New insights into the pathogenicity of non-synonymous variants through multi-level analysis

Pharmacogenetic insights into ABCB1, ABCC2, CYP1A2, and CYP2B6 variants with epilepsy susceptibility among Egyptian Children: A retrospective case-control study

BACKGROUND

Pediatric epilepsy is a complicated neuropsychiatric disorder that is characterized by recurrent seizures and unusual synchronized electrical activities within brain tissues. It has a substantial effect on the quality of life of children, thus understanding of the hereditary considerations influencing epilepsy susceptibility and the response to antiepileptic medications is crucial. This study focuses on assessing the correlation of the ABCB1, ABCC2, CYP1A2, and CYP2B6 genetic polymorphisms with the susceptibility to epileptic seizures and their contributions to antiepileptic medication throughout the course of the disease.

METHODS

This study included 134 Egyptian epileptic children, comprising 67 drug-responsive and 67 drug-resistant patients, along with 124 healthy controls matching for age, gender, and geographical district. Genotyping of the rs2032582, rs717620, rs2273697, rs762551, and rs3745274 variants was performed using the PCR technique. Statistical analyses, including haplotype, multivariate, logistic regression, and bioinformatics approaches, were conducted to evaluate the associations within the disease.

RESULTS

The ABCC2*rs717620 (T allele) revealed an increased risk of epilepsy compared to healthy controls (OR = 2.12, p-value < 0.001), with the rs717620 (C/T + T/T genotypes) showing significant differences between drug-responsive and drug-resistant patients (p-value < 0.05). Moreover, the ABCC2*rs2273697 (A allele) indicated a decreased risk of epileptic seizures compared to healthy controls (OR = 0.51, p-value = 0.033), with the rs2273697 (G/A + A/A genotypes) indicating a significant association with drug-resistant patients (OR = 0.21, p-value = 0.002). The rs717620*T/rs2273697*G haplotype was significantly correlated with an elevated risk of epileptic seizures within drug-responsive patients (OR = 2.26, p-value = 0.019). Additionally, the CYP1A2*rs762551 (A allele) represented a protective effect against epilepsy susceptibility (OR = 0.50, p-value < 0.001), with the rs762551 (G/A + A/A genotypes) disclosing a substantial association with a decreased risk of epileptic seizures among drug-resistant patients compared to drug-responsive patients (OR = 0.07, p-value < 0.001). Conversely, the ABCB1*rs2032582 (G allele) and the CYP2B6*rs3745274 (T allele) did not attain a significant difference with the epilepsy risk compared to healthy controls (p-value > 0.05).

CONCLUSIONS

The findings of our study emphasize the importance of pharmacogenetic screening in epilepsy research, particularly regarding to drug-resistant patients. The ABCC2*rs717620 variant conferred a significant correlation with elevated risk of epileptic seizures, while the ABCC2*rs2273697 and CYP1A2*rs762551 variants confirmed their contributions as protective markers against epilepsy development. Conversely, the ABCB1*rs2032582 and CYP2B6*rs3745274 alleles were not considered as independent risk factors with the course of epilepsy disease.

AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes

Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. Laboratory-based experimental methods for assessing these effects are time-consuming and often impractical, highlighting the importance of in-silico tools for variant impact prediction. However, the performance metrics of currently available tools on breast cancer missense variants from benchmarking databases have not been thoroughly investigated, creating a knowledge gap in the accurate prediction of pathogenicity. In this study, the benchmarking datasets ClinVar and HGMD were used to evaluate 21 Artificial Intelligence (AI)-derived in-silico tools. Missense variants in breast cancer genes were extracted from ClinVar and HGMD professional v2023.1. The HGMD dataset focused on pathogenic variants only, to ensure balance, benign variants for the same genes were included from the ClinVar database. Interestingly, our analysis of both datasets revealed variants across genes with varying penetrance levels like low and moderate in addition to high, reinforcing the value of disease-specific tools. The top-performing tools on ClinVar dataset identified were MutPred (Accuracy = 0.73), Meta-RNN (Accuracy = 0.72), ClinPred (Accuracy = 0.71), Meta-SVM, REVEL, and Fathmm-XF (Accuracy = 0.70). While on HGMD dataset they were ClinPred (Accuracy = 0.72), MetaRNN (Accuracy = 0.71), CADD (Accuracy = 0.69), Fathmm-MKL (Accuracy = 0.68), and Fathmm-XF (Accuracy = 0.67). These findings offer clinicians and researchers valuable insights for selecting, improving, and developing effective in-silico tools for breast cancer pathogenicity prediction. Bridging this knowledge gap contributes to advancing precision medicine and enhancing diagnostic and therapeutic approaches for breast cancer patients with potential implications for other conditions.

Variant Effect Prediction in the Age of Machine Learning

Over the years, many computational methods have been created for the analysis of the impact of single amino acid substitutions resulting from single-nucleotide variants in genome coding regions. Historically, all methods have been supervised and thus limited by the inadequate sizes of experimentally curated data sets and by the lack of a standardized definition of variant effect. The emergence of unsupervised, deep learning (DL)-based methods raised an important question: Can machines learn the language of life from the unannotated protein sequence data well enough to identify significant errors in the protein "sentences"? Our analysis suggests that some unsupervised methods perform as well or better than existing supervised methods. Unsupervised methods are also faster and can, thus, be useful in large-scale variant evaluations. For all other methods, however, their performance varies by both evaluation metrics and by the type of variant effect being predicted. We also note that the evaluation of method performance is still lacking on less-studied, nonhuman proteins where unsupervised methods hold the most promise.

Plasminogen missense variants and their involvement in cardiovascular and inflammatory disease

Human plasminogen (PLG), the zymogen of the fibrinolytic protease, plasmin, is a polymorphic protein with two widely distributed codominant alleles, PLG/Asp453 and PLG/Asn453. About 15 other missense or non-synonymous single nucleotide polymorphisms (nsSNPs) of PLG show major, yet different, relative abundances in world populations. Although the existence of these relatively abundant allelic variants is generally acknowledged, they are often overlooked or assumed to be non-pathogenic. In fact, at least half of those major variants are classified as having conflicting pathogenicity, and it is unclear if they contribute to different molecular phenotypes. From those, PLG/K19E and PLG/A601T are examples of two relatively abundant PLG variants that have been associated with PLG deficiencies (PD), but their pathogenic mechanisms are unclear. On the other hand, approximately 50 rare and ultra-rare PLG missense variants have been reported to cause PD as homozygous or compound heterozygous variants, often leading to a debilitating disease known as ligneous conjunctivitis. The true abundance of PD-associated nsSNPs is unknown since they can remain undetected in heterozygous carriers. However, PD variants may also contribute to other diseases. Recently, the ultra-rare autosomal dominant PLG/K311E has been found to be causative of hereditary angioedema (HAE) with normal C1 inhibitor. Two other rare pathogenic PLG missense variants, PLG/R153G and PLG/V709E, appear to affect platelet function and lead to HAE, respectively. Herein, PLG missense variants that are abundant and/or clinically relevant due to association with disease are examined along with their world distribution. Proposed molecular mechanisms are discussed when known or can be reasonably assumed.

