-
ESMO Open Jun 2023The search for prognostic biomarkers indicating sensitivity to immunotherapy in lung adenocarcinoma patients has zeroed in on genes in the switch/sucrose non-fermentable...
BACKGROUND
The search for prognostic biomarkers indicating sensitivity to immunotherapy in lung adenocarcinoma patients has zeroed in on genes in the switch/sucrose non-fermentable (SWI/SNF) pathway. The mutational profiles of key genes are not clearly defined, however, and no comparisons have been conducted on whether mutations in the genes involved provide the same predictive value.
METHODS
In this study, analysis of clinical factors, tumor mutation burden (TMB), chromosomal instability, and co-alterations was conducted for 4344 lung adenocarcinoma samples. Independent online cohorts (N = 1661 and 576) were used to supplement the analysis with survival and RNA-seq data.
RESULTS
Mutational burden and chromosomal instability analysis showed that ARID family mutations (including ARID1A, ARID1B, or ARID2 mutations) and SMARC family mutations (including SMARCA4 or SMARCB1 mutations) display different profiles from wild-type (WT) samples (TMB: ARID versus WT: P < 2.2 × 10, SMARC versus WT: P < 2.2 × 10; CIN: ARID versus WT: P = 1.8 × 10, SMARC versus WT: P = 0.027). Both mutant groups have a higher proportion of transversions than transitions, whereas the ratio is more equal for wild-type samples. Survival analysis shows that patients with ARID mutations were more sensitive to immunotherapy treatment than wild-type and SMARC-mutated patients (P < 0.001 and P = 0.013, respectively), and multivariate Cox analysis reveals that the presence of ARID mutations is likely the main cause.
CONCLUSIONS
The research presented in this study shows that mutations in the ARID gene family, including ARID1A, ARID1B, and ARID2, are primarily responsible for the sensitive response to immunotherapy treatment in patients with lung adenocarcinoma.
Topics: Humans; Mutation; Biomarkers, Tumor; Adenocarcinoma of Lung; Immunotherapy; Chromosomal Instability; DNA Helicases; Nuclear Proteins; Transcription Factors
PubMed: 37327699
DOI: 10.1016/j.esmoop.2023.101585 -
Frontiers in Endocrinology 2022The cAMP-signaling cancers, which are defined by functionally-significant somatic mutations in one or more elements of the cAMP signaling pathway, have an unexpectedly... (Review)
Review
The cAMP-signaling cancers, which are defined by functionally-significant somatic mutations in one or more elements of the cAMP signaling pathway, have an unexpectedly wide range of cell origins, clinical manifestations, and potential therapeutic options. Mutations in at least 9 cAMP signaling pathway genes (, and ) have been identified as driver mutations in human cancer. Although all cAMP-signaling pathway cancers are driven by mutation(s) that impinge on a single signaling pathway, the ultimate tumor phenotype reflects interactions between five critical variables: (1) the precise gene(s) that undergo mutation in each specific tumor type; (2) the effects of specific allele(s) in any given gene; (3) mutations in modifier genes (mutational "context"); (4) the tissue-specific expression of various cAMP signaling pathway elements in the tumor stem cell; and (5) and the precise biochemical regulation of the pathway components in tumor cells. These varying oncogenic mechanisms reveal novel and important targets for drug discovery. There is considerable diversity in the "druggability" of cAMP-signaling components, with some elements (GPCRs, cAMP-specific phosphodiesterases and kinases) appearing to be prime drug candidates, while other elements (transcription factors, protein-protein interactions) are currently refractory to robust drug-development efforts. Further refinement of the precise driver mutations in individual tumors will be essential for directing priorities in drug discovery efforts that target these mutations.
Topics: Humans; Signal Transduction; Mutation; Neoplasms; Phenotype
PubMed: 36313756
DOI: 10.3389/fendo.2022.1024423 -
International Journal of Molecular... May 2023A limited number of studies have focused on the mutational landscape of breast cancer in different ethnic populations within Europe and compared the data with other...
A limited number of studies have focused on the mutational landscape of breast cancer in different ethnic populations within Europe and compared the data with other ethnic groups and databases. We performed whole-genome sequencing of 63 samples from 29 Hungarian breast cancer patients. We validated a subset of the identified variants at the DNA level using the Illumina TruSight Oncology (TSO) 500 assay. Canonical breast-cancer-associated genes with pathogenic germline mutations were and . Nearly all the observed germline mutations were as frequent in the Hungarian breast cancer cohort as in independent European populations. The majority of the detected somatic short variants were single-nucleotide polymorphisms (SNPs), and only 8% and 6% of them were deletions or insertions, respectively. The genes most frequently affected by somatic mutations were (31%), (34%), (18%), and (34%). Copy number alterations were most common in the , , , and genes. For many samples, the somatic mutational landscape was dominated by mutational processes associated with homologous recombination deficiency (HRD). Our study, as the first breast tumor/normal sequencing study in Hungary, revealed several aspects of the significantly mutated genes and mutational signatures, and some of the copy number variations and somatic fusion events. Multiple signs of HRD were detected, highlighting the value of the comprehensive genomic characterization of breast cancer patient populations.
Topics: Humans; Female; Breast Neoplasms; Hungary; DNA Copy Number Variations; Genetic Predisposition to Disease; Mutation; Germ-Line Mutation; Genomics
PubMed: 37239898
DOI: 10.3390/ijms24108553 -
Cell Reports Aug 2023Disruption of antigen presentation via loss of major histocompatibility complex (MHC) expression is a strategy whereby cancer cells escape immune surveillance and...
Disruption of antigen presentation via loss of major histocompatibility complex (MHC) expression is a strategy whereby cancer cells escape immune surveillance and develop resistance to immunotherapy. Here, we develop the personalized genomics algorithm Hapster and accurately call somatic mutations within the MHC genes of 10,001 primary and 2,199 metastatic tumors, creating a catalog of 1,663 non-synonymous mutations that provide key insights into MHC mutagenesis. We find that MHC class I genes are among the most frequently mutated genes in both primary and metastatic tumors, while MHC class II mutations are more restricted. Recurrent deleterious mutations are found within haplotype- and cancer-type-specific hotspots associated with distinct mutational processes. Functional classification of MHC residues reveals significant positive selection for mutations disruptive to the B2M, peptide, and T cell binding interfaces, as well as to MHC chaperones.
Topics: Humans; Histocompatibility Antigens Class I; HLA Antigens; Neoplasms; T-Lymphocytes; Histocompatibility Antigens Class II; Mutation
PubMed: 37597185
DOI: 10.1016/j.celrep.2023.112965 -
Disease Models & Mechanisms Jun 2022Computational predictors of genetic variant effect have advanced rapidly in recent years. These programs provide clinical and research laboratories with a rapid and... (Review)
Review
Computational predictors of genetic variant effect have advanced rapidly in recent years. These programs provide clinical and research laboratories with a rapid and scalable method to assess the likely impacts of novel variants. However, it can be difficult to know to what extent we can trust their results. To benchmark their performance, predictors are often tested against large datasets of known pathogenic and benign variants. These benchmarking data may overlap with the data used to train some supervised predictors, which leads to data re-use or circularity, resulting in inflated performance estimates for those predictors. Furthermore, new predictors are usually found by their authors to be superior to all previous predictors, which suggests some degree of computational bias in their benchmarking. Large-scale functional assays known as deep mutational scans provide one possible solution to this problem, providing independent datasets of variant effect measurements. In this Review, we discuss some of the key advances in predictor methodology, current benchmarking strategies and how data derived from deep mutational scans can be used to overcome the issue of data circularity. We also discuss the ability of such functional assays to directly predict clinical impacts of mutations and how this might affect the future need for variant effect predictors.
