De Novo Variants in TAOK1 Cause Neurodevelopmental Disorders.

De novo variants represent a significant cause of neurodevelopmental delay and intellectual disability. A genetic basis can be identified in only half of individuals who have neurodevelopmental disorders (NDDs); this indicates that additional causes need to be elucidated. We compared the frequency of de novo variants in patient-parent trios with (n = 2,030) versus without (n = 2,755) NDDs. We identified de novo variants in TAOK1 (thousand and one [TAO] amino acid kinase 1), which encodes the serine/threonine-protein kinase TAO1, in three individuals with NDDs but not in persons who did not have NDDs. Through further screening and the use of GeneMatcher, five additional individuals with NDDs were found to have de novo variants. All eight variants were absent from gnomAD (Genome Aggregation Database). The variant carriers shared a non-specific phenotype of developmental delay, and six individuals had additional muscular hypotonia. We established a fibroblast line of one mutation carrier, and we demonstrated that reduced mRNA levels of TAOK1 could be increased upon cycloheximide treatment. These results indicate nonsense-mediated mRNA decay. Further, there was neither detectable phosphorylated TAO1 kinase nor phosphorylated tau in these cells, and mitochondrial morphology was altered. Knockdown of the ortholog gene Tao1 (Tao, CG14217) in Drosophila resulted in delayed early development. The majority of the Tao1-knockdown flies did not survive beyond the third instar larval stage. When compared to control flies, Tao1 knockdown flies revealed changed morphology of the ventral nerve cord and the neuromuscular junctions as well as a decreased number of endings (boutons). Furthermore, mitochondria in mutant flies showed altered distribution and decreased size in axons of motor neurons. Thus, we provide compelling evidence that de novo variants in TAOK1 cause NDDs.

Development of an evidence-based algorithm that optimizes sensitivity and specificity in ES-based diagnostics of a clinically heterogeneous patient population.

PURPOSE: Next-generation sequencing (NGS) is rapidly replacing Sanger sequencing in genetic diagnostics. Sensitivity and specificity of NGS approaches are not well-defined, but can be estimated from applying NGS and Sanger sequencing in parallel. Utilizing this strategy, we aimed at optimizing exome sequencing (ES)-based diagnostics of a clinically diverse patient population. METHODS: Consecutive DNA samples from unrelated patients with suspected genetic disease were exome-sequenced; comparatively nonstringent criteria were applied in variant calling. One thousand forty-eight variants in genes compatible with the clinical diagnosis were followed up by Sanger sequencing. Based on a set of variant-specific features, predictors for true positives and true negatives were developed. RESULTS: Sanger sequencing confirmed 81.9% of ES-derived variants. Calls from the lower end of stringency accounted for the majority of the false positives, but also contained ~5% of the true positives. A predictor incorporating three variant-specific features classified 91.7% of variants with 100% specificity and 99.75% sensitivity. Confirmation status of the remaining variants (8.3%) was not predictable. CONCLUSIONS: Criteria for variant calling in ES-based diagnostics impact on specificity and sensitivity. Confirmatory sequencing for a proportion of variants, therefore, remains a necessity. Our study exemplifies how these variants can be defined on an empirical basis.

Prediction uncertainty estimates elucidate the limitation of current NSCLC subtype classification in representing mutational heterogeneity.

The heterogeneous pathogenesis and treatment response of non-small cell lung cancer (NSCLC) has led clinical treatment decisions to be guided by NSCLC subtypes, with lung adenocarcinoma and lung squamous cell carcinoma being the most common subtypes. While histology-based subtyping remains challenging, NSCLC subtypes were found to be distinct at the transcriptomic level. However, unlike genomic alterations, gene expression is generally not assessed in clinical routine. Since subtyping of NSCLC has remained elusive using mutational data, we aimed at developing a neural network model that simultaneously learns from adenocarcinoma and squamous cell carcinoma samples of other tissue types and is regularized using a neural network model trained from gene expression data. While substructures of the expression-based manifold were captured in the mutation-based manifold, NSCLC classification accuracy did not significantly improve. However, performance was increased when rejecting inconclusive samples using an ensemble-based approach capturing prediction uncertainty. Importantly, SHAP analysis of misclassified samples identified co-occurring mutations indicative of both NSCLC subtypes, questioning the current NSCLC subtype classification to adequately represent inherent mutational heterogeneity. Since our model captures mutational patterns linked to clinical heterogeneity, we anticipate it to be suited as foundational model of genomic data for clinically relevant prognostic or predictive downstream tasks.

Novel pathogenic variants and multiple molecular diagnoses in neurodevelopmental disorders.