Minimizing the ratio of ionizable lipid in lipid nanoparticles for in vivo base editing

Lipid nanoparticles (LNPs) have gained clinical approval as carriers for both siRNA and mRNA. Among the crucial components of LNPs, ionizable lipids play a pivotal role in determining the efficiency of RNA delivery. In this study, we synthesized a series of ionizable lipids, denoted as HTO, with a higher count of hydroxyl groups compared to SM-102. Remarkably, LNPs based on HTO12 lipid demonstrated comparable mRNA delivery efficiency and biosafety to those based on SM-102. However, the former reduced the ratio of ionizable lipid/total lipids to mRNA in LNPs by 2.5 times compared to SM-102. The HTO12 LNP efficiently encapsulated adenine base editor mRNA and sgRNA targeting Pcsk9, leading to substantial gene editing within the liver of mice and effective reduction of the target protein. Our study underscores that ionizable lipids with multiple hydroxyl groups may facilitate an improved lipid-to-mRNA ratio to minimize the dosage of ionizable lipids for in vivo delivery.

Rare GPR37L1 Variants Reveal Potential Association between GPR37L1 and Disorders of Anxiety and Migraine

GPR37L1 is an orphan receptor that couples through heterotrimeric G-proteins to regulate physiological functions. Since its role in humans is not fully defined, we used an unbiased computational approach to assess the clinical significance of rare G-protein-coupled receptor 37-like 1 (GPR37L1) genetic variants found among 51,289 whole-exome sequences from the DiscovEHR cohort. Rare GPR37L1 coding variants were binned according to predicted pathogenicity and analyzed by sequence kernel association testing to reveal significant associations with disease diagnostic codes for epilepsy and migraine, among others. Since associations do not prove causality, rare GPR37L1 variants were functionally analyzed in SK-N-MC cells to evaluate potential signaling differences and pathogenicity. Notably, receptor variants exhibited varying abilities to reduce cAMP levels, activate mitogen-activated protein kinase (MAPK) signaling, and/or upregulate receptor expression in response to the agonist prosaptide (TX14(A)), as compared with the wild-type receptor. In addition to signaling changes, knock-out (KO) of GPR37L1 or expression of certain rare variants altered cellular cholesterol levels, which were also acutely regulated by administration of the agonist TX14(A) via activation of the MAPK pathway. Finally, to simulate the impact of rare nonsense variants found in the large patient cohort, a KO mouse line lacking Gpr37l1 was generated. Although KO animals did not recapitulate an acute migraine phenotype, the loss of this receptor produced sex-specific changes in anxiety-related disorders often seen in chronic migraineurs. Collectively, these observations define the existence of rare GPR37L1 variants associated with neuropsychiatric conditions in the human population and identify the signaling changes contributing to pathological processes.

Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning

MOTIVATION

Whole-exome and genome sequencing have become common tools in diagnosing patients with rare diseases. Despite their success, this approach leaves many patients undiagnosed. A common argument is that more disease variants still await discovery, or the novelty of disease phenotypes results from a combination of variants in multiple disease-related genes. Interpreting the phenotypic consequences of genomic variants relies on information about gene functions, gene expression, physiology, and other genomic features. Phenotype-based methods to identify variants involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been successfully applied to prioritizing variants, such methods are based on known gene-disease or gene-phenotype associations as training data and are applicable to genes that have phenotypes associated, thereby limiting their scope. In addition, phenotypes are not assigned uniformly by different clinicians, and phenotype-based methods need to account for this variability.

RESULTS

We developed an Embedding-based Phenotype Variant Predictor (EmbedPVP), a computational method to prioritize variants involved in genetic diseases by combining genomic information and clinical phenotypes. EmbedPVP leverages a large amount of background knowledge from human and model organisms about molecular mechanisms through which abnormal phenotypes may arise. Specifically, EmbedPVP incorporates phenotypes linked to genes, functions of gene products, and the anatomical site of gene expression, and systematically relates them to their phenotypic effects through neuro-symbolic, knowledge-enhanced machine learning. We demonstrate EmbedPVP's efficacy on a large set of synthetic genomes and genomes matched with clinical information.

AVAILABILITY AND IMPLEMENTATION

EmbedPVP and all evaluation experiments are freely available at https://github.com/bio-ontology-research-group/EmbedPVP.

DrivR-Base: a feature extraction toolkit for variant effect prediction model construction

MOTIVATION

Recent advancements in sequencing technologies have led to the discovery of numerous variants in the human genome. However, understanding their precise roles in diseases remains challenging due to their complex functional mechanisms. Various methodologies have emerged to predict the pathogenic significance of these genetic variants. Typically, these methods employ an integrative approach, leveraging diverse data sources that provide important insights into genomic function. Despite the abundance of publicly available data sources and databases, the process of navigating, extracting, and pre-processing features for machine learning models can be highly challenging and time-consuming. Furthermore, researchers often invest substantial effort in feature extraction, only to later discover that these features lack informativeness.

RESULTS

In this article, we introduce DrivR-Base, an innovative resource that efficiently extracts and integrates molecular information (features) related to single nucleotide variants. These features encompass information about the genomic positions and the associated protein positions of a variant. They are derived from a wide array of databases and tools, including structural properties obtained from AlphaFold, regulatory information sourced from ENCODE, and predicted variant consequences from Variant Effect Predictor. DrivR-Base is easily deployable via a Docker container to ensure reproducibility and ease of access across diverse computational environments. The resulting features can be used as input for machine learning models designed to predict the pathogenic impact of human genome variants in disease. Moreover, these feature sets have applications beyond this, including haploinsufficiency prediction and the development of drug repurposing tools. We describe the resource's development, practical applications, and potential for future expansion and enhancement.

AVAILABILITY AND IMPLEMENTATION

DrivR-Base source code is available at https://github.com/amyfrancis97/DrivR-Base.

Breaking genetic shackles: The advance of base editing in genetic disorder treatment

The rapid evolution of gene editing technology has markedly improved the outlook for treating genetic diseases. Base editing, recognized as an exceptionally precise genetic modification tool, is emerging as a focus in the realm of genetic disease therapy. We provide a comprehensive overview of the fundamental principles and delivery methods of cytosine base editors (CBE), adenine base editors (ABE), and RNA base editors, with a particular focus on their applications and recent research advances in the treatment of genetic diseases. We have also explored the potential challenges faced by base editing technology in treatment, including aspects such as targeting specificity, safety, and efficacy, and have enumerated a series of possible solutions to propel the clinical translation of base editing technology. In conclusion, this article not only underscores the present state of base editing technology but also envisions its tremendous potential in the future, providing a novel perspective on the treatment of genetic diseases. It underscores the vast potential of base editing technology in the realm of genetic medicine, providing support for the progression of gene medicine and the development of innovative approaches to genetic disease therapy.

MLe-KCNQ2: An Artificial Intelligence Model for the Prognosis of Missense KCNQ2 Gene Variants

Despite the increasing availability of genomic data and enhanced data analysis procedures, predicting the severity of associated diseases remains elusive in the absence of clinical descriptors. To address this challenge, we have focused on the KV7.2 voltage-gated potassium channel gene (KCNQ2), known for its link to developmental delays and various epilepsies, including self-limited benign familial neonatal epilepsy and epileptic encephalopathy. Genome-wide tools often exhibit a tendency to overestimate deleterious mutations, frequently overlooking tolerated variants, and lack the capacity to discriminate variant severity. This study introduces a novel approach by evaluating multiple machine learning (ML) protocols and descriptors. The combination of genomic information with a novel Variant Frequency Index (VFI) builds a robust foundation for constructing reliable gene-specific ML models. The ensemble model, MLe-KCNQ2, formed through logistic regression, support vector machine, random forest and gradient boosting algorithms, achieves specificity and sensitivity values surpassing 0.95 (AUC-ROC > 0.98). The ensemble MLe-KCNQ2 model also categorizes pathogenic mutations as benign or severe, with an area under the receiver operating characteristic curve (AUC-ROC) above 0.67. This study not only presents a transferable methodology for accurately classifying KCNQ2 missense variants, but also provides valuable insights for clinical counseling and aids in the determination of variant severity. The research context emphasizes the necessity of precise variant classification, especially for genes like KCNQ2, contributing to the broader understanding of gene-specific challenges in the field of genomic research. The MLe-KCNQ2 model stands as a promising tool for enhancing clinical decision making and prognosis in the realm of KCNQ2-related pathologies.

Characterizing genetic variation in the regulation of the ER stress response through computational and cis-eQTL analyses

Misfolded proteins in the endoplasmic reticulum (ER) elicit the ER stress response, a large transcriptional response driven by 3 well-characterized transcription factors (TFs). This transcriptional response is variable across different genetic backgrounds. One mechanism in which genetic variation can lead to transcriptional variability in the ER stress response is through altered binding and activity of the 3 main TFs: XBP1, ATF6, and ATF4. This work attempts to better understand this mechanism by first creating a computational pipeline to identify potential binding sites throughout the human genome. We utilized GTEx data sets to identify cis-eQTLs that fall within predicted TF binding sites (TFBSs). We also utilized the ClinVar database to compare the number of pathogenic vs benign variants at different positions of the binding motifs. Finally, we performed a cis-eQTL analysis on human cell lines experiencing ER stress to identify cis-eQTLs that regulate the variable ER stress response. The majority of these cis-eQTLs are unique to a given condition: control or ER stress. Some of these stress-specific cis-eQTLs fall within putative binding sites of the 3 main ER stress response TFs, providing a potential mechanism by which these cis-eQTLs might be impacting gene expression under ER stress conditions through altered TF binding. This study represents the first cis-eQTL analysis on human samples experiencing ER stress and is a vital step toward identifying the genetic components responsible for the variable ER stress response.

Deep computational phenotyping of genomic variants impacting the SET domain of KMT2C reveal molecular mechanisms for their dysfunction

Introduction: Kleefstra Syndrome type 2 (KLEFS-2) is a genetic, neurodevelopmental disorder characterized by intellectual disability, infantile hypotonia, severe expressive language delay, and characteristic facial appearance, with a spectrum of other distinct clinical manifestations. Pathogenic mutations in the epigenetic modifier type 2 lysine methyltransferase KMT2C have been identified to be causative in KLEFS-2 individuals. Methods: This work reports a translational genomic study that applies a multidimensional computational approach for deep variant phenotyping, combining conventional genomic analyses, advanced protein bioinformatics, computational biophysics, biochemistry, and biostatistics-based modeling. We use standard variant annotation, paralog annotation analyses, molecular mechanics, and molecular dynamics simulations to evaluate damaging scores and provide potential mechanisms underlying KMT2C variant dysfunction. Results: We integrated data derived from the structure and dynamics of KMT2C to classify variants into SV (Structural Variant), DV (Dynamic Variant), SDV (Structural and Dynamic Variant), and VUS (Variant of Uncertain Significance). When compared with controls, these variants show values reflecting alterations in molecular fitness in both structure and dynamics. Discussion: We demonstrate that our 3D models for KMT2C variants suggest distinct mechanisms that lead to their imbalance and are not predictable from sequence alone. Thus, the missense variants studied here cause destabilizing effects on KMT2C function by different biophysical and biochemical mechanisms which we adeptly describe. This new knowledge extends our understanding of how variations in the KMT2C gene cause the dysfunction of its methyltransferase enzyme product, thereby bearing significant biomedical relevance for carriers of KLEFS2-associated genomic mutations.

LDLR and PCSK9 3´UTR variants and their putative effects on microRNA molecular interactions in familial hypercholesterolemia: a computational approach

BACKGROUND

Familial hypercholesterolemia (FH) is caused by pathogenic variants in low-density lipoprotein (LDL) receptor (LDLR) or its associated genes, including apolipoprotein B (APOB), proprotein convertase subtilisin/kexin type 9 (PCSK9), and LDLR adaptor protein 1 (LDLRAP1). However, approximately 40% of the FH patients clinically diagnosed (based on FH phenotypes) may not carry a causal variant in a FH-related gene. Variants located at 3' untranslated region (UTR) of FH-related genes could elucidate mechanisms involved in FH pathogenesis. This study used a computational approach to assess the effects of 3'UTR variants in FH-related genes on miRNAs molecular interactions and to explore the association of these variants with molecular diagnosis of FH.

METHODS AND RESULTS

Exons and regulatory regions of FH-related genes were sequenced in 83 FH patients using an exon-target gene sequencing strategy. In silico prediction tools were used to study the effects of 3´UTR variants on interactions between miRNAs and target mRNAs. Pathogenic variants in FH-related genes (molecular diagnosis) were detected in 44.6% FH patients. Among 59 3'UTR variants identified, LDLR rs5742911 and PCSK9 rs17111557 were associated with molecular diagnosis of FH, whereas LDLR rs7258146 and rs7254521 and LDLRAP1 rs397860393 had an opposite effect (p < 0.05). 3´UTR variants in LDLR (rs5742911, rs7258146, rs7254521) and PCSK9 (rs17111557) disrupt interactions with several miRNAs, and more stable bindings were found with LDLR (miR-4435, miR-509-3 and miR-502) and PCSK9 (miR-4796).

CONCLUSION

LDLR and PCSK9 3´UTR variants disturb miRNA:mRNA interactions that could affect gene expression and are potentially associated with molecular diagnosis of FH.

Systematic characterization of regulatory variants of blood pressure genes

High blood pressure (BP) is the major risk factor for cardiovascular disease. Genome-wide association studies have identified genetic variants for BP, but functional insights into causality and related molecular mechanisms lag behind. We functionally characterize 4,608 genetic variants in linkage with 135 BP loci in vascular smooth muscle cells and cardiomyocytes by massively parallel reporter assays. High densities of regulatory variants at BP loci (i.e., ULK4, MAP4, CFDP1, PDE5A) indicate that multiple variants drive genetic association. Regulatory variants are enriched in repeats, alter cardiovascular-related transcription factor motifs, and spatially converge with genes controlling specific cardiovascular pathways. Using heuristic scoring, we define likely causal variants, and CRISPR prime editing finally determines causal variants for KCNK9, SFXN2, and PCGF6, which are candidates for developing high BP. Our systems-level approach provides a catalog of functionally relevant variants and their genomic architecture in two trait-relevant cell lines for a better understanding of BP gene regulation.

Rare GPR37L1 variants reveal potential roles in anxiety and migraine disorders

GPR37L1 is an orphan receptor that couples through heterotrimeric G-proteins to regulate physiological functions. Since its role in humans is not fully defined, we used an unbiased computational approach to assess the clinical significance of rare GPR37L1 genetic variants found among 51,289 whole exome sequences from the DiscovEHR cohort. Briefly, rare GPR37L1 coding variants were binned according to predicted pathogenicity, and analyzed by Sequence Kernel Association testing to reveal significant associations with disease diagnostic codes for epilepsy and migraine, among others. Since associations do not prove causality, rare GPR37L1 variants were then functionally analyzed in SK-N-MC cells to evaluate potential signaling differences and pathogenicity. Notably, receptor variants exhibited varying abilities to reduce cAMP levels, activate MAPK signaling, and/or upregulate receptor expression in response to the agonist prosaptide (TX14(A)), as compared to the wild-type receptor. In addition to signaling changes, knockout of GPR37L1 or expression of certain rare variants altered cellular cholesterol levels, which were also acutely regulated by administration of the agonist TX14(A) via activation of the MAPK pathway. Finally, to simulate the impact of rare nonsense variants found in the large patient cohort, a knockout (KO) mouse line lacking Gpr37L1 was generated, revealing loss of this receptor produced sex-specific changes implicated in migraine-related disorders. Collectively, these observations define the existence of rare GPR37L1 variants in the human population that are associated with neuropsychiatric conditions and identify the underlying signaling changes that are implicated in the in vivo actions of this receptor in pathological processes leading to anxiety and migraine.

SIGNIFICANCE STATEMENT

G-protein coupled receptors (GPCRs) represent a diverse group of membrane receptors that contribute to a wide range of diseases and serve as effective drug targets. However, a number of these receptors have no identified ligands or functions, i.e., orphan receptors. Over the past decade, advances have been made, but there is a need for identifying new strategies to reveal their roles in health and disease. Our results highlight the utility of rare variant analyses of orphan receptors for identifying human disease associations, coupled with functional analyses in relevant cellular and animal systems, to ultimately reveal their roles as novel drug targets for treatment of neurological disorders that lack wide-spread efficacy.

eyeVarP: A computational framework for the identification of pathogenic variants specific to eye disease

PURPOSE

Disease-specific pathogenic variant prediction tools that differentiate pathogenic variants from benign have been improved through disease specificity recently. However, they have not been evaluated on disease-specific pathogenic variants compared with other diseases, which would help to prioritize disease-specific variants from several genes or novel genes. Thus, we hypothesize that features of pathogenic variants alone would provide a better model.

METHODS

We developed an eye disease-specific variant prioritization tool (eyeVarP), which applied the random forest algorithm to the data set of pathogenic variants of eye diseases and other diseases. We also developed the VarP tool and generalized pipeline to filter missense and insertion-deletion variants and predict their pathogenicity from exome or genome sequencing data, thus we provide a complete computational procedure.

RESULTS

eyeVarP outperformed pan disease-specific tools in identifying eye disease-specific pathogenic variants under the top 10. VarP outperformed 12 pathogenicity prediction tools with an accuracy of 95% in correctly identifying the pathogenicity of missense and insertion-deletion variants. The complete pipeline would help to develop disease-specific tools for other genetic disorders.

CONCLUSION

eyeVarP performs better in identifying eye disease-specific pathogenic variants using pathogenic variant features and gene features. Implementing such complete computational procedure would significantly improve the clinical variant interpretation for specific diseases.

Innovations in CRISPR-Based Therapies

Gene and cell therapies have shown tremendous advancement in the last 5 years. Prominent examples include the successful use of CRISPR-edited stem cells for treating blood disorders like sickle cell anemia and beta-thalassemia, and ongoing clinical trials for treating blindness. This mini-review assesses the status of CRISPR-based therapies, both in vivo and ex vivo, and the challenges associated with clinical translation. In vivo CRISPR therapies have been used to treat eye and liver diseases due to the practicality of delivering editing components to the target tissue. In contrast, even though ex vivo CRISPR therapy involves cell isolation, expansion, and infusion, its advantages include characterizing the gene edits before infusion and restricting off-target effects in other tissues. Further, the safety, affordability, and feasibility of CRISPR therapies, especially for treating large number of patients, are discussed.

AllelePred: A Simple Allele Frequencies Ensemble Predictor for Different Single Nucleotide Variants

BACKGROUND & OBJECTIVE

Genomic medicine stands to be revolutionized by understanding single nucleotide variants (SNVs) and their expression in single-gene disorders (Mendelian diseases). Computational tools can play a vital role in the exploration of such variations and their pathogenicity. Consequently, we developed the ensemble prediction tool AllelePred to identify deleterious SNVs and disease causative genes.

RESULTS

The model utilizes different population genetics backgrounds and restricted criteria for features selection to help generate high accuracy results. In comparison to other tools, such as Eigen, PROVEAN, and fathmm-MKL our classifier achieves higher accuracy (98%), precision (96%), F1 score (93%), and coverage (100%) for different types of coding variants. The new method was also compared against a bioinformatics analytical workflow, which uses gnomAD overall AFs (less than 1%) and CADD (scaled C-score of at least 15). Furthermore, this research highlights the stature of genetic variant sharing and curation. We accumulated a list of highly probable deleterious variants and recommended further experimental validation before medical diagnostic usage.

CONCLUSIONS

The ensemble prediction tool AllelePred enables increased accuracy in recognizing deleterious SNVs and the genetic determinants in real clinical data.

Protocols of Investigation of Neonatal Cholestasis-A Critical Appraisal

Neonatal cholestasis (NC) starts during the first three months of life and comprises extrahepatic and intrahepatic groups of diseases, some of which have high morbimortality rates if not timely identified and treated. Prolonged jaundice, clay-colored or acholic stools, and choluria in an infant indicate the urgent need to investigate the presence of NC, and thenceforth the differential diagnosis of extra- and intrahepatic causes of NC. The differential diagnosis of NC is a laborious process demanding the accurate exclusion of a wide range of diseases, through the skillful use and interpretation of several diagnostic tests. A wise integration of clinical-laboratory, histopathological, molecular, and genetic evaluations is imperative, employing extensive knowledge about each evaluated disease as well as the pitfalls of each diagnostic test. Here, we review the difficulties involved in correctly diagnosing the cause of cholestasis in an affected infant.

Development of a universal antibiotic resistance screening reporter for improving efficiency of cytosine and adenine base editing

Base editing has emerged as a revolutionary technology for single nucleotide modifications. The cytosine and adenine base editors (CBEs and ABEs) have demonstrated great potential in clinical and fundamental research. However, screening and isolating target-edited cells remains challenging. In the current study, we developed a universal Adenine and Cytosine Base-Editing Antibiotic Resistance Screening Reporter (ACBE-ARSR) for improving the editing efficiency. To develop the reporter, the CBE-ARSR was first constructed and shown to be capable of enriching cells for those that had undergone CBE editing activity. Then, the ACBE-ARSR was constructed and was further validated in the editing assays by four different CBEs and two versions of ABE at several different genomic loci. Our results demonstrated that ACBE-ARSR, compared to the reporter of transfection (RoT) screening strategy, improved the editing efficiency of CBE and ABE by 4.6- and 1.9-fold on average, respectively. We found the highest CBE and ABE editing efficiencies as enriched by ACBE-ARSR reached 90% and 88.7%. Moreover, we also demonstrated ACBE-ARSR could be employed for enhancing simultaneous multiplexed genome editing. In conclusion, both CBE and ABE activity can be improved significantly using our novel ACBE-ARSR screening strategy, which we believe will facilitate the development of base editors and their application in biomedical and fundamental research studies.

Synonymous mutation in adenosine triphosphatase copper-transporting beta causes enhanced exon skipping in Wilson disease

Wilson disease (WD) is caused by biallelic pathogenic variants in adenosine triphosphatase copper-transporting beta (ATP7B); however, genetic testing identifies only one or no pathogenic ATP7B variant in a number of patients with WD. Synonymous single-nucleotide sequence variants have been recognized as pathogenic in individual families. The aim of the present study was to evaluate the prevalence and disease mechanism of the synonymous variant c.2292C>T (p.Phe764=) in WD. A cohort of 280 patients with WD heterozygous for a single ATP7B variant was investigated for the presence of c.2292C>T (p.Phe764=). In this cohort of otherwise genetically unexplained WD, the allele frequency of c.2292C>T (p.Phe764=) was 2.5% (14 of 560) compared to 7.1 × 10^-6 in the general population (2 of 280,964 in the Genome Aggregation Database; p < 10^-5 ; Fisher exact test). In an independent United Kingdom (UK) cohort, 2 patients with WD homozygous for p.Phe764= were identified. RNA analysis of ATP7B transcripts from patients homozygous or heterozygous for c.2292C>T and control fibroblasts showed that this variant caused high expression of an ATP7B transcript variant lacking exon 8. Conclusion: The synonymous ATP7B variant c.2292C>T (p.Phe764=) causes abnormal messenger RNA processing of ATP7B transcripts and is associated with WD in compound heterozygotes and homozygotes.

Unusual mammalian usage of TGA stop codons reveals that sequence conservation need not imply purifying selection

The assumption that conservation of sequence implies the action of purifying selection is central to diverse methodologies to infer functional importance. GC-biased gene conversion (gBGC), a meiotic mismatch repair bias strongly favouring GC over AT, can in principle mimic the action of selection, this being thought to be especially important in mammals. As mutation is GC→AT biased, to demonstrate that gBGC does indeed cause false signals requires evidence that an AT-rich residue is selectively optimal compared to its more GC-rich allele, while showing also that the GC-rich alternative is conserved. We propose that mammalian stop codon evolution provides a robust test case. Although in most taxa TAA is the optimal stop codon, TGA is both abundant and conserved in mammalian genomes. We show that this mammalian exceptionalism is well explained by gBGC mimicking purifying selection and that TAA is the selectively optimal codon. Supportive of gBGC, we observe (i) TGA usage trends are consistent at the focal stop codon and elsewhere (in UTR sequences); (ii) that higher TGA usage and higher TAA→TGA substitution rates are predicted by a high recombination rate; and (iii) across species the difference in TAA <-> TGA substitution rates between GC-rich and GC-poor genes is largest in genomes that possess higher between-gene GC variation. TAA optimality is supported both by enrichment in highly expressed genes and trends associated with effective population size. High TGA usage and high TAA→TGA rates in mammals are thus consistent with gBGC’s predicted ability to “drive” deleterious mutations and supports the hypothesis that sequence conservation need not be indicative of purifying selection. A general trend for GC-rich trinucleotides to reside at frequencies far above their mutational equilibrium in high recombining domains supports the generality of these results.

Proximal and Distal Regions of Pathogenic Th17 Related Chromatin Loci Are Sequentially Accessible During Pathogenicity of Th17

Pathogenic Th17, featured by their production of pro-inflammatory cytokines, are considered as a key player in most autoimmune diseases. The transcriptome of them is obviously distinct from that of conventional regulatory Th17. However, chromatin accessibility of the two Th17 groups have not been comprehensively compared yet. Here, we found that their chromatin-accessible regions(ChARs) significantly correlated with the expression of related genes, indicating that they might engage in the regulation of these genes. Indeed, pathogenic Th17 specific ChARs (patho-ChARs) exhibited a significant distribution preference in TSS-proximal region. We further filtered the patho-ChARs based on their conservation among mammalians or their concordance with the expression of their related genes. In either situation, the filtered patho-ChARs also showed a preference for TSS-proximal region. Enrichment of expression concordant patho-ChARs related genes suggested that they might involve in the pathogenicity of Th17. Thus, we also examined all ChARs of patho-ChARs related genes, and defined an opening ChAR set according to their changes in the Th17 to Th1 conversion. Interestingly, these opening ChARs displayed a sequential accessibility change from TSS-proximal region to TSS-distal region. Meanwhile, a group of patho-TFs (transcription factors) were identified based on the appearance of their binding motifs in the opening ChARs. Consistently, some of them also displayed a similar preference for binding the TSS-proximal region. Single-cell transcriptome analysis further confirmed that these patho-TFs were involved in the generation of pathogenic Th17. Therefore, our results shed light on a new regulatory mechanism underlying the generation of pathogenic Th17, which is worth to be considered for autoimmune disease therapy.

Whole exome sequencing in Alopecia Areata identifies rare variants in KRT82

Alopecia areata is a complex genetic disease that results in hair loss due to the autoimmune-mediated attack of the hair follicle. We previously defined a role for both rare and common variants in our earlier GWAS and linkage studies. Here, we identify rare variants contributing to Alopecia Areata using a whole exome sequencing and gene-level burden analyses approach on 849 Alopecia Areata patients compared to 15,640 controls. KRT82 is identified as an Alopecia Areata risk gene with rare damaging variants in 51 heterozygous Alopecia Areata individuals (6.01%), achieving genome-wide significance (p = 2.18E−07). KRT82 encodes a hair-specific type II keratin that is exclusively expressed in the hair shaft cuticle during anagen phase, and its expression is decreased in Alopecia Areata patient skin and hair follicles. Finally, we find that cases with an identified damaging KRT82 variant and reduced KRT82 expression have elevated perifollicular CD8 infiltrates. In this work, we utilize whole exome sequencing to successfully identify a significant Alopecia Areata disease-relevant gene, KRT82, and reveal a proposed mechanism for rare variant predisposition leading to disrupted hair shaft integrity.

Common variants have been discovered to be associated with Alopecia Areata; however, rare variants have been less well studied. Here, the authors use whole-exome sequencing to identify associated rare variants in the hair keratin gene KRT82. Further, they find that individuals with Alopecia Areata have reduced expression of KRT82 in the skin and hair follicle.

The Basis and Promise of Programmable RNA Editing and Modification

One key advantage of RNA over genomic editing is its temporary effects. Aside from current use of DNA-targeting CRISPR-Cas9, the more recently discovered CRISPR-Cas13 has been explored as a means of editing due to its RNA-targeting capabilities. Specifically, there has been a recent interest in identifying and functionally characterizing biochemical RNA modifications, which has spurred a new field of research known as "epitranscriptomics". As one of the most frequently occurring transcriptome modifications, N6-methyladenosine (m6A) has generated much interest. The presence of m6A modifications is under the tight control of a series of regulators, and the ability of fusing these proteins or demethylases to catalytically inactive CRISPR proteins have resulted in a new wave of programmable RNA methylation tools. In addition, studies have been conducted to develop different CRISPR/Cas and base editor systems capable of more efficient editing, and some have explored the effects of in vivo editing for certain diseases. As well, the application of CRISPR and base editors for screening shows promise in revealing the phenotypic outcomes from m6A modification, many of which are linked to physiological, and pathological effects. Thus, the therapeutic potential of CRISPR/Cas and base editors for not only m6A related, but other RNA and DNA related disease has also garnered insight. In this review, we summarize/discuss the recent findings on RNA editing with CRISPR, base editors and non-CRISPR related tools and offer a perspective regarding future applications for basic and clinical research.

Functional assessment of somatic STK11 variants identified in primary human non-small cell lung cancers

Serine/Threonine Kinase 11 (STK11) encodes an important tumor suppressor that is frequently mutated in lung adenocarcinoma. Clinical studies have shown that mutations in STK11 resulting in loss of function correlate with resistance to anti-PD-1 monoclonal antibody therapy in KRAS-driven non-small cell lung cancer (NSCLC), but the molecular mechanisms responsible remain unclear. Despite this uncertainty, STK11 functional status is emerging as a reliable biomarker for predicting non-response to anti-PD-1 therapy in NSCLC patients. The clinical utility of this biomarker ultimately depends upon accurate classification of STK11 variants. For nonsense variants occurring early in the STK11 coding region, this assessment is straightforward. However, rigorously demonstrating the functional impact of missense variants remains an unmet challenge. Here we present data characterizing four STK11 splice-site variants by analyzing tumor mRNA, and 28 STK11 missense variants using an in vitro kinase assay combined with a cell-based p53-dependent luciferase reporter assay. The variants we report were identified in primary human NSCLC biopsies in collaboration with the University of Vermont Genomic Medicine group. Additionally, we compare our experimental results with data from 22 in silico predictive algorithms. Our work highlights the power, utility and necessity of functional variant assessment and will aid STK11 variant curation, provide a platform to assess novel STK11 variants and help guide anti-PD-1 therapy utilization in KRAS-driven NSCLCs.

Decreased IL-10 accelerates B-cell leukemia/lymphoma in a mouse model of pediatric lymphoid leukemia

B-cell deficiency and DNA damage in the bone marrow of Il10^−/− mice are associated with inflammation and mitigated by antibiotics.

In ETV6-RUNX1⁺Cdkn2a^−/− mice, low IL-10 accelerates the development of B-cell leukemia/lymphoma in a dose-dependent manner.

Exposures to a wide repertoire of common childhood infections and strong inflammatory responses to those infections are associated with the risk of pediatric B-cell acute lymphoblastic leukemia (B-ALL) in opposing directions. Neonatal inflammatory markers are also related to risk by unknown mechanism(s). Here, we demonstrate that interleukin-10 (IL-10) deficiency, which is associated with childhood B-ALL, indirectly impairs B lymphopoiesis and increases B-cell DNA damage in association with a module of 6 proinflammatory/myeloid-associated cytokines (IL-1α, IL-6, IL-12p40, IL-13, macrophage inflammatory protein-1β/CCL4, and granulocyte colony-stimulating factor). Importantly, antibiotics attenuated inflammation and B-cell defects in preleukemic Cdkn2a^−/−Il10^−/− mice. In an ETV6-RUNX1⁺ (E6R1⁺) Cdkn2a^−/− mouse model of B-ALL, decreased levels of IL-10 accelerated B-cell neoplasms in a dose-dependent manner and altered the mutational profile of these neoplasms. Our results illuminate a mechanism through which a low level of IL-10 can create a risk for leukemic transformation and support developing evidence that microbial dysbiosis contributes to pediatric B-ALL.

Structural and functional analysis of somatic coding and UTR indels in breast and lung cancer genomes

Insertions and deletions (Indels) represent one of the major variation types in the human genome and have been implicated in diseases including cancer. To study the features of somatic indels in different cancer genomes, we investigated the indels from two large samples of cancer types: invasive breast carcinoma (BRCA) and lung adenocarcinoma (LUAD). Besides mapping somatic indels in both coding and untranslated regions (UTRs) from the cancer whole exome sequences, we investigated the overlap between these indels and transcription factor binding sites (TFBSs), the key elements for regulation of gene expression that have been found in both coding and non-coding sequences. Compared to the germline indels in healthy genomes, somatic indels contain more coding indels with higher than expected frame-shift (FS) indels in cancer genomes. LUAD has a higher ratio of deletions and higher coding and FS indel rates than BRCA. More importantly, these somatic indels in cancer genomes tend to locate in sequences with important functions, which can affect the core secondary structures of proteins and have a bigger overlap with predicted TFBSs in coding regions than the germline indels. The somatic CDS indels are also enriched in highly conserved nucleotides when compared with germline CDS indels.

Revealing modifier variations characterizations for elucidating the genetic basis of human phenotypic variations

Epistatic interactions complicate the identification of variants involved in phenotypic effect. In-depth knowledge in modifiers and in pathogenic variants would benefit the mechanistic studies on the genetic basis of complex traits. We systematically compared the modifier variants which have evidence of modifier effect with the pathogenic variants from multiple angles. Our study found that genomic loci of modifier variations differ from pathogenic loci in many aspects, such as population genetics statistics, epigenetic features, evolutionary characteristics and functional properties of the variations. Genes containing modifier variation(s) exhibit higher probability of being haploinsufficient and higher probability of recessive disease causation, and they are relatively more important in network communication. Furthermore, we reinforced that co-expression analysis is an effective methodology to predict functional associations between modifier genes and their potential target genes. In many aspects, we detected statistically significant differences between modifier variants/genes and pathogenic variants/genes, and investigated relationships between modifiers and their potential targets. Our results offer some actionable insights that may provide appropriate guidelines to clinical genetics and researchers to elucidate the molecular mechanism underlying the human phenotypic variation.

L-Type Calcium Channel: Predicting Pathogenic/Likely Pathogenic Status for Variants of Uncertain Clinical Significance

(1) Background: Defects in gene CACNA1C, which encodes the pore-forming subunit of the human Cav1.2 channel (hCav1.2), are associated with cardiac disorders such as atrial fibrillation, long QT syndrome, conduction disorders, cardiomyopathies, and congenital heart defects. Clinical manifestations are known only for 12% of CACNA1C missense variants, which are listed in public databases. Bioinformatics approaches can be used to predict the pathogenic/likely pathogenic status for variants of uncertain clinical significance. Choosing a bioinformatics tool and pathogenicity threshold that are optimal for specific protein families increases the reliability of such predictions. (2) Methods and Results: We used databases ClinVar, Humsavar, gnomAD, and Ensembl to compose a dataset of pathogenic/likely pathogenic and benign variants of hCav1.2 and its 20 paralogues: voltage-gated sodium and calcium channels. We further tested the performance of sixteen in silico tools in predicting pathogenic variants. ClinPred demonstrated the best performance, followed by REVEL and MCap. In the subset of 309 uncharacterized variants of hCav1.2, ClinPred predicted the pathogenicity for 188 variants. Among these, 36 variants were also categorized as pathogenic/likely pathogenic in at least one paralogue of hCav1.2. (3) Conclusions: The bioinformatics tool ClinPred and the paralogue annotation method consensually predicted the pathogenic/likely pathogenic status for 36 uncharacterized variants of hCav1.2. An analogous approach can be used to classify missense variants of other calcium channels and novel variants of hCav1.2.

Pitt-Hopkins syndrome: phenotypic and genotypic description of four unrelated patients and structural analysis of corresponding missense mutations

Pitt-Hopkins syndrome is an underdiagnosed neurodevelopmental disorder which is characterized by specific facial features, early-onset developmental delay, and moderate to severe intellectual disability. The genetic cause, a deficiency of the TCF4 gene, has been established; however, the underlying pathological mechanisms of this disease are still unclear. Herein, we report four unrelated children with different de novo mutations (T606A, K607E, R578C, and V617I) located at highly conserved sites and with clinical phenotypes which present variable degrees of developmental delay and intellectual disability. Three of these four missense mutations have not yet been reported. The patient with V617I mutation exhibits mild intellectual disability and has attained more advanced motor and verbal skills, which is significantly different from other cases reported to date. Molecular dynamics simulations are used to explore the atomic level mechanism of how missense mutations impair the functions of TCF4. Mutations T606A, K607E, and R578C are found to affect DNA binding directly or indirectly, while V617I only induces subtle conformational changes, which is consistent with the milder clinical phenotype of the corresponding patient. The study expands the mutation spectrum and phenotypic characteristics of Pitt-Hopkins syndrome, and reinforces the genotype-phenotype correlation and strengthens the understanding of phenotype variability, which is helpful for further investigation of pathogenetic mechanisms and improved genetic counseling.

