Next-generation Sequencing Data Analysis Research Articles

Next-generation sequencing (NGS) of anaplastic thyroid carcinoma identifies unreported mutations in 25 recurring genes

Abstract Introduction/Objective Anaplastic thyroid carcinoma (ATC) is a rare, aggressive form of thyroid cancer. Our prior work identified 6 molecular clusters of ATC with a possible de novo group. Secondary analysis of NGS data from 713 ATC cases was undertaken to identify less frequent and unreported gene mutations. Methods/Case Report Deidentified NGS results of 713 ATC cases were obtained from Foundation Medicine (FoundationOne CDx). The results were analyzed for frequency of gene mutations. A search of the literature and genomic databases (TCGA, NCI Genomic Data Commons, and cBioPortal) was performed to determine gene association with thyroid cancers including ATC. Results (if a Case Study enter NA) There are 25 gene mutations present in 5 or more cases that have not previously been reported in ATC and one gene found, MTAP, which has been discussed in a limited number of cases. The gene mutations found are listed from highest to lowest frequency (14.77% to 0.69%) as follows: MTAP, LRP1B, ASXL1, NUP93, MUTYH, NOTCH3, RAD21, CRKL, PIK3R1, FANCA, STAG2, BRCA2, FGF4, FGF3, FGF19, FBXW7, BRCA1, BCORL1, QKI, VHL, PRKN, GATA6, NKX2_1, KDM5C, CCND3, and BAP1. There are also 99 other gene mutations with 4 or fewer cases (0.14% to 0.77%) that have not previously been associated with ATC. MTAP was previously reported to be lost by immunohistochemistry in 6/27 (22.2%) ATC cases; MTAP loss was also reported to indicate a potential CDKN2A abnormality. Our larger data set supports that MTAP is lost in ATC with 98.6% of MTAP- deleted ATC also harboring CDKN2A deletion and 92.3% showing CDKN2B deletion (p-values < 0.0001), supporting MTAP and CDKN2A/B are often deleted together. Other significant genes include the hereditary cancer genes MUTYH, FANCA (autosomal recessive), and BRCA1/2 (autosomal dominant) and genes associated with clonal hematopoiesis. ASXL1 and STAG2 have previously been unreported in ATC while others such as DNMT3A, TET2, and BCOR were also present in our dataset. Conclusion Our findings demonstrate 124 genes mutated in ATC that have not been previously reported. Besides the previously reported MTAP in 6 cases, genes associated with hereditary cancer and clonal hematopoiesis were identified. MTAP is located adjacent to CDKN2A/B and is often codeleted with MTAP at a much higher frequency than previously reported. Identifying associations with other diseases could improve surveillance and options for earlier therapy of ATC. Finally, these findings warrant further research to determine their role in the pathogenesis and treatment targets of ATC.

Identification and interaction analysis of molecular markers in myocardial infarction by bioinformatics and next-generation sequencing data analysis

BackgroundCardiovascular diseases are prevalent worldwide with any age, and it is characterized by sudden blockage of blood flow to heart and permanent damage to the heart muscle, whose cause and underlying molecular mechanisms are not fully understood. This investigation aimed to explore and identify essential genes and signaling pathways that contribute to the progression of MI.MethodsThe aim of this investigation was to use bioinformatics and next-generation sequencing (NGS) data analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in MI. NGS dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database. DEGs between MI and normal control samples were identified using the DESeq2 R bioconductor tool. The gene ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed using g:Profiler. Next, four kinds of algorithms in the protein–protein interaction (PPI) were performed to identify potential novel biomarkers. Next, miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network were constructed by miRNet and NetworkAnalyst database, and Cytoscape software. Finally, the diagnostic effectiveness of hub genes was predicted by receiver operator characteristic curve (ROC) analysis and AUC more than 0.800 was considered as having the capability to diagnose MI with excellent specificity and sensitivity.ResultsA total of 958 DEGs were identified, consisting of 480 up-regulated genes and 478 down-regulated genes. The enriched GO terms and pathways of the DEGs include immune system, neuronal system, response to stimulus and multicellular organismal process. Ten hub genes (namely cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1) were obtained via protein–protein interaction analysis results. MiRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-409-3p, hsa-mir-3200-3p, creb1 and tp63 might play an important role in the MI.ConclusionsAnalysis of next-generation sequencing dataset combined with global network information and validation presents a successful approach to uncover the risk hub genes and prognostic markers of MI. Our investigation identified four risk- and prognostic-related gene signatures, including cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of MI.

