Novel variants in established epilepsy genes in focal epilepsy.

INTRODUCTION: Next generation sequencing (NGS) has greatly expanded our understanding of genetic contributors in multiple epilepsy syndromes, including focal epilepsy. Describing the genetic architecture of common syndromes promises to facilitate the diagnostic process as well as aid in the identification of patients who stand to benefit from genetic testing, but most studies to date have been limited to examining children or adults with intellectual disability. Our aim was to determine the yield of targeted sequencing of 5 established epilepsy genes (DEPDC5, LGI1, SCN1A, GRIN2A, and PCHD19) in an extensively phenotyped cohort of focal epilepsy patients with normal intellectual function or mild intellectual disability, as well as describe novel variants and determine the characteristics of variant carriers. PATIENTS AND METHODS: Targeted panel sequencing was performed on 96 patients with a strong clinical suspicion of genetic focal epilepsy. Patients had previously gone through a comprehensive diagnostic epilepsy evaluation in The Neurology Clinic, University Clinical Center of Serbia. Variants of interest (VOI) were classified using the American College of Medical Genetics and the Association for Molecular Pathology criteria. RESULTS: Six VOI in eight (8/96, 8.3%) patients were found in our cohort. Four likely pathogenic VOI were determined in six (6/96, 6.2%) patients, two DEPDC5 variants in two patients, one SCN1A variant in two patients and one PCDH19 variant in two patients. One variant of unknown significance (VUS) was found in GRIN2A in one (1/96, 1.0%) patient. Only one VOI in GRIN2A was classified as likely benign. No VOI were detected in LGI1. CONCLUSION: Sequencing of only five known epilepsy genes yielded a diagnostic result in 6.2% of our cohort and revealed multiple novel variants. Further research is necessary for a better understanding of the genetic basis in common epilepsy syndromes in patients with normal intellectual function or mild intellectual disability.

Novel GATOR1 variants in focal epilepsy.

INTRODUCTION: Variants in GATOR1 genes are well established in focal epilepsy syndromes. A strong association of GATOR1 variants with drug-resistant epilepsy as well as an increased risk of sudden unexplained death in epilepsy warrants developing strategies to facilitate the identification of patients who could potentially benefit from genetic testing and precision medicine. We aimed to determine the yield of GATOR1 gene sequencing in patients with focal epilepsy typically referred for genetic testing, establish novel GATOR1 variants and determine clinical, electroencephalographic, and radiological characteristics of variant carriers. PATIENTS AND METHODS: Ninety-six patients with clinical suspicion of genetic focal epilepsy with previous comprehensive diagnostic epilepsy evaluation in The Neurology Clinic, University Clinical Center of Serbia, were included in the study. Sequencing was performed using a custom gene panel encompassing DEPDC5, NPRL2, and NPRL3. Variants of interest (VOI) were classified according to criteria proposed by the American College of Medical Genetics and the Association for Molecular Pathology. RESULTS: Four previously unreported VOI in 4/96 (4.2%) patients were found in our cohort. Three likely pathogenic variants were determined in 3/96 (3.1%) patients, one frameshift variant in DEPDC5 in a patient with nonlesional frontal lobe epilepsy, one splicogenic DEPDC5 variant in a patient with nonlesional posterior quadrant epilepsy, and one frameshift variant in NPRL2 in a patient with temporal lobe epilepsy associated with hippocampal sclerosis. Only one VOI, a missense variant in NPRL3, found in 1/96 (1.1%) patients, was classified as a variant of unknown significance. CONCLUSION: GATOR1 gene sequencing was diagnostic in 3.1% of our cohort and revealed three novel likely pathogenic variants, including a previously unreported association of temporal lobe epilepsy with hippocampal sclerosis with an NPRL2 variant. Further research is essential for a better understanding of the clinical scope of GATOR1 gene-associated epilepsy.

Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values.

