Selecting variants of unknown significance through network-based gene-association significantly improves risk prediction for disease-control cohorts

Algorithmic assessment reveals functional implications of GABRD gene variants linked to idiopathic generalized epilepsy

OBJECTIVE

The primary objective of this study is to address the challenge posed by the increasing number of variants of unknown clinical significance (VUS) within the GABRD gene, which encodes the δ subunit of γ-Aminobutyric acid type A receptors. The focus is on predicting the most pathogenic GABRD VUS to enhance precision medicine and improve our understanding of relevant pathophysiology.

METHODS

The study employs a combination of in silico algorithms to analyze 82 variants of unknown clinical significance of GABRD gene sourced from the ClinVar database. Initially, separate algorithms based on sequence homology are utilized to assess this variant set. Subsequently, consensus variants predicted as pathogenic undergo further evaluation through a web server employing an algorithm based on structural homology. The resulting 11 variants are then validated using in silico tools that assess variant effects based on genetic and molecular data. The evaluation includes consideration of disease association and protein stability due to amino acid substitutions.

RESULTS

The study identifies specific variants (L111R, R114C, D123N, G150S, and L243P) in the coding region of the GABRD gene, which are predicted as deleterious by multiple algorithms. These variants are evolutionarily conserved, mapped onto the extracellular domain of the δ subunit, and associated with idiopathic generalized epilepsy.

CONCLUSIONS

The findings suggest structural or functional consequences that lead to pathogenicity, offering valuable insights for wet-lab experimentation. Besides, the findings contribute to the validation of clinically significant genetic variants in the GABRD gene, which is critical for epilepsy precision medicine.

Incorporating functional genomics into the pathology-supported genetic testing framework implemented in South Africa: A future view of precision medicine for breast carcinomas

A pathology-supported genetic testing (PSGT) framework was established in South Africa to improve access to precision medicine for patients with breast carcinomas. Nevertheless, the frequent identification of variants of uncertain significance (VUSs) with the use of genome-scale next-generation sequencing has created a bottleneck in the return of results to patients. This review highlights the importance of incorporating functional genomics into the PSGT framework as a proposed initiative. Here, we explore various model systems and experimental methods available for conducting functional studies in South Africa to enhance both variant classification and clinical interpretation. We emphasize the distinct advantages of using in vitro, in vivo, and translational ex vivo models to improve the effectiveness of precision oncology. Moreover, we highlight the relevance of methodologies such as protein modelling and structural bioinformatics, multi-omics, metabolic activity assays, flow cytometry, cell migration and invasion assays, tube-formation assays, multiplex assays of variant effect, and database mining and machine learning models. The selection of the appropriate experimental approach largely depends on the molecular mechanism of the gene under investigation and the predicted functional effect of the VUS. However, before making final decisions regarding the pathogenicity of VUSs, it is essential to assess the functional evidence and clinical outcomes under current variant interpretation guidelines. The inclusion of a functional genomics infrastructure within the PSGT framework will significantly advance the reclassification of VUSs and enhance the precision medicine pipeline for patients with breast carcinomas in South Africa.

Clinical outcomes and response to chemotherapy in a cohort of pancreatic cancer patients with germline variants of unknown significance (VUS) in BRCA1 and BRCA2 genes

PURPOSE

The clinical outcome and the efficacy of chemotherapy in pancreatic cancer patients with BRCA1/2 Variants of Unknown Significance (VUS) is unknown. We explored the effects of chemotherapy with or without Platinum in non metastatic and metastatic pancreatic cancer patients with BRCA1/2 VUS.

METHODS

A retrospective analysis of non-metastatic or metastatic pancreatic cancer patients with gBRCA1/2 VUS treated in 13 Italian centers between November 2015 and December 2020 was performed. All patients were assessed for toxicity and RECIST 1.1 response. Metastatic patients were evaluated for survival outcome.

RESULTS

30 pancreatic cancer patients with gBRCA1/2 VUS were considered: 20 were M+ and 10 were non-M+. Pl-CT was recommended to 16 patients: 10 M+ (6 FOLFIRINOX and 4 PAXG) and 6 non-M+ (3 FOLFIRINOX and 3 PAXG); 11 patients received Nabpaclitaxel-Gemcitabine (AG; 8 M+) and 3 patients (2 M+) were treated with Gemcitabine (G). The RECIST 1.1 response rate was 27% for AG and 44% for Pl-CT (22% for (m) FOLFIRINOX and 71% PAXG). 1 year Progression-Free Survival was 37.5% for patients treated with AG and 33% in the Pl-CT subgroup. Median Overall Survival (OS) was 23.5 months for patients treated with AG and 14 months for the Pl-CT subgroup. 1 Year and 2 Year OS were numerically better for AG (1 Year OS: 75% vs 60% and 2 Year OS: 50% and 20% in AG and Pl-CT subgroups, respectively) as well.