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

BackgroundEndometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis.MethodsNext-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes.ResultsA total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network.ConclusionsThis investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.

A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.

Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large number of genomics, transcriptomics, proteomics, and metagenomics features relative to the number of biological samples, presents significant challenges for reducing feature dimensionality. The high dimensionality of NGS data poses significant challenges for data analysis, including increased computational burden, potential overfitting, and difficulty in interpreting results. Feature selection and feature extraction are two pivotal techniques employed to address these challenges by reducing the dimensionality of the data, thereby enhancing model performance, interpretability, and computational efficiency. Feature selection and feature extraction can be categorized into statistical and machine learning methods. The present study conducts a comprehensive and comparative review of various statistical, machine learning, and deep learning-based feature selection and extraction techniques specifically tailored for NGS and microarray data interpretation of humankind. A thorough literature search was performed to gather information on these techniques, focusing on array-based and NGS data analysis. Various techniques, including deep learning architectures, machine learning algorithms, and statistical methods, have been explored for microarray, bulk RNA-Seq, and single-cell, single-cell RNA-Seq (scRNA-Seq) technology-based datasets surveyed here. The study provides an overview of these techniques, highlighting their applications, advantages, and limitations in the context of high-dimensional NGS data. This review provides better insights for readers to apply feature selection and feature extraction techniques to enhance the performance of predictive models, uncover underlying biological patterns, and gain deeper insights into massive and complex NGS and microarray data.

A negative regulatory role of β-cell-derived exosomes in the glucose-stimulated insulin secretion of recipient β-cells.

Exosomes are extracellular vesicles that play a role in intercellular communication through the transportation of their cargo including mRNAs, microRNAs, proteins, and nucleic acids. Exosomes can also regulate glucose homeostasis and insulin secretion under diabetic conditions. However, the role of exosomes in insulin secretion in islet β-cells under physiological conditions remains to be clarified. The aim of this study was to investigate whether exosomes derived from pancreatic islet β-cells could affect insulin secretion in naïve β-cells. We first confirmed that exosomes derived from the RIN-m5f β-cell line interfered with the glucose-stimulated insulin secretion (GSIS) of recipient β-cells without affecting cell viability. The exosomes significantly reduced the protein expression levels of phosphorylated Akt, phosphorylated GSK3α/β, CaMKII, and GLUT2 (insulin-related signaling molecules), and they increased the protein expression levels of phosphorylated NFκB-p65 and Cox-2 (inflammation-related signaling molecules), as determined by a Western blot analysis. A bioinformatics analysis of Next-Generation Sequencing data suggested that exosome-carried microRNAs, such as miR-1224, -122-5p, -133a-3p, -10b-5p, and -423-5p, may affect GSIS in recipient β-cells. Taken together, these findings suggest that β-cell-derived exosomes may upregulate exosomal microRNA-associated signals to dysregulate glucose-stimulated insulin secretion in naïve β-cells.

