CDKN2A unclassified variants in familial malignant melanoma: combining functional and computational approaches for their assessment

Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes

Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.

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.

Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes

Disease variant annotation in the context of biological reactions and pathways can provide a standardized overview of molecular phenotypes of pathogenic mutations that is amenable to computational mining and mathematical modeling. Reactome, an open source, manually curated, peer-reviewed database of human biological pathways, provides annotations for over 4000 disease variants of close to 400 genes in the context of ∼800 disease reactions constituting ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics (ACMG). Reactome's pathway-based, reaction-specific disease variant dataset and data model provide a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity.

Mutational screening of androgen receptor gene in 8224 men of infertile couples

CONTEXT

Mutations in Androgen receptor (AR) gene might be associated with infertility mainly because they cause various degree of androgen insensitivity.

OBJECTIVE

The aim of the study was to evaluate the frequency and type of AR variants in a large cohort of infertile males.

PATIENTS AND SETTING

8224 males of Italian idiopathic infertile couples referred University Hospital of Padova.

MAIN OUTCOME MEASURES

Mutational screening of AR, computational and functional analyses.

RESULTS

We found 131 patients (1.6%) harboring 45 variants in AR gene, of which 18 were novel missense AR variants. Patients with AR gene variants had lower sperm count (p = 0.048), higher testosterone concentration (p < 0.0001) and higher androgen sensitivity index (ASI) [LH x testosterone (T), p < 0.001] compared to patients without variants. Statistical analyses found T ≥ 15.38 nmol/l and ASI ≥180 IU × nmol/l2 as threshold values to discriminate with good accuracy patients with AR variants. Patients with oligozoospermia and T ≥ 15.38 nmol/l have a 9-fold increased risk of harboring mutations compared to patients with normal sperm count and T < 15.38 nmol/l (OR 9.29, 95% CI 5.07-17.02). Using computational and functional approaches, we identified two novel variants, L595P and L791I, as potentially pathogenic.

CONCLUSION

This is the largest study screening AR gene variants in men of idiopathic infertile couples. We found that the prevalence of variants increased to 3.4% in oligozoospermic subjects with T ≥ 15.38 nmol/l. Conversely, more than 80% of men with AR gene variants had low sperm count and high T levels. Based on our findings, we suggest AR sequencing as a routine genetic test in cases of idiopathic oligozoospermia with T ≥ 15.38 nmol/L.

Identification of Rare LRP5 Variants in a Cohort of Males with Impaired Bone Mass

Osteoporosis is the most common bone disease characterized by reduced bone mass and increased bone fragility. Genetic contribution is one of the main causes of primary osteoporosis; therefore, both genders are affected by this skeletal disorder. Nonetheless, osteoporosis in men has received little attention, thus being underestimated and undertreated. The aim of this study was to identify novel genetic variants in a cohort of 128 males with idiopathic low bone mass using a next-generation sequencing (NGS) panel including genes whose mutations could result in reduced bone mineral density (BMD). Genetic analysis detected in eleven patients ten rare heterozygous variants within the LRP5 gene, which were categorized as VUS (variant of uncertain significance), likely pathogenic and benign variants according to American College of Medical Genetics and Genomics (ACMG) guidelines. Protein structural and Bayesian analysis performed on identified LRP5 variants pointed out p.R1036Q and p.R1135C as pathogenic, therefore suggesting the likely association of these two variants with the low bone mass phenotype. In conclusion, this study expands our understanding on the importance of a functional LRP5 protein in bone formation and highlights the necessity to sequence this gene in subjects with idiopathic low BMD.

Clonal heterogeneity of melanoma in a paradigmatic case study: future prospects for circulating melanoma cells

The management of metastatic melanoma is a difficult matter. Nevertheless, the advent of target therapy has significantly improved patient outcome, provided that tumor molecular characteristics become available: the detection of drug-resistant clones can contribute to understanding the reasons for resistance onset, influencing the choice of subsequent therapy. This work aimed to provide a possible explanation for the early resistance to vemurafenib developed by a patient with melanoma, and concurrently to assess the extent, and role, of the tumor clonal heterogeneity. We analyzed tissue samples from different sites and time points: first/second primary, three lymph node metastases, and circulating melanoma cells (CMCs). We first investigated these samples by the routine Sanger sequencing for BRAF, NRAS, and KIT, and then, we focused on specific hotspots by droplet digital PCR. We detected a BRAF V600E mutation by Sanger sequencing in the second primary and distant lymph node metastases, but not in the first primary or sentinel lymph node. Interestingly, by droplet digital PCR, the V600E mutation was also detected in the first primary, and the V600K in the second primary and metastases. Moreover, we identified a rare KIT V569G mutation, appearing to be CMC exclusive. This finding confirms the potential of CMCs as a source of tumor material for genetic analysis, reflecting real-time systemic disease evolution and, most likely, the most aggressive, treatment-resistant clones. In summary, this work underlines the importance of CMCs in the early identification of tumor clones putatively responsible for therapy resistance.

The Binding Mechanism Between Inositol Phosphate (InsP) and the Jasmonate Receptor Complex: A Computational Study

Jasmonates are critical plant hormones, mediating stress response in plants and regulating plant growth and development. The jasmonate receptor is a multi-component complex, composed of Arabidopsis SKP-LIKE PROTEIN1 (ASK1), CORONATINE INSENSITIVE 1 (COI1), inositol phosphate (InsP), and jasmonate ZIM-domain protein (JAZ). COI1 acts as multi-component signaling hub that binds with each component. InsP is suggested to play important roles in the hormone perception. How InsP binds with COI1 and the structural changes in COI1 upon binding with InsP, JA-Ile, and JAZ are not well understood. In this study, we integrated multiple computational methods, such as molecular docking, molecular dynamics simulations, residue interaction network analysis and binding free energy calculation, to explore the effect of InsP on the dynamic behavior of COI1 and the recognition mechanism of each component of the jasmonate receptor complex. We found that upon binding with InsP, JA-Ile, and JAZ1, the structure of COI1 becomes more compact. The binding of InsP with COI1 stabilizes the conformation of COI1 and promotes the binding between JA-Ile or JAZ1 and COI1. Analysis of the network parameters led to the identification of some hub nodes in this network, including Met88, His118, Arg120, Arg121, Arg346, Tyr382, Arg409, Trp467, and Lys492. The structural and dynamic details will be helpful for understanding the recognition mechanism of each component and the discovery and design of novel jasmonate signaling pathway modulators.

Functional analysis of a CDKN2A 5'UTR germline variant associated with pancreatic cancer development

CDKN2A coding region germline variants are associated with pancreatic adenocarcinoma (PC) susceptibility. Recently, we described functional germline 5’UTR CDKN2A variants from melanoma patients affecting the post-transcriptional regulation of p16^INK4a mRNA that is dependent, at least in part, on an Internal Ribosome Entry Site (IRES) in the 5’UTR region. Here we describe a 5’UTR c.-201_-198delinsCTTT CDKN2A variant (frequency 0.0028 based on 350 PC patients), which seems to be private to PC, since it has never been found in public databases nor in thousands of melanoma patients tested. Functional analyses confirmed IRES activity of the 5’UTR in BX-PC3 PC cells and revealed a functional impact of the identified variant. Using gene reporter assays we observed reduced translation potential in cells treated with the mTOR inhibitor Torin1, a condition that favors the assessment of IRES activity. At the endogenous gene level we quantified allelic imbalance among polysome-associated mRNAs using a patient-derived cell line heterozygous for the c.-201_-198delinsCTTT. Overall, we conclude that this very rare private variant can be considered a potential mutation, specifically associated with PC. Our data indicate that sequencing of the entire 5'UTR of CDKN2A should be included in routine screening of PC cases with suspected inherited susceptibility.

Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI

Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of 10 variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants.

Missense variant pathogenicity predictors generalize well across a range of function-specific prediction challenges

The steady advances in machine learning and accumulation of biomedical data have contributed to the development of numerous computational models that assess the impact of missense variants. Different methods, however, operationalize impact differently. Two common tasks in this context are the prediction of the pathogenicity of variants and the prediction of their effects on a protein's function. These are related but distinct problems, and it is unclear whether methods developed for one are optimized for the other. The Critical Assessment of Genome Interpretation (CAGI) experiment provides a means to address this question empirically. To this end, we participated in various protein-specific challenges in CAGI with two objectives in mind. First, to compare the performance of methods in the MutPred family with the state-of-the-art. Second and more importantly, to investigate the applicability of general-purpose pathogenicity predictors to the classification of specific function-altering variants without additional training or calibration. We find that our pathogenicity predictors performed competitively with other methods, outputting score distributions in agreement with experimental outcomes. Overall, we conclude that binary classifiers learned from disease-causing mutations are capable of modeling important aspects of the underlying biology and the alteration of protein function resulting from mutations.

Objective assessment of the evolutionary action equation for the fitness effect of missense mutations across CAGI-blinded contests

A major challenge in genome interpretation is to estimate the fitness effect of coding variants of unknown significance (VUS). Labor, limited understanding of protein functions, and lack of assays generally limit direct experimental assessment of VUS, and make robust and accurate computational approaches a necessity. Often, however, algorithms that predict mutational effect disagree among themselves and with experimental data, slowing their adoption for clinical diagnostics. To objectively assess such methods, the Critical Assessment of Genome Interpretation (CAGI) community organizes contests to predict unpublished experimental data, available only to CAGI assessors. We review here the CAGI performance of evolutionary action (EA) predictions of mutational impact. EA models the fitness effect of coding mutations analytically, as a product of the gradient of the fitness landscape times the perturbation size. In practice, these terms are computed from phylogenetic considerations as the functional sensitivity of the mutated site and as the magnitude of amino acid substitution, respectively, and yield the percentage loss of wild-type activity. In five CAGI challenges, EA consistently performed on par or better than sophisticated machine learning approaches. This objective assessment suggests that a simple differential model of evolution can interpret the fitness effect of coding variations, opening diverse clinical applications.

The RING 2.0 web server for high quality residue interaction networks

Residue interaction networks (RINs) are an alternative way of representing protein structures where nodes are residues and arcs physico–chemical interactions. RINs have been extensively and successfully used for analysing mutation effects, protein folding, domain–domain communication and catalytic activity. Here we present RING 2.0, a new version of the RING software for the identification of covalent and non-covalent bonds in protein structures, including π–π stacking and π–cation interactions. RING 2.0 is extremely fast and generates both intra and inter-chain interactions including solvent and ligand atoms. The generated networks are very accurate and reliable thanks to a complex empirical re-parameterization of distance thresholds performed on the entire Protein Data Bank. By default, RING output is generated with optimal parameters but the web server provides an exhaustive interface to customize the calculation. The network can be visualized directly in the browser or in Cytoscape. Alternatively, the RING-Viz script for Pymol allows visualizing the interactions at atomic level in the structure. The web server and RING-Viz, together with an extensive help and tutorial, are available from URL: http://protein.bio.unipd.it/ring.

The CDKN2A/p16(INK) (4a) 5'UTR sequence and translational regulation: impact of novel variants predisposing to melanoma

Many variants of uncertain functional significance in cancer susceptibility genes lie in regulatory regions, and clarifying their association with disease risk poses significant challenges. We studied 17 germline variants (nine of which were novel) in the CDKN2A 5'UTR with independent approaches, which included mono and bicistronic reporter assays, Western blot of endogenous protein, and allelic representation after polysomal profiling to investigate their impact on CDKN2A mRNA translation regulation. Two of the novel variants (c.-27del23, c.-93-91delAGG) were classified as causal mutations (score ≥3), along with the c.-21C>T, c.-34G>T, and c.-56G>T, which had already been studied by a subset of assays. The novel c.-42T>A as well as the previously described c.-67G>C were classified as potential mutations (score 1 or 2). The remaining variants (c.-14C>T, c.-20A>G, c.-25C>T+c.-180G>A, c.-30G>A, c.-40C>T, c.-45G>A, c.-59C>G, c.-87T>A, c.-252A>T) were classified as neutral (score 0). In conclusion, we found evidence that nearly half of the variants found in this region had a negative impact on CDKN2A mRNA translation, supporting the hypothesis that 5'UTR can act as a cellular Internal Ribosome Entry Site (IRES) to modulate p16(INK) (4a) translation.

Isoform-specific interactions of the von Hippel-Lindau tumor suppressor protein

Deregulation of the von Hippel-Lindau tumor suppressor protein (pVHL) is considered one of the main causes for malignant renal clear-cell carcinoma (ccRCC) insurgence. In human, pVHL exists in two isoforms, pVHL19 and pVHL30 respectively, displaying comparable tumor suppressor abilities. Mutations of the p53 tumor suppressor gene have been also correlated with ccRCC insurgence and ineffectiveness of treatment. A recent proteomic analysis linked full length pVHL30 with p53 pathway regulation through complex formation with the p14ARF oncosuppressor. The alternatively spliced pVHL19, missing the first 53 residues, lacks this interaction and suggests an asymmetric function of the two pVHL isoforms. Here, we present an integrative bioinformatics and experimental characterization of the pVHL oncosuppressor isoforms. Predictions of the pVHL30 N-terminus three-dimensional structure suggest that it may exist as an ensemble of structured and disordered forms. The results were used to guide Yeast two hybrid experiments to highlight isoform-specific binding properties. We observed that the physical pVHL/p14ARF interaction is specifically mediated by the 53 residue long pVHL30 N-terminal region, suggesting that this N-terminus acts as a further pVHL interaction interface. Of note, we also observed that the shorter pVHL19 isoform shows an unexpected high tendency to form homodimers, suggesting an additional isoform-specific binding specialization.

Insights into the proline hydroxylase (PHD) family, molecular evolution and its impact on human health

PHDs (proline hydroxylases) are a small protein family found in all organisms, considered the central regulator of the molecular hypoxia response due to PHDs being completely inactivated under low oxygen concentration. At physiological oxygen concentration, PHDs drive the degradation of the HIF-1α (hypoxia-inducible factor 1-α), which is responsible for upregulating the expression of genes involved in the cellular response to hypoxia. Hypoxia is a common feature of most tumors, in particular during metastasis development. Indeed, cancer reacts by activating pathways promoting new blood vessel formation and activating strategies aimed to improve survival. In this scenario, the PHD family regulates the activation of HIF-1α and cell-cycle regulation. Several PHD mutations were found in cancer patients, underlining their importance for human health. Here, we propose a Bayesian model able to predict the pathological effect of human PHD mutations and their correlation with cancer outcome. The model was developed through an integrative in silico approach, where data collected from the literature has been coupled with sequence evolution and structural analysis. The model was used to assess 135 human PHD variants. Finally, bioinformatics characterization was used to demonstrate how few amino acid changes are able to explain the functional specialization of PHD family members and their physiological role in human health.

Molecular characterization of melanoma cases in Denmark suspected of genetic predisposition

Both environmental and host factors influence risk of cutaneous melanoma (CM), and worldwide, the incidence varies depending on constitutional determinants of skin type and pigmentation, latitude, and patterns of sun exposure. We performed genetic analysis of CDKN2A, CDK4, BAP1, MC1R, and MITFp.E318K in Danish high-risk melanoma cases and found CDKN2A germline mutations in 11.3% of CM families with three or more affected individuals, including four previously undescribed mutations. Rare mutations were also seen in CDK4 and BAP1, while MC1R variants were common, occurring at more than twice the frequency compared to Danish controls. The MITF p.E318K variant similarly occurred at an approximately three-fold higher frequency in melanoma cases than controls. To conclude, we propose that mutation screening of CDKN2A and CDK4 in Denmark should predominantly be performed in families with at least 3 cases of CM. In addition, we recommend that testing of BAP1 should not be conducted routinely in CM families but should be reserved for families with CM and uveal melanoma, or mesothelioma.

Prevalence of Germline BAP1, CDKN2A, and CDK4 Mutations in an Australian Population-Based Sample of Cutaneous Melanoma Cases

Mutations in Cyclin-Dependent Kinase Inhibitor 2A (CDKN2A) and Cyclin-Dependent Kinase 4 (CDK4) contribute to susceptibility in approximately 40% of high-density cutaneous melanoma (CMM) families and about 2% of unselected CMM cases. BRCA-1 associated protein-1 (BAP1) has been more recently shown to predispose to CMM and uveal melanoma (UMM) in some families; however, its contribution to CMM development in the general population is unreported. We sought to determine the contribution of these genes to CMM susceptibility in a population-based sample of cases from Australia. We genotyped 1,109 probands from Queensland families and found that approximately 1.31% harbored mutations in CDKN2A, including some with novel missense mutations (p.R22W, p.G35R and p.I49F). BAP1 missense variants occurred in 0.63% of cases but no CDK4 variants were observed in the sample. This is the first estimate of the contribution of BAP1 and CDK4 to a population-based sample of CMM and supports the previously reported estimate of CDKN2A germline mutation prevalence.

Single nucleotide variations: biological impact and theoretical interpretation

Genome-wide association studies (GWAS) and whole-exome sequencing (WES) generate massive amounts of genomic variant information, and a major challenge is to identify which variations drive disease or contribute to phenotypic traits. Because the majority of known disease-causing mutations are exonic non-synonymous single nucleotide variations (nsSNVs), most studies focus on whether these nsSNVs affect protein function. Computational studies show that the impact of nsSNVs on protein function reflects sequence homology and structural information and predict the impact through statistical methods, machine learning techniques, or models of protein evolution. Here, we review impact prediction methods and discuss their underlying principles, their advantages and limitations, and how they compare to and complement one another. Finally, we present current applications and future directions for these methods in biological research and medical genetics.