Genetic Inheritance and Its Contribution to Tinnitus

Tinnitus is the abnormal perception of sound that affects more than 15% of adult population around the globe. Severe tinnitus is considered a complex disorder that arises as result of the interaction of genetic and environmental factors, and it is associated with several comorbidities such as hearing loss, anxiety, and insomnia. We begin this review with an introduction to human molecular genetics and the role of genetic variation on the inheritance. There are some genetic reports on tinnitus heritability including concordance studies in twins and adoptees or aggregation in families providing some evidence for familial aggregation in patients with severe tinnitus and high concordance in monozygotic twins with bilateral tinnitus. So, sex differences in familial aggregation and heritability of bilateral tinnitus suggest a potential sexual dimorphism in tinnitus inheritance.Molecular genetic studies have been demonstrated to be a useful tool to understand the role of genetic variation in rare diseases and complex disorders. The reported associations in common variants in neurotrophic factors such as GDNF, BDNF, or potassium channels genes were underpowered, and the lack of replication questions these findings. Although candidate gene approaches have failed in replicating these genetic associations, the development of high throughput sequencing technology and the selection of extreme phenotypes are strategies that will allow the clinicians and researchers to combine genetic information with clinical data to implement a personalized diagnosis and therapy in patients with tinnitus.

Revisiting the clinical impact of variants in EFHC1 in patients with different phenotypes of genetic generalized epilepsy

The most common form of genetic generalized epilepsy (GGE) is juvenile myoclonic epilepsy (JME), which accounts for 5 to 10% of all epilepsy cases. The gene EFHC1 has been implicated as a putative cause of JME. However, it remains debatable whether testing for EFHC1 mutations should be included in the diagnostic epilepsy gene panels. To investigate the clinical utility of EFHC1 testing, we studied 125 individuals: 100 with JME and 25 with other GGEs. We amplified and sequenced all EFHC1 coding exons. Then, we predicted the pathogenicity or benign impact of the variants using the analyses proposed by the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP). Mutation screening revealed 11 missense variants in 44 probands with JME (44%) and one of the seven individuals with generalized tonic-clonic seizures on awakening (14%). Six of the 11 variants (54%) were classified as 'benign,' and the remaining variants were considered variants of uncertain significance (VUS). There is currently a limitation to test for genes that predispose an individual to complex, nonmonogenic phenotypes. Thus, we show suggestive evidence that EFHC1 testing lacks a scientific foundation based on the disputed nature of the gene-disease relationship and should be currently limited to research purposes.

Tumor Mutational Patterns and Infiltrating Lymphocyte Density in Young and Elderly Patients With Breast Cancer

BACKGROUND/AIM

Age may pertain to different tumor genotype characteristics which may interfere with treatment efficacy and prognosis. We investigated the distribution and prognostic effect of mutations and tumor infiltrating lymphocyte (stromal TIL density) in young (≤35 years) and elderly (>65 years) early breast cancer patients.

MATERIALS AND METHODS

Paraffin tumor genotypes of all clinical subtypes from 345 patients were examined.

RESULTS

A total of 638 mutations were detected in 221 patients (64.1%). Compared to young, elderly patients presented with lower TIL density (p<0.001) but more TILs in TP53 mutated tumors (p=0.042). Mutation in one, rather than in 2 or more genes, conferred better outcome (DFS: HR=0.51, p=0.016; OS: HR=0.47, p=0.015) but the effect was age-independent.

CONCLUSION

There are fewer TILs and different mutations patterns in tumors from elderly patients compared to young. Age and TIL-independent gene agnostic co-mutations affect patient outcome.

Deciphering the impact of missense mutations on structure and dynamics of SMAD4 protein involved in pathogenesis of gall bladder cancer

Gall bladder cancer (GBC) is the most common malignancy of biliary tract cancer associated with high mortality rate and poor prognosis due to lack of suitable biomarkers. In this study, we explored the structural and functional effects of different missense mutations occurs in SMAD4 that was associated with the development of GBC. We utilized in silico methods to predict the harmful effects of nonsynonymous missense mutations and monitored the stability of protein. We found that all mutations (D351N, G352E, R361C, R361H, E526Q) associated with SMAD4 were deleterious in nature resulting in the formation of deformed or unstable protein structure. Molecular dynamics simulation studies revealed how these mutations affect protein stability, structure, conformation and function. We observed, different mutants increase the compactness and rigidity of SMAD4 protein, alter secondary structure composition, decrease the surface area and protein-ligand interaction and affect its conformation. Findings of current work indicated that the analyzed mutations might affect the structure of protein and its caliber to interact with other molecules, which probably related to functional impairment of SMAD4 upon D351N, G352E, R361C, R361H, E526Q mutations and their involvement in cancer. Hence, the present study has significance of rational drug design and further increase our understanding of GBC development.Communicated by Ramaswamy H. Sarma.

Pathogenic mutations and overall survival in 3,084 patients with cancer: the Hellenic Cooperative Oncology Group Precision Medicine Initiative

Background: We evaluated the association between pathogenic mutations and overall survival (OS) in patients with cancer referred to Hellenic Cooperative Oncology Group–affiliated Departments.

Patients and methods: Patients referred from 12/1980 to 1/2017 had molecular testing (for research) of archival tumor tissue collected at the time of first diagnosis (non-metastatic, 81%; metastatic, 19%). Tumor-specific gene panels (16-101 genes) were used to identify pathogenic mutations in clinically relevant genes. NGS genotyping was performed at the Laboratory of Molecular Oncology, Aristotle University of Thessaloniki. Annotation of mutations was performed at MD Anderson Cancer Center.

Results: We analyzed 3,084 patients (median age, 57 years; men, 22%) with sequencing data. Overall, 1,775 (58% of 3,084) patients had pathogenic mutations. The median follow-up was 7.52 years (95% CI, 7.39-7.61). In patients with non-metastatic tumors, after stratification by tumor type, increasing age, higher grade, and histology other than adenocarcinoma were associated with shorter OS. OS was also shorter in patients with pathogenic TP53 (HR=1.36; p<0.001), MLL3 (HR=1.64; p=0.005), and BRCA1 (HR=1.46; p=0.047) mutations compared to wild-type genes. In multivariate analyses, independent prognostic factors predicting shorter OS were pathogenic mutations in TP53 (HR=1.37, p=0.002) and MLL3 (HR=1.50, p=0.027); increasing age (HR=1.02, p<0.001); and increasing grade (HR=1.46, p<0.001). In patients with metastatic cancer, older age and higher grade were associated with shorter OS and maintained their independent prognostic significance (increasing age, HR=1.03, p<0.001 and higher grade, HR=1.73, p<0.001).

Conclusions: Analysis of molecular data reveals prognostic biomarkers, regardless of tissue or organ of origin to improve patient management.