Next-generation sequencing technology in cancer

Next-generation sequencing (NGS) technology has revolutionized cancer research and treatment by enabling comprehensive analysis of genetic mutations, alterations, and expression profiles. It allows for the identification of cancer-driving mutations, helping in the development of targeted therapies. NGS provides detailed insights into tumor heterogeneity, resistance mechanisms, and clonal evolution. Its high-throughput capacity facilitates large-scale studies, improving our understanding of cancer genomics. By enabling personalized treatment plans based on individual genetic profiles, NGS holds promise for more effective and tailored cancer therapies. Early reviews on cancer genomics often lacked comprehensive coverage of emerging technologies. They missed in-depth analysis of NGS advancements, their impact on cancer research, and clinical applications. The review addresses this gap by reviving a thorough examination of NGS methods, their role in identifying genetic mutations, and their potential in personalized cancer treatment, thus providing essential insights into the evolving landscape of cancer genomics. The article covers the advancements in technology and bioinformatic approaches for NGS data analysis. It delves into NGS applications in research and diagnostics, particularly for solid cancer diagnosis. The review highlights specific cancer types, including hereditary breast cancer, melanoma, prostate cancer, thyroid cancer, lung cancer, and colorectal cancer. It explores NGS contribution in understanding the genetic basis of these cancers and its potential for enhancing personalized diagnosis and treatment strategies. This review rectifies early lacunas by providing a comprehensive and updated examination of NGS technology, addressing gaps in previous analyses and emphasizes bioinformatic approaches for NGS data analysis, crucial for interpreting vast genomic data accurately. The review meets the current need for a thorough understanding of NGS’s role in personalized cancer treatment and research.

Targeted Next-Generation Sequencing Analysis Reveals a Novel Genetic Variant in MYO6 Gene in an Indian Family with Postlingual Nonsyndromic Hearing Loss.

Background: Hereditary nonsyndromic hearing loss (NSHL) is an extremely heterogeneous disorder, both genetically and clinically. Myosin VI (MYO6) pathogenic variations have been reported to cause both prelingual and postlingual forms of NSHL. Postlingual autosomal dominant cases are often overlooked for genetic etiology in clinical setups. In this study, we used next-generation sequencing (NGS)-based targeted deafness gene panel assay to identify the cause of postlingual hearing loss in an Indian family. Methods: The proband and his father from a multigenerational Indian family affected by postlingual hearing loss were examined via targeted capture of 129 deafness genes, after excluding gap junction protein beta 2 (GJB2) pathogenic variants by Sanger sequencing. NGS data analysis and co-segregation of the candidate variants in the family were carried out. The variant effect was predicted by in silico tools and interpreted following American College of Medical Genetics and Genomics-Association for Molecular Pathology guidelines. Results: A novel heterozygous transversion c.3225T>G, p.(Tyr1075*) in MYO6 gene was identified as the disease-causing variant in this family. This stop-gained variant is predicted to form a truncated myosin VI protein, which is devoid of crucial cargo-binding domain. PCR-RFLP screening in 200 NSHL cases and 200 normal-hearing controls showed the absence of this variant indicating its de novo nature in the population. Furthermore, we reviewed MYO6 variants reported from various populations to date. Conclusions: To the best of our knowledge, this is the first family with MYO6-associated hearing loss from an Indian population. The study also highlights the importance of deafness gene panels in molecular diagnosis of GJB2-negative pedigrees, contributing to genetic counseling in the affected families.

Two Novel Variants in PI4KA in a Family Presenting With Hereditary Spastic Paraparesis: A Case Report.

To report novel biallelic PI4KA variants in a family presenting with pure hereditary spastic paraparesis. Two affected sisters presented with unsolved hereditary spastic paraparesis and underwent clinical and imaging assessments. This was followed by short-read next-generation sequencing. Analysis of next-generation sequencing data uncovered compound heterozygous variants in PI4KA (NM_058004.4: c.[3883C>A];[5785A>C]; p.[(His1295Asn);(Thr1929Pro)]. Using ACMG guidelines, both variants were classified as likely pathogenic. Here, next-generation sequencing revealed 2 novel compound heterozygous variants in the phosphatidylinositol 4-kinase alpha gene (PI4KA) in 2 sisters presenting with progressive pure hereditary spastic paraparesis. Pathogenic variants in PI4KA have previously been associated with a spectrum of disorders including autosomal recessive perisylvian polymicrogyria, with cerebellar hypoplasia, arthrogryposis, and pure spastic paraplegia. The cases presented in this study expand the phenotypic spectrum associated with PI4KA variants and contribute new likely pathogenic variants for testing in patients with otherwise unsolved hereditary spastic paraparesis.

