Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST-Indel)

Discovery of Pathogenic Variants Associated with Idiopathic Recurrent Pregnancy Loss Using Whole-Exome Sequencing

Idiopathic recurrent pregnancy loss (RPL) is defined as at least two pregnancy losses before 20 weeks of gestation. Approximately 5% of pregnant couples experience idiopathic RPL, which is a heterogeneous disease with various causes including hormonal, chromosomal, and intrauterine abnormalities. Although how pregnancy loss occurs is still unknown, numerous biological factors are associated with the incidence of pregnancy loss, including genetic variants. Whole-exome sequencing (WES) was conducted on blood samples from 56 Korean patients with RPL and 40 healthy controls. The WES data were aligned by means of bioinformatic analysis, and the detected variants were annotated using machine learning tools to predict the pathogenicity of protein alterations. Each indicated variant was confirmed using Sanger sequencing. A replication study was also conducted in 112 patients and 114 controls. The Variant Effect Scoring Tool, Combined Annotation Dependent Depletion tool, Sorting Intolerant from Tolerant annotation tool, and various databases detected 10 potential variants previously associated with spontaneous abortion genes in patients by means of a bioinformatic analysis of WES data. Several variants were detected in more than one patient. Interestingly, several of the detected genes were functionally clustered, including some with a secretory function (mucin 4; MUC4; rs200737893 G>A and hyaluronan-binding protein 2; HABP2; rs542838125 G>T), in which growth arrest-specific 2 Like 2 (GAS2L2; rs140842796 C>T) and dynamin 2 (DNM2; rs763894364 G>A) are functionally associated with cell protrusion and the cytoskeleton. ATP Binding Cassette Subfamily C Member 6 (ABCC6) was the only gene with two variants. HABP2 (rs542838125 G>T), MUC4 (rs200737893 G>A), and GAS2L2 (rs140842796 C>T) were detected in only the patient group in the replication study. The combination of WES and machine learning tools is a useful method to detect potential variants associated with RPL. Using bioinformatic tools, we found 10 potential variants in 9 genes. WES data from patients are needed to better understand the causes of RPL.

Retinal Dystrophies Associated With Peripherin-2: Genetic Spectrum and Novel Clinical Observations in 241 Patients

Purpose

To describe the clinical, electrophysiological and genetic spectrum of inherited retinal diseases associated with variants in the PRPH2 gene.

Methods

A total of 241 patients from 168 families across 15 sites in 9 countries with pathogenic or likely pathogenic variants in PRPH2 were included. Records were reviewed for age at symptom onset, visual acuity, full-field ERG, fundus colour photography, fundus autofluorescence (FAF), and SD-OCT. Images were graded into six phenotypes. Statistical analyses were performed to determine genotype-phenotype correlations.

Results

The median age at symptom onset was 40 years (range, 4-78 years). FAF phenotypes included normal (5%), butterfly pattern dystrophy, or vitelliform macular dystrophy (11%), central areolar choroidal dystrophy (28%), pseudo-Stargardt pattern dystrophy (41%), and retinitis pigmentosa (25%). Symptom onset was earlier in retinitis pigmentosa as compared with pseudo-Stargardt pattern dystrophy (34 vs 44 years; P = 0.004). The median visual acuity was 0.18 logMAR (interquartile range, 0-0.54 logMAR) and 0.18 logMAR (interquartile range 0-0.42 logMAR) in the right and left eyes, respectively. ERG showed a significantly reduced amplitude across all components (P < 0.001) and a peak time delay in the light-adapted 30-Hz flicker and single-flash b-wave (P < 0.001). Twenty-two variants were novel. The central areolar choroidal dystrophy phenotype was associated with 13 missense variants. The remaining variants showed marked phenotypic variability.

Conclusions

We described six distinct FAF phenotypes associated with variants in the PRPH2 gene. One FAF phenotype may have multiple ERG phenotypes, demonstrating a discordance between structure and function. Given the vast spectrum of PRPH2 disease our findings are useful for future clinical trials.

Detection of somatic and germline pathogenic variants in adult cohort of drug-resistant focal epilepsies

OBJECTIVE

This study investigates the prevalence of pathogenic variants in the mechanistic target of rapamycin (mTOR) pathway in surgical specimens of malformations of cortical development (MCDs) and cases with negative histology. The study also aims to evaluate the predictive value of genotype-histotype findings on the surgical outcome.

METHODS

The study included patients with drug-resistant focal epilepsy who underwent epilepsy surgery. Cases were selected based on histopathological diagnosis, focusing on MCDs and negative findings. We included brain tissues both as formalin-fixed, paraffin-embedded (FFPE) or fresh frozen (FF) samples. Single-molecule molecular inversion probes (smMIPs) analysis was conducted, targeting the MTOR gene in FFPE samples and 10 genes within the mTOR pathway in FF samples. Correlations between genotype-histotype and surgical outcome were examined.

RESULTS

We included 78 patients for whom we obtained 28 FFPE samples and 50 FF tissues. Seventeen pathogenic variants (22 %) were identified and validated, with 13 being somatic within the MTOR gene and 4 germlines (2 DEPDC5, 1 TSC1, 1 TSC2). Pathogenic variants in mTOR pathway genes were exclusively found in FCDII and TSC cases, with a significant association between FCD type IIb and MTOR genotype (P = 0.003). Patients carrying mutations had a slightly better surgical outcome than the overall cohort, however it results not significant. The FCDII diagnosed cases more frequently had normal neuropsychological test, a higher incidence of auras, fewer multiple seizure types, lower occurrence of seizures with awareness impairment, less ictal automatisms, fewer Stereo-EEG investigations, and a longer period long-life of seizure freedom before surgery.

SIGNIFICANCE

This study confirms that somatic MTOR variants represent the primary genetic alteration detected in brain specimens from FCDII/TSC cases, while germline DEPDC5, TSC1/TSC2 variants are relatively rare. Systematic screening for these mutations in surgically treated patients' brain specimens can aid histopathological diagnoses and serve as a biomarker for positive surgical outcomes. Certain clinical features associated with pathogenic variants in mTOR pathway genes may suggest a genetic etiology in FCDII patients.

Detection of driver mutations and genomic signatures in endometrial cancers using artificial intelligence algorithms

Analyzed endometrial cancer (EC) genomes have allowed for the identification of molecular signatures, which enable the classification, and sometimes prognostication, of these cancers. Artificial intelligence algorithms have facilitated the partitioning of mutations into driver and passenger based on a variety of parameters, including gene function and frequency of mutation. Here, we undertook an evaluation of EC cancer genomes deposited on the Catalogue of Somatic Mutations in Cancers (COSMIC), with the goal to classify all mutations as either driver or passenger. Our analysis showed that approximately 2.5% of all mutations are driver and cause cellular transformation and immortalization. We also characterized nucleotide level mutation signatures, gross chromosomal re-arrangements, and gene expression profiles. We observed that endometrial cancers show distinct nucleotide substitution and chromosomal re-arrangement signatures compared to other cancers. We also identified high expression levels of the CLDN18 claudin gene, which is involved in growth, survival, metastasis and proliferation. We then used in silico protein structure analysis to examine the effect of certain previously uncharacterized driver mutations on protein structure. We found that certain mutations in CTNNB1 and TP53 increase protein stability, which may contribute to cellular transformation. While our analysis retrieved previously classified mutations and genomic alterations, which is to be expected, this study also identified new signatures. Additionally, we show that artificial intelligence algorithms can be effectively leveraged to accurately predict key drivers of cancer. This analysis will expand our understanding of ECs and improve the molecular toolbox for classification, diagnosis, or potential treatment of these cancers.

An integrative framework for clinical diagnosis and knowledge discovery from exome sequencing data

Non-silent single nucleotide genetic variants, like nonsense changes and insertion-deletion variants, that affect protein function and length substantially are prevalent and are frequently misclassified. The low sensitivity and specificity of existing variant effect predictors for nonsense and indel variations restrict their use in clinical applications. We propose the Pathogenic Mutation Prediction (PMPred) method to predict the pathogenicity of single nucleotide variations, which impair protein function by prematurely terminating a protein's elongation during its synthesis. The prediction starts by monitoring functional effects (Gene Ontology annotation changes) of the change in sequence, using an existing ensemble machine learning model (UniGOPred). This, in turn, reveals the mutations that significantly deviate functionally from the wild-type sequence. We have identified novel harmful mutations in patient data and present them as motivating case studies. We also show that our method has increased sensitivity and specificity compared to state-of-the-art, especially in single nucleotide variations that produce large functional changes in the final protein. As further validation, we have done a comparative docking study on such a variation that is misclassified by existing methods and, using the altered binding affinities, show how PMPred can correctly predict the pathogenicity when other tools miss it. PMPred is freely accessible as a web service at https://pmpred.kansil.org/, and the related code is available at https://github.com/kansil/PMPred.

Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations

Genetic variations (including substitutions, insertions, and deletions) exert a profound influence on DNA sequences. These variations are systematically classified as synonymous, nonsynonymous, and nonsense, each manifesting distinct effects on proteins. The implementation of high-throughput sequencing has significantly augmented our comprehension of the intricate interplay between gene variations and protein structure and function, as well as their ramifications in the context of diseases. Frameshift variations, particularly small insertions and deletions (indels), disrupt protein coding and are instrumental in disease pathogenesis. This review presents a succinct review of computational methods, databases, current challenges, and future directions in predicting the consequences of coding frameshift small indels variations. We analyzed the predictive efficacy, reliability, and utilization of computational methods and variant account, reliability, and utilization of database. Besides, we also compared the prediction methodologies on GOF/LOF pathogenic variation data. Addressing the challenges pertaining to prediction accuracy and cross-species generalizability, nascent technologies such as AI and deep learning harbor immense potential to enhance predictive capabilities. The importance of interdisciplinary research and collaboration cannot be overstated for devising effective diagnosis, treatment, and prevention strategies concerning diseases associated with coding frameshift indels variations.

Comprehensive Genotyping and Phenotyping Analysis of GUCY2D-Associated Rod- and Cone-Dominated Dystrophies

PURPOSE

To describe the genetic and clinical spectrum of GUCY2D-associated retinopathies and to accurately establish their prevalence in a large cohort of patients.

DESIGN

Retrospective case series.

METHODS

Institutional study of 47 patients from 27 unrelated families with retinal dystrophies carrying disease-causing GUCY2D variants from the Fundación Jiménez Díaz hospital dataset of 8000 patients. Patients underwent ophthalmological examination and molecular testing by Sanger or exome sequencing approaches. Statistical and principal component analyses were performed to determine genotype-phenotype correlations.

RESULTS

Four clinically different associated phenotypes were identified: 66.7% of families with cone/cone-rod dystrophy, 22.2% with Leber congenital amaurosis, 7.4% with early-onset retinitis pigmentosa, and 3.7% with congenital night blindness. Twenty-three disease-causing GUCY2D variants were identified, including 6 novel variants. Biallelic variants accounted for 28% of patients, whereas most carried dominant alleles associated with cone/cone-rod dystrophy. The disease onset had statistically significant differences according to the functional variant effect. Patients carrying GUCY2D variants were projected into 3 subgroups by allelic combination, disease onset, and presence of nystagmus or night blindness. In contrast to patients with the most severe phenotype of Leber congenital amaurosis, 7 patients with biallelic GUCY2D had a later and milder rod form with night blindness in infancy as the first symptom.

CONCLUSIONS

This study represents the largest GUCY2D cohort in which 4 distinctly different phenotypes were identified, including rare intermediate presentations of rod-dominated retinopathies. We established that GUCY2D is linked to about 1% of approximately 3000 molecularly characterized families of our cohort. All of these findings are critical for defining cohorts for inclusion in future clinical trials.

PredinID: Predicting Pathogenic Inframe Indels in Human Through Graph Convolution Neural Network With Graph Sampling Technique

Inframe insertion/deletion (indel) variants may alter protein sequence and function, which are closely related to an extensive variety of diseases. Although recent researches have paid attention to the associations between inframe indels and diseases, modeling indels in silico and interpreting their pathogenicity remain challenging, mainly due to the lack of experimental information and computational methodologies. In this article, we propose a novel computational method named PredinID (Predictor for inframe InDels) via graph convolutional network (GCN). PredinID leverages k-nearest neighbor algorithm to construct the feature graph for aggregating more informative representation, regarding the pathogenic inframe indel prediction as a node classification task. An edge-based sampling strategy is designed for extracting information from both the potential connections of feature space and the topological structure of subgraphs. Evaluated by 5-fold cross-validations, the PredinID method achieves satisfactory performance and is superior to four classic machine learning algorithms and two GCN methods. Comprehensive experiments show that PredinID has superior performances when compared with the state-of-the-art methods on the independent test set. Moreover, we also implement a web server at http://predinid.bio.aielab.cc/, to facilitate the use of the model.

The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers

BACKGROUND

Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathological markers with intrinsic tumor molecular markers can help identify novel molecular subtypes and the discovery of actionable biomarkers.

METHODS

We conducted a large multi-institutional genomic analysis of paired normal and tumor samples from breast cancer patients to profile the complex genomic architecture of breast tumors. Long-term patient follow-up, therapeutic regimens, and treatment response for this cohort are documented using the Breast Cancer Collaborative Registry. The majority of the patients in this study were at tumor stage 1 (51.4%) and stage 2 (36.3%) at the time of diagnosis. Whole-exome sequencing data from 554 patients were used for mutational profiling and identifying cancer drivers.

RESULTS

We identified 54 tumors having at least 1000 mutations and 185 tumors with less than 100 mutations. Tumor mutational burden varied across the classified subtypes, and the top ten mutated genes include MUC4, MUC16, PIK3CA, TTN, TP53, NBPF10, NBPF1, CDC27, AHNAK2, and MUC2. Patients were classified based on seven biological and tumor-specific parameters, including grade, stage, hormone receptor status, histological subtype, Ki67 expression, lymph node status, race, and mutational profiles compared across different subtypes. Mutual exclusion of mutations in PIK3CA and TP53 was pronounced across different tumor grades. Cancer drivers specific to each subtype include TP53, PIK3CA, CDC27, CDH1, STK39, CBFB, MAP3K1, and GATA3, and mutations associated with patient survival were identified in our cohort.

CONCLUSIONS

This extensive study has revealed tumor burden, driver genes, co-occurrence, mutual exclusivity, and survival effects of mutations on a US Midwestern breast cancer cohort, paving the way for developing personalized therapeutic strategies.

Actionability classification of variants of unknown significance correlates with functional effect

Genomically-informed therapy requires consideration of the functional impact of genomic alterations on protein expression and/or function. However, a substantial number of variants are of unknown significance (VUS). The MD Anderson Precision Oncology Decision Support (PODS) team developed an actionability classification scheme that categorizes VUS as either "Unknown" or "Potentially" actionable based on their location within functional domains and/or proximity to known oncogenic variants. We then compared PODS VUS actionability classification with results from a functional genomics platform consisting of mutant generation and cell viability assays. 106 (24%) of 438 VUS in 20 actionable genes were classified as oncogenic in functional assays. Variants categorized by PODS as Potentially actionable (N = 204) were more likely to be oncogenic than those categorized as Unknown (N = 230) (37% vs 13%, p = 4.08e-09). Our results demonstrate that rule-based actionability classification of VUS can identify patients more likely to have actionable variants for consideration with genomically-matched therapy.

The genetics of autism spectrum disorder in an East African familial cohort

Autism spectrum disorder (ASD) is a group of complex neurodevelopmental conditions affecting communication and social interaction in 2.3% of children. Studies that demonstrated its complex genetic architecture have been mainly performed in populations of European ancestry. We investigate the genetics of ASD in an East African cohort (129 individuals) from a population with higher prevalence (5%). Whole-genome sequencing identified 2.13 million private variants in the cohort and potentially pathogenic variants in known ASD genes (including CACNA1C, CHD7, FMR1, and TCF7L2). Admixture analysis demonstrated that the cohort comprises two ancestral populations, African and Eurasian. Admixture mapping discovered 10 regions that confer ASD risk on the African haplotypes, containing several known ASD genes. The increased ASD prevalence in this population suggests decreased heterogeneity in the underlying genetic etiology, enabling risk allele identification. Our approach emphasizes the power of African genetic variation and admixture analysis to inform the architecture of complex disorders.

Basic Research Protocol: Exome Sequencing in Adults With Loin Pain Hematuria Syndrome: A Pilot Study

Background

Loin pain hematuria syndrome (LPHS) is a poorly understood clinical syndrome characterized by hematuria and either unilateral or bilateral severe kidney pain in the absence of identifiable urological disease. Loin pain hematuria syndrome imposes a significant health and economic impact with a loss of productivity and quality of life in a young population. Owing to an incomplete understanding of its pathophysiology, treatment has been limited to nonspecific pain management. Nearly 60 years after its initial description, we are no further ahead in understanding the molecular pathways involved in LPHS.

Objective

To outline the study design for exome sequencing in adults with LPHS and their families.

Methods

In this single-center case series, 24 patients with LPHS and 2 additional first-degree family members per participant will be recruited. DNA extracted from venous blood samples will undergo exome sequencing on the Illumina NovaSeq 6000 System at 100× depth and will be assessed for pathogenic variants in genes associated with hematuria (number of genes in: glomerular endothelium [n = 10] and basement membrane [n = 8]), and pain pathways (number of genes in: pain transduction [n = 17], conduction [n = 8], synaptic transmission [n = 37], and modulation [n = 27]). We will further examine identified potentially pathogenic variants that co-segregate with LPHS features among affected families.

Conclusions

This pilot study may identify new directions for an investigation into the molecular mechanisms underlying LPHS.

