Catalogue Of Mutations Research Articles

Analyses of mutational patterns induced by formaldehyde and acetaldehyde reveal similarity to a common mutational signature.

Formaldehyde and acetaldehyde are reactive small molecules produced endogenously in cells as well as being environmental contaminants. Both of these small aldehydes are classified as human carcinogens, since they are known to damage DNA and exposure is linked to cancer incidence. However, the mutagenic properties of formaldehyde and acetaldehyde remain incompletely understood, at least in part because they are relatively weak mutagens. Here, we use a highly sensitive yeast genetic reporter system featuring controlled generation of long single-stranded DNA regions to show that both small aldehydes induced mutational patterns characterized by predominantly C/G → A/T, C/G → T/A, and T/A → C/G substitutions, each in similar proportions. We observed an excess of C/G → A/T transversions when compared to mock-treated controls. Many of these C/G → A/T transversions occurred at TC/GA motifs. Interestingly, the formaldehyde mutational pattern resembles single base substitution signature 40 from the Catalog of Somatic Mutations in Cancer. Single base substitution signature 40 is a mutational signature of unknown etiology. We also noted that acetaldehyde treatment caused an excess of deletion events longer than 4 bases while formaldehyde did not. This latter result could be another distinguishing feature between the mutational patterns of these simple aldehydes. These findings shed new light on the characteristics of 2 important, commonly occurring mutagens.

Whole-exome sequencing identified mutational profiles of urothelial carcinoma post kidney transplantation

Kidney transplantation is a lifesaving option for patients with end-stage kidney disease. In Taiwan, urothelial carcinoma (UC) is the most common de novo cancer after kidney transplantation (KT). UC has a greater degree of molecular heterogeneity than do other solid tumors. Few studies have explored genomic alterations in UC after KT. We performed whole-exome sequencing to compare the genetic alterations in UC developed after kidney transplantation (UCKT) and in UC in patients on hemodialysis (UCHD). After mapping and variant calling, 18,733 and 11,093 variants were identified in patients with UCKT and UCHD, respectively. We excluded known single-nucleotide polymorphisms (SNPs) and retained genes that were annotated in the Catalogue of Somatic Mutations in Cancer (COSMIC), in the Integrative Onco Genomic cancer mutations browser (IntOGen), and in the Cancer Genome Atlas (TCGA) database of genes associated with bladder cancer. A total of 14 UCKT-specific genes with SNPs identified in more than two patients were included in further analyses. The single-base substitution (SBS) profile and signatures showed a relative high T > A pattern compared to COMSIC UC mutations. Ingenuity pathway analysis was used to explore the connections among these genes. GNAQ, IKZF1, and NTRK3 were identified as potentially involved in the signaling network of UCKT. The genetic analysis of posttransplant malignancies may elucidate a fundamental aspect of the molecular pathogenesis of UCKT.

Mutational landscape of pan-cancer patients with PIK3CA alterations in Chinese population

PurposeTo analyze the mutational landscape of pan-cancer patients with PIK3CA mutations in Chinese population in real-world.MethodsWe analyzed PIK3CA mutation status in sequencing data of cell-free DNA from plasma and genomic DNA from matched peripheral blood lymphocyte in 11,904 Chinese pan-cancer patients, and compared them with genomic data from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. Besides, concomitant genomic aberrations in PIK3CA-mutated samples were detected to investigate cancer driver genes, as well as their enriched pathways. Meanwhile, the mutations of Alpelisib targeting genes were screened and their co-alterations were analyzed according to OncoKB definition to identify the potential actionable ones.ResultsThe proportion of patients with PIK3CA mutations varied among 21 types of cancer, with the top being BRCA, CESC, SCL, and UCEC. The most common PIK3CA mutation hotspots were found to be E545K, E542K and H1047R. The Chinese cohort had significantly lower frequencies of PIK3CA mutations in breast and stomach cancers, but markedly higher PIK3CA mutation frequencies in large intestine, kidney and lung cancers than the COSMIC cohort. Compared with COSMIC cohort, the mutation frequencies of Alpelisib-targeted genes in breast cancer were significantly reduced in the Chinese cohort. All PIK3CA-mutated patients had concomitant genomic aberrations. While the most common concomitant genomic alterations occurred in TP53, EGFR and FAT1, these co-mutated genes were mainly enriched in RTK/RAS pathway, PI3K pathway and P53 pathway. Moreover, 83.6% of patients carrying mutations in Alpelisib-targeted genes had at least one actionable concomitant alteration. Level 1 actionable alteration was identified in LUAD, BRCA, COAD, LUSC, READ, and STAD.ConclusionCompared with the Western cohort, the mutation frequency of PIK3CA in breast cancer was reduced in the Chinese cohort. RTK/RAS pathway, PI3K pathway and P53 pathway were identified as the most common co-mutation pathways, suggesting that they may potentially serve as targets for possible Alpelisib-based combination therapy.

GraphChrom: A Novel Graph-Based Framework for Cancer Classification Using Chromosomal Rearrangement Endpoints.

Simple SummaryCancer is among the leading causes of death in the United States and worldwide. Early prediction of cancers is important for the improvement of treatment outcomes and survival rates, thus resulting in significant social and economic impacts. Recent developments have focused primarily on using gene expression and mutation data to predict or classify cancer types. Here, we show that chromosomal rearrangement endpoints alone can predict cancer types with more reliability and specificity. Chromosomal rearrangements are generally a consequence of improperly repaired double-strand breaks in DNA. These genomic aberrations can be a driver of cancers. Here, we investigated the use of chromosomal rearrangements for classification of cancer tumors and the effect of inter- and intrachromosomal rearrangements in cancer classification. We used data from the Catalogue of Somatic Mutations in Cancer (COSMIC) for breast, pancreatic, and prostate cancers, for which the COSMIC dataset reports the highest number of chromosomal aberrations. We developed a framework known as GraphChrom for cancer classification. GraphChrom was developed using a graph neural network which models the complex structure of chromosomal aberrations (CA) and provides local connectivity between the aberrations. The proposed framework illustrates three important contributions to the field of cancers. Firstly, it successfully classifies cancer types and subtypes. Secondly, it evolved into a novel data extraction technique which can be used to extract more informative graphs (informative aberrations associated with a sample); and thirdly, it predicts that interCAs (rearrangements between two or more chromosomes) are more effective in cancer prediction than intraCAs (rearrangements within the same chromosome), although intraCAs are three times more likely to occur than intraCAs.

Integrated genomic analyses of acral and mucosal melanomas nominate novel driver genes

BackgroundAcral and mucosal melanomas are aggressive subtypes of melanoma, which have a significantly lower burden of somatic mutations than cutaneous melanomas, but more frequent copy number variations, focused gene amplifications, and structural alterations. The landscapes of their genomic alterations remain to be fully characterized.MethodsWe compiled sequencing data of 240 human acral and mucosal melanoma samples from 11 previously published studies and applied a uniform pipeline to call tumor cell content, ploidy, somatic and germline mutations, as well as CNVs, LOH, and SVs. We identified genes that are significantly mutated or recurrently affected by CNVs and implicated in oncogenesis. We further examined the difference in the frequency of recurrent pathogenic alterations between the two melanoma subtypes, correlation between pathogenic alterations, and their association with clinical features.ResultsWe nominated PTPRJ, mutated and homozygously deleted in 3.8% (9/240) and 0.8% (2/240) of samples, respectively, as a probable tumor suppressor gene, and FER and SKP2, amplified in 3.8% and 11.7% of samples, respectively, as probable oncogenes. We further identified a long tail of infrequent pathogenic alterations, involving genes such as CIC and LZTR1. Pathogenic germline mutations were observed on MITF, PTEN, ATM, and PRKN. We found BRAF V600E mutations in acral melanomas with fewer structural variations, suggesting that they are distinct and related to cutaneous melanomas. Amplifications of PAK1 and GAB2 were more commonly observed in acral melanomas, whereas SF3B1 R625 codon mutations were unique to mucosal melanomas (12.9%). Amplifications at 11q13-14 were frequently accompanied by fusion to a region on chromosome 6q12, revealing a recurrent novel structural rearrangement whose role remains to be elucidated.ConclusionsOur meta-analysis expands the catalog of driver mutations in acral and mucosal melanomas, sheds new light on their pathogenesis and broadens the catalog of therapeutic targets for these difficult-to-treat cancers.

Abstract 4093: COSMIC Actionability: Supporting precision oncology through Identification of actionable mutations from clinical trials databases and publications

Abstract The availability of drugs that are effective in cancer patients with specific somatic mutations is rapidly evolving. Finding the most recent information is not straightforward; clinical progress and drug approvals are recorded in clinical trial repositories, files from drug regulatory agencies, publications and conference abstracts. Often there is a delay between trials reaching their outcomes, publication and deposition of results in clinical trial databases. COSMIC Actionability is a new addition to COSMIC’s (Catalogue Of Somatic Mutations In Cancer) portfolio of navigational databases for precision oncology. It indicates the availability of drugs that target mutations and tracks the progress of novel drugs through clinical development. All stages of drug development are represented, through safety and clinical efficacy to market approval, with additional data from case studies expanding mutation coverage. For each mutation-cancer type combination, a simple tiered system indicates whether marketed drugs are available and if not, indicates the progress of drug development efforts. Trial records available in databases such as Clinical Trials.gov and EudraCT are supplemented with trials identified in publications. Trial progression and outcomes are recorded; details are sourced from clinical trial databases, publications, conference abstracts, corporate announcements and regulatory authorities such as the FDA. Curation identifies results for approximately 40% more trials than are available in clinical trial databases alone. Like other COSMIC resources, Actionability is manually curated by Ph.D. level experts. The current version of COSMIC Actionability (October 2021) has 49 fully curated genes with a further 237 having some clinical information. Details of 6244 drug-cancer type combinations are available, of which 2224 have clinical results. 963 drugs are represented, in 2224 treatment combinations. 149 point mutations and 520 further mutations or fusions are represented. This dataset will be made freely available in 2022 then released quarterly, with extended gene coverage and updates of previous content. Citation Format: Steven C. Jupe, Nidhi Bindal, Zbyslaw Sondka, Shicai Wang, Sumodh Nair, Laura Ponting, Doron Sondheimer, Charlie Hathaway, Simon A. Forbes. COSMIC Actionability: Supporting precision oncology through Identification of actionable mutations from clinical trials databases and publications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4093.

A paediatric dysembryoplastic neuroepithelial tumour (DNET) with deregulated stem cell markers: a case report

BackgroundDysembryoplastic neuroepithelial tumours (DNETs) are rare, with only a few reported lethal cases. Currently, there are focused efforts by neuro-oncology professionals to reveal the molecular characterisations of individual central nervous system tumours (CNSTs). Here, we report the status of cancer stem cell (CSC) genes associated with resilience and drug resistance in a paediatric DNET, since the deregulations and variations of CSC genes may prove critical to these tumours’ molecular characterisations.Case DescriptionImmunofluorescence, clonogenic assay and whole exome sequencing (WES) were applied to the patient’s tissue and its corresponding cell line. The case is for of a 6-year-old boy with intractable epilepsy and unremarkable physical and neurological examinations. Following magnetic resonance imaging (MRI) and histopathological tests, the patient was diagnosed with DNET. The child underwent a right posterior temporoparietooccipital neuronavigation-assisted craniotomy. Several CSC markers were upregulated in situ, including the metastasis-related protein, anterior gradient 2 (AGR2; 67%), and the Wnt-signalling-related protein, frizzled class receptor 9 (FZD9; 79%). The cell line possessed a similar DNA profile as the original tissue, stained positive for the tumorigenic marker [BMI1 proto-oncogene (BMI)] and CSC markers, and displayed drug resistance. Variants identified in the tissue DNA, which are listed in the catalogue of somatic mutations in cancer (COSMIC) database for genes previously known to be necessary for the development of the embryonic brain, included variants in the cell division cycle 27 (CDC27) gene.Conclusionswe report the in situ and in vitro presence of CSCs in a paediatric DNET.

CTHRC1 is a Potential Prognostic Biomarker and Correlated with Macrophage Infiltration in Breast Cancer.

BackgroundTumor immune cell infiltration is closely associated with the occurrence and development of tumors. Collagen triple helix repeats containing 1 (CTHRC1), a regulator of collagen expression and cell migration, is involved in the metastasis and invasion of tumors. However, the role of CTHRC1 in breast cancer remains unclear. This study aimed to investigate the prognostic value of CTHRC1, and further explore its association with immune infiltration in breast cancer.MethodsCTHRC1 expression pattern and prognostic value were analyzed using ONCOMINE, PrognoScan, GEPIA, and Kaplan–Meier Plotter databases. We then detected CTHRC1 mRNA levels in breast cancer tissues and paired normal breast tissues by Q-PCR. Subsequently, the University of California Santa Cruz (UCSC) database was used to determine the methylation status of CTHRC1. Furthermore, CTHRC1 mutations were investigated using the Catalogue of Somatic mutations in Cancer (COSMIC) and cBioPortal databases. We also assessed the correlation between CTHRC1 expression and immune cell infiltration using TIMER. In addition, The relationship of CTHRC1 expression with the immune marker sets of various immune cells was evaluated using GEPIA and TIMER.ResultsCTHRC1 was highly expressed in a variety of tumors, including breast cancer. Elevated CTHRC1 expression was related to a poor prognosis. Notably, CTHRC1 expression was significantly associated with macrophage infiltration, especially the immune infiltration gene marker set of M2. Copy number variations, DNA mutations and methylation states might be potential mechanisms for regulating CTHRC1 expression. Protein digestion and absorption, human papillomavirus infection, ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathways were identified as the potential CTHRC1-driven signaling pathways.ConclusionThese findings suggest that CTHRC1 could be a promising immune-related biomarker for the treatment of breast cancer patients.

Identification of lung cancer drivers by comparison of the observed and the expected numbers of missense and nonsense mutations in individual human genes.

Largely, cancer development is driven by acquisition and positive selection of somatic mutations that increase proliferation and survival of tumor cells. As a result, genes related to cancer development tend to have an excess of somatic mutations in them. An excess of missense and/or nonsense mutations in a gene is an indicator of its cancer relevance. To identify genes with an excess of potentially functional missense or nonsense mutations one needs to compare the observed and expected numbers of mutations in the gene. We estimated the expected numbers of missense and nonsense mutations in individual human genes using (i) the number of potential sites for missense and nonsense mutations in individual transcripts and (ii) histology-specific nucleotide context-dependent mutation rates. To estimate mutation rates defined as the number of mutations per site per tumor we used silent mutations reported in the Catalog Of Somatic Mutations In Cancer (COSMIC). The estimates were nucleotide context dependent. We have identified 26 genes with an excess of missense and/or nonsense mutations for lung adenocarcinoma, 18 genes for small cell lung cancer, and 26 genes for squamous cell carcinoma of the lung. These genes include known genes and novel lung cancer gene candidates.

Pancancer Analyses Reveal Genomics and Clinical Characteristics of the SETDB1 in Human Tumors.

Background Malignant tumor is one of the most common diseases that seriously affect human health. The prior literature has reported the biological function and potential therapeutic targets of SET domain bifurcated histone lysine methyltransferase 1 (SETDB1) as an oncogene. However, SETDB1 has rarely been analyzed from a pan-cancer perspective. Methods Bioinformatics analysis tools and databases, including GeneCards, National Center for Biotechnology Information (NCBI), UniProt, Illustrator for Biological Sequences (IBS), Human Protein Atlas (HPA), GEPIA, TIMER2, Sangerbox 3.0, UALCAN, Kaplan-Meier (K-M) plotter, cBioPortal, Catalogue Of Somatic Mutations In Cancer (COSMIC), PhosphoSitePlus, TISIDB, STRING, and GeneMANIA, were utilized to clarify the biological functions and clinical significance of SETDB1 from a pan-cancer perspective. Results In this study, the pan-cancer analysis demonstrated that SETDB1 showed significantly differential expression in most tumor tissues and paracancerous tissues, and SETDB1 expression was associated with clinicopathological features and clinical prognosis. We also found that SETDB1 mutations occurred in most tumors and were related to tumorigenesis. In addition, DNA methylation of SETDB1 primarily occurred at the cg10444928 site and was associated with prognosis in several human tumors. The predicted phosphorylation site of SETDB1 was Ser1006. We found that SETDB1 was significantly related to the specific tumor-infiltrating immune cell populations and expression of clinically targetable immune checkpoints and may be a promising immunotherapy target. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses also indicated that SETDB1 may function as crucial regulator in carcinogenesis of human cancers. Conclusions SETDB1 is an important oncogene involved in tumorigenesis and tumor progression through different biological mechanisms. Furthermore, SETDB1 may be a potential therapeutic target for cancer treatment.

Pan-cancer analysis of mutations in open chromatin regions and their possible association with cancer pathogenesis.

BackgroundOpen chromatin is associated with gene transcription. Previous studies have shown that the density of mutations in open chromatin regions is lower than that in flanking regions because of the higher accessibility of DNA repair machinery. However, in several cancer types, open chromatin regions show an increased local density of mutations in activated regulatory regions. Although the mutation distribution within open chromatin regions in cancer cells has been investigated, only few studies have focused on their functional implications in cancer. To reveal the impact of highly mutated open chromatin regions on cancer, we investigated the association between mutations in open chromatin regions and their possible functions.MethodsWhole‐genome sequencing data of 18 cancer types were downloaded from the PanCancer Analysis of Whole Genomes and Catalog of Somatic Mutations in Cancer. We quantified the mutations located in open chromatin regions defined by The Cancer Genome Atlas and classified open chromatin regions into three categories based on the number of mutations. Then, we investigated the chromatin state, amplification, and possible target genes of the open chromatin regions with a high number of mutations. We also analyzed the association between the number of mutations in open chromatin regions and patient prognosis.ResultsIn some cancer types, the proportion of promoter or enhancer chromatin state in open chromatin regions with a high number of mutations was significantly higher than that in the regions with a low number of mutations. The possible target genes of open chromatin regions with a high number of mutations were more strongly associated with cancer than those of other open chromatin regions. Moreover, a high number of mutations in open chromatin regions was significantly associated with a poor prognosis in some cancer types.ConclusionsThese results suggest that highly mutated open chromatin regions play an important role in cancer pathogenesis and can be effectively used to predict patient prognosis.

Mutational signatures and processes in hepatobiliary cancers.

The evolutionary history of hepatobiliary cancers is embedded in their genomes. By analysing their catalogue of somatic mutations and the DNA sequence context in which they occur, it is possible to infer the mechanisms underpinning tumorigenesis. These mutational signatures reflect the exogenous and endogenous origins of genetic damage as well as the capacity of hepatobiliary cells to repair and replicate DNA. Genomic analysis of thousands of patients with hepatobiliary cancers has highlighted the diversity of mutagenic processes active in these malignancies, highlighting a prominent source of the inter-cancer-type, inter-patient, intertumour and intratumoural heterogeneity that is observed clinically. However, a substantial proportion of mutational signatures detected in hepatocellular carcinoma and biliary tract cancer remain of unknown cause, emphasizing the important contribution of processes yet to be identified. Exploiting mutational signatures to retrospectively understand hepatobiliary carcinogenesis could advance preventative management of these aggressive tumours as well as potentially predict treatment response and guide the development of therapies targeting tumour evolution.

