Development of a somatic variant registry in a National Cancer Center: towards Molecular Real World Data preparedness

The Biomedical Research field is currently advancing to develop Clinical Trials and translational projects based on Real World Evidence. To make this transition feasible, clinical centers need to work toward Data Accessibility and Interoperability. This task is particularly challenging when applied to Genomics, that entered in routinary screening in the last years via mostly amplicon-based Next-Generation Sequencing panels. Said experiments produce up to hundreds of features per patient, and their summarized results are often stored in static clinical reports, making critical information inaccessible to automated access and Federated Search consortia. In this study, we present a reanalysis of 4620 solid tumor sequencing samples in five different histology settings. Furthermore, we describe all the Bioinformatics and Data Engineering processes that were put in place in order to create a Somatic Variant Registry able to deal with the large biotechnological variability of routinary Genomics Profiling.

Automated summary

The biomedical research field is advancing towards developing clinical trials and translational projects based on real-world evidence. To make this transition feasible, clinical centers need to work towards data accessibility and interoperability. This task is particularly challenging when applied to genomics, which has become routine through amplicon-based next-generation sequencing panels. These experiments produce numerous features per patient, and their results are often stored in static clinical reports, making critical information inaccessible to automated access and federated search consortia. This study presents a reanalysis of 4620 solid tumor sequencing samples in different histology settings and describes the bioinformatics and data engineering processes employed to create a somatic variant registry capable of dealing with the large biotechnological variability of routine genomics profiling.