Digital ECMT Cancer Trial Matching Tool: an Open Source Research Application to Support Oncologists in the Identification of Precision Medicine Clinical Trials

Author:

O'Regan Paul1ORCID,Hoskins Richard2,Grave Christopher2,Stevenson Julie-Anne1,Frost Hannah13ORCID,Graham Donna M.14,Krebs Matthew G.4ORCID,Freitas Andre135,Landers Dónal1ORCID

Affiliation:

1. Digital Experimental Cancer Medicine Team, Cancer Research UK Manchester Institute, The Oglesby Cancer Research Building, The University of Manchester, Manchester, United Kingdom

2. Research IT, The University of Manchester, Manchester, United Kingdom

3. Department of Computer Science, Kilburn Building, The University of Manchester, Manchester, United Kingdom

4. Division of Cancer Sciences, The University of Manchester and The Christie NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, United Kingdom

5. Idiap Research Institute, Martigny, Switzerland

Abstract

PURPOSEMatching patients with cancer to precision medicine clinical trials on the basis of their tumor genotype has the potential to improve outcomes for patients who have exhausted standard-of-care treatment options. However, the matching process presents a substantial challenge because of the number of clinical trials available. We describe a free, open source research tool designed to extract relevant trial information to support oncologists in the matching process, and we illustrate its utility with recent case studies of patients who were matched to trials using this tool.METHODSTrial records are sourced from ClinicalTrials.gov and indexed using natural language processing techniques, including named entity recognition, term normalization, and relationship extraction. Relationships between trials and genetic alterations are assigned scores on the basis of a rule-based system. All data are updated daily. A user interface is provided via R Shiny app.RESULTSAn instance of the trial match tool, configured for UK clinical trials, is hosted by the digital Experimental Cancer Medicine Team (see link in Data Sharing Statement). Users select the relevant cancer type and genetic alteration(s). Matching studies are ranked according to the score assigned for the selected genetic alterations. Results may be downloaded and attached to the patient's health record if desired. The tool is currently being used to support the ongoing TARGET National study, which aims to match up to 6,000 patients to early phase clinical trials. We present three case studies that exemplify relationships between genetic alterations and studies.CONCLUSIONWith increasing numbers of precision medicine treatments and as comprehensive molecular profiling of tumor samples becomes more common, decision support tools are likely to become increasingly important. This work represents an important step toward the development and wider implementation of such systems.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

General Medicine

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