Closing the Gap - Detection of 5q-Spinal Muscular Atrophy by Short-Read Next-Generation Sequencing and Unexpected Results in a Diagnostic Patient Cohort.

BACKGROUND: The importance of early diagnosis of 5q-Spinal muscular atrophy (5q-SMA) has heightened as early intervention can significantly improve clinical outcomes. In 96% of cases, 5q-SMA is caused by a homozygous deletion of SMN1. Around 4 % of patients carry a SMN1 deletion and a single-nucleotide variant (SNV) on the other allele. Traditionally, diagnosis is based on multiplex ligation probe amplification (MLPA) to detect homozygous or heterozygous exon 7 deletions in SMN1. Due to high homologies within the SMN1/SMN2 locus, sequence analysis to identify SNVs of the SMN1 gene is unreliable by standard Sanger or short-read next-generation sequencing (srNGS) methods. OBJECTIVE: The objective was to overcome the limitations in high-throughput srNGS with the aim of providing SMA patients with a fast and reliable diagnosis to enable their timely therapy. METHODS: A bioinformatics workflow to detect homozygous SMN1 deletions and SMN1 SNVs on srNGS analysis was applied to diagnostic whole exome and panel testing for suggested neuromuscular disorders (1684 patients) and to fetal samples in prenatal diagnostics (260 patients). SNVs were detected by aligning sequencing reads from SMN1 and SMN2 to an SMN1 reference sequence. Homozygous SMN1 deletions were identified by filtering sequence reads for the ,, gene-determining variant" (GDV). RESULTS: 10 patients were diagnosed with 5q-SMA based on (i) SMN1 deletion and hemizygous SNV (2 patients), (ii) homozygous SMN1 deletion (6 patients), and (iii) compound heterozygous SNVs in SMN1 (2 patients). CONCLUSIONS: Applying our workflow in srNGS-based panel and whole exome sequencing (WES) is crucial in a clinical laboratory, as otherwise patients with an atypical clinical presentation initially not suspected to suffer from SMA remain undiagnosed.

Diagnostic yield and clinical relevance of expanded germline genetic testing for nearly 7000 suspected HBOC patients.

Here we report the results of a retrospective germline analysis of 6941 individuals fulfilling the criteria necessary for genetic testing of hereditary breast- and ovarian cancer (HBOC) according to the German S3 or AGO Guidelines. Genetic testing was performed by next-generation sequencing using 123 cancer-associated genes based on the Illumina TruSight® Cancer Sequencing Panel. In 1431 of 6941 cases (20.6%) at least one variant was reported (ACMG/AMP classes 3-5). Of those 56.3% (n = 806) were class 4 or 5 and 43.7% (n = 625) were a class 3 (VUS). We defined a 14 gene HBOC core gene panel and compared this to a national and different internationally recommended gene panels (German Hereditary Breast and Ovarian Cancer Consortium HBOC Consortium, ClinGen expert Panel, Genomics England PanelsApp) in regard of diagnostic yield, revealing a diagnostic range of pathogenic variants (class 4/5) from 7.8 to 11.6% depending on the panel evaluated. With the 14 HBOC core gene panel having a diagnostic yield of pathogenic variants (class 4/5) of 10.8%. Additionally, 66 (1%) pathogenic variants (ACMG/AMP class 4 or 5) were found in genes outside the 14 HBOC core gene set (secondary findings) that would have been missed with the restriction to the analysis of HBOC genes. Furthermore, we evaluated a workflow for a periodic re-evaluation of variants of uncertain clinical significance (VUS) for the improvement of clinical validity of germline genetic testing.

Somatic copy number alteration and fragmentation analysis in circulating tumor DNA for cancer screening and treatment monitoring in colorectal cancer patients.

BACKGROUND: Analysis of circulating free DNA (cfDNA) is a promising tool for personalized management of colorectal cancer (CRC) patients. Untargeted cfDNA analysis using whole-genome sequencing (WGS) does not need a priori knowledge of the patient´s mutation profile. METHODS: Here we established LIquid biopsy Fragmentation, Epigenetic signature and Copy Number Alteration analysis (LIFE-CNA) using WGS with ~ 6× coverage for detection of circulating tumor DNA (ctDNA) in CRC patients as a marker for CRC detection and monitoring. RESULTS: We describe the analytical validity and a clinical proof-of-concept of LIFE-CNA using a total of 259 plasma samples collected from 50 patients with stage I-IV CRC and 61 healthy controls. To reliably distinguish CRC patients from healthy controls, we determined cutoffs for the detection of ctDNA based on global and regional cfDNA fragmentation patterns, transcriptionally active chromatin sites, and somatic copy number alterations. We further combined global and regional fragmentation pattern into a machine learning (ML) classifier to accurately predict ctDNA for cancer detection. By following individual patients throughout their course of disease, we show that LIFE-CNA enables the reliable prediction of response or resistance to treatment up to 3.5 months before commonly used CEA. CONCLUSION: In summary, we developed and validated a sensitive and cost-effective method for untargeted ctDNA detection at diagnosis as well as for treatment monitoring of all CRC patients based on genetic as well as non-genetic tumor-specific cfDNA features. Thus, once sensitivity and specificity have been externally validated, LIFE-CNA has the potential to be implemented into clinical practice. To the best of our knowledge, this is the first study to consider multiple genetic and non-genetic cfDNA features in combination with ML classifiers and to evaluate their potential in both cancer detection and treatment monitoring. Trial registration DRKS00012890.