Discovery of novel RNA viruses through analysis of fungi-associated next-generation sequencing data

BackgroundLike all other species, fungi are susceptible to infection by viruses. The diversity of fungal viruses has been rapidly expanding in recent years due to the availability of advanced sequencing technologies. However, compared to other virome studies, the research on fungi-associated viruses remains limited.ResultsIn this study, we downloaded and analyzed over 200 public datasets from approximately 40 different Bioprojects to explore potential fungal-associated viral dark matter. A total of 12 novel viral sequences were identified, all of which are RNA viruses, with lengths ranging from 1,769 to 9,516 nucleotides. The amino acid sequence identity of all these viruses with any known virus is below 70%. Through phylogenetic analysis, these RNA viruses were classified into different orders or families, such as Mitoviridae, Benyviridae, Botourmiaviridae, Deltaflexiviridae, Mymonaviridae, Bunyavirales, and Partitiviridae. It is possible that these sequences represent new taxa at the level of family, genus, or species. Furthermore, a co-evolution analysis indicated that the evolutionary history of these viruses within their groups is largely driven by cross-species transmission events.ConclusionsThese findings are of significant importance for understanding the diversity, evolution, and relationships between genome structure and function of fungal viruses. However, further investigation is needed to study their interactions.

Genomic Profiling of Rare Undifferentiated Sarcomatoid Subtypes of Pancreatic Carcinomas: In Search of Therapeutic Targets

PURPOSE The highly aggressive undifferentiated sarcomatoid carcinoma (USC) subtype of pancreatic ductal adenocarcinoma (PDAC) remains poorly characterized because of its rarity. Previous case reports suggest that immune checkpoint inhibitors could be a promising treatment strategy, but the prevalence of established predictive biomarkers of response is largely unknown. The objective of this study was to leverage comprehensive genomic profiling of USC PDAC tumors to determine the prevalence of biomarkers associated with potential response to targeted therapies. METHODS USC tumors (n = 20) underwent central pathology review by a board-certified gastrointestinal pathologist to confirm the diagnosis. These samples were compared with non-USC PDAC tumors (N = 5,562). Retrospective analysis of DNA and RNA next-generation sequencing data was performed. RESULTS USC PDACs were more frequently PD-L1+ by immunohistochemistry than non-USC PDAC (63% v 16%, respectively, P < .001). Furthermore, USC PDAC had an increase in neutrophils (8.99% v 5.55%, P = .005) and dendritic cells (1.08% v 0.00%, q = 0.022) and an increased expression of PDCD1LG2 (4.6% v 1.3%, q = 0.001), PDCD1 (2.0% v 0.8%, q = 0.060), and HAVCR2 (45.9% v 21.7%, q = 0.107) than non-USC PDAC. Similar to non-USC PDAC, KRAS was the most commonly mutated gene (86% v 90%, respectively, P = 1). CONCLUSION To our knowledge, this work represents the largest molecular analysis of USC tumors to date and showed an increased expression of immune checkpoint genes in USC tumors. These findings provide evidence for further investigation into immune checkpoint inhibitors in USC tumors.

INSaFLU-TELEVIR: an open web-based bioinformatics suite for viral metagenomic detection and routine genomic surveillance

