Computational analysis of cancer genome sequencing data

Unraveling the hidden complexity of cancer through long-read sequencing

Cancer is fundamentally a disease of the genome, characterized by extensive genomic, transcriptomic, and epigenomic alterations. Most current studies predominantly use short-read sequencing, gene panels, or microarrays to explore these alterations; however, these technologies can systematically miss or misrepresent certain types of alterations, especially structural variants, complex rearrangements, and alterations within repetitive regions. Long-read sequencing is rapidly emerging as a transformative technology for cancer research by providing a comprehensive view across the genome, transcriptome, and epigenome, including the ability to detect alterations that previous technologies have overlooked. In this Perspective, we explore the current applications of long-read sequencing for both germline and somatic cancer analysis. We provide an overview of the computational methodologies tailored to long-read data and highlight key discoveries and resources within cancer genomics that were previously inaccessible with prior technologies. We also address future opportunities and persistent challenges, including the experimental and computational requirements needed to scale to larger sample sizes, the hurdles in sequencing and analyzing complex cancer genomes, and opportunities for leveraging machine learning and artificial intelligence technologies for cancer informatics. We further discuss how the telomere-to-telomere genome and the emerging human pangenome could enhance the resolution of cancer genome analysis, potentially revolutionizing early detection and disease monitoring in patients. Finally, we outline strategies for transitioning long-read sequencing from research applications to routine clinical practice.

Protocol for the assessment, improvement, and harmonization of somatic variant calling using ONCOLINER

The interoperability of variant identification pipelines is fundamental for achieving consistent clinical care across oncology research centers and hospitals. Here, we present a protocol for using ONCOLINER, a platform for the assessment, improvement, and harmonization of somatic variant discovery of multiple pipelines. We describe steps for acquiring benchmarking datasets and executing the user variant calling pipeline. We then detail the procedures for performing analyses to produce user-friendly reports showing the quality, scope, and applicable improvements for each tumor genome analysis. For complete details on the use and execution of this protocol, please refer to Martín et al.1.

Measuring Longitudinal Genome-wide Clonal Evolution of Pediatric Acute Lymphoblastic Leukemia at Single-Cell Resolution

Over 80% of children with acute lymphoblastic leukemia (pALL) can be cured by treating them with multiple chemotherapeutic agents administered over several years, whereas pALL is incurable with 1-3 medications, suggesting significant variation in drug susceptibility across clonal populations. While bulk sequencing studies indicate that pALL cells contain relatively few genetic variants compared to other cancers, the true extent of genetic diversity at the single-cell level remains unknown. Here, we used three complementary approaches to investigate pALL genetic heterogeneity: error-corrected bulk sequencing, single-cell exome sequencing, and primary template-directed amplification (PTA)-enabled single-cell genome sequencing. We discovered that some ETV6-RUNX1 samples harbor multiple independent ras clones and that individual pALL cells harbor substantially more mutations (mean 3,553 per cell) than detected in bulk samples (mean 965 mutations), with variant signatures suggesting both early and late APOBEC-driven mutagenesis in ETV6-RUNX1 patients. Using PTA-based phylogenetic analysis of over 150 single-cell genomes from four pALL patients, we identified heritable phenotypes associated with specific genetic alterations, including some low-frequency clones that are preferentially selected for during chemotherapy treatment. Our findings reveal previously undetected genetic diversity in pALL and suggest that pre-existing mutations influence treatment response, with implications for future therapeutic strategies. This study provides a high-resolution framework for understanding cancer clonal evolution during treatment, yielding important new insights for developing more effective therapeutic approaches for pALL.

SERPINI1 serves as a biomarker promoting cell proliferation and invasion in hepatocellular carcinoma

BACKGROUND

SERPINI1 is a protein-coding gene, which has been reported to be related to malignancies, and the encoding protein is a secreted protein. Nevertheless, the specific effect of SERPINI1 on Hepatocellular carcinoma (HCC) remains unclear.