Structural Dynamics Predominantly Determine the Adaptability of Proteins to Amino Acid Deletions

The insertion or deletion (indel) of amino acids has a variety of effects on protein function, ranging from disease-forming changes to gaining new functions. Despite their importance, indels have not been systematically characterized towards protein engineering or modification goals. In the present work, we focus on deletions composed of multiple contiguous amino acids (mAA-dels) and their effects on the protein (mutant) folding ability. Our analysis reveals that the mutant retains the native fold when the mAA-del obeys well-defined structural dynamics properties: localization in intrinsically flexible regions, showing low resistance to mechanical stress, and separation from allosteric signaling paths. Motivated by the possibility of distinguishing the features that underlie the adaptability of proteins to mAA-dels, and by the rapid evaluation of these features using elastic network models, we developed a positive-unlabeled learning-based classifier that can be adopted for protein design purposes. Trained on a consolidated set of features, including those reflecting the intrinsic dynamics of the regions where the mAA-dels occur, the new classifier yields a high recall of 84.3% for identifying mAA-dels that are stably tolerated by the protein. The comparative examination of the relative contribution of different features to the prediction reveals the dominant role of structural dynamics in enabling the adaptation of the mutant to mAA-del without disrupting the native fold.

Protein Arginine Methyltransferase 5 (PRMT5) Mutations in Cancer Cells

Arginine methylation is a form of posttranslational modification that regulates many cellular functions such as development, DNA damage repair, inflammatory response, splicing, and signal transduction, among others. Protein arginine methyltransferase 5 (PRMT5) is one of nine identified methyltransferases, and it can methylate both histone and non-histone targets. It has pleiotropic functions, including recruitment of repair machinery to a chromosomal DNA double strand break (DSB) and coordinating the interplay between repair and checkpoint activation. Thus, PRMT5 has been actively studied as a cancer treatment target, and small molecule inhibitors of its enzymatic activity have already been developed. In this report, we analyzed all reported PRMT5 mutations appearing in cancer cells using data from the Catalogue of Somatic Mutations in Cancers (COSMIC). Our goal is to classify mutations as either drivers or passengers to understand which ones are likely to promote cellular transformation. Using gold standard artificial intelligence algorithms, we uncovered several key driver mutations in the active site of the enzyme (D306H, L315P, and N318K). In silico protein modeling shows that these mutations may affect the affinity of PRMT5 for S-adenosylmethionine (SAM), which is required as a methyl donor. Electrostatic analysis of the enzyme active site shows that one of these mutations creates a tunnel in the vicinity of the SAM binding site, which may allow interfering molecules to enter the enzyme active site and decrease its activity. We also identified several non-coding mutations that appear to affect PRMT5 splicing. Our analyses provide insights into the role of PRMT5 mutations in cancer cells. Additionally, since PRMT5 single molecule inhibitors have already been developed, this work may uncover future directions in how mutations can affect targeted inhibition.

Evaluation of in silico pathogenicity prediction tools for the classification of small in-frame indels

BACKGROUND

The use of in silico pathogenicity predictions as evidence when interpreting genetic variants is widely accepted as part of standard variant classification guidelines. Although numerous algorithms have been developed and evaluated for classifying missense variants, in-frame insertions/deletions (indels) have been much less well studied.

METHODS

We created a dataset of 3964 small (< 100 bp) indels predicted to result in in-frame amino acid insertions or deletions using data from gnomAD v3.1 (minor allele frequency of 1-5%), ClinVar and the Deciphering Developmental Disorders (DDD) study. We used this dataset to evaluate the performance of nine pathogenicity predictor tools: CADD, CAPICE, FATHMM-indel, MutPred-Indel, MutationTaster2021, PROVEAN, SIFT-indel, VEST-indel and VVP.

RESULTS

Our dataset consisted of 2224 benign/likely benign and 1740 pathogenic/likely pathogenic variants from gnomAD (n = 809), ClinVar (n = 2882) and, DDD (n = 273). We were able to generate scores across all tools for 91% of the variants, with areas under the ROC curve (AUC) of 0.81-0.96 based on the published recommended thresholds. To avoid biases caused by inclusion of our dataset in the tools' training data, we also evaluated just DDD variants not present in either gnomAD or ClinVar (70 pathogenic and 81 benign). Using this subset, the AUC of all tools decreased substantially to 0.64-0.87. Several of the tools performed similarly however, VEST-indel had the highest AUCs of 0.93 (full dataset) and 0.87 (DDD subset).

CONCLUSIONS

Algorithms designed for predicting the pathogenicity of in-frame indels perform well enough to aid clinical variant classification in a similar manner to missense prediction tools.

In Silico Examination of Single Nucleotide Missense Mutations in NHLH2, a Gene Linked to Infertility and Obesity

Continual advances in our understanding of the human genome have led to exponential increases in known single nucleotide variants. The characterization of each of the variants lags behind. For researchers needing to study a single gene, or multiple genes in a pathway, there must be ways to narrow down pathogenic variants from those that are silent or pose less pathogenicity. In this study, we use the NHLH2 gene which encodes the nescient helix-loop-helix 2 (Nhlh2) transcription factor in a systematic analysis of all missense mutations to date in the gene. The NHLH2 gene was first described in 1992. Knockout mice created in 1997 indicated a role for this protein in body weight control, puberty, and fertility, as well as the motivation for sex and exercise. Only recently have human carriers of NHLH2 missense variants been characterized. Over 300 missense variants for the NHLH2 gene are listed in the NCBI single nucleotide polymorphism database (dbSNP). Using in silico tools, predicted pathogenicity of the variants narrowed the missense variants to 37 which were predicted to affect NHLH2 function. These 37 variants cluster around the basic-helix-loop-helix and DNA binding domains of the transcription factor, and further analysis using in silico tools provided 21 SNV resulting in 22 amino acid changes for future wet lab analysis. The tools used, findings, and predictions for the variants are discussed considering the known function of the NHLH2 transcription factor. Overall use of these in silico tools and analysis of these data contribute to our knowledge of a protein which is both involved in the human genetic syndrome, Prader-Willi syndrome, and in controlling genes involved in body weight control, fertility, puberty, and behavior in the general population, and may provide a systematic methodology for others to characterize variants for their gene of interest.

A Comprehensive Analysis of Cutaneous Melanoma Patients in Greece Based on Multi-Omic Data

Cutaneous melanoma (CM) is the most aggressive type of skin cancer, and it is characterised by high mutational load and heterogeneity. In this study, we aimed to analyse the genomic and transcriptomic profile of primary melanomas from forty-six Formalin-Fixed, Paraffin-Embedded (FFPE) tissues from Greek patients. Molecular analysis for both germline and somatic variations was performed in genomic DNA from peripheral blood and melanoma samples, respectively, exploiting whole exome and targeted sequencing, and transcriptomic analysis. Detailed clinicopathological data were also included in our analyses and previously reported associations with specific mutations were recognised. Most analysed samples (43/46) were found to harbour at least one clinically actionable somatic variant. A subset of samples was profiled at the transcriptomic level, and it was shown that specific melanoma phenotypic states could be inferred from bulk RNA isolated from FFPE primary melanoma tissue. Integrative bioinformatics analyses, including variant prioritisation, differential gene expression analysis, and functional and gene set enrichment analysis by group and per sample, were conducted and molecular circuits that are implicated in melanoma cell programmes were highlighted. Integration of mutational and transcriptomic data in CM characterisation could shed light on genes and pathways that support the maintenance of phenotypic states encrypted into heterogeneous primary tumours.

Molecular Genetic Characteristics of FANCI, a Proposed New Ovarian Cancer Predisposing Gene

FANCI was recently identified as a new candidate ovarian cancer (OC)-predisposing gene from the genetic analysis of carriers of FANCI c.1813C>T; p.L605F in OC families. Here, we aimed to investigate the molecular genetic characteristics of FANCI, as they have not been described in the context of cancer. We first investigated the germline genetic landscape of two sisters with OC from the discovery FANCI c.1813C>T; p.L605F family (F1528) to re-affirm the plausibility of this candidate. As we did not find other conclusive candidates, we then performed a candidate gene approach to identify other candidate variants in genes involved in the FANCI protein interactome in OC families negative for pathogenic variants in BRCA1, BRCA2, BRIP1, RAD51C, RAD51D, and FANCI, which identified four candidate variants. We then investigated FANCI in high-grade serous ovarian carcinoma (HGSC) from FANCI c.1813C>T carriers and found evidence of loss of the wild-type allele in tumour DNA from some of these cases. The somatic genetic landscape of OC tumours from FANCI c.1813C>T carriers was investigated for mutations in selected genes, copy number alterations, and mutational signatures, which determined that the profiles of tumours from carriers were characteristic of features exhibited by HGSC cases. As other OC-predisposing genes such as BRCA1 and BRCA2 are known to increase the risk of other cancers including breast cancer, we investigated the carrier frequency of germline FANCI c.1813C>T in various cancer types and found overall more carriers among cancer cases compared to cancer-free controls (p = 0.007). In these different tumour types, we also identified a spectrum of somatic variants in FANCI that were not restricted to any specific region within the gene. Collectively, these findings expand on the characteristics described for OC cases carrying FANCI c.1813C>T; p.L605F and suggest the possible involvement of FANCI in other cancer types at the germline and/or somatic level.