BackgroundImplementation of clinical metagenomics and pathogen genomic surveillance can be particularly challenging due to the lack of bioinformatics tools and/or expertise. In order to face this challenge, we have previously developed INSaFLU, a free web-based bioinformatics platform for virus next-generation sequencing data analysis. Here, we considerably expanded its genomic surveillance component and developed a new module (TELEVIR) for metagenomic virus identification.ResultsThe routine genomic surveillance component was strengthened with new workflows and functionalities, including (i) a reference-based genome assembly pipeline for Oxford Nanopore technologies (ONT) data; (ii) automated SARS-CoV-2 lineage classification; (iii) Nextclade analysis; (iv) Nextstrain phylogeographic and temporal analysis (SARS-CoV-2, human and avian influenza, monkeypox, respiratory syncytial virus (RSV A/B), as well as a “generic” build for other viruses); and (v) algn2pheno for screening mutations of interest. Both INSaFLU pipelines for reference-based consensus generation (Illumina and ONT) were benchmarked against commonly used command line bioinformatics workflows for SARS-CoV-2, and an INSaFLU snakemake version was released. In parallel, a new module (TELEVIR) for virus detection was developed, after extensive benchmarking of state-of-the-art metagenomics software and following up-to-date recommendations and practices in the field. TELEVIR allows running complex workflows, covering several combinations of steps (e.g., with/without viral enrichment or host depletion), classification software (e.g., Kaiju, Kraken2, Centrifuge, FastViromeExplorer), and databases (RefSeq viral genome, Virosaurus, etc.), while culminating in user- and diagnosis-oriented reports. Finally, to potentiate real-time virus detection during ONT runs, we developed findONTime, a tool aimed at reducing costs and the time between sample reception and diagnosis.ConclusionsThe accessibility, versatility, and functionality of INSaFLU-TELEVIR are expected to supply public and animal health laboratories and researchers with a user-oriented and pan-viral bioinformatics framework that promotes a strengthened and timely viral metagenomic detection and routine genomics surveillance. INSaFLU-TELEVIR is compatible with Illumina, Ion Torrent, and ONT data and is freely available at https://insaflu.insa.pt/ (online tool) and https://github.com/INSaFLU (code).

Focused Exome Sequencing Gives a High Diagnostic Yield in the Indian Subcontinent

The genetically isolated yet heterogeneous and highly consanguineous Indian population has shown a higher prevalence of rare genetic disorders. However, there is a significant socioeconomic burden for genetic testing to be accessible to the general population. In the current study, we analyzed next-generation sequencing data generated through focused exome sequencing from individuals with different phenotypic manifestations referred for genetic testing to achieve a molecular diagnosis. We reported pathogenic or likely pathogenic variants in 280 out of 833 cases with a diagnostic yield of 33.6%. Homozygous sequence and copy number variants were found as positive diagnostic findings in 131 (15.7%) cases owing to the high consanguinity in the Indian population. No relevant findings related to reported phenotype were identified in 6.2% of the cases. Patients referred for testing due to metabolic disorder and neuromuscular disorder had higher diagnostic yields. Carrier testing of asymptomatic individuals with a family history of the disease, through focused exome sequencing, achieved positive diagnosis in 54 of 118 cases tested. We also reported copy number variants in trans with single nucleotide variants and mitochondrial variants in a few of the cases. The diagnostic yield and the findings from this study signify that a focused exome test is a good lower-cost alternative for whole exome and whole genome sequencing and as a first-tier approach to genetic testing.

Identification of pine wood nematode (Bursaphelenchus xylophilus) loading response genes in Japanese pine sawyer (Monochamus alternatus) through comparative genomics and transcriptomics.

Pine wood nematode (PWN; Bursaphelenchus xylophilus) is the causative agent of pine wilt disease (PWD), which is considered the most dangerous biohazard to conifer trees globally. The transmission of PWN relies on insect vectors, particularly the Japanese pine sawyer (JPS; Monochamus alternatus). However, the molecular mechanism underlying PWN-JPS assembly remains largely unknown. Here, we found that both geographical and gender could significantly affect the PCA (PWN carrying amount) of JPS; thus, JPS transcriptomes from diverse locations and genders were explored regard to PWN loading. Due to the shortage of genomes, we developed a full-length reference transcriptome for analyzing next-generation sequencing data. A comparative genomic study was performed, and 11 248 potential PWN-carrying associate genes (β) were nominated in JPS by using the reported genomes of PWN and non-PWN carrier insect species. Then, 151 differentially expressed transcripts (DETs), 28 of them overlapped with β, correlated with the PCA of JPS were nominated by RNA-Seq, and found that fatty acid β-oxidation might be the key factor that affected the PCA of JPS. Furthermore, JPS fatty acid β-oxidation rates were experimentally decreased using the inhibitor Etomoxir, leading to an increased PCA of JPS. Meanwhile, silencing MaCPT1 in JPS by RNA interference led to a decreased fatty acid β-oxidation rate and increased PCA of JPS. In conclusion, MaCPT1 was able to decrease the PWN-JPS assembly formation through the fatty acid β-oxidation of JPS. These results provide new insights for exploring the impact of PWN invasion on JPS. © 2024 Society of Chemical Industry.

