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Inherited genetic variation has important implications for cancer screening, early diagnosis, and disease prognosis. A role for germline variation has also been described in shaping the molecular landscape, immune response, microenvironment, and treatment response of individual tumors. However, there is a lack of consensus on the handling and analysis of germline information that extends beyond known or suspected cancer susceptibility in large-scale cancer genomics initiatives. As part of the Personalized OncoGenomics program in British Columbia, we performed whole-genome and transcriptome sequencing in paired tumor and normal tissues from advanced cancer patients to characterize the molecular tumor landscape and identify putative targets for therapy. Overall, our experience supports a multidisciplinary and integrative approach to germline data management. This includes a need for broader definitions and standardized recommendations regarding primary and secondary germline findings in precision oncology. Here, we propose a framework for identifying, evaluating, and returning germline variants of potential clinical significance that may have indications for health management beyond cancer risk reduction or prevention in patients and their families.

Precision oncology trials for pediatric cancers require rapid and accurate detection of genetic alterations. Tumor variant identification should interrogate the distinctive driver genes and more frequent copy number variants and gene fusions that are characteristics of pediatric tumors. Here, we evaluate tumor variant identification using whole genome sequencing (n = 12 samples) and two amplification-based next-generation sequencing assays (n = 28 samples), including one assay designed to rapidly assess common diagnostic, prognostic, and therapeutic biomarkers found in pediatric tumors. Variant identification by the three modalities was comparable when filtered for 151 pediatric driver genes. Across the 28 samples, the pediatric cancer-focused assay detected more tumor variants per sample (two-sided, P