Pancreatic Cancer Action Network's SPARK: A Cloud-Based Patient Health Data and Analytics Platform for Pancreatic Cancer.

PURPOSE: Pancreatic cancer currently holds the position of third deadliest cancer in the United States and the 5-year survival rate is among the lowest for major cancers at just 12%. Thus, continued research efforts to better understand the clinical and molecular underpinnings of pancreatic cancer are critical to developing both early detection methodologies as well as improved therapeutic options. This study introduces Pancreatic Cancer Action Network's (PanCAN's) SPARK, a cloud-based data and analytics platform that integrates patient health data from the PanCAN's research initiatives and aims to accelerate pancreatic cancer research by making real-world patient health data and analysis tools easier to access and use. MATERIALS AND METHODS: The SPARK platform integrates clinical, molecular, multiomic, imaging, and patient-reported data generated from PanCAN's research initiatives. The platform is built on a cloud-based infrastructure powered by Velsera. Cohort exploration and browser capabilities are built using Velsera ARIA, a specialized product for leveraging clinicogenomic data to build cohorts, query variant information, and drive downstream association analyses. Data science and analytic capabilities are also built into the platform allowing researchers to perform simple to complex analysis. RESULTS: Version 1 of the SPARK platform was released to pilot users, who represented diverse end users, including molecular biologists, clinicians, and bioinformaticians. Included in the pilot release of SPARK are deidentified clinical (including treatment and outcomes data), molecular, multiomic, and whole-slide pathology images for over 600 patients enrolled in PanCAN's Know Your Tumor molecular profiling service. CONCLUSION: The pilot release of the SPARK platform introduces qualified researchers to PanCAN real-world patient health data and analytical resources in a centralized location.

Detection of structural variation using target captured next-generation sequencing data for genetic diagnostic testing.

PURPOSE: Structural variation (SV) is associated with inherited diseases. Next-generation sequencing (NGS) is an efficient method for SV detection because of its high-throughput, low cost, and base-pair resolution. However, due to lack of standard NGS protocols and a limited number of clinical samples with pathogenic SVs, comprehensive standards for SV detection, interpretation, and reporting are to be established. METHODS: We performed SV assessment on 60,000 clinical samples tested with hereditary cancer NGS panels spanning 48 genes. To evaluate NGS results, NGS and orthogonal methods were used separately in a blinded fashion for SV detection in all samples. RESULTS: A total of 1,037 SVs in coding sequence (CDS) or untranslated regions (UTRs) and 30,847 SVs in introns were detected and validated. Across all variant types, NGS shows 100% sensitivity and 99.9% specificity. Overall, 64% of CDS/UTR SVs were classified as pathogenic/likely pathogenic, and five deletions/duplications were reclassified as pathogenic using breakpoint information from NGS. CONCLUSION: The SVs presented here can be used as a valuable resource for clinical research and diagnostics. The data illustrate NGS as a powerful tool for SV detection. Application of NGS and confirmation technologies in genetic testing ensures delivering accurate and reliable results for diagnosis and patient care.

Comparative genomics reveals mobile pathogenicity chromosomes in Fusarium.

Fusarium species are among the most important phytopathogenic and toxigenic fungi. To understand the molecular underpinnings of pathogenicity in the genus Fusarium, we compared the genomes of three phenotypically diverse species: Fusarium graminearum, Fusarium verticillioides and Fusarium oxysporum f. sp. lycopersici. Our analysis revealed lineage-specific (LS) genomic regions in F. oxysporum that include four entire chromosomes and account for more than one-quarter of the genome. LS regions are rich in transposons and genes with distinct evolutionary profiles but related to pathogenicity, indicative of horizontal acquisition. Experimentally, we demonstrate the transfer of two LS chromosomes between strains of F. oxysporum, converting a non-pathogenic strain into a pathogen. Transfer of LS chromosomes between otherwise genetically isolated strains explains the polyphyletic origin of host specificity and the emergence of new pathogenic lineages in F. oxysporum. These findings put the evolution of fungal pathogenicity into a new perspective.