Gene amplification of chromatin remodeling factor SMARCC2 and low protein expression of ACTL6A are unfavorable factors in ovarian high‑grade serous carcinoma.

Ovarian high-grade serous carcinoma (OHGSC) is the most common type of ovarian cancer worldwide. Genome sequencing has identified mutations in chromatin remodeling factors (CRFs) in gynecological cancer, such as clear cell carcinoma, endometrioid carcinoma and endometrial serous carcinoma. However, to the best of our knowledge, the association between CRFs and OHGSC remains unexplored. The present study aimed to investigate the clinicopathological and molecular characteristics of CRF dysfunction in OHGSC. CRF alterations were analyzed through numerous methods, including the analysis of public next-generation sequencing (NGS) data from 585 ovarian serous carcinoma cases from The Cancer Genome Atlas (TCGA), immunohistochemistry (IHC), and DNA copy number assays, which were performed on 203 surgically resected OHGSC samples. In the public NGS dataset, the most frequent genetic alteration was actin-like protein 6A (ACTL6A) amplification at 19.5%. Switch/sucrose non-fermentable related, matrix associated, actin dependent regulator of chromatin subfamily c member 2 (SMARCC2) amplification (3.1%) was associated with significantly decreased overall survival (OS). In addition, chromodomain-helicase-DNA-binding protein 4 (CHD4) amplification (5.7%) exhibited unfavorable outcome trends, although not statistically significant. IHC revealed the protein expression loss of ARID1A (2.5%), SMARCA2 (2.5%) and SMARCA4 (3.9%). The protein expression levels of ACTL6A, SMARCC2 and CHD4 were evaluated using H-score. Patients with low protein expression levels of ACTL6A showed a significantly decreased OS. Copy number gain or gene amplification was demonstrated in ACTL6A (66.2%) and SMARCC2 (33.5%), while shallow deletion or deep deletion was demonstrated in CHD4 (70.7%). However, there was no statistically significant difference in protein levels of these CRFs, between the different copy number alterations (CNAs). Overall, OHGSC exhibited CNAs and protein loss, indicating possible gene alterations in CRFs. Moreover, there was a significant association between the protein expression levels of ACTL6A and poor prognosis. Based on these findings, it is suggested that CRFs could serve as prognostic markers for OHGSC.

Assessing transmission attribution risk from simulated sequencing data in HIV molecular epidemiology.

HIV molecular epidemiology (ME) is the analysis of sequence data together with individual-level clinical, demographic, and behavioral data to understand HIV epidemiology. The use of ME has raised concerns regarding identification of the putative source in direct transmission events. This could result in harm ranging from stigma to criminal prosecution in some jurisdictions. Here we assessed the risks of ME using simulated HIV genetic sequencing data. We simulated social networks of men-who-have-sex-with-men, calibrating the simulations to data from San Diego. We used these networks to simulate consensus and next-generation sequence (NGS) data to evaluate the risks of identifying direct transmissions using different HIV sequence lengths, and population sampling depths. To identify the source of transmissions, we calculated infector probability and used phyloscanner software for the analysis of consensus and NGS data, respectively. Consensus sequence analyses showed that the risk of correctly inferring the source (direct transmission) within identified transmission pairs was very small and independent of sampling depth. Alternatively, NGS analyses showed that identification of the source of a transmission was very accurate, but only for 6.5% of inferred pairs. False positive transmissions were also observed, where one or more unobserved intermediaries were present when compared to the true network. Source attribution using consensus sequences rarely infers direct transmission pairs with high confidence but is still useful for population studies. In contrast, source attribution using NGS data was much more accurate in identifying direct transmission pairs, but for only a small percentage of transmission pairs analyzed.

Clonal Hematopoiesis of Indeterminate Potential Is Associated with Current Smoking Status and History of Exacerbation in Patients with Chronic Obstructive Pulmonary Disease.