SHINE: protein language model-based pathogenicity prediction for short inframe insertion and deletion variants

Accurate variant pathogenicity predictions are important in genetic studies of human diseases. Inframe insertion and deletion variants (indels) alter protein sequence and length, but not as deleterious as frameshift indels. Inframe indel Interpretation is challenging due to limitations in the available number of known pathogenic variants for training. Existing prediction methods largely use manually encoded features including conservation, protein structure and function, and allele frequency to infer variant pathogenicity. Recent advances in deep learning modeling of protein sequences and structures provide an opportunity to improve the representation of salient features based on large numbers of protein sequences. We developed a new pathogenicity predictor for SHort Inframe iNsertion and dEletion (SHINE). SHINE uses pretrained protein language models to construct a latent representation of an indel and its protein context from protein sequences and multiple protein sequence alignments, and feeds the latent representation into supervised machine learning models for pathogenicity prediction. We curated training data from ClinVar and gnomAD, and created two test datasets from different sources. SHINE achieved better prediction performance than existing methods for both deletion and insertion variants in these two test datasets. Our work suggests that unsupervised protein language models can provide valuable information about proteins, and new methods based on these models can improve variant interpretation in genetic analyses.

Genetic analyses of DNA repair pathway associated genes implicate new candidate cancer predisposing genes in ancestrally defined ovarian cancer cases

Not all familial ovarian cancer (OC) cases are explained by pathogenic germline variants in known risk genes. A candidate gene approach involving DNA repair pathway genes was applied to identify rare recurring pathogenic variants in familial OC cases not associated with known OC risk genes from a population exhibiting genetic drift. Whole exome sequencing (WES) data of 15 OC cases from 13 families tested negative for pathogenic variants in known OC risk genes were investigated for candidate variants in 468 DNA repair pathway genes. Filtering and prioritization criteria were applied to WES data to select top candidates for further analyses. Candidates were genotyped in ancestry defined study groups of 214 familial and 998 sporadic OC or breast cancer (BC) cases and 1025 population-matched controls and screened for additional carriers in 605 population-matched OC cases. The candidate genes were also analyzed in WES data from 937 familial or sporadic OC cases of diverse ancestries. Top candidate variants in ERCC5, EXO1, FANCC, NEIL1 and NTHL1 were identified in 5/13 (39%) OC families. Collectively, candidate variants were identified in 7/435 (1.6%) sporadic OC cases and 1/566 (0.2%) sporadic BC cases versus 1/1025 (0.1%) controls. Additional carriers were identified in 6/605 (0.9%) OC cases. Tumour DNA from ERCC5, NEIL1 and NTHL1 variant carriers exhibited loss of the wild-type allele. Carriers of various candidate variants in these genes were identified in 31/937 (3.3%) OC cases of diverse ancestries versus 0-0.004% in cancer-free controls. The strategy of applying a candidate gene approach in a population exhibiting genetic drift identified new candidate OC predisposition variants in DNA repair pathway genes.

A Novel Variant in VPS13B Underlying Cohen Syndrome

Pathogenic variants in vacuolar protein sorting 13 homolog B (VPS13B) cause Cohen syndrome (CS), a clinically diverse neurodevelopmental disorder. We used whole exome and Sanger sequencing to identify disease-causing variants in a Pakistani family with intellectual disability, microcephaly, facial dysmorphism, neutropenia, truncal obesity, speech delay, motor delay, and insomnia. We identified a novel homozygous nonsense variant c.8841G > A: p.(W2947^∗) in VPS13B (NM_017890.5) which segregated with the disease. Sleep disturbances are commonly seen in neurodevelopmental disorders and can exacerbate medical issues if left untreated. We demonstrate that individuals with Cohen syndrome may also be affected by sleep disturbances. In conclusion, we expand the genetic and phenotypic features of Cohen syndrome in the Pakistani population.

A case report of maturity-onset diabetes of the young (MODY12) in a Chinese Han patient with a novel ABCC8 gene mutation

RATIONALE

Maturity onset diabetes of the young (MODY) is the most common type of monogenic diabetes, characterized by autosomal dominant inheritance, the age of onset is often <25 years old, and the clinical manifestations are atypical. MODY12 is caused by a rare missense mutation of adenosine triphosphate (ATP)-binding cassette transporter subfamily C member 8 (ABCC8) gene and more than 50 ABCC8 variants were associated with MODY12.

PATIENT CONCERNS

The patient was a 30-year-old Chinese Han man. He was overweight with a poor control of blood glucose.

DIAGNOSES

The patient was diagnosed with MODY12.

INTERVENTIONS

The patient was given glimepiride (4 mg/d) with diet and exercise therapy to reduce blood glucose and weight.

OUTCOMES

The level of fasting blood glucose and C-peptide was improved after 1 year treatment as well as body weight.

LESSONS

A Chinese Han adult with a heterozygous missense mutation c.3976G > A (p.Glu1326Lys) was diagnosed with MODY12, which was the new pathogenic mutation for the disease. This report expands the spectrum of variants causing MODY12 and reduces misdiagnosis.

Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment

We identified resistance mechanisms to abiraterone acetate/prednisone (AA/P) in patients with metastatic castration-resistant prostate cancer (mCRPC) in the Prostate Cancer Medically Optimized Genome-Enhanced Therapy (PROMOTE) study.

We analyzed whole-exome sequencing (WES) and RNA-sequencing data from 83 patients with metastatic biopsies before (V1) and after 12 weeks of AA/P treatment (V2). Resistance was determined by time to treatment change (TTTC).

At V2, 18 and 11 of 58 patients had either short-term (median 3.6 months; range 1.4-4.5) or long-term (median 29 months; range 23.5-41.7) responses, respectively. Nonresponders had low expression of TGFBR3 and increased activation of the Wnt pathway, cell cycle, upregulation of AR variants, both pre- and posttreatment, with further deletion of AR inhibitor CDK11B posttreatment. Deletion of androgen processing genes, HSD17B11, CYP19A1 were observed in nonresponders posttreatment. Genes involved in cell cycle, DNA repair, Wnt-signaling, and Aurora kinase pathways were differentially expressed between the responder and non-responder at V2. Activation of Wnt signaling in nonresponder and deactivation of MYC or its target genes in responders was detected via SCN loss, somatic mutations, and transcriptomics. Upregulation of genes in the AURKA pathway are consistent with the activation of MYC regulated genes in nonresponders. Several genes in the AKT1 axis had increased mutation rate in nonresponders. We also found evidence of resistance via PDCD1 overexpression in responders.

IMPLICATIONS

Finally, we identified candidates drugs to reverse AA/P resistance: topoisomerase inhibitors and drugs targeting the cell cycle via the MYC/AURKA/AURKB/TOP2A and/or PI3K_AKT_MTOR pathways.

Assessing the Pathogenicity of In-Frame CACNA1F Indel Variants Using Structural Modeling

Small in-frame insertion-deletion (indel) variants are a common form of genomic variation whose impact on rare disease phenotypes has been understudied. The prediction of the pathogenicity of such variants remains challenging. X-linked incomplete congenital stationary night blindness type 2 (CSNB2) is a nonprogressive, inherited retinal disorder caused by variants in CACNA1F, encoding the Ca_v1.4α1 channel protein. Here, structural analysis was used through homology modeling to interpret 10 disease-correlated and 10 putatively benign CACNA1F in-frame indel variants. CSNB2-correlated changes were found to be more highly conserved compared with putative benign variants. Notably, all 10 disease-correlated variants but none of the benign changes were within modeled regions of the protein. Structural analysis revealed that disease-correlated variants are predicted to destabilize the structure and function of the Ca_v1.4α1 channel protein. Overall, the use of structural information to interpret the consequences of in-frame indel variants provides an important adjunct that can improve the diagnosis for individuals with CSNB2.

KAT5 histone acetyltransferase mutations in cancer cells

Cancer cells are characterized by accumulation of mutations due to improperly repaired DNA damage. The DNA double strand break is one of the most severe form of damage and several redundant mechanisms have evolved to facilitate accurate repair. During DNA replication and in mitosis, breaks are primarily repaired by homologous recombination which is facilitated by several genes. Key to this process is the breast cancer susceptibility genes BRCA1 and BRCA2 as well as the accessory RAD52 gene. Proper chromatin remodeling is also essential for repair and the KAT5 histone acetyltransferase facilitates histone removal at the break. Here we undertook a pan cancer analysis to investigate mutations within the KAT5 gene in cancer cells. We employed two standard artificial algorithms to classify mutations as either driver (CHASMPlus algorithm) or pathogenic (VEST4 algorithm). We find that most predicted driver and disease-causing mutations occur in the catalytic site or within key regulatory domains. In silico analysis of protein structure using AlphaFold shows that these mutations are likely to destabilize the function of KAT5 or interactions with DNA or its other partners. The data presented here, although preliminary, could be used to inform clinical strategies.

Phenotypic screening models for rapid diagnosis of genetic variants and discovery of personalized therapeutics

Precision medicine strives for highly individualized treatments for disease under the notion that each individual's unique genetic makeup and environmental exposures imprints upon them not only a disposition to illness, but also an optimal therapeutic approach. In the realm of rare disorders, genetic predisposition is often the predominant mechanism driving disease presentation. For such, mostly, monogenic disorders, a causal gene to phenotype association is likely. As a result, it becomes important to query the patient's genome for the presence of pathogenic variations that are likely to cause the disease. Determining whether a variant is pathogenic or not is critical to these analyses and can be challenging, as many disease-causing variants are novel and, ergo, have no available functional data to help categorize them. This problem is exacerbated by the need for rapid evaluation of pathogenicity, since many genetic diseases present in young children who will experience increased morbidity and mortality without rapid diagnosis and therapeutics. Here, we discuss the utility of animal models, with a focus mainly on C. elegans, as a contrast to tissue culture and in silico approaches, with emphasis on how these systems are used in determining pathogenicity of variants with uncertain significance and then used to screen for novel therapeutics.

MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning

Multiple computational approaches have been developed to improve our understanding of genetic variants. However, their ability to identify rare pathogenic variants from rare benign ones is still lacking. Using context annotations and deep learning methods, we present pathogenicity prediction models, MetaRNN and MetaRNN-indel, to help identify and prioritize rare nonsynonymous single nucleotide variants (nsSNVs) and non-frameshift insertion/deletions (nfINDELs). We use independent test sets to demonstrate that these new models outperform state-of-the-art competitors and achieve a more interpretable score distribution. Importantly, prediction scores from both models are comparable, enabling easy adoption of integrated genotype-phenotype association analysis methods. All pre-computed nsSNV scores are available at http://www.liulab.science/MetaRNN. The stand-alone program is also available at https://github.com/Chang-Li2019/MetaRNN.

Clinical and genetic characterization of CACNA1A-related disease

Pathogenic variants in the CACNA1A gene have been associated with episodic ataxia type 2, familial hemiplegic migraine, and spinocerebellar ataxia 6. With increasing use of clinical genetic testing, associations have expanded to include developmental delay, epilepsy, paroxysmal dystonia, and neuropsychiatric disorders. We report 47 individuals with 33 unique likely pathogenic or pathogenic CACNA1A variants. A machine learning method, funNCion, was used to predict loss-of-function (LoF)/gain-of-function (GoF) impact of genetic variants, and a heuristic severity score was designed to analyze genotype/phenotype correlations. Commonly reported phenotypes include developmental delay/intellectual disability (96%), hemiplegic migraines (36%), episodic ataxia type 2 (32%), epilepsy (55%), autism spectrum disorder (23%), and paroxysmal tonic upward gaze (36%). Severity score was significantly higher for predicted GoF variants, variants in the S5/S6 helices, and the recurrent p.Val1392Met variant. Seizures/status epilepticus were correlated with GoF and were more frequent in those with the p.Val1392Met variant. Our findings demonstrate a breadth of disease severity in CACNA1A-related disease and suggest that the clinical phenotypic heterogeneity likely reflects diverse molecular phenotypes. A better understanding of the natural history of CACNA1A-related disease and genotype/phenotype correlations will help inform prognosis and prepare for future clinical trials.

Pan-cancer analysis of co-occurring mutations in RAD52 and the BRCA1-BRCA2-PALB2 axis in human cancers

In human cells homologous recombination (HR) is critical for repair of DNA double strand breaks (DSBs) and rescue of stalled or collapsed replication forks. HR is facilitated by RAD51 which is loaded onto DNA by either BRCA2-BRCA1-PALB2 or RAD52. In human culture cells, double-knockdowns of RAD52 and genes in the BRCA1-BRCA2-PALB2 axis are lethal. Mutations in BRCA2, BRCA1 or PALB2 significantly impairs error free HR as RAD51 loading relies on RAD52 which is not as proficient as BRCA2-BRCA1-PALB2. RAD52 also facilitates Single Strand Annealing (SSA) that produces intra-chromosomal deletions. Some RAD52 mutations that affect the SSA function or decrease RAD52 association with DNA can suppress certain BRCA2 associated phenotypes in breast cancers. In this report we did a pan-cancer analysis using data reported on the Catalogue of Somatic Mutations in Cancers (COSMIC) to identify double mutants between RAD52 and BRCA1, BRCA2 or PALB2 that occur in cancer cells. We find that co-occurring mutations are likely in certain cancer tissues but not others. However, all mutations occur in a heterozygous state. Further, using computational and machine learning tools we identified only a handful of pathogenic or driver mutations predicted to significantly affect the function of the proteins. This supports previous findings that co-inactivation of RAD52 with any members of the BRCA2-BRCA1-PALB2 axis is lethal. Molecular modeling also revealed that pathogenic RAD52 mutations co-occurring with mutations in BRCA2-BRCA1-PALB2 axis are either expected to attenuate its SSA function or its interaction with DNA. This study extends previous breast cancer findings to other cancer types and shows that co-occurring mutations likely destabilize HR by similar mechanisms as in breast cancers.

Dynamic insights into the effects of nonsynonymous polymorphisms (nsSNPs) on loss of TREM2 function

Single nucleotide variations in Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) are associated with many neurodegenerative diseases, including Nasu-Hakola disease (NHD), frontotemporal dementia (FTD), and late-onset Alzheimer's disease because they disrupt ligand binding to the extracellular domain of TREM2. However, the effects of nonsynonymous single nucleotide polymorphisms (nsSNPs) in TREM2 on disease progression remain unknown. In this study, we identified several high-risk nsSNPs in the TREM2 gene using various deleterious SNP predicting algorithms and analyzed their destabilizing effects on the ligand recognizing region of the TREM2 immunoglobulin (Ig) domain by molecular dynamics (MD) simulation. Cumulative prediction by all tools employed suggested the three most deleterious nsSNPs involved in loss of TREM2 function are rs549402254 (W50S), rs749358844 (R52C), and rs1409131974 (D104G). MD simulation showed that these three variants cause substantial structural alterations and conformational remodeling of the apical loops of the TREM2 Ig domain, which is responsible for ligand recognition. Detailed analysis revealed that these variants substantially increased distances between apical loops and induced conformation remodeling by changing inter-loop nonbonded contacts. Moreover, all nsSNPs changed the electrostatic potentials near the putative ligand-interacting region (PLIR), which suggested they might reduce specificity or loss of binding affinity for TREM2 ligands. Overall, this study identifies three potential high-risk nsSNPs in the TREM2 gene. We propose further studies on the molecular mechanisms responsible for loss of TREM2 function and the associations between TREM2 nsSNPs and neurodegenerative diseases.

Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine

Precision medicine uses genetic, environmental and lifestyle factors to more accurately diagnose and treat disease in specific groups of patients, and it is considered one of the most promising medical efforts of our time. The use of genetics is arguably the most data-rich and complex components of precision medicine. The grand challenge today is the successful assimilation of genetics into precision medicine that translates across different ancestries, diverse diseases and other distinct populations, which will require clever use of artificial intelligence (AI) and machine learning (ML) methods. Our goal here was to review and compare scientific objectives, methodologies, datasets, data sources, ethics and gaps of AI/ML approaches used in genomics and precision medicine. We selected high-quality literature published within the last 5 years that were indexed and available through PubMed Central. Our scope was narrowed to articles that reported application of AI/ML algorithms for statistical and predictive analyses using whole genome and/or whole exome sequencing for gene variants, and RNA-seq and microarrays for gene expression. We did not limit our search to specific diseases or data sources. Based on the scope of our review and comparative analysis criteria, we identified 32 different AI/ML approaches applied in variable genomics studies and report widely adapted AI/ML algorithms for predictive diagnostics across several diseases.

TransPPMP: predicting pathogenicity of frameshift and non-sense mutations by a Transformer based on protein features

MOTIVATION

Protein structure can be severely disrupted by frameshift and non-sense mutations at specific positions in the protein sequence. Frameshift and non-sense mutation cases can also be found in healthy individuals. A method to distinguish neutral and potentially disease-associated frameshift and non-sense mutations is of practical and fundamental importance. It would allow researchers to rapidly screen out the potentially pathogenic sites from a large number of mutated genes and then use these sites as drug targets to speed up diagnosis and improve access to treatment. The problem of how to distinguish between neutral and potentially disease-associated frameshift and non-sense mutations remains under-researched.

RESULTS

We built a Transformer-based neural network model to predict the pathogenicity of frameshift and non-sense mutations on protein features and named it TransPPMP. The feature matrix of contextual sequences computed by the ESM pre-training model, type of mutation residue and the auxiliary features, including structure and function information, are combined as input features, and the focal loss function is designed to solve the sample imbalance problem during the training. In 10-fold cross-validation and independent blind test set, TransPPMP showed good robust performance and absolute advantages in all evaluation metrics compared with four other advanced methods, namely, ENTPRISE-X, VEST-indel, DDIG-in and CADD. In addition, we demonstrate the usefulness of the multi-head attention mechanism in Transformer to predict the pathogenicity of mutations-not only can multiple self-attention heads learn local and global interactions but also functional sites with a large influence on the mutated residue can be captured by attention focus. These could offer useful clues to study the pathogenicity mechanism of human complex diseases for which traditional machine learning methods fall short.

AVAILABILITY AND IMPLEMENTATION

TransPPMP is available at https://github.com/lennylv/TransPPMP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): Joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC)

PURPOSE

Several professional societies have published guidelines for the clinical interpretation of somatic variants, which specifically address diagnostic, prognostic, and therapeutic implications. Although these guidelines for the clinical interpretation of variants include data types that may be used to determine the oncogenicity of a variant (eg, population frequency, functional, and in silico data or somatic frequency), they do not provide a direct, systematic, and comprehensive set of standards and rules to classify the oncogenicity of a somatic variant. This insufficient guidance leads to inconsistent classification of rare somatic variants in cancer, generates variability in their clinical interpretation, and, importantly, affects patient care. Therefore, it is essential to address this unmet need.

METHODS

Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group and ClinGen Germline/Somatic Variant Subcommittee, the Cancer Genomics Consortium, and the Variant Interpretation for Cancer Consortium used a consensus approach to develop a standard operating procedure (SOP) for the classification of oncogenicity of somatic variants.

RESULTS

This comprehensive SOP has been developed to improve consistency in somatic variant classification and has been validated on 94 somatic variants in 10 common cancer-related genes.

CONCLUSION

The comprehensive SOP is now available for classification of oncogenicity of somatic variants.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Computational and experimental methods for classifying variants of unknown clinical significance

The increase in sequencing capacity, reduction in costs, and national and international coordinated efforts have led to the widespread introduction of next-generation sequencing (NGS) technologies in patient care. More generally, human genetics and genomic medicine are gaining importance for more and more patients. Some communities are already discussing the prospect of sequencing each individual's genome at time of birth. Together with digital health records, this shall enable individualized treatments and preventive measures, so-called precision medicine. A central step in this process is the identification of disease causal mutations or variant combinations that make us more susceptible for diseases. Although various technological advances have improved the identification of genetic alterations, the interpretation and ranking of the identified variants remains a major challenge. Based on our knowledge of molecular processes or previously identified disease variants, we can identify potentially functional genetic variants and, using different lines of evidence, we are sometimes able to demonstrate their pathogenicity directly. However, the vast majority of variants are classified as variants of uncertain clinical significance (VUSs) with not enough experimental evidence to determine their pathogenicity. In these cases, computational methods may be used to improve the prioritization and an increasing toolbox of experimental methods is emerging that can be used to assay the molecular effects of VUSs. Here, we discuss how computational and experimental methods can be used to create catalogs of variant effects for a variety of molecular and cellular phenotypes. We discuss the prospects of integrating large-scale functional data with machine learning and clinical knowledge for the development of accurate pathogenicity predictions for clinical applications.

Filamin A Is a Potential Driver of Breast Cancer Metastasis via Regulation of MMP-1

Recurrent metastasis is a major fatal cause of breast cancer. Regretfully, the driving force and the molecular beneath have not been fully illustrated yet. In this study, a cohort of breast cancer patients with locoregional metastasis was recruited. For them, we collected the matched samples of the primary tumor and metastatic tumor, and then we determined the mutation profiles with whole-exome sequencing (WES). On basis of the profiles, we identified a list of deleterious variants in eight susceptible genes. Of them, filamin A (FLNA) was considered a potential driver gene of metastasis, and its low expression could enhance 5 years’ relapse survival rate by 15%. To prove the finding, we constructed a stable FLNA knockout tumor cell line, which manifested that the cell abilities of proliferation, migration, and invasion were significantly weakened in response to the gene knockout. Subsequently, xenograft mouse experiments further proved that FLNA knockout could inhibit local or distal metastasis. Putting all the results together, we consolidated that FLNA could be a potential driver gene to metastasis of breast cancer, in particular triple-negative breast cancer. Additional experiments also suggested that FLNA might intervene in metastasis via the regulation of MMP-1 expression. In summary, this study demonstrates that FLNA may play as a positive regulator in cancer proliferation and recurrence. It provides new insight into breast cancer metastasis and suggests a potential new therapeutic target for breast cancer therapy.

Whole Genome Sequencing Unravels New Genetic Determinants of Early-Onset Familial Osteoporosis and Low BMD in Malta

Background: Osteoporosis is a skeletal disease with a strong genetic background. The study aimed to identify the genetic determinants of early-onset familial osteoporosis and low bone mineral density (BMD) in a two-generation Maltese family. Methods: Fifteen relatives aged between 28–74 years were recruited. Whole genome sequencing was conducted on 12 relatives and shortlisted variants were genotyped in the Malta Osteoporotic Fracture Study (MOFS) for replication. Results: Sequential variant filtering following a dominant inheritance pattern identified rare missense variants within SELP, TGF-β2 and ADAMTS20, all of which were predicted to be likely pathogenic and participate in osteoimmunology. TGF-β2 c.1136C>T was identified in five individuals from the MOFS in heterozygosity, four of whom had osteopenia/osteoporosis at the lumbar spine and hip, and/or had sustained a low-trauma fracture. Heterozygosity for the ADAMTS20 c.4090A>T was accompanied by lower total hip BMD (p = 0.018) and lower total serum calcium levels in MOFS (p < 0.01), recapitulating the findings from the family. Women carrying at least one copy of the alternative allele (TC/CC) for SELP c.2177T>C exhibited a tendency for lower lumbar spine BMD and/or wrist fracture history relative to women with TT genotype. Conclusions: Our findings suggest that the identified variants, alone or in combination, could be causal factors of familial osteoporosis and low BMD, requiring replication in larger collections.

Leveraging cell-type-specific regulatory networks to interpret genetic variants in abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is a common and severe disease with major genetic risk factors. In this study we generated enhancer-promoter contact data to identify regulatory elements in AAA-relevant cell types and identified changes in their predicted chromatin accessibility between AAA patients and controls. We integrated this information with disease-associated variants in regulatory elements and gene bodies to further understand the etiology and pathogenetic mechanisms of AAA. Our study combined whole-genome sequencing data with gene regulatory relations in disease-relevant cell types to reveal the important roles of the interleukin 6 pathway and ERG and KLF regulation in AAA pathogenesis.

Abdominal aortic aneurysm (AAA) is a common degenerative cardiovascular disease whose pathobiology is not clearly understood. The cellular heterogeneity and cell-type-specific gene regulation of vascular cells in human AAA have not been well-characterized. Here, we performed analysis of whole-genome sequencing data in AAA patients versus controls with the aim of detecting disease-associated variants that may affect gene regulation in human aortic smooth muscle cells (AoSMC) and human aortic endothelial cells (HAEC), two cell types of high relevance to AAA disease. To support this analysis, we generated H3K27ac HiChIP data for these cell types and inferred cell-type-specific gene regulatory networks. We observed that AAA-associated variants were most enriched in regulatory regions in AoSMC, compared with HAEC and CD4⁺ cells. The cell-type-specific regulation defined by this HiChIP data supported the importance of ERG and the KLF family of transcription factors in AAA disease. The analysis of regulatory elements that contain noncoding variants and also are differentially open between AAA patients and controls revealed the significance of the interleukin-6-mediated signaling pathway. This finding was further validated by including information from the deleteriousness effect of nonsynonymous single-nucleotide variants in AAA patients and additional control data from the Medical Genome Reference Bank dataset. These results shed important insights into AAA pathogenesis and provide a model for cell-type-specific analysis of disease-associated variants.

A functionally impaired missense variant identified in French Canadian families implicates FANCI as a candidate ovarian cancer-predisposing gene

Background

Familial ovarian cancer (OC) cases not harbouring pathogenic variants in either of the BRCA1 and BRCA2 OC-predisposing genes, which function in homologous recombination (HR) of DNA, could involve pathogenic variants in other DNA repair pathway genes.

Methods

Whole exome sequencing was used to identify rare variants in HR genes in a BRCA1 and BRCA2 pathogenic variant negative OC family of French Canadian (FC) ancestry, a population exhibiting genetic drift. OC cases and cancer-free individuals from FC and non-FC populations were investigated for carrier frequency of FANCI c.1813C>T; p.L605F, the top-ranking candidate. Gene and protein expression were investigated in cancer cell lines and tissue microarrays, respectively.

Results

In FC subjects, c.1813C>T was more common in familial (7.1%, 3/42) than sporadic (1.6%, 7/439) OC cases (P = 0.048). Carriers were detected in 2.5% (74/2950) of cancer-free females though female/male carriers were more likely to have a first-degree relative with OC (121/5249, 2.3%; Spearman correlation = 0.037; P = 0.011), suggesting a role in risk. Many of the cancer-free females had host factors known to reduce risk to OC which could influence cancer risk in this population. There was an increased carrier frequency of FANCI c.1813C>T in BRCA1 and BRCA2 pathogenic variant negative OC families, when including the discovery family, compared to cancer-free females (3/23, 13%; OR = 5.8; 95%CI = 1.7–19; P = 0.005). In non-FC subjects, 10 candidate FANCI variants were identified in 4.1% (21/516) of Australian OC cases negative for pathogenic variants in BRCA1 and BRCA2, including 10 carriers of FANCI c.1813C>T. Candidate variants were significantly more common in familial OC than in sporadic OC (P = 0.04). Localization of FANCD2, part of the FANCI-FANCD2 (ID2) binding complex in the Fanconi anaemia (FA) pathway, to sites of induced DNA damage was severely impeded in cells expressing the p.L605F isoform. This isoform was expressed at a reduced level, destabilized by DNA damaging agent treatment in both HeLa and OC cell lines, and exhibited sensitivity to cisplatin but not to a poly (ADP-ribose) polymerase inhibitor. By tissue microarray analyses, FANCI protein was consistently expressed in fallopian tube epithelial cells and only expressed at low-to-moderate levels in 88% (83/94) of OC samples.

