Abstract
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.
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.
| Original language | English |
|---|---|
| Article number | e2200137 |
| Journal | JCO Clinical Cancer Informatics |
| Volume | 7 |
| DOIs | |
| Publication status | Published - 19 Jan 2023 |
Research Beacons, Institutes and Platforms
- Manchester Cancer Research Centre
