Uploaded March 2025 | Updated September 2026, 2 weeks ago
#ShinyConf #ShinyConf2024
Abstract: Next-generation sequencing has made molecular profiling possible and identify biomarkers in guiding therapy for cancer patients. 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 capture structured 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 using R shiny, that enables to create simple interfaces (i) add trials through a semi-automated curation interface, (ii) edit trials (iii) browse and search trials, (iv) and match patients to biomarker-based trials. A mongo database is used to store trial data, various R libraries to query data , 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 shiny application will reduce the person-hours required for trial management for a patient, help in increasing clinical trial enrollment by systematically providing treatment options aiding in decision-making, and hence an important tool in the clinical application of precision oncology.
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Join us this year! Sign up for ShinyConf 2025 here: go.appsilon.com/register-shinyconf2025
#ShinyConf #ShinyConf2024
Abstract: Next-generation sequencing has made molecular profiling possible and identify biomarkers in guiding therapy for cancer patients. 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 capture structured 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 using R shiny, that enables to create simple interfaces (i) add trials through a semi-automated curation interface, (ii) edit trials (iii) browse and search trials, (iv) and match patients to biomarker-based trials. A mongo database is used to store trial data, various R libraries to query data , 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 shiny application will reduce the person-hours required for trial management for a patient, help in increasing clinical trial enrollment by systematically providing treatment options aiding in decision-making, and hence an important tool in the clinical application of precision oncology.
____________________________
Join us this year! Sign up for ShinyConf 2025 here: go.appsilon.com/register-shinyconf2025