Conclusions

This is the first study to describe candidate OC variants in FANCI, a member of the ID2 complex of the FA DNA repair pathway. Our data suggest that pathogenic FANCI variants may modify OC risk in cancer families.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00998-5.

The Somatic Mutation Paradigm in Congenital Malformations: Hirschsprung Disease as a Model

Patients with Hirschsprung disease (HSCR) do not always receive a genetic diagnosis after routine screening in clinical practice. One of the reasons for this could be that the causal mutation is not present in the cell types that are usually tested—whole blood, dermal fibroblasts or saliva—but is only in the affected tissue. Such mutations are called somatic, and can occur in a given cell at any stage of development after conception. They will then be present in all subsequent daughter cells. Here, we investigated the presence of somatic mutations in HSCR patients. For this, whole-exome sequencing and copy number analysis were performed in DNA isolated from purified enteric neural crest cells (ENCCs) and blood or fibroblasts of the same patient. Variants identified were subsequently validated by Sanger sequencing. Several somatic variants were identified in all patients, but causative mutations for HSCR were not specifically identified in the ENCCs of these patients. Larger copy number variants were also not found to be specific to ENCCs. Therefore, we believe that somatic mutations are unlikely to be identified, if causative for HSCR. Here, we postulate various modes of development following the occurrence of a somatic mutation, to describe the challenges in detecting such mutations, and hypothesize how somatic mutations may contribute to ‘missing heritability’ in developmental defects.

Genome sequencing data analysis for rare disease gene discovery

Rare diseases occur in a smaller proportion of the general population, which is variedly defined as less than 200 000 individuals (US) or in less than 1 in 2000 individuals (Europe). Although rare, they collectively make up to approximately 7000 different disorders, with majority having a genetic origin, and affect roughly 300 million people globally. Most of the patients and their families undergo a long and frustrating diagnostic odyssey. However, advances in the field of genomics have started to facilitate the process of diagnosis, though it is hindered by the difficulty in genome data analysis and interpretation. A major impediment in diagnosis is in the understanding of the diverse approaches, tools and datasets available for variant prioritization, the most important step in the analysis of millions of variants to select a few potential variants. Here we present a review of the latest methodological developments and spectrum of tools available for rare disease genetic variant discovery and recommend appropriate data interpretation methods for variant prioritization. We have categorized the resources based on various steps of the variant interpretation workflow, starting from data processing, variant calling, annotation, filtration and finally prioritization, with a special emphasis on the last two steps. The methods discussed here pertain to elucidating the genetic basis of disease in individual patient cases via trio- or family-based analysis of the genome data. We advocate the use of a combination of tools and datasets and to follow multiple iterative approaches to elucidate the potential causative variant.

Comprehensive Genetic Analysis of DGAT2 Mutations and Gene Expression Patterns in Human Cancers

DGAT2 is a transmembrane protein encoded by the DGAT2 gene that functions in lipid metabolism, triacylglycerol synthesis, and lipid droplet regulation. Cancer cells exhibit altered lipid metabolism and mutations in DGAT2 may contribute to this state. Using data from the Catalogue of Somatic Mutations in Cancer (COSMIC), we analyzed all cancer genetic DGAT2 alterations, including mutations, copy number variations and gene expression. We find that several DGAT2 mutations fall within the catalytic site of the enzyme. Using the Variant Effect Scoring Tool (VEST), we identify multiple mutations with a high likelihood of contributing to cellular transformation. We also found that D222V is a mutation hotspot neighboring a previously discovered Y223H mutation that causes Axonal Charcot-Marie-Tooth disease. Remarkably, Y223H has not been detected in cancers, suggesting that it is inhibitory to cancer progression. We also identify several single nucleotide polymorphisms (SNP) with high VEST scores, indicating that certain alleles in human populations have a pathogenic predisposition. Most mutations do not correlate with a change in gene expression, nor is gene expression dependent on high allele copy number. However, we did identify eight alleles with high expression levels, suggesting that at least in certain cases, the excess DGAT2 gene product is not inhibitory to cellular proliferation. This work uncovers unknown functions of DGAT2 in cancers and suggests that its role may be more complex than previously appreciated.

Multimodal Retinal Imaging and Microperimetry Reveal a Novel Phenotype and Potential Trial End Points in CRB1-Associated Retinopathies

Purpose

Biallelic crumbs cell polarity complex component 1 (CRB1) mutations can present as Leber congenital amaurosis (LCA), retinitis pigmentosa (RP), or cystic maculopathy. This study reports a novel phenotype of asymptomatic fenestrated slit maculopathy (AFSM) and examines macular volume profile and microperimetry as clinical trial end points in CRB1-associated retinopathies.

Methods

Twelve patients from nine families with CRB1 mutation were recruited. Ultra-widefield (UWF) color fundus photography and autofluorescence (AF), spectral-domain optical coherence tomography (SD-OCT), microperimetry, and adaptive optics (AO) imaging were performed. Macular volume profiles were compared with age-matched healthy controls. Genotyping was performed using APEX genotyping microarrays, targeted next-generation sequencing, and Sanger sequencing.

Results

We identified one patient with LCA, five patients with RP, and four patients with macular dystrophy (MD) with biallelic CRB1 mutations. Two siblings with compound heterozygote genotype (c.[2843G>A]; [498_506del]) had AFSM characterized by localized outer retinal disruption on SD-OCT and parafoveal cone loss on AO imaging despite normal fundus appearance, visual acuity, and foveal sensitivity. UWF AF demonstrated preserved para-arteriolar retinal pigment epithelium (PPRPE) in all patients with RP. Microperimetry documented preserved central retinal function in six patients. The ratio of perifoveal-to-foveal retinal volume was greater than controls in 89% (8/9) of patients with RP or MD, whereas central subfield and total macular volume were outside normal limits in 67% (6/9).

Conclusions

AO imaging was helpful in detecting parafoveal cone loss in asymptomatic patients. Macular volume profile and microperimetry parameters may have utility as CRB1 trials end points.

Translational Relevance

Macular volume and sensitivity can be used as structural and functional end points for trials on CRB1-associated RP and MD.

Genetic Characterization of Cancer of Unknown Primary Using Liquid Biopsy Approaches

Cancers of unknown primary (CUPs) comprise a heterogeneous group of rare metastatic tumors whose primary site cannot be identified after extensive clinical-pathological investigations. CUP patients are generally treated with empirical chemotherapy and have dismal prognosis. As recently reported, CUP genome presents potentially druggable alterations for which targeted therapies could be proposed. The paucity of tumor tissue, as well as the difficult DNA testing and the lack of dedicated panels for target gene sequencing are further relevant limitations. Here, we propose that circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) could be used to identify actionable mutations in CUP patients. Blood was longitudinally collected from two CUP patients. CTCs were isolated with CELLSEARCH® and DEPArrayTM NxT and Parsortix systems, immunophenotypically characterized and used for single-cell genomic characterization with Ampli1TM kits. Circulating cell-free DNA (ccfDNA), purified from plasma at different time points, was tested for tumor mutations with a CUP-dedicated, 92-gene custom panel using SureSelect Target Enrichment technology. In parallel, FFPE tumor tissue was analyzed with three different assays: FoundationOne CDx assay, DEPArray LibPrep and OncoSeek Panel, and the SureSelect custom panel. These approaches identified the same mutations, when the gene was covered by the panel, with the exception of an insertion in APC gene. which was detected by OncoSeek and SureSelect panels but not FoundationOne. FGFR2 and CCNE1 gene amplifications were detected in single CTCs, tumor tissue, and ccfDNAs in one patient. A somatic variant in ARID1A gene (p.R1276∗) was detected in the tumor tissue and ccfDNAs. The alterations were validated by Droplet Digital PCR in all ccfDNA samples collected during tumor evolution. CTCs from a second patient presented a pattern of recurrent amplifications in ASPM and SEPT9 genes and loss of FANCC. The 92-gene custom panel identified 16 non-synonymous somatic alterations in ccfDNA, including a deletion (I1485Rfs∗19) and a somatic mutation (p. A1487V) in ARID1A gene and a point mutation in FGFR2 gene (p.G384R). Our results support the feasibility of non-invasive liquid biopsy testing in CUP cases, either using ctDNA or CTCs, to identify CUP genetic alterations with broad NGS panels covering the most frequently mutated genes.