METHODS

The expression level of SERPINI1 in cancers was detected by the Gene Expression Omnibus (GEO) database, the Gene Expression Profiling Interactive Analysis (GEPIA) database and the collected serum of HCC patients. The receiver operating characteristic (ROC) curve and area under curve (AUC) were used to evaluate the diagnostic effectiveness of serum SERPINI1 and the combination of AFP and SERPINI1 for HCC. The Kaplan-Meier (KM) survival was used to evaluate the prognostic capacity of SERPINI1 for HCC in GEPIA database. Furthermore, the correlations between clinicopathological characteristics and the level of serum SERPINI1 were analyzed. Besides, we detected the expression of SERPINI1 in HepG2 by qPCR and western blot, and confirmed the biological function of SERPINI1 through MTT, EdU, wound healing and transwell invasion assay.

RESULTS

The results indicated that the level of SERPINI1 was significantly increased in tissue and serum of HCC patients. ROC analysis displayed that SERPINI1 had a significantly diagnostic value for HCC, the combination of AFP and SERPINI1 gained the higher specificity and sensitivity. The KM survival curves indicated that patients with SERPINI1 overexpression had worse overall survival. Furthermore, we found the positive correlations between serum SERPINI1 level and some clinicopathological characteristics, such as tumor size, differentiation degrees and so on. In addition, in vitro experiments revealed that SERPINI1 could promote the proliferation and invasion of HCC.

CONCLUSIONS

Taken together, our study demonstrates that SERPINI1, which is highly expressed in HCC and closely related to cell proliferation and invasion, may serve as a novel biomarker for diagnosis and prognosis of HCC.

Precision medicine in colorectal cancer: genomics profiling and targeted treatment

Precision medicine has revolutionized the treatment of colorectal cancer by enabling a personalized approach tailored to each patient's unique genetic characteristics. Genomic profiling allows for the identification of specific mutations in genes such as KRAS, BRAF, and PIK3CA, which play a crucial role in cell signaling pathways that regulate cell proliferation, apoptosis, and differentiation. This information enables doctors to select targeted therapies that inhibit specific molecular pathways, maximizing treatment effectiveness and minimizing side effects. Precision medicine also facilitates adaptive monitoring of tumor progression, allowing for adjustments in therapy to maintain treatment effectiveness. While challenges such as high costs, limited access to genomic technology, and the need for more representative genomic data for diverse populations remain, collaboration between researchers, medical practitioners, policymakers, and the pharmaceutical industry is crucial to ensure that precision medicine becomes a standard of care accessible to all. With continued advances and support, precision medicine has the potential to improve treatment outcomes, reduce morbidity and mortality rates, and enhance the quality of life for colorectal cancer patients worldwide.

Development of a multi-epitope vaccine candidate to combat SARS-CoV-2 and dengue virus co-infection through an immunoinformatic approach

Background

Although the SARS-CoV-2 and dengue viruses seriously endanger human health, there is presently no vaccine that can stop a person from contracting both viruses at the same time. In this study, four antigens from SARS-CoV-2 and dengue virus were tested for immunogenicity, antigenicity, allergenicity, and toxicity and chosen to predict dominant T- and B-cell epitopes.

Methods

For designing a multi-epitope vaccine, the sequences were retrieved, and using bioinformatics and immunoinformatics, the physicochemical and immunological properties, as well as secondary structures, of the vaccine were predicted and studied. Additionally, the three-dimensional structure was estimated, improved upon, and confirmed using bioinformatics methods before being docked with TLR-2 and TLR-4. Eight helper T-cell lymphocyte (HTL) epitopes, ten cytotoxic T-cell lymphocyte (CTL) epitopes, nine B-cell epitopes, and TLR agonists were used to create a new multi-epitope vaccine. Furthermore, according to the immunological stimulation hypothesis, the vaccine could stimulate T and B cells to create large quantities of Th1 cytokines and antibodies.

Results

The study indicates that the developed vaccine is a favorable vaccine candidate with antigenicity, immunogenicity, non-toxicity, and non-allergenicity properties. The vaccine construct was made up of 460 amino acids, had an MW of 49391.51 Da, a theoretical pI of 9.86, and the formula C2203H3433N643O618S18, a lipid index of 39.84, a GRAVY of -0.473, an aliphatic index of 63.80, and an instability index of 39.84, which classifies the protein to be stable.

