OnPOINT: Development of an open navigator through precision oncology informatics technology to support precision oncology decisions and genotype-matched clinical trial enrollment.

Abstract

e13672 Background: The growing compendium of genomic alterations linked to FDA-approved therapies or drugs in development, requires the implementation of informatics solutions that tailor next-generation sequencing and tumor molecular profiling to evidence based ranked molecularly guided targeted therapies in an automated, scalable and generalizable manner. Nevertheless, such comprehensive informatics solutions are currently lacking, highlighting a significant gap in precision oncology. Methods: We developed the Open Navigator through Precision Oncology INformatics Technology (OnPOINT) platform that utilizes open-source tools, retrieves data from public repositories via APIs, processes multi-source sequencing files, synthesizes genomic with clinical data and matches actionable molecular findings with clinical trials (CTs). Clinical and genomics data were standardized using the Precision Oncology Core Data Model (Precision-DM). Public APIs were utilized to retrieve information from variant registries, knowledgebases and clinicaltrials.gov. Variant oncogenicity was characterized by an ensemble approach through the meta-annotator OpenCravat. The clinical utility of OnPOINT was tested at the Johns Hopkins Molecular Tumor Board (JH MTB) and in the community setting. Results: OnPOINT operates in an IRB-approved web environment, hosting standardized data for > 1,200 cancer patients reviewed at the JH MTB. Following programmatic mutation characterization by oncogenicity and actionability, biomedical literature is annotated with the National Library of Medicine PubTator tool to retrieve gene, variant and drug entities and their relationships. In tandem, OnPOINT automatically identifies CTs tailored to a patient’s clinical-genomic profile. All source data, external knowledge, and generated information is displayed in an interactive dashboard and summarized in an auto-populated report. To evaluate the platform’s effectiveness, we performed a pilot benchmark using 15 cases manually reviewed by the JH MTB. OnPOINT automatically captured 88% of genotype-matched clinical trials recommended by the MTB experts; missed trials were mainly those with status changes from active to non-recruiting between the MTB review and the benchmark analysis. We then evaluated OnPOINT using a set of 12 patients receiving care at a community hospital; of 116 identified mutations, 23.3% were deemed actionable and matched to 40 genotype-targeted trials. Notably, none of these patients received genotype-targeted therapies, highlighting underscoring the utility of our approach in matching patients with clinical trials. Conclusions: Integrative informatics approaches open a window of opportunity for scaling MTB operations and maximizing genotype-matched clinical trial visibility in the community setting.