CONCLUSIONS

Pl-CT does not seem to be associated with a better outcome compared to AG chemotherapy in PDAC patients with BRCA 1/2 VUS.

The Role of Genetics in the Management of Heart Failure Patients

Over the last decades, the relevance of genetics in cardiovascular diseases has expanded, especially in the context of cardiomyopathies. Its relevance extends to the management of patients diagnosed with heart failure (HF), given its capacity to provide invaluable insights into the etiology of cardiomyopathies and identify individuals at a heightened risk of poor outcomes. Notably, the identification of an etiological genetic variant necessitates a comprehensive evaluation of the family lineage of the affected patients. In the future, these genetic variants hold potential as therapeutic targets with the capability to modify gene expression. In this complex setting, collaboration among cardiologists, specifically those specializing in cardiomyopathies and HF, and geneticists becomes paramount to improving individual and family health outcomes, as well as therapeutic clinical results. This review is intended to offer geneticists and cardiologists an updated perspective on the value of genetic research in HF and its implications in clinical practice.

Basic science methods for the characterization of variants of uncertain significance in hypertrophic cardiomyopathy

With the advent of next-generation whole genome sequencing, many variants of uncertain significance (VUS) have been identified in individuals suffering from inheritable hypertrophic cardiomyopathy (HCM). Unfortunately, this classification of a genetic variant results in ambiguity in interpretation, risk stratification, and clinical practice. Here, we aim to review some basic science methods to gain a more accurate characterization of VUS in HCM. Currently, many genomic data-based computational methods have been developed and validated against each other to provide a robust set of resources for researchers. With the continual improvement in computing speed and accuracy, in silico molecular dynamic simulations can also be applied in mutational studies and provide valuable mechanistic insights. In addition, high throughput in vitro screening can provide more biologically meaningful insights into the structural and functional effects of VUS. Lastly, multi-level mathematical modeling can predict how the mutations could cause clinically significant organ-level dysfunction. We discuss emerging technologies that will aid in better VUS characterization and offer a possible basic science workflow for exploring the pathogenicity of VUS in HCM. Although the focus of this mini review was on HCM, these basic science methods can be applied to research in dilated cardiomyopathy (DCM), restrictive cardiomyopathy (RCM), arrhythmogenic cardiomyopathy (ACM), or other genetic cardiomyopathies.

How Functional Genomics Can Keep Pace With VUS Identification

Over the last two decades, an exponentially expanding number of genetic variants have been identified associated with inherited cardiac conditions. These tremendous gains also present challenges in deciphering the clinical relevance of unclassified variants or variants of uncertain significance (VUS). This review provides an overview of the advancements (and challenges) in functional and computational approaches to characterize variants and help keep pace with VUS identification related to inherited heart diseases.

Targeting mutations in cancer

Targeted therapies have come to play an increasingly important role in cancer therapy over the past two decades. This success has been made possible in large part by technological advances in sequencing, which have greatly advanced our understanding of the mutational landscape of human cancer and the genetic drivers present in individual tumors. We are rapidly discovering a growing number of mutations that occur in targetable pathways, and thus tumor genetic testing has become an important component in the choice of appropriate therapies. Targeted therapy has dramatically transformed treatment outcomes and disease prognosis in some settings, whereas in other oncologic contexts, targeted approaches have yet to demonstrate considerable clinical efficacy. In this Review, we summarize the current knowledge of targetable mutations that occur in a range of cancers, including hematologic malignancies and solid tumors such as non-small cell lung cancer and breast cancer. We outline seminal examples of druggable mutations and targeting modalities and address the clinical and research challenges that must be overcome to maximize therapeutic benefit.