Conclusion

The acquired data showed that both vaccine designs had a considerable chance of preventing the co-infection of SARS-CoV-2 and dengue virus and that they demonstrate good results following in-silico testing. Furthermore, the vaccine may be an effective strategy in preventing SARS-CoV-2 and dengue since it can cause noticeably high levels of Th1 cytokines and antibodies.

Revolutionizing ESCC prognosis: the efficiency of tumor-infiltrating immune cells (TIIC) signature score

BACKGROUND

Patients suffer from esophageal squamous cell carcinoma (ESCC), which is the ninth highly aggressive malignancy. Tumor-infiltrating immune cells (TIIC) exert as major component of the tumor microenvironment (TME), showing possible prognostic value in ESCC.

METHODS

Transcriptome data and scRNA-seq data of ESCC samples were extracted from the GEO and TCGA databases. Tissue Specific Index (TSI) was defined to identify potential TIIC-RNAs from the TME. Twenty machine learning algorithms were further applied to evaluate the prognostic efficacy of TIIC signature score. Gene colocalization analysis was performed. Differences in CNV on chromosomes and SNP sites of prognostic model genes were calculated.

RESULTS

The most reliable model of TIIC signature score was developed based on three prognostic TIIC-RNAs. It showed a higher C-index than any other reported prognostic models. ESCC patients with high TIIC signature score showed poorer survival outcomes than low TIIC signature score. The activity of most immune cells decreased with the increase of TIIC score. TIIC signature score showed difference in the expression levels and methylation levels of DEGs. There was also significant different correlation with the degree of CNV amplification and CNV deletion of the immune checkpoint genes. Gene colocalization analysis showed two prognostic model genes (ATP6V0E1 and BIRC2). MR analysis found that rs148710154 and rs75146099 SNP sites of TIIC-RNA gene had a significant correlation between them gastro-oesophageal reflux and ESCC.

CONCLUSION

TIIC signature score was the first time developed which provided a novel strategy and guidance for the prognosis and immunotherapy of ESCC. It also gave the evidence in the important role of immune cells from the TME in the treatment of cancers.

Integrative bioinformatics and immunohistochemical analysis unravel the prognostic significance and immunological implication of LIMCH1 in breast cancer: a retrospective study

The current mortality rates for breast cancer underscore the need for better prognostic tools; moreover, LIM and calponin homology domain 1 (LIMCH1), which is a protein with dual roles in cancer, is a promising candidate for investigation. This study employed an integrative approach combining bioinformatics analysis of The Cancer Genome Atlas (TCGA) cohort and clinical immunohistochemistry (IHC) cohort data. We analysed LIMCH1 expression patterns, its associations with clinicopathological features and prognosis, and its impact on the tumour immune microenvironment (TIME). Functional annotations and single-cell RNA sequencing (scRNA-seq) data were used to explore the underlying molecular mechanisms. Our analysis revealed that high LIMCH1 expression in breast cancer patients was significantly associated with unfavourable clinical outcomes and served as an independent prognostic indicator. The functional annotations revealed pathways related to carcinogenesis and metabolic reconfiguration, thus suggesting the role of LIMCH1 in tumour progression. Additionally, LIMCH1 expression was correlated with an immunosuppressive TIME characterized by increased numbers of M2 macrophages and reduced numbers of CD8 + T cells and NK cells. Finally, we developed a nomogram incorporating LIMCH1 expression for predicting overall survival rates, thus providing a clinically applicable tool. Our research elucidates the diverse functions of LIMCH1 in the pathogenesis of breast cancer, thus highlighting its potential utility as both a prognostic indicator and a therapeutic intervention.

