Challenges and Gaps in Clinical Trial Genomic Data Management

Abstract

It is now a commonplace to propose high-dimensional molecular characterization within the translational research objectives of contemporary clinical trials. Similarly, most of these methods are being applied to archived samples from completed clinical trials. The rationale is well understood—comprehensive molecular profiling should accelerate our goal of precision cancer medicine, especially when applied to the randomized clinical trials that incorporate current and emerging effective treatments. However, present barriers impede researchers from unlocking the full potential of these data sets and trials, and it is critical to solve these challenges. Currently, omics data generated from trials are largely decentralized: data are housed at a variety of sites, analyses take place locally, and other researchers do not have access until public deposition of data on repositories such as the database of Genotypes and Phenotypes (dbGaP) and National Cancer Institute’s (NCI) Genomic Data Commons (GDC) at publication—often years after generation—and then potentially without adequate clinical annotation to correlate omics features with clinical outcomes. Furthermore, analyses vary widely in bioinformatics methods, including choice of tools, dependencies, file formats, parameterizations, data quality filtering thresholds, and other workflow elements, which makes integration across groups challenging.