BACKGROUND: Rare denovo variants represent a significant cause of neurodevelopmental delay and intellectual disability (ID). METHODS: Exome sequencing was performed on 4351 patients with global developmental delay, seizures, microcephaly, macrocephaly, motor delay, delayed speech and language development, or ID according to Human Phenotype Ontology (HPO) terms. All patients had previously undergone whole exome sequencing as part of diagnostic genetic testing with a focus on variants in genes implicated in neurodevelopmental disorders up to January 2017. This resulted in a genetic diagnosis in 1336 of the patients. In this study, we specifically searched for variants in 14 recently implicated novel neurodevelopmental disorder (NDD) genes. RESULTS: We identified 65 rare, protein-changing variants in 11 of these 14 novel candidate genes. Fourteen variants in CDK13, CHD4, KCNQ3, KMT5B, TCF20, and ZBTB18 were scored pathogenic or likely pathogenic. Of note, two of these patients had a previously identified cause of their disease, and thus, multiple molecular diagnoses were made including pathogenic/likely pathogenic variants in FOXG1 and CDK13 or in TMEM237 and KMT5B. CONCLUSIONS: Looking for pathogenic variants in newly identified NDD genes enabled us to provide a molecular diagnosis to 14 patients and their close relatives and caregivers. This underlines the relevance of re-evaluation of existing exome data on a regular basis to improve the diagnostic yield and serve the needs of our patients.

Next-generation sequencing of the BRCA1 and BRCA2 genes for the genetic diagnostics of hereditary breast and/or ovarian cancer.

Genetic testing for hereditary breast and/or ovarian cancer mostly relies on laborious molecular tools that use Sanger sequencing to scan for mutations in the BRCA1 and BRCA2 genes. We explored a more efficient genetic screening strategy based on next-generation sequencing of the BRCA1 and BRCA2 genes in 210 hereditary breast and/or ovarian cancer patients. We first validated this approach in a cohort of 115 samples with previously known BRCA1 and BRCA2 mutations and polymorphisms. Genomic DNA was amplified using the Ion AmpliSeq BRCA1 and BRCA2 panel. The DNA Libraries were pooled, barcoded, and sequenced using an Ion Torrent Personal Genome Machine sequencer. The combination of different robust bioinformatics tools allowed detection of all previously known pathogenic mutations and polymorphisms in the 115 samples, without detecting spurious pathogenic calls. We then used the same assay in a discovery cohort of 95 uncharacterized hereditary breast and/or ovarian cancer patients for BRCA1 and BRCA2. In addition, we describe the allelic frequencies across 210 hereditary breast and/or ovarian cancer patients of 74 unique definitely and likely pathogenic and uncertain BRCA1 and BRCA2 variants, some of which have not been previously annotated in the public databases. Targeted next-generation sequencing is ready to substitute classic molecular methods to perform genetic testing on the BRCA1 and BRCA2 genes and provides a greater opportunity for more comprehensive testing of at-risk patients.

Validation of a semiconductor next-generation sequencing assay for the clinical genetic screening of CFTR.

Genetic testing for cystic fibrosis and CFTR-related disorders mostly relies on laborious molecular tools that use Sanger sequencing to scan for mutations in the CFTR gene. We have explored a more efficient genetic screening strategy based on next-generation sequencing (NGS) of the CFTR gene. We validated this approach in a cohort of 177 patients with previously known CFTR mutations and polymorphisms. Genomic DNA was amplified using the Ion AmpliSeq™ CFTR panel. The DNA libraries were pooled, barcoded, and sequenced using an Ion Torrent PGM sequencer. The combination of different robust bioinformatics tools allowed us to detect previously known pathogenic mutations and polymorphisms in the 177 samples, without detecting spurious pathogenic calls. In summary, the assay achieves a sensitivity of 94.45% (95% CI: 92% to 96.9%), with a specificity of detecting nonvariant sites from the CFTR reference sequence of 100% (95% CI: 100% to 100%), a positive predictive value of 100% (95% CI: 100% to 100%), and a negative predictive value of 99.99% (95% CI: 99.99% to 100%). In addition, we describe the observed allelic frequencies of 94 unique definitely and likely pathogenic, uncertain, and neutral CFTR variants, some of them not previously annotated in the public databases. Strikingly, a seven exon spanning deletion as well as several more technically challenging variants such as pathogenic poly-thymidine-guanine and poly-thymidine (poly-TG-T) tracts were also detected. Targeted NGS is ready to substitute classical molecular methods to perform genetic testing on the CFTR gene.