Topics: Computational Biology; Mutation
PubMed: 35736673
DOI: 10.1242/dmm.049510 -
Advances in Clinical Chemistry 2018The molecular diagnosis of the cancer mutational status is essential for modern clinical laboratory medicine. Mutations in EGFR, KRAS, BRAF, and PIK3CA genes are widely... (Review)
Review
The molecular diagnosis of the cancer mutational status is essential for modern clinical laboratory medicine. Mutations in EGFR, KRAS, BRAF, and PIK3CA genes are widely analyzed in solid tumors such as lung cancer, colorectal cancer, breast cancer, and melanoma. The allele-specific polymerase chain reaction, high-resolution melting, and Sanger sequencing are used for detecting and identifying gene mutations in many clinical laboratories. The locked nucleic acid (LNA) is a class of nucleic acid analogs that contain a methylene bridge connecting the 2' oxygen and 4' carbon in the ribose moiety. This methylene bridge locks the ribose group into a C3'-endo conformation. LNA, including an oligonucleotide, increases the thermal stability of hybrid strands. The use of LNA technology in molecular diagnostic methods improves the specificity and sensitivity of assays. This review describes routinely analyzed mutations and molecular diagnostic methods used in the clinical laboratory along with the performance improvement of mutational analysis with LNA.
Topics: DNA Mutational Analysis; Humans; Mutation; Neoplasms; Oligonucleotides; Polymerase Chain Reaction
PubMed: 29304903
DOI: 10.1016/bs.acc.2017.10.002 -
Molecular Biology and Evolution Apr 2023SARS-CoV-2 evolves rapidly in part because of its high mutation rate. Here, we examine whether this mutational process itself has changed during viral evolution. To do...
SARS-CoV-2 evolves rapidly in part because of its high mutation rate. Here, we examine whether this mutational process itself has changed during viral evolution. To do this, we quantify the relative rates of different types of single-nucleotide mutations at 4-fold degenerate sites in the viral genome across millions of human SARS-CoV-2 sequences. We find clear shifts in the relative rates of several types of mutations during SARS-CoV-2 evolution. The most striking trend is a roughly 2-fold decrease in the relative rate of G→T mutations in Omicron versus early clades, as was recently noted by Ruis et al. (2022. Mutational spectra distinguish SARS-CoV-2 replication niches. bioRxiv, doi:10.1101/2022.09.27.509649). There is also a decrease in the relative rate of C→T mutations in Delta, and other subtle changes in the mutation spectrum along the phylogeny. We speculate that these changes in the mutation spectrum could arise from viral mutations that affect genome replication, packaging, and antagonization of host innate-immune factors, although environmental factors could also play a role. Interestingly, the mutation spectrum of Omicron is more similar than that of earlier SARS-CoV-2 clades to the spectrum that shaped the long-term evolution of sarbecoviruses. Overall, our work shows that the mutation process is itself a dynamic variable during SARS-CoV-2 evolution and suggests that human SARS-CoV-2 may be trending toward a mutation spectrum more similar to that of other animal sarbecoviruses.
Topics: Animals; Humans; SARS-CoV-2; COVID-19; Mutation; Mutation Rate; Severe acute respiratory syndrome-related coronavirus; Genome, Viral
PubMed: 37039557
DOI: 10.1093/molbev/msad085 -
The Journal of Clinical Endocrinology... Jun 2022Head and neck paragangliomas (HNPGLs) are rare neoplasms with a high degree of heritability. Paragangliomas present as polygenic diseases caused by combined alterations...
CONTEXT
Head and neck paragangliomas (HNPGLs) are rare neoplasms with a high degree of heritability. Paragangliomas present as polygenic diseases caused by combined alterations in multiple genes; however, many driver changes remain unknown.
OBJECTIVE
The objective of the study was to analyze somatic mutation profiles in HNPGLs.
METHODS
Whole-exome sequencing of 42 tumors and matched normal tissues obtained from Russian patients with HNPGLs was carried out. Somatic mutation profiling included variant calling and utilizing MutSig and SigProfiler packages.
RESULTS
57% of patients harbored germline and somatic variants in paraganglioma (PGL) susceptibility genes or potentially related genes. Somatic variants in novel genes were found in 17% of patients without mutations in any known PGL-related genes. The studied cohort was characterized by 6 significantly mutated genes: SDHD, BCAS4, SLC25A14, RBM3, TP53, and ASCC1, as well as 4 COSMIC single base substitutions (SBS)-96 mutational signatures (SBS5, SBS29, SBS1, and SBS7b). Tumors with germline variants specifically displayed SBS11 and SBS19, when an SBS33-specific mutational signature was identified for cases without those. Beta allele frequency analysis of copy number variations revealed loss of heterozygosity of the wild-type allele in 1 patient with germline mutation c.287-2A>G in the SDHB gene. In patients with germline mutation c.A305G in the SDHD gene, frequent potential loss of chromosome 11 was observed.