Amyotrophic lateral sclerosis (ALS) is a prototypical neurodegenerative disease characterized by progressive degeneration of motor neurons to severely effect the functionality to control voluntary muscle movement. Most of the non-additive genetic aberrations responsible for ALS make its molecular classification very challenging along with limited sample size, curse of dimensionality, class imbalance and noise in the data. Deep learning methods have been successful in many other related areas but have low minority class accuracy and suffer from the lack of explainability when used directly with RNA expression features for ALS molecular classification. In this paper, we propose a deep-learning-based molecular ALS classification and interpretation framework. Our framework is based on training a convolution neural network (CNN) on images obtained from converting RNA expression values into pixels based on DeepInsight similarity technique. Then, we employed Shapley additive explanations (SHAP) to extract pixels with higher relevance to ALS classifications. These pixels were mapped back to the genes which made them up. This enabled us to classify ALS samples with high accuracy for a minority class along with identifying genes that might be playing an important role in ALS molecular classifications. Taken together with RNA expression images classified with CNN, our preliminary analysis of the genes identified by SHAP interpretation demonstrate the value of utilizing Machine Learning to perform molecular classification of ALS and uncover disease-associated genes.

Tumor Signature Analysis Implicates Hereditary Cancer Genes in Endometrial Cancer Development.

Risk of endometrial cancer (EC) is increased ~2-fold for women with a family history of cancer, partly due to inherited pathogenic variants in mismatch repair (MMR) genes. We explored the role of additional genes as explanation for familial EC presentation by investigating germline and EC tumor sequence data from The Cancer Genome Atlas (n = 539; 308 European ancestry), and germline data from 33 suspected familial European ancestry EC patients demonstrating immunohistochemistry-detected tumor MMR proficiency. Germline variants in MMR and 26 other known/candidate EC risk genes were annotated for pathogenicity in the two EC datasets, and also for European ancestry individuals from gnomAD as a population reference set (n = 59,095). Ancestry-matched case-control comparisons of germline variant frequency and/or sequence data from suspected familial EC cases highlighted ATM, PALB2, RAD51C, MUTYH and NBN as candidates for large-scale risk association studies. Tumor mutational signature analysis identified a microsatellite-high signature for all cases with a germline pathogenic MMR gene variant. Signature analysis also indicated that germline loss-of-function variants in homologous recombination (BRCA1, PALB2, RAD51C) or base excision (NTHL1, MUTYH) repair genes can contribute to EC development in some individuals with germline variants in these genes. These findings have implications for expanded therapeutic options for EC cases.

A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity.

SUMMARY: Assessing the pathogenicity of genetic variants can be a complex and challenging task. Spliceogenic variants, which alter mRNA splicing, may yield mature transcripts that encode non-functional protein products, an important predictor of Mendelian disease risk. However, most variant annotation tools do not adequately assess spliceogenicity outside the native splice site and thus the disease-causing potential of variants in other intronic and exonic regions is often overlooked. Here, we present a plugin for the Ensembl Variant Effect Predictor that packages MaxEntScan and extends its functionality to provide splice site predictions using a maximum entropy model. The plugin incorporates a sliding window algorithm to predict splice site loss or gain for any variant that overlaps a transcript feature. We also demonstrate the utility of the plugin by comparing our predictions to two mRNA splicing datasets containing several cancer-susceptibility genes. AVAILABILITY AND IMPLEMENTATION: Source code is freely available under the Apache License, Version 2.0: https://github.com/Ensembl/VEP_plugins. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Association of CYP19A1 Variation with Circulating Estradiol and Aromatase Inhibitor Outcome: Can CYP19A1 Variants Be Used to Predict Treatment Efficacy?

