Anu Amallraja is a Lead Clinical Bioinformatics Analyst in the Department of Cancer Genomics at the Avera Cancer Institute. She has an undergraduate degree in Biotechnology Engineering from Manipal Institute of Technology, India, and an MS in Biological and Medical Informatics from San Diego State University. She has over 6 years of experience in cancer genomics, working with genomics, transcriptomics, and clinical patient data. Her goal is to utilize bioinformatics as a tool to understand complex biology and to inform decisions made by clinicians so patients receive the best possible therapeutic options.
Anu Amallraja, Shivani Kapadia, Padmapriya Swaminathan, Casey Williams, Tobias Meissner
Avera Cancer Institute Center for Precision Oncology, Sioux Falls, SD, USA
The routine use of genetic and genomic sequencing in the treatment of cancer has been steadily rising over the past few years, with a corresponding increase in the availability of biomarker-based clinical trials. 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 way 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, 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.
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, but not for biomarkers or for 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 application will reduce the person-hours required for trial management for a patient, aid in increasing clinical trial enrollment by systematically providing treatment options, and is thus an important tool in the clinical application of precision oncology.