There is limited data regarding the clinical outcomes of clonal hematopoiesis of indeterminate potential (CHIP) in patients with chronic obstructive pulmonary disease (COPD). This study aimed to evaluate the clinical significance of CHIP as a COPD biomarker. This retrospective study was conducted on patients with COPD who were enrolled prospectively in the Seoul National University Hospital Airway Registry from January 2013 to December 2019 and underwent pulmonary function and blood tests. We evaluated the CHIP score according to smoking status and severity of airflow obstruction. We analyzed next-generation sequencing data to detect CHIP in 125 patients with COPD. Current smokers had a higher prevalence of CHIP in combination of DNMT3A, TET2, and PPM1D (DTP), DNA methyltransferase 3 alpha (DNMT3A), and protein phosphatase, Mg2+/Mn2+ dependent 1D (PPM1D) genes than in never- or ex-smokers. CHIP of DTP and DNMT3A genes was significantly associated with current smokers (adjusted odds ratio [aOR], 2.80; 95% confidence interval [CI], 1.01 to 7.79) (aOR, 4.03; 95% CI, 1.09 to 14.0). Patients with moderate-to-severe airflow obstruction had a higher prevalence of CHIP in most of the explored genes than those with mild obstruction, although the difference was not statistically significant. CHIP in ASXL transcriptional regulator 1 (ASXL1) genes was significantly associated with history of mild, severe, and total acute exacerbation. Given that CHIP in specific genes was significantly associated with current smoking status and acute exacerbation, CHIP can be considered as a candidate biomarker for COPD patients.

Abstract A036: The genetic landscape of head and neck cancer using brush biopsy

Abstract Oral potentially malignant disorders (OPMDs) are characterized by the presence of dysplasia and DNA copy number aberrations (CNAs) and subsequently progress to oral squamous cell carcinoma (OSCC). OSCC has a 5-year survival rate of only about 50% and varies greatly depending on site and stage. The combination of next-generation sequencing and advanced computational data analysis approaches has revolutionized our understanding of the genomic underpinnings of cancer development and progression. This can reveal individual targetable alterations, mutational load, complex mutation signatures, and tumor-specific antigens, which might inform the utilization of targeted therapies and early diagnosis. Oral brush biopsy represents an attractive non-invasive advantage in the characterization of premalignant and malignant oral lesions. Still, a sufficient number of abnormal cells is mandatory to diagnose. The current study tested the possibility of recreating the genomic landscape of oral cancer using brush biopsy. Materials and methods: As a pilot study to address this question, we analyzed matched tissue and brush biopsy of a patient with synchronous OPMD and OSCC. Brush and tissue biopsy was taken from normal epithelium, OPMD, and OSCC. We scrutinized the evolution and heterogeneity of these three brush/tissue biopsy pairs. The techniques used include whole exome sequencing (WES), mutational signature analysis to evaluate smoking and other aberrant signatures, copy number analysis (using ASCAT), and subclonal architecture reconstruction (using DPclust). Results: To analyze intra-tumoral heterogeneity (ITH), ASCAT and DPclust were used to call copy number aberrations (CNA) and cluster somatic SNVs based on cancer cell fraction (CCF), respectively. We observed a large number of shared SNVs between the OPMD and OSCC lesions suggesting a common evolutionary origin. After adjusting the ploidy and purity based on DPClust results, ASCAT profiles revealed differences in aberrant tumor cell fraction, gains and loss of heterozygosity, and copy number neutral events between the samples. It showed almost similar profiles between the respective lesions' brush and tissue samples, suggesting that brush biopsy can recapitulate the genomic landscape. The OPMD and OSCC lesions showed loss of heterozygosity in chromosome 9 and chromosome 17, which play a potential role in tumorigenesis with loss of function of P53. We inferred the subclonal architecture of each sample, illuminating the clonal relationships between samples and distinct lineages of precursor clones developing independently. Conclusion: Collectively, these results emphasize the possibility of using brush biopsy to reconstruct the genomic characterization of OPMD and OSCC show prognostic potential for diagnosis and intervention strategies, and accelerate progress in precision cancer care. Citation Format: Evit John, Tom Lesluyes, Xiao Zhao, Peter Van Loo. The genetic landscape of head and neck cancer using brush biopsy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A036.