Clinical and analytical validation of FoundationOne®CDx, a comprehensive genomic profiling assay for solid tumors

FoundationOne®CDx (F1CDx) is a United States (US) Food and Drug Administration (FDA)-approved companion diagnostic test to identify patients who may benefit from treatment in accordance with the approved therapeutic product labeling for 28 drug therapies. F1CDx utilizes next-generation sequencing (NGS)-based comprehensive genomic profiling (CGP) technology to examine 324 cancer genes in solid tumors. F1CDx reports known and likely pathogenic short variants (SVs), copy number alterations (CNAs), and select rearrangements, as well as complex biomarkers including tumor mutational burden (TMB) and microsatellite instability (MSI), in addition to genomic loss of heterozygosity (gLOH) in ovarian cancer. CGP services can reduce the complexity of biomarker testing, enabling precision medicine to improve treatment decision-making and outcomes for cancer patients, but only if test results are reliable, accurate, and validated clinically and analytically to the highest standard available. The analyses presented herein demonstrate the extensive analytical and clinical validation supporting the F1CDx initial and subsequent FDA approvals to ensure high sensitivity, specificity, and reliability of the data reported. The analytical validation included several in-depth evaluations of F1CDx assay performance including limit of detection (LoD), limit of blank (LoB), precision, and orthogonal concordance for SVs (including base substitutions [SUBs] and insertions/deletions [INDELs]), CNAs (including amplifications and homozygous deletions), genomic rearrangements, and select complex biomarkers. The assay validation of >30,000 test results comprises a considerable and increasing body of evidence that supports the clinical utility of F1CDx to match patients with solid tumors to targeted therapies or immunotherapies based on their tumor's genomic alterations and biomarkers. F1CDx meets the clinical needs of providers and patients to receive guideline-based biomarker testing, helping them keep pace with a rapidly evolving field of medicine.

Multicenter Consensus Approach to Evaluation of Neonatal Hypotonia in the Genomic Era: A Review

Importance

Infants with hypotonia can present with a variety of potentially severe clinical signs and symptoms and often require invasive testing and multiple procedures. The wide range of clinical presentations and potential etiologies leaves diagnosis and prognosis uncertain, underscoring the need for rapid elucidation of the underlying genetic cause of disease.

Observations

The clinical application of exome sequencing or genome sequencing has dramatically improved the timely yield of diagnostic testing for neonatal hypotonia, with diagnostic rates of greater than 50% in academic neonatal intensive care units (NICUs) across Australia, Canada, the UK, and the US, which compose the International Precision Child Health Partnership (IPCHiP). A total of 74% (17 of 23) of patients had a change in clinical care in response to genetic diagnosis, including 2 patients who received targeted therapy. This narrative review discusses the common causes of neonatal hypotonia, the relative benefits and limitations of available testing modalities used in NICUs, and hypotonia management recommendations.

Conclusions and Relevance

This narrative review summarizes the causes of neonatal hypotonia and the benefits of prompt genetic diagnosis, including improved prognostication and identification of targeted treatments which can improve the short-term and long-term outcomes. Institutional resources can vary among different NICUs; as a result, consideration should be given to rule out a small number of relatively unique conditions for which rapid targeted genetic testing is available. Nevertheless, the consensus recommendation is to use rapid genome or exome sequencing as a first-line testing option for NICU patients with unexplained hypotonia. As part of the IPCHiP, this diagnostic experience will be collected in a central database with the goal of advancing knowledge of neonatal hypotonia and improving evidence-based practice.

Exome sequencing and RNA analysis identify two novel CPLANE1 variants causing Joubert syndrome

BACKGROUND

Joubert syndrome (JS) is a genetically heterogeneous disorder; its genetic etiology involves more than 35 genes, and a limited number of studies have investigated the pathogenic mechanism of variants in patients with JS. RNA splicing analysis is critical to determine the functional significance for noncanonical splicing variants.

METHODS

Whole exome sequencing was performed to screen the causative gene variants in a JS family. Sanger sequencing was used to verify the variants. cDNA PCR products were analyzed and functional experiments were performed to determine the pathogenicity of the variants.

RESULTS

The clinical phenotypes and CPLANE1 variants in the JS patient were analyzed and proved consistent. We identified two novel heterozygous variants of CPLANE1 in the proband first, including c.4459del (frameshift variant) and c.7534-14G > A (intronic variant). We analyzed the pathogenic consequences of the 2 variants and classified the c.4459del as likely pathogenic according to the ACMG/AMP guidelines; however, the pathogenic significance of c.7534-14G > A was uncertain. Furthermore, we performed RNA splicing analysis and revealed that the noncanonical splicing variant (c.7534-14G > A) caused aberrant exon 37 skipping. It produced an aberrant transcript that was predicted to encode a C-terminal truncated protein.

CONCLUSIONS

The genetic variation spectrum of JS caused by CPLANE1 was updated. Two novel variants further deepened our insight into the disease's molecular mechanism and confirmed the significance of diagnostic whole-exome sequencing.

Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review

BACKGROUND

Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine.

CONTENT

In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine.

SUMMARY

AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective ML-supported clinical diagnostics.

Whole genome sequencing of low input circulating cell-free DNA obtained from normal human subjects

Cell-free DNA circulates in plasma at low levels as a normal by-product of cellular apoptosis. Multiple clinical pathologies, as well as environmental stressors can lead to increased circulating cell-free DNA (ccfDNA) levels. Plasma DNA studies frequently employ targeted amplicon deep sequencing platforms due to limited concentrations (ng/ml) of ccfDNA in the blood. Here, we report whole genome sequencing (WGS) and read distribution across chromosomes of ccfDNA extracted from two human plasma samples from normal, healthy subjects, representative of limited clinical samples at <1 ml. Amplification was sufficiently robust with ~90% of the reference genome (GRCh38.p2) exhibiting 10X coverage. Chromosome read coverage was uniform and directly proportional to the number of reads for each chromosome across both samples. Almost 99% of the identified genomic sequence variants were known annotated dbSNP variants in the hg38 reference genome. A high prevalence of C>T and T>C mutations was present along with a strong concordance of variants shared between the germline genome databases; gnomAD (81.1%) and the 1000 Genome Project (93.6%). This study demonstrates isolation and amplification procedures from low input ccfDNA samples that can detect sequence variants across the whole genome from amplified human plasma ccfDNA that can translate to multiple clinical research disciplines.

Proteotyping of knockout mouse strains reveals sex- and strain-specific signatures in blood plasma

We proteotyped blood plasma from 30 mouse knockout strains and corresponding wild-type mice from the International Mouse Phenotyping Consortium. We used targeted proteomics with internal standards to quantify 375 proteins in 218 samples. Our results provide insights into the manifested effects of each gene knockout at the plasma proteome level. We first investigated possible contamination by erythrocytes during sample preparation and labeled, in one case, up to 11 differential proteins as erythrocyte originated. Second, we showed that differences in baseline protein abundance between female and male mice were evident in all mice, emphasizing the necessity to include both sexes in basic research, target discovery, and preclinical effect and safety studies. Next, we identified the protein signature of each gene knockout and performed functional analyses for all knockout strains. Further, to demonstrate how proteome analysis identifies the effect of gene deficiency beyond traditional phenotyping tests, we provide in-depth analysis of two strains, C8a-/- and Npc2+/-. The proteins encoded by these genes are well-characterized providing good validation of our method in homozygous and heterozygous knockout mice. Ig alpha chain C region, a poorly characterized protein, was among the differentiating proteins in C8a-/-. In Npc2+/- mice, where histopathology and traditional tests failed to differentiate heterozygous from wild-type mice, our data showed significant difference in various lysosomal storage disease-related proteins. Our results demonstrate how to combine absolute quantitative proteomics with mouse gene knockout strategies to systematically study the effect of protein absence. The approach used here for blood plasma is applicable to all tissue protein extracts.

Prioritizing variants of uncertain significance for reclassification using a rule-based algorithm in inherited retinal dystrophies

Inherited retinal dystrophies (IRD) are a highly heterogeneous group of rare diseases with a molecular diagnostic rate of >50%. Reclassification of variants of uncertain significance (VUS) poses a challenge for IRD diagnosis. We collected 668 IRD cases analyzed by our geneticists using two different clinical exome-sequencing tests. We identified 114 unsolved cases pending reclassification of 125 VUS and studied their genomic, functional, and laboratory-specific features, comparing them to pathogenic and likely pathogenic variants from the same cohort (N = 390). While the clinical exome used did not show differences in diagnostic rate, the more IRD-experienced geneticist reported more VUS (p = 4.07e-04). Significantly fewer VUS were reported in recessive cases (p = 2.14e-04) compared to other inheritance patterns, and of all the genes analyzed, ABCA4 and IMPG2 had the lowest and highest VUS frequencies, respectively (p = 3.89e-04, p = 6.93e-03). Moreover, few frameshift and stop-gain variants were found to be informed VUS (p = 6.73e-08 and p = 2.93e-06). Last, we applied five pathogenicity predictors and found there is a significant proof of deleteriousness when all score for pathogenicity in missense variants. Altogether, these results provided input for a set of rules that correctly reclassified ~70% of VUS as pathogenic in validation datasets. Disease- and setting-specific features influence VUS reporting. Comparison with pathogenic and likely pathogenic variants can prioritize VUS more likely to be reclassified as causal.

