NSF Award
2425998
The broader impact of this I-Corps project is the development of a software platform to provide the most effective drug combination therapy recommendation for cancer patients. Currently, drug resistance is one of the leading causes for cancer patients having to seek alternative therapies. By providing a streamlined ability to provide informed and personalized treatment plans to cancer patients, trial-and-error approaches will be minimized, which may reduce costs and improve patient outcomes. Using this technology, data on tumor composition and drug combination effectiveness may be collected and analyzed on a population-level, significantly bolstering drug prioritization. In addition, the technology may be adapted to provide individually tailored treatment options, demonstrating the future of personalized precision medicine and the potential to shape future cancer research, treatment, and pharmaceutical development.<br/><br/>This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of single cell data-based transfer learning techniques. This technology enables the precise contextualization of a patient’s tumor cells in terms of similar cell lines and uses that data to provide the most effective drug combination therapy recommendation for the cancer patient. This technology does not require a reference cohort or a deep understanding of a drug’s molecular targets in order to identify individually tailored treatment options and may bridge the gap between in-vitro cell line drug sensitivities and prioritizing drugs for clinical use. In addition, the technology provides comprehensive data on the composition of patient’s tumor cells and aids in the understanding of tumor heterogeneity, leading to a data-driven approach to personalized therapeutics for cancer patients.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