The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis

BackgroundMolecular diagnostics are considered the most promising route to achieving rapid, universal drug susceptibility testing for Mycobacterium tuberculosiscomplex (MTBC). We aimed to generate a WHO endorsed catalogue of mutations to serve as a global standard for interpreting molecular information for drug resistance prediction.MethodsA candidate gene approach was used to identify mutations as associated with resistance, or consistent with susceptibility, for 13 WHO endorsed anti-tuberculosis drugs. 38,215 MTBC isolates with paired whole-genome sequencing and phenotypic drug susceptibility testing data were amassed from 45 countries. For each mutation, a contingency table of binary phenotypes and presence or absence of the mutation computed positive predictive value, and Fisher’s exact tests generated odds ratios and Benjamini-Hochberg corrected p-values. Mutations were graded as Associated with Resistance if present in at least 5 isolates, if the odds ratio was >1 with a statistically significant corrected p-value, and if the lower bound of the 95% confidence interval on the positive predictive value for phenotypic resistance was >25%. A series of expert rules were applied for final confidence grading of each mutation.Findings15,667 associations were computed for 13,211 unique mutations linked to one or more drugs. 1,149/15,667 (7·3%) mutations were classified as associated with phenotypic resistance and 107/15,667 (0·7%) were deemed consistent with susceptibility. For rifampicin, isoniazid, ethambutol, fluoroquinolones, and streptomycin, the mutations’ pooled sensitivity was >80%. Specificity was over 95% for all drugs except ethionamide (91·4%), moxifloxacin (91·6%) and ethambutol (93·3%). Only two resistance mutations were classified for bedaquiline, delamanid, clofazimine, and linezolid as prevalence of phenotypic resistance was low for these drugs.InterpretationThis first WHO endorsed catalogue of molecular targets for MTBC drug susceptibility testing provides a global standard for resistance interpretation. Its existence should encourage the implementation of molecular diagnostics by National Tuberculosis Programmes.FundingUNITAID, Wellcome, MRC, BMGF.

Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants

Non-coding variants in the human genome significantly influence human traits and complex diseases via their regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences. However, it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods. To solve this issue, we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets: (1) rare germline variants from clinical relevant sequence variants (ClinVar), (2) rare somatic variants from Catalogue Of Somatic Mutations In Cancer (COSMIC), (3) common regulatory variants from curated expression quantitative trait locus (eQTL) data, and (4) disease-associated common variants from curated genome-wide association studies (GWAS). All 24 tested methods performed differently under various conditions, indicating varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve (AUROC) of 0.4481–0.8033 and poor for rare somatic variants from COSMIC (AUROC = 0.4984–0.7131), common regulatory variants from curated eQTL data (AUROC = 0.4837–0.6472), and disease-associated common variants from curated GWAS (AUROC = 0.4766–0.5188). We also compared the prediction performance of 24 methods for non-coding de novo mutations in autism spectrum disorder, and found that the combined annotation-dependent depletion (CADD) and context-dependent tolerance score (CDTS) methods showed better performance. Summarily, we assessed the performance of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants.

Somatic mutation analyses of stem-like cells in gingivobuccal oral squamous cell carcinoma reveals DNA damage response genes.

Gingivobuccal oral squamous cell carcinoma (OSCC-GB) occurs among persons who excessively chew smokeless tobacco in India. To understand the role of cancer stem cells (CSCs) in the disease, we have performed transcriptomics analysis on RNA-seq data from OSCC-GB primary tumors. The mutational signature analysis of the identified novel and Catalogue of Somatic Mutations in Cancer (COSMIC) variants reveals DNA damage associated etiology based on identified COSMIC signatures showing a higher prevalence of C>T mutations and 1bpT/(A) nucleotide insertions, pointing to the role of smokeless tobacco carcinogens. The differential somatic mutational, functional impact predictions, and survival analysis reveals the role of DNA damage response-related genes, with the CREBBP gene as a major player. The new CSC somatic variants identified in the study may play a crucial role in cancer metastasis, local-regional recurrence, chemo- and/or radioresistance that contributes to high mortality of the Indian OSCC-GB patients.

Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN

Effective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule-based Mycobacterium tuberculosis (MTB) drug resistance prediction methods using bacterial genomic sequencing data often achieve varying results: high accuracy on some antibiotics but relatively low accuracy on others. Traditional machine learning (ML) approaches have been applied to classify drug resistance for MTB and have shown more stable performance. However, there is no study that uses deep learning architecture like Convolutional Neural Network (CNN) on a large and diverse cohort of MTB samples for AMR prediction. We developed 24 binary classifiers of MTB drug resistance status across eight anti-MTB drugs and three different ML algorithms: logistic regression, random forest and 1D CNN using a training dataset of 10,575 MTB isolates collected from 16 countries across six continents, where an extended pan-genome reference was used for detecting genetic features. Our 1D CNN architecture was designed to integrate both sequential and non-sequential features. In terms of F1-scores, 1D CNN models are our best classifiers that are also more accurate and stable than the state-of-the-art rule-based tool Mykrobe predictor (81.1 to 93.8%, 93.7 to 96.2%, 93.1 to 94.8%, 95.9 to 97.2% and 97.1 to 98.2% for ethambutol, rifampicin, pyrazinamide, isoniazid and ofloxacin respectively). We applied filter-based feature selection to find AMR relevant features. All selected variant features are AMR-related ones in CARD database. 78.8% of them are also in the catalogue of MTB mutations that were recently identified as drug resistance-associated ones by WHO. To facilitate ML model development for AMR prediction, we packaged every step into an automated pipeline and shared the source code at https://github.com/KuangXY3/MTB-AMR-classification-CNN.

Detection of genetic mutations in patients with breast cancer from Saudi Arabia using Ion AmpliSeq™ Cancer Hotspot Panel v.2.0.

Next-Generation Sequencing allows for quick and precise sequencing of multiple genes concurrently. Recently, this technology has been employed for the identification of novel gene mutations responsible for disease manifestation among breast cancer (BC) patients, the most common type of cancer amongst Arabian women, and the major cause of disease-associated death in women worldwide. Genomic DNA was extracted from the peripheral blood of 32 Saudi Arabian BC patients with histologically confirmed invasive BC stages I-III and IV, as well from 32 healthy Saudi Arabian women using a QIAamp® DNA Mini Kit. The isolated DNA was quantified using a Qubit™ dsDNA BR Assay Kit with a Qubit 2.0 Fluorometer. Ion semiconductor sequencing technology with an Ion S5 System and AmpliSeq™ Cancer Hotspot Panel v2 were utilized to analyze ~2,800 mutations described in the Catalogue of Somatic Mutations in Cancer from 50 oncogenes and tumor suppressor genes. Ion Reporter Software v.5.6 was used to evaluate the genomic alterations in all the samples after alignment to the hg19 human reference genome. The results showed that out of the 50 genes, 26 mutations, including 17 (65%) missense point mutations (single nucleotide variants), and 9 (35%) frameshift (insertion/deletion) mutations, were identified in 11 genes across the cohort in 61 samples (95%). Mutations were predominantly focused on two genes, PIK3CA and TP53, in the BC genomes of the sample set. PIK3CA mutation, c.1173A>G located in exon 9, was identified in 15 patients (46.9%). The TP53 mutations detected were a missense mutation (c.215C>G) in 26 patients (86.70%) and 1 frameshift mutation (c.215_216insG) in 1 patient (3.33%), located within exon 3 and 5, respectively. This study revealed specific mutation profiles for every BC patient, Thus, the results showed that Ion Torrent DNA Sequencing technology may be a possible diagnostic and prognostic method for developing personalized therapy based on the patient's individual BC genome.

Computational Prediction of the Pathogenic Status of Cancer-Specific Somatic Variants.

In-silico classification of the pathogenic status of somatic variants is shown to be promising in promoting the clinical utilization of genetic tests. Majority of the available classification tools are designed based on the characteristics of germline variants or the combination of germline and somatic variants. Significance of somatic variants in cancer initiation and progression urges for development of classifiers specialized for classifying pathogenic status of cancer somatic variants based on the model trained on cancer somatic variants. We established a gold standard exclusively for cancer somatic single nucleotide variants (SNVs) collected from the catalogue of somatic mutations in cancer. We developed two support vector machine (SVM) classifiers based on genomic features of cancer somatic SNVs located in coding and non-coding regions of the genome, respectively. The SVM classifiers achieved the area under the ROC curve of 0.94 and 0.89 regarding the classification of the pathogenic status of coding and non-coding cancer somatic SNVs, respectively. Our models outperform two well-known classification tools including FATHMM-FX and CScape in classifying both coding and non-coding cancer somatic variants. Furthermore, we applied our models to predict the pathogenic status of somatic variants identified in young breast cancer patients from METABRIC and TCGA-BRCA studies. The results indicated that using the classification threshold of 0.8 our “coding” model predicted 1853 positive SNVs (out of 6,910) from the TCGA-BRCA dataset, and 500 positive SNVs (out of 1882) from the METABRIC dataset. Interestingly, through comparative survival analysis of the positive predictions from our models, we identified a young-specific pathogenic somatic variant with potential for the prognosis of early onset of breast cancer in young women.