Precise uncertain significance prediction using latent space matrix factorization models: genomics variant and heterogeneous clinical data-driven approaches

Several studies to date have proposed different types of interpreters for measuring the degree of pathogenicity of variants. However, in predicting the disease type and disease-gene associations, scholars face two essential challenges, namely the vast number of existing variants and the existence of variants which are recognized as variant of uncertain significance (VUS). To tackle these challenges, we propose algorithms to assign a significance to each gene rather than each variant, describing its degree of pathogenicity. Since the interpreters identified most of the variants as VUS, most of the gene scores were identified as uncertain significance. To predict the uncertain significance scores, we design two matrix factorization-based models: the common latent space model uses genomics variant data as well as heterogeneous clinical data, while the single-matrix factorization model can be used when heterogeneous clinical data are unavailable. We have managed to show that the models successfully predict the uncertain significance scores with low error and high accuracy. Moreover, to evaluate the effectiveness of our novel input features, we train five different multi-label classifiers including a feedforward neural network with the same feature set and show they all achieve high accuracy as the main impact of our approach comes from the features. Availability: The source code is freely available at https://github.com/sabdollahi/CoLaSpSMFM.

Collision-Induced Unfolding Differentiates Functional Variants of the KCNQ1 Voltage Sensor Domain

The KCNQ1 voltage-gated potassium channel regulates the repolarization of cardiac cells, and a plurality of point mutations in its voltage-sensing domain (VSD) are associated with toxic gain or loss of pore function, resulting in disease. As is the case with many disease-associated membrane proteins, there are hundreds of human variants of interest identified for KCNQ1; however, a significant portion of these variants have not been characterized in relation to their functional and disease associations. Additionally, as the VSD consists of four transmembrane helices, studies into dynamic structural differences among KCNQ1 VSD variants are hindered by the current limitations and deficits in the high-resolution structure determination of membrane proteins. Here, we use native ion mobility-mass spectrometry and collision-induced unfolding (CIU) to address the need for a high throughput-compatible method for the structural characterization of membrane protein variants of unknown significance using the KCNQ1 VSD as a model system. We perform CIU on wild-type and three mutant KCNQ1 VSD forms associated with the toxic gain or loss of function and show through both automated feature detection and comprehensive difference analysis of the CIU data sets that the variants are clearly grouped by function and disease association. We also construct a classification scheme based on the CIU data sets, which is able to differentiate the variant functional groups and classify a recently characterized variant to its correct grouping. Further, we probe the stability of the KCNQ1 VSD variants when liberated from C12E8 micelles at pH 8.0 and find preliminary evidence that the R231C mutation associated with the gain of the pore function is destabilized relative to the wild-type and loss of function variants.

Characterization of the major human STAG3 variants using some proteomics and bioinformatics assays

Background

STAG3 is the meiotic component of cohesin and a member of the Cancer Testis Antigen (CTA) family. This gene has been found to be overexpressed in many types of cancer, and recently, its variants have been implicated in other disorders and many human diseases. Therefore, this study aimed to analyze the major variants of STAG3. Western blot (WB) and immunoprecipitation (IP) assays were performed using two different anti-STAG3 antibodies that targeted the relevant protein in MCF-7, T-47D, MDA-MB-468, and MDA-MB-231 breast cancer cells with Jurkat and MCF-10A cells as positive and negative controls, respectively. In silico analyses were searched to study the major isoforms.

Results

WB and IP assays revealed two abundant polypeptides < 191 kDa and ~ 75 kDa in size. Specific bioinformatics tools successfully determined the three-dimensional (3-D) structure, the subcellular localization, and the secondary structures of the isoforms. Furthermore, some of the physicochemical properties of the STAG3 proteins were also determined.

Conclusions

The results of this study revealed the power of applying the biological techniques (WB and IP) with the bioinformatics assays and the possibility of their exploitation in understanding diseased genes. Exploring the major variants of STAG3 at the protein level could help decipher some disorders associated with their occurrence, along with designing drugs effective at least for some relevant diseases.