502 (Alliance). AAPS J 19:1411-1423 |
| Sharma, Manish R; Ratain, Mark J (2016) Taking a Measured Approach to Toxicity Data in Phase I Oncology Clinical Trials. Clin Cancer Res 22:527-9 |
