Matching genotypes with personalized therapies: Development of a decision support infrastructure to augment the value of precision medicine

Project Summary
Despite the progress made in precision oncology, clinicians typically face a vast volume and variety of next-
generation sequencing and molecular data that is frequently intuitively processed to support high-stakes
decisions. Overall, currently available resources that assist with next-generation sequence data interpretation
are limited by manually performed, complex, time-consuming, and error-prone gene queries and ultimately
lack the necessary information for prioritizing emerging therapies in a scalable manner. Importantly, the
integration of genomic with clinical data has been severely hampered by the lack of advanced analytical tools
that match genomic targets with molecularly-driven therapies. These barriers, together with health disparities,
widen the gap between an exponentially increasing drug development field and the actual benefits for
patients with cancer.
The overarching goal of the proposed research is to link clinical with computational precision oncology and
enable clinical decision-making in genomically defined groups. We propose to develop a precision oncology
decision support framework for automated, scalable, and precise matching of actionable next-generation
sequencing findings with targeted therapies. We will then test its clinical utility and value in the several clinical
settings within the Johns Hopkins Molecular Tumor Board, in Johns Hopkins partnering community medical
centers as well within two ongoing clinical trials for women with breast cancer. To enhance the generalizability
of our analytical toolkit past our local academic environment, we have designed the platform's architecture
such that it allows for ingestion and harmonization of next-generation sequence data from multiple sources,
implements a common data model to map clinical elements to standardized terminologies and leverages
ensemble natural language processing to generate actionable mutation-targeted therapy pairs. These
attributes provide the foundation for the toolkit's potential widespread use and implementation in health care
settings outside our local academic environment.
While significant advances have been made in advanced diagnostics for tumor profiling, a solid backbone
that supports the practical implementation within and across health care systems is lacking. The underlying
premise of the proposed research is that it will ignite cross-institutional real-world genomic data analysis
initiatives and genotype-driven clinical trials that will be beneficial for health systems and patients. Notably,
our precision oncology decision support platform will enhance the implementation of precision oncology at
institutions that do not readily have access to in-house expertise in clinical genomics. We envision that this
streamlined automatic and scalable process will improve care, enhance patient outcomes and define national
standards in how treatments are selected and tailored to individual patients.

Grant Number: 5U01CA274631-03
NIH Institute/Center: NIH
Principal Investigator: Valsamo Anagnostou