Digital ECMT Cancer Trial Matching Tool: an Open Source Research Application to Support Oncologists in the Identification of Precision Medicine Clinical Trials
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Published on: May 17, 2019
Paul O'Regan1, Richard Hoskins2, Christopher Grave2
1Digital Experimental Cancer Medicine Team, Cancer Research UK Manchester Institute, The Oglesby Cancer Research Building, The University of Manchester, Manchester, United Kingdom.
Purpose:
Matching 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.
Methods:
Trial 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.
Results:
An 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.
Conclusion:
With 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.
Insights
A new free, open-source tool helps oncologists match cancer patients to precision medicine clinical trials based on tumor genotype. This system simplifies trial selection, improving outcomes for patients with limited treatment options.
Area of Science:
- Oncology
- Bioinformatics
- Clinical Trial Management
Background:
- Precision medicine in oncology relies on matching patients to clinical trials based on tumor genetic alterations.
- The increasing number of trials makes manual matching a significant challenge for oncologists.
- Standard-of-care treatments are often exhausted, necessitating alternative therapeutic options.
Purpose of the Study:
- To introduce a free, open-source research tool for extracting and matching clinical trial information to patient tumor genotypes.
- To support oncologists in identifying suitable precision medicine clinical trials for their patients.
- To demonstrate the utility of the tool through case studies.
Main Methods:
- Trial records from ClinicalTrials.gov are processed using natural language processing (NLP) techniques.
- Named entity recognition, term normalization, and relationship extraction are employed to index trials.
- A rule-based system scores relationships between trials and genetic alterations, with daily data updates.
- An R Shiny app provides a user interface for trial matching.
Main Results:
- The tool ranks matching clinical trials based on user-selected cancer type and genetic alterations.
- Results can be downloaded and integrated into patient health records.
- The tool is actively supporting the TARGET National study, aiming to match 6,000 patients to early-phase trials.
- Three case studies illustrate successful patient-trial matching.
Conclusions:
- Decision support tools are crucial for navigating the growing landscape of precision medicine treatments.
- This research tool represents a significant advancement in developing and implementing systems for precision oncology trial matching.
- The tool facilitates better patient outcomes by improving access to relevant clinical trials.
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