A novel combined oxidative stress and extracellular matrix related predictive gene signature for keratoconus

Keratoconus (KC) is an ectatic cornea disease with high prevalence and asymptomatic at early stage, leading to decreased visual acuity and even cornea transplantation. However, the etiology mechanism of keratoconus is still poorly understood. Oxidative stress (OS) and extracellular matrix (ECM) remodeling play critical roles in keratoconus development. Here, based on keratoconus datasets from GEO database, we obtained 454 differentially expressed genes (DEGs), which were further intersected with oxidative stress (OS) and extracellular matrix (ECM) genesets from MSigDB database. A total of 17 OS- and ECM-related DEGs (OEDEGs) were identified. Feature genes were screened by least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithms, and a six-gene (COL1A1, CYP1B1, MMP3, HMOX1, FOS and GDF15) classification model was developed utilizing Logistic regression (LR), Support Vector Machine (SVM) and Naïve Bayes (NB) algorithms respectively, which was further verified in internal and external cohort. Subsequently, a predictive nomogram was constructed for KC patients. Six signature genes showed a strong correlation with the infiltration level of macrophages M1, neutrophils and eosinophils. Additionally, in vitro qRT-PCR validated the decreased expression of signature genes in either keratoconus clinical samples or human cornea epithelial (HCE) cells grown on soft hydrogel substrate. Finally, we revealed that CYP1B1 and GDF15 regulate cellular proliferation and response to oxidative stress. In conclusion, the developed combined OS and ECM gene signature showed excellent performance for keratoconus prediction, providing beneficial perspectives for keratoconus pathogenesis.

A personalized multi-platform assessment of somatic mosaicism in the human frontal cortex

Somatic mutations in individual cells lead to genomic mosaicism, contributing to the intricate regulatory landscape of genetic disorders and cancers. To evaluate and refine the detection of somatic mosaicism across different technologies with personalized donor-specific assembly (DSA), we obtained tissue from the dorsolateral prefrontal cortex (DLPFC) of a post-mortem neurotypical 31-year-old individual. We sequenced bulk DLPFC tissue using Oxford Nanopore Technologies (~60X), NovaSeq (~30X), and linked-read sequencing (~28X). Additionally, we applied Cas9 capture methodology coupled with long-read sequencing (TEnCATS), targeting active transposable elements. We also isolated and amplified DNA from flow-sorted single DLPFC neurons using MALBAC, sequencing 115 of these MALBAC libraries on Nanopore and 94 on NovaSeq. We constructed a haplotype-resolved assembly with a total length of 5.77 Gb and a phase block length of 2.67 Mb (N50) to facilitate cross-platform analysis of somatic genetic variations. We observed an increase in the phasing rate from 11.6% to 38.0% between short-read and long-read technologies. By generating a catalog of phased germline SNVs, CNVs, and TEs from the assembled genome, we applied standard approaches to recall these variants across sequencing technologies. We achieved aggregated recall rates from 97.3% to 99.4% based on long-read bulk tissue data, setting an upper bound for detection limits. Moreover, utilizing haplotype-based analysis from DSA, we achieved a remarkable reduction in false positive somatic calls in bulk tissue, ranging from 14.9% to 72.4%. We developed pipelines leveraging DSA information to enhance somatic large genetic variant calling in long-read single cells. By examining somatic variation using long-reads in 115 individual neurons, we identified 468 candidate somatic heterozygous large deletions (1.5Mb - 20Mb), 137 of which intersected with short-read single-cell data. Additionally, we identified 61 putative somatic TEs (60 Alus, one LINE-1) in the single-cell data. Collectively, our analysis spans personalized assembly to single-cell somatic variant calling, providing a comprehensive ab initio ad finem approach and resource in real human tissue.

Exploring the clinical significance of TPX2 in pancreatic cancer: from biomarker to immunotherapy

Pancreatic cancer (PC) is a highly aggressive malignancy characterized by a dismal prognosis. The present study is designed to elucidate the pivotal role of Xenopus kinesin-like protein 2 (TPX2) as a biomarker with substantial clinical prognostic significance in PC. By conducting a comprehensive analysis of RNA sequencing data and protein expression profiles obtained from multiple databases, we observed a pronounced upregulation of TPX2 expression in PC tissues compared to normal pancreatic tissues. Importantly, TPX2 emerged as an independent prognostic factor, demonstrating remarkable diagnostic accuracy. Notably, its expression levels were found to be significantly associated with the PC immune microenvironment and sensitivity to various therapeutic modalities. Functional assays revealed that the silencing of TPX2 markedly inhibited PC cell proliferation, metastasis, and the growth of subcutaneous tumors in PC mouse models. These effects were potentially mediated by the activation of CD8+ T cell immune responses and the inhibition of cell cycle progression and adhesion mechanisms. Taken together, our findings indicate that TPX2 may serve as a critical biomarker for the diagnosis and clinical management of patients with PC.

