Session 3: CancerTrialMatch: a web application for the curation and matching of clinical trials at a precision oncology center
Presentation Type
Invited
Track
Other
Abstract
The adoption of next-generation sequencing for cancer patients has made molecular profiling possible and identify biomarkers. At the Avera Cancer Institute, biomarker-based clinical trials are often presented as treatment options to oncologists at the molecular tumor board. This necessitated a method to capturestructured trial data and match them to patients based on their disease and sequencing profile in a systematic manner. We developed an open-source web application, CancerTrialMatch, that enables to (i) add trials through a semi-automated curation interface, (ii) browse and search trials, (iii) and match patients to biomarker-based trials. This application uses R Shiny to create simple interfaces, a mongo database to store trial data, various R libraries to query data and perform computations, and Docker to manage software installation and application instantiation. The curation interface is semi-automated because querying the clinicaltrials.gov API will return discrete data for many fields, except biomarkers and disease subtypes. The user has to manually input disease type based on the OncoTree classification, as well as biomarker information for mutations, copy numbers, fusions, TMB, MSI/PD-L1 status, RNA expression, and disease-specific markers such as ER/PR/HER2 status. We believe that this computational resource will reduce the person-hours required for trial management for a patient, aid in increasing clinical trial enrollment by systematically providing treatment options, and hence an important tool in the clinical application of precision oncology.
Start Date
2-7-2023 9:50 AM
End Date
2-7-2023 10:50 AM
Session 3: CancerTrialMatch: a web application for the curation and matching of clinical trials at a precision oncology center
Herold Crest 253 C
The adoption of next-generation sequencing for cancer patients has made molecular profiling possible and identify biomarkers. At the Avera Cancer Institute, biomarker-based clinical trials are often presented as treatment options to oncologists at the molecular tumor board. This necessitated a method to capturestructured trial data and match them to patients based on their disease and sequencing profile in a systematic manner. We developed an open-source web application, CancerTrialMatch, that enables to (i) add trials through a semi-automated curation interface, (ii) browse and search trials, (iii) and match patients to biomarker-based trials. This application uses R Shiny to create simple interfaces, a mongo database to store trial data, various R libraries to query data and perform computations, and Docker to manage software installation and application instantiation. The curation interface is semi-automated because querying the clinicaltrials.gov API will return discrete data for many fields, except biomarkers and disease subtypes. The user has to manually input disease type based on the OncoTree classification, as well as biomarker information for mutations, copy numbers, fusions, TMB, MSI/PD-L1 status, RNA expression, and disease-specific markers such as ER/PR/HER2 status. We believe that this computational resource will reduce the person-hours required for trial management for a patient, aid in increasing clinical trial enrollment by systematically providing treatment options, and hence an important tool in the clinical application of precision oncology.