CONCLUSION
These results give an understanding of somatic changes and the mutational landscape associated with HNPGLs and are important for the identification of molecular mechanisms involved in tumor development.
Topics: Carrier Proteins; DNA Copy Number Variations; Germ-Line Mutation; Head and Neck Neoplasms; Humans; Mutation; Paraganglioma; RNA-Binding Proteins; Succinate Dehydrogenase
PubMed: 35460558
DOI: 10.1210/clinem/dgac250 -
Bioinformatics (Oxford, England) May 2022Protein structure can be severely disrupted by frameshift and non-sense mutations at specific positions in the protein sequence. Frameshift and non-sense mutation cases...
MOTIVATION
Protein structure can be severely disrupted by frameshift and non-sense mutations at specific positions in the protein sequence. Frameshift and non-sense mutation cases can also be found in healthy individuals. A method to distinguish neutral and potentially disease-associated frameshift and non-sense mutations is of practical and fundamental importance. It would allow researchers to rapidly screen out the potentially pathogenic sites from a large number of mutated genes and then use these sites as drug targets to speed up diagnosis and improve access to treatment. The problem of how to distinguish between neutral and potentially disease-associated frameshift and non-sense mutations remains under-researched.
RESULTS
We built a Transformer-based neural network model to predict the pathogenicity of frameshift and non-sense mutations on protein features and named it TransPPMP. The feature matrix of contextual sequences computed by the ESM pre-training model, type of mutation residue and the auxiliary features, including structure and function information, are combined as input features, and the focal loss function is designed to solve the sample imbalance problem during the training. In 10-fold cross-validation and independent blind test set, TransPPMP showed good robust performance and absolute advantages in all evaluation metrics compared with four other advanced methods, namely, ENTPRISE-X, VEST-indel, DDIG-in and CADD. In addition, we demonstrate the usefulness of the multi-head attention mechanism in Transformer to predict the pathogenicity of mutations-not only can multiple self-attention heads learn local and global interactions but also functional sites with a large influence on the mutated residue can be captured by attention focus. These could offer useful clues to study the pathogenicity mechanism of human complex diseases for which traditional machine learning methods fall short.
AVAILABILITY AND IMPLEMENTATION
TransPPMP is available at https://github.com/lennylv/TransPPMP.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Frameshift Mutation; Humans; Mutation; Neural Networks, Computer; Software
PubMed: 35561183
DOI: 10.1093/bioinformatics/btac188 -
Genes & Genetic Systems Apr 2019Germline mutations are the origin of genetic variation and are widely considered to be the driving force of genome evolution. The rates and spectra of de novo mutations... (Review)
Review
Germline mutations are the origin of genetic variation and are widely considered to be the driving force of genome evolution. The rates and spectra of de novo mutations (DNMs) directly affect evolutionary speed and direction and thereby establish species-specific genomic futures in the long term. This has resulted in a keen interest in understanding the origin of germline mutations in mammals. Accumulating evidence from next-generation sequencing and family-based analysis indicates that the frequency of human DNMs varies according to sex, age and local genomic context. Thus, it is likely that there are multiple causes and drivers of mutagenesis, including spontaneous DNA lesions, DNA repair status and DNA polymerase errors. In this review, recent studies of human and mouse germline DNMs are discussed, and the rates and spectra of spontaneous germline DNMs in the mouse mutator lines Pold1 and TOY-KO (Mth1/Ogg1/Mutyh triple knockout) are summarized in the context of endogenous causes and mechanisms.
Topics: Animals; Germ-Line Mutation; Humans; Mice; Mutagenesis; Mutation Accumulation; Mutation Rate
PubMed: 30381610
DOI: 10.1266/ggs.18-00015