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Familial adenomatous polyposis (FAP) is an autosomal dominant syndrome responsible for 1% of colorectal cancers (CRCs). Up to 90% of classic FAPs are caused by inactivating mutations in APC, and mosaicism has been previously reported in 20% of de novo cases, usually linked to milder phenotypic manifestations. This study aimed to explore the prevalence of mosaicism in 11 unsolved cases of classic FAP and to evaluate the diagnostic yield of somatic testing. Paired samples of colorectal polyps, tumors, and/or mucosa were analyzed using a custom next-generation sequencing panel targeting 15 polyposis and CRC-predisposing genes. Whenever possible, the extension of mosaicism to blood or sperm was also examined. Of 11 patients with classic adenomatous polyposis, a mosaic pathogenic variant in APC was identified in 7 (64%). No other altered genes were identified. In two of seven patients (29%), mosaicism was found restricted to colonic tissues, whereas in five of seven patients (71%), it was extended to the blood. Germline affectation was confirmed in one patient. We report the first analysis at a somatic level of 15 genes associated with CRC susceptibility, which highlights the role of APC mosaicism in classic FAP etiology. The results further reinforce the importance of testing target tissues when blood test results are negative.

IntroductionGermline CNVs are important contributors to hereditary cancer. In genetic diagnostics, multiplex ligation-dependent probe amplification (MLPA) is commonly used to identify them. However, MLPA is time-consuming and expensive if applied to many genes, hence many routine laboratories test only a subset of genes of interest.Methods and resultsWe evaluated a next-generation sequencing (NGS)-based CNV detection tool (DECoN) as first-tier screening to decrease costs and turnaround time and expand CNV analysis to all genes of clinical interest in our diagnostics routine. We used DECoN in a retrospective cohort of 1860 patients where a limited number of genes were previously analysed by MLPA, and in a prospective cohort of 2041 patients, without MLPA analysis. In the retrospective cohort, 6 new CNVs were identified and confirmed by MLPA. In the prospective cohort, 19 CNVs were identified and confirmed by MLPA, 8 of these would have been lost in our previous MLPA-restricted detection strategy. Also, the number of genes tested by MLPA across all samples decreased by 93.0% in the prospective cohort.ConclusionIncluding an in silico germline NGS CNV detection tool improved our genetic diagnostics strategy in hereditary cancer, both increasing the number of CNVs detected and reducing turnaround time and costs.

Background CHEK2 variants are associated with intermediate breast cancer risk among other cancers. We aimed to comprehensively describe CHEK2 variants in a Spanish hereditary cancer (HC) cohort and adjust American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP) guidelines for their classification. Methods First, three CHEK2 frequent variants were screened in a retrospective Hereditary Breast and Ovarian Cancer cohort of 516 patients. After, the whole CHEK2 coding region was analyzed by next-generation sequencing in 1,848 prospective patients with HC suspicion. We refined ACMG-AMP criteria and applied different combined rules to classify CHEK2 variants and define risk alleles. Results We identified 10 CHEK2 null variants, 6 missense variants with discordant interpretation in ClinVar database, and 35 additional variants of unknown significance. Twelve variants were classified as (likely)-pathogenic; two can also be considered "established risk-alleles" and one as "likely risk-allele". The prevalence of (likely)-pathogenic variants in the HC cohort was 0.8% (1.3% in breast cancer patients and 1.0% in hereditary non-polyposis colorectal cancer patients). Conclusions Here we provide ACMG adjustment guidelines to classify CHEK2 variants. We hope that this work would be useful for variant classification of other genes with low effect variants. This article is protected by copyright. All rights reserved.

Although germline copy-number variants (CNVs) are the genetic cause of multiple hereditary diseases, detecting them from targeted next-generation sequencing data (NGS) remains a challenge. Existing tools perform well for large CNVs but struggle with single and multi-exon alterations. The aim of this work is to evaluate CNV calling tools working on gene panel NGS data and their suitability as a screening step before orthogonal confirmation in genetic diagnostics strategies. Five tools (DECoN, CoNVaDING, panelcn.MOPS, ExomeDepth, and CODEX2) were tested against four genetic diagnostics datasets (two in-house and two external) for a total of 495 samples with 231 single and multi-exon validated CNVs. The evaluation was performed using the default and sensitivity-optimized parameters. Results showed that most tools were highly sensitive and specific, but the performance was dataset dependant. When evaluating them in our diagnostics scenario, DECoN and panelcn.MOPS detected all CNVs with the exception of one mosaic CNV missed by DECoN. However, DECoN outperformed panelcn.MOPS specificity achieving values greater than 0.90 when using the optimized parameters. In our in-house datasets, DECoN and panelcn.MOPS showed the highest performance for CNV screening before orthogonal confirmation. Benchmarking and optimization code is freely available at https://github.com/TranslationalBioinformaticsIGTP/CNVbenchmarkeR.