Genetics of inborn errors of immunity: Diagnostic strategies and new approaches to CNV detection.

BACKGROUND: Genetic diagnosis of inborn errors of immunity (IEI) is complex due to the large number of genes involved and their molecular features. Missense variants have been reported as the most common cause of IEI. However, the frequency of copy number variants (CNVs) may be underestimated since their detection requires specific quantitative techniques. At this point, the use of Next Generation Sequencing (NGS) is acquiring relevance. METHODS: In this article, we present our experience in the genetic diagnosis of IEI based on three diagnostic algorithms that allowed the detection of single nucleotide variants (SNVs) and CNVs. Following this approximation, 703 index cases were evaluated between 2014 and 2021. Sanger sequencing, MLPA, CGH array, breakpoint spanning PCR or a customized NGS-based multigene-targeted panel were performed. RESULTS: A genetic diagnosis was reached in 142 of the 703 index cases (20%), 19 of them presented deletions as causal variants. Deletions were also detected in 5 affected relatives and 16 healthy carriers during the family studies. Additionally, we compile, characterize and present all the CNVs detected by our diagnostic algorithms, representing the largest cohort of deletions related to IEI to date. Furthermore, three bioinformatic tools (LACONv, XHMM, VarSeq™) based on NGS data were evaluated. VarSeq™ was the most sensitive and specific bioinformatic tool; detecting 21/23 (91%) deletions located in captured regions. CONCLUSION: Based on our results, we propose a strategy to guide the molecular diagnosis that can be followed by expert and non-expert centres in the field of IEI.

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency.

BACKGROUND: Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. RESULTS: In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. CONCLUSION: These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.