Abstract 1899: TrialMatch: A computational resource for the management of biomarker-based clinical trials at a precision oncology center
Authors: Amallraja Anu , Kapadia Shivani , Swaminathan Padmadpriya , Williams Casey , Meissner Tobias 
Published: June 2022

Abstract
Introduction: The adoption of routine tumor sequencing, and the incorporation of biomarker-based clinical trials in the treatment of cancer has been steadily rising over the past few years. Access and ability to participate in clinical trials is a key element in providing therapeutic options for cancer patients.At the Avera Cancer Institute, options of biomarker-based clinical trials are routinely presented to the treating clinicians at the molecular tumor board (MTB). With everincreasing numbers of trials, a systematic method for keepingtrack of available trials andevaluating patients for trial options was needed. A search for solutions led to only one open-source tool, MatchMiner, but it did not meet our needs.
Methods and Results: We developed an open-source web application, TrialMatch, that has the ability to (i) add trials through a semi-automated curation interface, (ii) browse and search trials, (iii) and match patients to biomarker-based trials.TrialMatch is an R Shiny application that uses a mongo database to store the trial data. Software installation and application instantiation is managed through Docker. Curate This section has fields for a user to enter trial name and a NCT ID from clinicaltrials.gov. It then queries the clinicaltrials.gov API and fetches data about the sponsor, treatment, etc.Although disease and biomarker data are available on clinicaltrials.gov, they are not stored as discrete fields that can be queried, and hence require manual input. The user provides disease type based on the OncoTree classification, as well as biomarker information for mutations, copy numbers, fusions, TMB, MSI status, PD-L1 status, RNA expression, and disease specific markers such as ER/PR/HER2 status.All information for a particular trial is entered in a mongo database collection, with each trial stored as a document. The ability to add and edit data fields can be restricted to specific users. Browse This section allows the user to browse through all trials with key information such as NCT ID, trial name, disease, and biomarkers. Each row represents one trial and can be expanded to show more detailed information. This is useful to keep track of trials, and to quickly filter trials to a specific disease or biomarker. Match This section displays a patient-centric view of the trials that each patients’ age, sex, disease, and biomarkers have matched to. The matching can be automated for scheduled recurrentruns. At Avera, this is used during MTB to evaluate treatment options, but can also be used by clinicians to get a sense of the trials available for their patients, while awaiting formal review.
Conclusion: We believe that this computational tool fulfills a crucial need in the clinical application of precision oncology, and will ultimately increase clinical trial enrollment for patients.
Citation Format: Anu Amallraja, Shivani Kapadia, Padmadpriya Swaminathan, Casey Williams, Tobias Meissner. TrialMatch: A computational resource for the management of biomarker-based clinical trials at a precision oncology center [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1899.