DEEPOMICS FFPE, a deep neural network model, identifies DNA sequencing artifacts from formalin fixed paraffin embedded tissue with high accuracy

Formalin-fixed, paraffin-embedded (FFPE) tissue specimens are routinely used in pathological diagnosis, but their large number of artifactual mutations complicate the evaluation of companion diagnostics and analysis of next-generation sequencing data. Identification of variants with low allele frequencies is challenging because existing FFPE filtering tools label all low-frequency variants as artifacts. To address this problem, we aimed to develop DEEPOMICS FFPE, an AI model that can classify a true variant from an artifact. Paired whole exome sequencing data from fresh frozen and FFPE samples from 24 tumors were obtained from public sources and used as training and validation sets at a ratio of 7:3. A deep neural network model with three hidden layers was trained with input features using outputs of the MuTect2 caller. Contributing features were identified using the SHapley Additive exPlanations algorithm and optimized based on training results. The performance of the final model (DEEPOMICS FFPE) was compared with those of existing models (MuTect filter, FFPolish, and SOBDetector) by using well-defined test datasets. We found 41 discriminating properties for FFPE artifacts. Optimization of property quantification improved the model performance. DEEPOMICS FFPE removed 99.6% of artifacts while maintaining 87.1% of true variants, with an F1-score of 88.3 in the entire dataset not used for training, which is significantly higher than those of existing tools. Its performance was maintained even for low-allele-fraction variants with a specificity of 0.995, suggesting that it can be used to identify subclonal variants. Different from existing methods, DEEPOMICS FFPE identified most of the sequencing artifacts in the FFPE samples while retaining more of true variants, including those of low allele frequencies. The newly developed tool DEEPOMICS FFPE may be useful in designing capture panels for personalized circulating tumor DNA assay and identifying candidate neoepitopes for personalized vaccine design. DEEPOMICS FFPE is freely available on the web (http://deepomics.co.kr/ffpe) for research.

An analysis pipeline for understanding 6-thioguanine effects on a mouse tumour genome

Mouse tumour models are extensively used as a pre-clinical research tool in the field of oncology, playing an important role in anticancer drugs discovery. Accordingly, in cancer genomics research, the demand for next-generation sequencing (NGS) is increasing, and consequently, the need for data analysis pipelines is likewise growing. Most NGS data analysis solutions to date do not support mouse data or require highly specific configuration for their use. Here, we present a genome analysis pipeline for mouse tumour NGS data including the whole-genome sequence (WGS) data analysis flow for somatic variant discovery, and the RNA-seq data flow for differential expression, functional analysis and neoantigen prediction. The pipeline is based on standards and best practices and integrates mouse genome references and annotations. In a recent study, the pipeline was applied to demonstrate the efficacy of low dose 6-thioguanine (6TG) treatment on low-mutation melanoma in a pre-clinical mouse model. Here, we further this study and describe in detail the pipeline and the results obtained in terms of tumour mutational burden (TMB) and number of predicted neoantigens, and correlate these with 6TG effects on tumour volume. Our pipeline was expanded to include a neoantigen analysis, resulting in neopeptide prediction and MHC class I antigen presentation evaluation. We observed that the number of predicted neoepitopes were more accurate indicators of tumour immune control than TMB. In conclusion, this study demonstrates the usability of the proposed pipeline, and suggests it could be an essential robust genome analysis platform for future mouse genomic analysis.

V-pipe 3.0: a sustainable pipeline for within-sample viral genetic diversity estimation.

The large amount and diversity of viral genomic datasets generated by next-generation sequencing technologies poses a set of challenges for computational data analysis workflows, including rigorous quality control, scaling to large sample sizes, and tailored steps for specific applications. Here, we present V-pipe 3.0, a computational pipeline designed for analyzing next-generation sequencing data of short viral genomes. It is developed to enable reproducible, scalable, adaptable, and transparent inference of genetic diversity of viral samples. By presenting 2 large-scale data analysis projects, we demonstrate the effectiveness of V-pipe 3.0 in supporting sustainable viral genomic data science.