Computational Tools to Assess the Functional Consequences of Rare and Noncoding Pharmacogenetic Variability

Interindividual differences in drug response are a common concern in both drug development and across layers of care. While genetics clearly influences drug response and toxicity of many drugs, a substantial fraction of the heritable pharmacological and toxicological variability remains unexplained by known genetic polymorphisms. In recent years, population-scale sequencing projects have unveiled tens of thousands of coding and noncoding pharmacogenetic variants with unclear functional effects that might explain at least part of this missing heritability. However, translating these personalized variant signatures into drug response predictions and actionable advice remains challenging and constitutes one of the most important frontiers of contemporary pharmacogenomics. Conventional prediction methods are primarily based on evolutionary conservation, which drastically reduces their predictive accuracy when applied to poorly conserved pharmacogenes. Here, we review the current state-of-the-art of computational variant effect predictors across variant classes and critically discuss their utility for pharmacogenomics. Besides missense variants, we discuss recent progress in the evaluation of synonymous, splice, and noncoding variations. Furthermore, we discuss emerging possibilities to assess haplotypes and structural variations. We advocate for the development of algorithms trained on pharmacogenomic instead of pathogenic data sets to improve the predictive accuracy in order to facilitate the utilization of next-generation sequencing data for personalized clinical decision support and precision pharmacogenomics.

Clinical, Laboratory Features and Clinical Courses of Patients with Wiskott Aldrich Syndrome and X-linked Thrombocytopenia-A single center study

Objective: Wiskott Aldrich Syndrome is an X-linked primary immunodeficiency disorder characterized by microthrombocytopenia, severe immunodeficiency, and eczema. To define clinical-laboratory features, genetic defects (known/novel) of 23 patients of Wiskott Aldrich Syndrome/X-linked Thrombocytopenia (WAS/XLT) cohort, establish relationships between molecular defects and clinical features if present, evaluate patients who underwent hematopoietic stem cell transplantation (HSCT) and did not.Methods: Qualitative analysis from patients' hospital files and Sanger sequencing for molecular diagnosis was performed. Twenty-two WAS patients and one XLT patient were included in the study.Results: The median age of diagnosis was 15 months (2.5-172 months). The most common symptom was otitis media and all patients had microthrombocytopenia. Autoimmune findings were detected in 34.7% (8 patients) of the patients; three patients (13%) had positive anti-nuclear antibody (ANA), three patients (13%) hemolytic anemia, one patient autoimmune neutropenia, two patients vasculitis, and one patient demyelinating polyneuropathy. Nine of the 23 (39,1%) patients had HSCT with nearly 90% success. We identified 13 different mutations in our cohort; seven were novel.Conclusions: HSCT is the only curative treatment for WAS. The study confirms that early diagnosis is very important for the success of therapy, so we must increase awareness in society and physicians to keep an eye out for clues. Our study cohort and follow-up period are not sufficient to establish phenotype-genotype correlation, so a larger cohort from various centers with longer follow-up will be more decisive.

A Population-Based Analysis of BRCA1/2 Genes and Associated Breast and Ovarian Cancer Risk in Korean Patients: A Multicenter Cohort Study

Simple Summary

Although it has been suggested that cancer risk and genetic variation vary by population, there is still a lack of research on non-European populations. In this study, we applied Korean patients as a model to find out the way to conduct BRCA1/2-related clinical studies in non-European populations who do not have as much clinical data as Europeans. The BRCA1/2 variants were classified following the 2015 ACMG standards/guidelines and using a multifactorial probability-based approach. To estimate the additional sample numbers needed to resolve BRCA1/2 unclassified status, we applied a simulation analysis considering population-specific clinical characteristics. In addition, we estimated the risks of breast or ovarian cancer for BRCA1/2 carriers by mutation regions. Data from this study reveal that BRCA1/2 variants in the non-European population are highly specific; therefore, population-specific study is essential for clinical application of treatment or prevention for breast or ovarian cancer.

Abstract

In this study, we performed a comprehensive analysis of BRCA1/2 variants and associated cancer risk in Korean patients considering two aspects: variants of uncertain significance (VUS) and pathogenic or likely pathogenic variants (PLPVs) in BRCA1 and BRCA2. This study included 5433 Korean participants who were tested for BRCA1/2 genes. The BRCA1/2 variants were classified following the standards/guidelines for interpretation of genetic variants and using a multifactorial probability-based approach. In Korea, 15.8% of participants had BRCA1 or BRCA2 PLPVs. To estimate the additional sample numbers needed to resolve unclassified status, we applied a simulation analysis. The simulation study for VUS showed that the smaller the number of samples, the more the posterior probability was affected by the prior probability; in addition, more samples for BRCA2 VUS than those of BRCA1 VUS were required to resolve the unclassified status, and the presence of clinical information associated with their VUS was an important factor. The cumulative lifetime breast cancer risk was 59.1% (95% CI: 44.1–73.6%) for BRCA1 and 58.3% (95% CI: 43.2–73.0%) for BRCA2 carriers. The cumulative lifetime ovarian cancer risk was estimated to be 36.9% (95% CI: 23.4–53.9%) for BRCA1 and 14.9% (95% CI: 7.4–28.5%) for BRCA2 carriers.

Deep clinical phenotyping and gene expression analysis in a patient with RCBTB1-associated retinopathy

Background: Mutations in the RCC1 and BTB domain-containing protein 1 (RCBTB1) gene have been implicated in a rare form of retinal dystrophy. Herein, we report the clinical features of a 45-year-old Singaporean-Chinese female and her presymptomatic sibling, who each possesses compound heterozygous mutations in RCBTB1. Expression of RCBTB1 in patient-derived cells was evaluated.Materials and Methods: The natural history was documented by a series of ophthalmic examinations including electroretinography, fundus autofluorescence imaging, spectral-domain optical coherence tomography, visual field, microperimetry, and adaptive optics retinal imaging. Patient DNA was genetically analysed using a 537-gene Next Generation Sequencing panel and targeted Sanger sequencing. Expression of RCBTB1 in lymphocytes, fibroblasts, and induced pluripotent stem cells (iPSC) derived from the proband and healthy controls was characterized by quantitative PCR, Sanger sequencing, and western blotting.Results: The proband presented with left visual distortion at age 40 due to extrafoveal chorioretinal atrophy. Atrophy expanded at 1.3 (OD) and 1.0 (OS) mm²/year. Total macular volume declined by 0.09 (OD) and 0.13 (OS) mm³/year. Microperimetry demonstrated enlarging scotoma in both eyes. Generalised cone dysfunction was demonstrated by electroretinography. A retinal dystrophy panel testing revealed biallelic frameshifting mutations, c.170delG (p.Gly57Glufs*12) and c.707delA (p.Asn236Thrfs*11) in RCBTB1. The level of RCBTB1 mRNA expression was reduced in patient-derived lymphocytes compared to controls. RCBTB1 protein was detected in control fibroblasts and iPSC but was absent in patient-derived cells.Conclusions: Atrophy expansion rate and macular volume change are feasible endpoints for monitoring RCBTB1-associated retinopathy. We provide further functional evidence of pathogenicity for two disease-causing variants using patient-derived iPSCs.

Expanding the PURA syndrome phenotype: A child with the recurrent PURA p.(Phe233del) pathogenic variant showing similarities with cutis laxa

BACKGROUND

PURA syndrome is rare autosomal dominant condition characterized by moderate to severe neurodevelopmental delay with absence of speech in nearly all patients and lack of independent ambulation in many. Early-onset problems include excessive hiccups, hypotonia, hypersomnolence, hypothermia, feeding difficulties, recurrent apneas, epileptic seizures, and abnormal nonepileptic movements. Other less common manifestations comprise congenital heart defects, urogenital malformations, and various skeletal, ophthalmological, gastrointestinal, and endocrine anomalies. Up to now, 78 individuals with PURA syndrome and 64 different pathogenic variants have been reported, but no clear-cut genotype-phenotype correlations have emerged so far. Herein, we report the clinical and molecular characterization of a 3-year-old girl with severe hypotonia, global developmental delay, and soft, loose skin, who came to our attention with a suspicion of cutis laxa (CL), which denotes another condition with variable neurodevelopmental problems.

METHODS

Amplicon-based whole exome sequencing was performed, and an in-house pipeline was used to conduct filtering and prioritization of variants. New prediction algorithms for indels were used to validate the pathogenicity of the PURA variant, and results were confirmed with the Sanger method. Finally, we collected clinical and mutational data of all PURA syndrome patients reported yet and compared the clinical features with those of our patient.

RESULTS

Clinical evaluation and biochemical investigations excluded CL and prompted to perform whole exome sequencing, which confirmed the absence of pathogenic variants in all CL-related genes and revealed the known PURA c.697_699del, p.(Phe233del) variant, identified hitherto in seven additional children with PURA syndrome.

CONCLUSIONS

Our data expand the phenotypic spectrum of PURA syndrome by showing that it can be regarded as a differential diagnosis for cutis laxa in early infancy. Our patient and literature review emphasize that a wide clinical variability exists not only between individuals with different PURA variants, but also among patients with the same causal mutation.