Multi-Omics Analysis of the Therapeutic Value of MAL2 Based on Data Mining in Human Cancers

Recent studies have reported that T-cell differentiation protein 2 (MAL2) is an important regulator in cancers. Here, we downloaded data from multiple databases to analyze MAL2 expression and function in pan-cancers, especially in ovarian cancer (OC). Gene Expression Profiling Interactive Analysis (GEPIA) databases was used to examine MAL2 expression in 13 types of cancer. Kaplan–Meier plotter database was used to analyze the overall survival rate of MAL2 in pan-cancers. The Catalog of Somatic Mutations in Cancer (COSMIC), cBioPortal, and UCSC databases were used to examine MAL2 mutation in human cancers. Metascape, STRING, and GeneMANIA websites were used to explore MAL2 function in OC. Furthermore, ggplot2 package and ROC package were performed to analyze hub gene expression and undertake receiver operating characteristic (ROC) analysis. Drug sensitivity of MAL2 in OC was examined by the GSCALite database. In order to verify the results from databases above, real-time quantitative polymerase chain reaction (qRT-PCR) and western blotting were conducted to detect the expression of MAL2 in OC cells. CRISPR/Cas9 system was used to knockout the MAL2 gene in the OC cell lines HO8910 and OVCAR3, using specific guide RNA targeting the exons of MAL2. Then, we performed proliferation, colony formation, migration, and invasion assays to investigate the impact of MAL2 in OC cell lines in vivo and in vitro. Epithelial-mesenchymal transition (EMT)-associated biomarkers were significantly altered in vitro via western blotting and qRT-PCR. Taken together, we observed that MAL2 was remarkably dysregulated in multiple cancers and was related to patient overall survival (OS), mutation, and drug sensitivity. Furthermore, experimental results showed that MAL2 deletion negatively regulated the proliferation, migration, invasion, and EMT of OC, indicating that MAL2 is a novel oncogene that can activate EMT, significantly promote both the proliferation and migration of OC in vitro and in vivo, and provide new clues for treatment strategies.

Biased expression of mutant alleles in cancer-related genes in esophageal squamous cell carcinoma.

Recent progress of large-scale international studies has provided comprehensive catalogs of somatic mutations in cancers. Additionally, it has become evident that allelic imbalance in the abundance of somatic mutations between DNA and RNA were pervasive in various types of cancer. However, the allelic imbalance of the abundance of somatic mutations in esophageal squamous cell carcinoma (ESCC) has not been fully analyzed. We performed exome sequencing for 25 Japanese patients with ESCC to detect a comprehensive catalog of somatic mutations in ESCC. Additionally, we performed mRNA sequencing to evaluate the allelic imbalance of the identified somatic mutations at the transcriptional level by comparing the mutant allele frequencies between RNA and DNA. The exome sequencing showed that TP53 and ZNF750 were significantly mutated genes. The expression levels of TP53 and ZNF750 were different depending on the mutation status. In almost all the tumors with missense mutations in TP53 and ZNF750, the mutant allele frequencies were higher in the RNA sequencing than those in the exome sequencing, indicating that the mutant alleles were preferentially expressed. By examining the allelic imbalances for all the identified missense mutations, we demonstrated that genes showing preferential expressions of the mutant alleles were involved in the pathways including cell cycle, cell death, and chromatin modification. The results of this study suggest that the allelic imbalance of the abundance of somatic mutations plays important roles in the initiation and progression of ESCC by modulating cancer-related biological pathways.