After menopause, estradiol is primarily synthesized in peripheral tissues by the enzyme aromatase, encoded by CYP19A1. CYP19A1 variation associates with circulating estradiol in postmenopausal women and this variation is best represented by the intronic variant rs727479. This variation appears to have pleiotropic effects as it also associates with endometrial cancer risk. Indeed, estradiol plays an important role in the development of breast and endometrial cancer. Aromatase inhibitor (AI) drugs are used in the treatment of both diseases, however, response rates for AIs are low and there is currently no way to identify breast or endometrial cancer patients who are more likely to receive a clinical benefit. Multiple studies have proposed that genetic variation in CYP19A1 will have effects on AI efficacy: eight candidate variant studies of sample size greater than 50 describe associations between CYP19A1 variation and the outcome of patients treated with AIs. Nominally significant associations with patient outcome were reported for several variants, including rs727479. However, only an association between rs4646 and time to progression was replicated in an independent study. Moreover, rs4646 is also the only variant that has an association with patient outcome that passes a multiple testing threshold and this variant is in linkage disequilibrium with rs727479, supporting the hypothesis that associations with patient outcome may be driven through the effects on circulating estradiol. Despite this preliminary evidence, well phenotyped and comprehensively genotyped patient sets need to be studied before conclusions can be drawn about the effects of CYP19A1 variation on AI efficacy.

Endometrial cancer gene panels: clinical diagnostic vs research germline DNA testing.

Endometrial cancer is the most common gynecological cancer, but is nevertheless uncommon enough to have value as a signature cancer for some hereditary cancer syndromes. Commercial multigene testing panels include up to 13 different genes annotated for germline DNA testing of patients with endometrial cancer. Many other genes have been reported as relevant to familial endometrial cancer from directed genome-wide sequencing studies or multigene panel testing, or research. This review assesses the evidence supporting association with endometrial cancer risk for 32 genes implicated in hereditary endometrial cancer, and presents a summary of rare germline variants in these 32 genes detected by analysis of quasi-population-based endometrial cancer patients from The Cancer Genome Atlas project. This comprehensive investigation has led to the conclusion that convincing evidence currently exists to support clinical testing of only six of these genes for diagnosis of hereditary endometrial cancer. Testing of endometrial cancer patients for the remaining genes should be considered in the context of research studies, as a means to better establish the level of endometrial cancer risk, if any, associated with genetic variants that are deleterious to gene or protein function. It is acknowledged that clinical testing of endometrial cancer patients for several genes included on commercial panels may provide actionable findings in relation to risk of other cancers, but these should be considered secondary or incidental findings and not conclusive evidence for diagnosis of inherited endometrial cancer. In summary, this review and analysis provides a comprehensive report of current evidence to guide the selection of genes for clinical and research gene testing of germline DNA from endometrial cancer patients.

Whole-genome sequencing analysis of phenotypic heterogeneity and anticipation in Li-Fraumeni cancer predisposition syndrome.

The Li-Fraumeni syndrome (LFS) and its variant form (LFL) is a familial predisposition to multiple forms of childhood, adolescent, and adult cancers associated with germ-line mutation in the TP53 tumor suppressor gene. Individual disparities in tumor patterns are compounded by acceleration of cancer onset with successive generations. It has been suggested that this apparent anticipation pattern may result from germ-line genomic instability in TP53 mutation carriers, causing increased DNA copy-number variations (CNVs) with successive generations. To address the genetic basis of phenotypic disparities of LFS/LFL, we performed whole-genome sequencing (WGS) of 13 subjects from two generations of an LFS kindred. Neither de novo CNV nor significant difference in total CNV was detected in relation with successive generations or with age at cancer onset. These observations were consistent with an experimental mouse model system showing that trp53 deficiency in the germ line of father or mother did not increase CNV occurrence in the offspring. On the other hand, individual records on 1,771 TP53 mutation carriers from 294 pedigrees were compiled to assess genetic anticipation patterns (International Agency for Research on Cancer TP53 database). No strictly defined anticipation pattern was observed. Rather, in multigeneration families, cancer onset was delayed in older compared with recent generations. These observations support an alternative model for apparent anticipation in which rare variants from noncarrier parents may attenuate constitutive resistance to tumorigenesis in the offspring of TP53 mutation carriers with late cancer onset.