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  1. Advancing Regulatory Science

Development of a precision oncology decision support platform to enhance genotype-driven clinical trial recruitment and decentralized personalized medicine approaches

CERSI Collaborators: Valsamo Anagnostou, MD, PhD, Taxiarchis Botsis, MSc, MPS, PhD, Rachel Karchin, PhD, Adrian Dobs, MD, MHS, Rena Xian, MD, MS, Kala Visvanathan, MBBS, FRACP, MHS, Jessica Tao, MD, Kory Kreimeyer, MS, Jonathan Spiker, BA, Jenna Canzoniero, MD, Archana Balan, MS, Maria Fatteh, MD, Ting He, MS

FDA Collaborators: Preeti Narayan, MD, Wenming Xiao, PhD, Emma Scott, PhD

Project Start Date: August 19, 2022

Regulatory Science Challenge

There is an unmet clinical need to develop and apply informatics tools (computer-based applications and systems that are designed to support data management, analysis, and decision-making) that assist with matching the genetic make-up of tumors to genotype-targeted clinical trials. Currently available approaches to match genotypes with therapies focus on manual processing of a vast array of multi-source data that is challenging outside Molecular Tumor Boards of academic institutions. The vision is to accelerate cancer care so that the earliest best decisions are made with precision, thus maximizing the effect of therapies.

Project Description and Goals

This project aims to develop an automated tool that will match changes in the genetic material of tumors (called mutations), detected by high throughput molecular testing (called next-generation sequencing), with drugs that specifically target these mutations and are evaluated in clinical trials. The primary research objective is to retrieve and combine data from electronic health records, cancer knowledgebases, clinical trial registries such as ClinicalTrials.gov, and other sources and integrate the information needed to identify appropriate clinical trials for patients with cancer. Researchers will test the clinical utility of this precision oncology decision-support platform within the Johns Hopkins Molecular Tumor Board and in the community setting within the Johns Hopkins Clinical Research Network. Overall, this work aims to generate an automated precision oncology platform that will match patients with clinical trials based on the genomic footprint of their tumors in a manner that is decentralized and inclusive of disparities. Developed technology will be transferred to both the research community and the public.

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