An integrated framework for reporting clinically relevant biomarkers from paired tumor/normal genomic and transcriptomic sequencing data in support of clinical trials in personalized medicine.

Abstract

The ability to rapidly sequence the tumor and germline DNA of an individual holds the eventual promise of revolutionizing our ability to match targeted therapies to tumors harboring the associated genetic biomarkers. Analyzing high throughput genomic data consisting of millions of base pairs and discovering alterations in clinically actionable genes in a structured and real time manner is at the crux of personalized testing. This requires a computational architecture that can monitor and track a system within a regulated environment as terabytes of data are reduced to a small number of therapeutically relevant variants, delivered as a diagnostic laboratory developed test. These high complexity assays require data structures that enable real-time and retrospective ad-hoc analysis, with a capability of updating to keep up with the rapidly changing genomic and therapeutic options, all under a regulated environment that is relevant under both CMS and FDA depending on application. We describe a flexible computational framework that uses a paired tumor/normal sample allowing for complete analysis and reporting in approximately 24 hours, providing identification of single nucleotide changes, small insertions and deletions, chromosomal rearrangements, gene fusions and gene expression with positive predictive values over 90%. In this paper we present the challenges in integrating clinical, genomic and annotation databases to provide interpreted draft reports which we utilize within ongoing clinical research protocols. We demonstrate the need to retire from existing performance measurements of accuracy and specificity and measure metrics that are meaningful to a genomic diagnostic environment. This paper presents a three-tier infrastructure that is currently being used to analyze an individual genome and provide available therapeutic options via a clinical report. Our framework utilizes a non-relational variant-centric database that is scaleable to a large amount of data and addresses the challenges and limitations of a relational database system. Our system is continuously monitored via multiple trackers each catering differently to the diversity of users involved in this process. These trackers designed in analytics web-app framework provide status updates for an individual sample accurate to a few minutes. In this paper, we also present our outcome delivery process that is designed and delivered adhering to the standards defined by various regulation agencies involved in clinical genomic testing.