Diskin, Sharon J.

Abstract Purpose: DROSHA, DGCR8, and DICER1 regulate miRNA biogenesis and are commonly mutated in cancer. Although DGCR8 and DICER1 germline pathogenic variants (GPV) cause autosomal dominant tumor predisposition, no association between DROSHA GPVs and clinical phenotypes has been reported. Experimental Design: After obtaining informed consent, sequencing was performed on germline and tumor samples from all patients. The occurrence of germline DROSHA GPVs was investigated in large pediatric and adult cancer datasets. The population prevalence of DROSHA GPVs was investigated in the UK Biobank and Geisinger DiscovEHR cohorts. Results: We describe nine children from eight families with heterozygous DROSHA GPVs and a diagnosis of pineoblastoma (n = 8) or Wilms tumor (n = 1). A somatic second hit in DROSHA was detected in all eight tumors analyzed. All pineoblastoma tumors analyzed were classified as miRNA processing–altered 1 subtype. We estimate the population prevalence of germline DROSHA loss-of-function variants to be 1:3,875 to 1:4,843 but find no evidence for increased adult cancer risk. Conclusions: This is the first report of DROSHA-related tumor predisposition. As pineoblastoma and Wilms tumor are also associated with DICER1 GPVs, our results suggest that the tissues of origin for these tumors are uniquely tolerant of general miRNA loss. The miRNA processing–altered 1 pineoblastoma subtype is associated with older age of diagnosis and better outcomes than other subtypes, suggesting DROSHA GPV status may have important clinical and prognostic significance. We suggest that genetic testing for DROSHA GPVs be considered for patients with pineoblastoma, Wilms tumor, or other DICER1-/DGCR8-related conditions and propose surveillance recommendations through research studies for individuals with DROSHA GPVs.

Abstract Background Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors. Methods We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set. Results We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls. Conclusions In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats).