Computational Approaches to Prioritize Cancer Driver Missense Mutations

Exploring Bioinformatics Tools to Analyze the Role of CDC6 in the Progression of Polycystic Ovary Syndrome to Endometrial Cancer by Promoting Immune Infiltration

Cell division cycle 6 (CDC6) is essential for the initiation of DNA replication in eukaryotic cells and contributes to the development of various human tumors. Polycystic ovarian syndrome (PCOS) is a reproductive endocrine disease in women of childbearing age, with a significant risk of endometrial cancer (EC). However, the role of CDC6 in the progression of PCOS to EC is unclear. Therefore, we examined CDC6 expression in patients with PCOS and EC. We evaluated the relationship between CDC6 expression and its prognostic value, potential biological functions, and immune infiltrates in patients with EC. In vitro analyses were performed to investigate the effects of CDC6 knockdown on EC proliferation, migration, invasion, and apoptosis. CDC6 expression was significantly upregulated in patients with PCOS and EC. Moreover, this protein caused EC by promoting the aberrant infiltration of macrophages into the immune microenvironment in patients with PCOS. A functional enrichment analysis revealed that CDC6 exerted its pro-cancer and pro-immune cell infiltration functions via the PI3K-AKT pathway. Moreover, it promoted EC proliferation, migration, and invasion but inhibited apoptosis. This protein significantly reduced EC survival when mutated. These findings demonstrate that CDC6 regulates the progression of PCOS to EC and promotes immune infiltration.

Machine learning optimized DriverDetect software for high precision prediction of deleterious mutations in human cancers

The detection of cancer-driving mutations is important for understanding cancer pathology and therapeutics development. Prediction tools have been created to streamline the computation process. However, most tools available have heterogeneous sensitivity or specificity. We built a machine learning-derived algorithm, DriverDetect that combines the outputs of seven pre-existing tools to improve the prediction of candidate driver cancer mutations. The algorithm was trained with cancer gene-specific mutation datasets of cancer patients to identify cancer drivers. DriverDetect performed better than the individual tools or their combinations in the validation test. It has the potential to incorporate future novel prediction algorithms and can be retrained with new datasets, offering an expanded application to pan-cancer analysis for cross-cancer study. (115 words).

In Silico Deciphering of the Potential Impact of Variants of Uncertain Significance in Hereditary Colorectal Cancer Syndromes

Colorectal cancer (CRC) ranks third in terms of cancer incidence worldwide and is responsible for 8% of all deaths globally. Approximately 10% of CRC cases are caused by inherited pathogenic mutations in driver genes involved in pathways that are crucial for CRC tumorigenesis and progression. These hereditary mutations significantly increase the risk of initial benign polyps or adenomas developing into cancer. In recent years, the rapid and accurate sequencing of CRC-specific multigene panels by next-generation sequencing (NGS) technologies has enabled the identification of several recurrent pathogenic variants with established functional consequences. In parallel, rare genetic variants that are not characterized and are, therefore, called variants of uncertain significance (VUSs) have also been detected. The classification of VUSs is a challenging task because each amino acid has specific biochemical properties and uniquely contributes to the structural stability and functional activity of proteins. In this scenario, the ability to computationally predict the effect of a VUS is crucial. In particular, in silico prediction methods can provide useful insights to assess the potential impact of a VUS and support additional clinical evaluation. This approach can further benefit from recent advances in artificial intelligence-based technologies. In this review, we describe the main in silico prediction tools that can be used to evaluate the structural and functional impact of VUSs and provide examples of their application in the analysis of gene variants involved in hereditary CRC syndromes.

Nonsynonymous mutations in VEGF receptor binding domain alter the efficacy of bevacizumab treatment

Vascular endothelial growth factor (VEGF) mediated angiogenesis is crucial for tumor progression. Isoforms of VEGF bind to different VEGF receptors (VEGFRs) to initiate angiogenesis specific cellular signaling. Inhibitors that target both the receptors and ligands are in clinical use to impede angiogenesis. Bevacizumab, a monoclonal antibody (mAb) approved by the Food and Drug Administration (FDA), binds in the VEGF receptor binding domain (RBD) of all soluble isoforms of VEGF and inhibits the VEGF-VEGFR interaction. Bevacizumab is also used in combination with other chemotherapeutic agents for a better therapeutic outcome. Understanding the intricate polymorphic character of VEGFA gene and the influence of missense or nonsynonymous mutations in the form of nonsynonymous polymorphisms (nsSNPs) on RBD of VEGF may aid in increasing the efficacy of this drug. This study has identified 18 potential nsSNPs in VEGFA gene that affect the VEGF RBD structure and alter its binding pattern to bevacizumab. The mutated RBDs, modeled using trRosetta, in addition to the changed pattern of secondary structure, post translational modification and stability compared to the wild type, have shown contrasting binding affinity and molecular interaction pattern with bevacizumab. Molecular docking analysis by ClusPro and visualization using PyMol and PDBsum tools have detected 17 nsSNPs with decreased binding affinity to bevacizumab and therefore may impact the treatment efficacy. Whereas VEGF RBD expressed due to rs1267535717 (R229H) nsSNP of VEGFA has increased affinity to the mAb. This study suggests that genetic characterization of VEGFA before bevacizumab mediated cancer treatment is essential in predicting the appropriate efficacy of the drug, as the treatment efficiency may vary at individual level.

Unveiling the potential of HSPA4: a comprehensive pan-cancer analysis of HSPA4 in diagnosis, prognosis, and immunotherapy

With the global rise in cancer incidence and mortality rates, research on the topic has become increasingly urgent. Among the significant players in this field are heat shock proteins (HSPs), particularly HSPA4 from the HSP70 subfamily, which has recently garnered considerable interest for its role in cancer progression. However, despite numerous studies on HSPA4 in specific cancer types, a comprehensive analysis across all cancer types is lacking. This study employs various bioinformatics techniques to delve into the role of HSPA4 in pan-cancer. Our objective is to assess its potential in clinical diagnosis, prognosis, and as a future molecular target for therapy. The research findings reveal significant differences in HSPA4 expression across different cancer types, suggesting its diagnostic value and close association with cancer staging and patient survival rates. Furthermore, genetic variations and methylation status of HSPA4 play critical roles in tumorigenesis. Lastly, the interaction of HSPA4 with immune cells is linked to the tumor microenvironment (TME) and immunotherapy. In summary, HSPA4 emerges as a promising cancer biomarker and a vital member of the HSPs family, holding potential applications in diagnosis, prognosis, and immunotherapy.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Identifying the role of NUDCD1 in human tumors from clinical and molecular mechanisms: a study based on comprehensive bioinformatics and experimental validation

NUDCD1 (NudC domain-containing 1) is abnormally activated in multiple tumors and has been identified as a cancer antigen. But there is still no pan-cancer analysis available for NUDCD1 in human cancers. The role of NUDCD1 across multiple tumors was explored using data from the public databases including HPA, TCGA, GEO, GTEx, TIMER2, TISIDB, UALCAN, GEPIA2, cBioPortal, GSCA and so on. Molecular experiments (e.g., quantitative real-time PCR, immunohistochemistry and western blot) were conducted to validate the expression and biological function of NUDCD1 in STAD. Results showed that NUDCD1 was highly expressed in most tumors and its levels were associated with the prognosis. Multiple genetic and epigenetic features of NUDCD1 exist in different cancers. NUDCD1 was associated with expression levels of recognized immune checkpoints (anti-CTLA-4) and immune infiltrates (e.g., CD4+ and CD8+ T cells) in some cancers. Moreover, NUDCD1 correlated with the CTRP and GDSC drug sensitivity and acted as a link between chemicals and cancers. Importantly, NUDCD1-related genes were enriched in several tumors (e.g., COAD, STAD and ESCA) and affected apoptosis, cell cycle and DNA damage cancer-related pathways. Furthermore, expression, mutation and copy number variations for the gene sets were also associated with prognosis. At last, the overexpression and contribution of NUDCD1 in STAD were experimentally validated in vitro and in vivo. NUDCD1 was involved in diverse biological processes and it influenced the occurrence and development of cancers. This first pan-cancer analysis for NUDCD1 provides a comprehensive understanding about its roles across various cancer types, especially in STAD.

Computational Saturation Mutagenesis of SARS-CoV-1 Spike Glycoprotein: Stability, Binding Affinity, and Comparison With SARS-CoV-2

Severe Acute respiratory syndrome coronavirus (SARS-CoV-1) attaches to the host cell surface to initiate the interaction between the receptor-binding domain (RBD) of its spike glycoprotein (S) and the human Angiotensin-converting enzyme (hACE2) receptor. SARS-CoV-1 mutates frequently because of its RNA genome, which challenges the antiviral development. Here, we per-formed computational saturation mutagenesis of the S protein of SARS-CoV-1 to identify the residues crucial for its functions. We used the structure-based energy calculations to analyze the effects of the missense mutations on the SARS-CoV-1 S stability and the binding affinity with hACE2. The sequence and structure alignment showed similarities between the S proteins of SARS-CoV-1 and SARS-CoV-2. Interestingly, we found that target mutations of S protein amino acids generate similar effects on their stabilities between SARS-CoV-1 and SARS-CoV-2. For example, G839W of SARS-CoV-1 corresponds to G857W of SARS-CoV-2, which decrease the stability of their S glycoproteins. The viral mutation analysis of the two different SARS-CoV-1 isolates showed that mutations, T487S and L472P, weakened the S-hACE2 binding of the 2003–2004 SARS-CoV-1 isolate. In addition, the mutations of L472P and F360S destabilized the 2003–2004 viral isolate. We further predicted that many mutations on N-linked glycosylation sites would increase the stability of the S glycoprotein. Our results can be of therapeutic importance in the design of antivirals or vaccines against SARS-CoV-1 and SARS-CoV-2.

Yeast as a Tool to Understand the Significance of Human Disease-Associated Gene Variants

At present, the great challenge in human genetics is to provide significance to the growing amount of human disease-associated gene variants identified by next generation DNA sequencing technologies. Increasing evidences suggest that model organisms are of pivotal importance to addressing this issue. Due to its genetic tractability, the yeast Saccharomyces cerevisiae represents a valuable model organism for understanding human genetic variability. In the present review, we show how S. cerevisiae has been used to study variants of genes involved in different diseases and in different pathways, highlighting the versatility of this model organism.

Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies

For the last couple of decades, there has been a significant growth in sequencing data, leading to an extraordinary increase in the number of gene variants. This places a challenge on the bioinformatics research community to develop and improve computational tools for functional annotation of new variants. Genes coding for epigenetic regulators have important roles in cancer pathogenesis and mutations in these genes show great potential as clinical biomarkers, especially in hematologic malignancies. Therefore, we developed a model that specifically focuses on these genes, with an assumption that it would outperform general models in predicting the functional effects of amino acid substitutions. EpiMut is a standalone software that implements a sequence based alignment-free method. We applied a two-step approach for generating sequence based features, relying on the biophysical and biochemical indices of amino acids and the Fourier Transform as a sequence transformation method. For each gene in the dataset, the machine learning algorithm-Naïve Bayes was used for building a model for prediction of the neutral or disease-related status of variants. EpiMut outperformed state-of-the-art tools used for comparison, PolyPhen-2, SIFT and SNAP2. Additionally, EpiMut showed the highest performance on the subset of variants positioned outside conserved functional domains of analysed proteins, which represents an important group of cancer-related variants. These results imply that EpiMut can be applied as a first choice tool in research of the impact of gene variants in epigenetic regulators, especially in the light of the biomarker role in hematologic malignancies. EpiMut is freely available at https://www.vin.bg.ac.rs/180/tools/epimut.php.

An Ensemble Approach to Predict the Pathogenicity of Synonymous Variants

Single-nucleotide variants (SNVs) are a major form of genetic variation in the human genome that contribute to various disorders. There are two types of SNVs, namely non-synonymous (missense) variants (nsSNVs) and synonymous variants (sSNVs), predominantly involved in RNA processing or gene regulation. sSNVs, unlike missense or nsSNVs, do not alter the amino acid sequences, thereby making challenging candidates for downstream functional studies. Numerous computational methods have been developed to evaluate the clinical impact of nsSNVs, but very few methods are available for understanding the effects of sSNVs. For this analysis, we have downloaded sSNVs from the ClinVar database with various features such as conservation, DNA-RNA, and splicing properties. We performed feature selection and implemented an ensemble random forest (RF) classification algorithm to build a classifier to predict the pathogenicity of the sSNVs. We demonstrate that the ensemble predictor with selected features (20 features) enhances the classification of sSNVs into two categories, pathogenic and benign, with high accuracy (87%), precision (79%), and recall (91%). Furthermore, we used this prediction model to reclassify sSNVs with unknown clinical significance. Finally, the method is very robust and can be used to predict the effect of other unknown sSNVs.

Machine Learning Approaches for Quality Assessment of Protein Structures

Protein structures play a very important role in biomedical research, especially in drug discovery and design, which require accurate protein structures in advance. However, experimental determinations of protein structure are prohibitively costly and time-consuming, and computational predictions of protein structures have not been perfected. Methods that assess the quality of protein models can help in selecting the most accurate candidates for further work. Driven by this demand, many structural bioinformatics laboratories have developed methods for estimating model accuracy (EMA). In recent years, EMA by machine learning (ML) have consistently ranked among the top-performing methods in the community-wide CASP challenge. Accordingly, we systematically review all the major ML-based EMA methods developed within the past ten years. The methods are grouped by their employed ML approach-support vector machine, artificial neural networks, ensemble learning, or Bayesian learning-and their significances are discussed from a methodology viewpoint. To orient the reader, we also briefly describe the background of EMA, including the CASP challenge and its evaluation metrics, and introduce the major ML/DL techniques. Overall, this review provides an introductory guide to modern research on protein quality assessment and directions for future research in this area.

Recognition Dynamics of Cancer Mutations on the ERp57-Tapasin Interface

Down regulation of the major histocompatibility class (MHC) I pathway plays an important role in tumour development, and can be achieved by suppression of HLA expression or mutations in the MHC peptide-binding pocket. The peptide-loading complex (PLC) loads peptides on the MHC-I molecule in a dynamic multi-step assembly process. The effects of cancer variants on ERp57 and tapasin components from the MHC-I pathway is less known, and they could have an impact on antigen presentation. Applying computational approaches, we analysed whether the ERp57-tapasin binding might be altered by missense mutations. The variants H408R(ERp57) and P96L, D100A, G183R(tapasin) at the protein–protein interface improved protein stability (ΔΔG) during the initial screen of 14 different variants. The H408R(ERp57) and P96L(tapasin) variants, located close to disulphide bonds, were further studied by molecular dynamics (MD). Identifying intramolecular a-a’ domain interactions, MD revealed open and closed conformations of ERp57 in the presence and absence of tapasin. In wild-type and mutant ERp57-tapasin complexes, residues Val97, Ser98, Tyr100, Trp405, Gly407(ERp57) and Asn94, Cys95, Arg97, Asp100(tapasin) formed common H-bond interactions. Moreover, comparing the H-bond networks for P96L and H408R with each other, suggests that P96L(tapasin) improved ERp57-tapasin binding more than the H408R(ERp57) mutant. During MD, the C-terminus domain (that binds MHC-I) in tapasin from the ERp57(H408R)-tapasin complex moved away from the PLC, whereas in the ERp57-tapasin(P96L) system was oppositely displaced. These findings can have implications for the function of PLC and, ultimately, for the presentation of MHC-I peptide complex on the tumour cell surface.

A decision tree to improve identification of pathogenic mutations in clinical practice

BACKGROUND

A variant of unknown significance (VUS) is a variant form of a gene that has been identified through genetic testing, but whose significance to the organism function is not known. An actual challenge in precision medicine is to precisely identify which detected mutations from a sequencing process have a suitable role in the treatment or diagnosis of a disease. The average accuracy of pathogenicity predictors is 85%. However, there is a significant discordance about the identification of mutational impact and pathogenicity among them. Therefore, manual verification is necessary for confirming the real effect of a mutation in its casuistic.

METHODS

In this work, we use variables categorization and selection for building a decision tree model, and later we measure and compare its accuracy with four known mutation predictors and seventeen supervised machine-learning (ML) algorithms.

RESULTS

The results showed that the proposed tree reached the highest precision among all tested variables: 91% for True Neutrals, 8% for False Neutrals, 9% for False Pathogenic, and 92% for True Pathogenic.

CONCLUSIONS

The decision tree exceptionally demonstrated high classification precision with cancer data, producing consistently relevant forecasts for the sample tests with an accuracy close to the best ones achieved from supervised ML algorithms. Besides, the decision tree algorithm is easier to apply in clinical practice by non-IT experts. From the cancer research community perspective, this approach can be successfully applied as an alternative for the determination of potential pathogenicity of VOUS.

Combing the Cancer Genome for Novel Kinase Drivers and New Therapeutic Targets

Protein kinases are critical regulators of signaling cascades that control cellular proliferation, growth, survival, metabolism, migration, and invasion. Deregulation of kinase activity can lead to aberrant regulation of biological processes and to the onset of diseases, including cancer. In this review, we focus on oncogenic kinases and the signaling pathways they regulate that underpin tumor development. We highlight genomic biomarker-based precision medicine intervention strategies that match kinase inhibitors alone or in combination to mutationally activated kinase drivers, as well as progress towards implementation of these treatment strategies in the clinic. We also discuss the challenges for identification of novel protein kinase cancer drivers in the genomic era.

Insights into the Effects of Cancer Associated Mutations at the UPF2 and ATP-Binding Sites of NMD Master Regulator: UPF1

Nonsense-mediated mRNA decay (NMD) is a quality control mechanism that recognizes post-transcriptionally abnormal transcripts and mediates their degradation. The master regulator of NMD is UPF1, an enzyme with intrinsic ATPase and helicase activities. The cancer genomic sequencing data has identified frequently mutated residues in the CH-domain and ATP-binding site of UPF1. In silico screening of UPF1 stability change as a function over 41 cancer mutations has identified five variants with significant effects: K164R, R253W, T499M, E637K, and E833K. To explore the effects of these mutations on the associated energy landscape of UPF1, molecular dynamics simulations (MDS) were performed. MDS identified stable H-bonds between residues S152, S203, S205, Q230/R703, and UPF2/AMPPNP, and suggest that phosphorylation of Serine residues may control UPF1-UPF2 binding. Moreover, the alleles K164R and R253W in the CH-domain improved UPF1-UPF2 binding. In addition, E637K and E833K alleles exhibited improved UPF1-AMPPNP binding compared to the T499M variant; the lower binding is predicted from hindrance caused by the side-chain of T499M to the docking of the tri-phosphate moiety (AMPPNP) into the substrate site. The dynamics of wild-type/mutant systems highlights the flexible nature of the ATP-binding region in UPF1. These insights can facilitate the development of drug discovery strategies for manipulating NMD signaling in cell systems using chemical tools.

Pathogenicity and functional impact of non-frameshifting insertion/deletion variation in the human genome

Differentiation between phenotypically neutral and disease-causing genetic variation remains an open and relevant problem. Among different types of variation, non-frameshifting insertions and deletions (indels) represent an understudied group with widespread phenotypic consequences. To address this challenge, we present a machine learning method, MutPred-Indel, that predicts pathogenicity and identifies types of functional residues impacted by non-frameshifting insertion/deletion variation. The model shows good predictive performance as well as the ability to identify impacted structural and functional residues including secondary structure, intrinsic disorder, metal and macromolecular binding, post-translational modifications, allosteric sites, and catalytic residues. We identify structural and functional mechanisms impacted preferentially by germline variation from the Human Gene Mutation Database, recurrent somatic variation from COSMIC in the context of different cancers, as well as de novo variants from families with autism spectrum disorder. Further, the distributions of pathogenicity prediction scores generated by MutPred-Indel are shown to differentiate highly recurrent from non-recurrent somatic variation. Collectively, we present a framework to facilitate the interrogation of both pathogenicity and the functional effects of non-frameshifting insertion/deletion variants. The MutPred-Indel webserver is available at http://mutpred.mutdb.org/.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Spacial models of malfunctioned protein complexes help to elucidate signal transduction critical for insulin release

Mutations in gene KCNJ11 encoding the Kir6.2 subunit of the ATP-sensitive potassium channel (KATP), a representative of a quite complex biosystem, may affect insulin release from pancreatic beta-cells. Both gain and loss of channel activity are observed, which lead to varied clinical phenotypes ranging from neonatal diabetes to congenital hyperinsulinism. In order to understand the mechanisms of the channel function better we mapped, based on the literature review, known medically relevant Kir6.2/SUR1 mutations into recently (2017) determined CryoEM 3D structures of this complex. We used a clustering algorithm to find hots spots in the 3D structure, thus we may hypothesize about their nano-mechanical role in the channel gating and the insulin level control. We also adapted a simple model of the channel gating to cover all currently known factors that can influence the KATP biosystem functions.