A regression based approach to phylogenetic reconstruction from multi-sample bulk DNA sequencing of tumors

MOTIVATION

DNA sequencing of multiple bulk samples from a tumor provides the opportunity to investigate tumor heterogeneity and reconstruct a phylogeny of a patient's cancer. However, since bulk DNA sequencing of tumor tissue measures thousands of cells from a heterogeneous mixture of distinct sub-populations, accurate reconstruction of the tumor phylogeny requires simultaneous deconvolution of cancer clones and inference of ancestral relationships, leading to a challenging computational problem. Many existing methods for phylogenetic reconstruction from bulk sequencing data do not scale to large datasets, such as recent datasets containing upwards of ninety samples with dozens of distinct sub-populations.

RESULTS

We develop an approach to reconstruct phylogenetic trees from multi-sample bulk DNA sequencing data by separating the reconstruction problem into two parts: a structured regression problem for a fixed tree [Formula: see text], and an optimization over tree space. We derive an algorithm for the regression sub-problem by exploiting the unique, combinatorial structure of the matrices appearing within the problem. This algorithm has both asymptotic and empirical improvements over linear programming (LP) approaches to the problem. Using our algorithm for this regression sub-problem, we develop fastBE, a simple method for phylogenetic inference from multi-sample bulk DNA sequencing data. We demonstrate on simulated data with hundreds of samples and upwards of a thousand distinct sub-populations that fastBE outperforms existing approaches in terms of reconstruction accuracy, sample efficiency, and runtime. Owing to its scalability, fastBE enables both phylogenetic reconstruction directly from indvidual mutations without requiring the clustering of mutations into clones, as well as a new phylogeny constrained mutation clustering algorithm. On real data from fourteen B-progenitor acute lymphoblastic leukemia patients, fastBE infers mutation phylogenies with fewer violations of a widely used evolutionary constraint and better agreement to the observed mutational frequencies. Using our phylogeny constrained mutation clustering algorithm, we also find mutation clusters with lower distortion compared to state-of-the-art approaches. Finally, we show that on two patient-derived colorectal cancer models, fastBE infers mutation phylogenies with less violation of a widely used evolutionary constraint compared to existing methods.

From Genomic Exploration to Personalized Treatment: Next-Generation Sequencing in Oncology

Next-generation sequencing (NGS) has revolutionized personalized oncology care by providing exceptional insights into the complex genomic landscape. NGS offers comprehensive cancer profiling, which enables clinicians and researchers to better understand the molecular basis of cancer and to tailor treatment strategies accordingly. Targeted therapies based on genomic alterations identified through NGS have shown promise in improving patient outcomes across various cancer types, circumventing resistance mechanisms and enhancing treatment efficacy. Moreover, NGS facilitates the identification of predictive biomarkers and prognostic indicators, aiding in patient stratification and personalized treatment approaches. By uncovering driver mutations and actionable alterations, NGS empowers clinicians to make informed decisions regarding treatment selection and patient management. However, the full potential of NGS in personalized oncology can only be realized through bioinformatics analyses. Bioinformatics plays a crucial role in processing raw sequencing data, identifying clinically relevant variants, and interpreting complex genomic landscapes. This comprehensive review investigates the diverse NGS techniques, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and single-cell RNA sequencing (sc-RNA-Seq), elucidating their roles in understanding the complex genomic/transcriptomic landscape of cancer. Furthermore, the review explores the integration of NGS data with bioinformatics tools to facilitate personalized oncology approaches, from understanding tumor heterogeneity to identifying driver mutations and predicting therapeutic responses. Challenges and future directions in NGS-based cancer research are also discussed, underscoring the transformative impact of these technologies on cancer diagnosis, management, and treatment strategies.

Molecular classification of ovarian high-grade serous/endometrioid carcinomas through multi-omics analysis: JGOG3025-TR2 study

BACKGROUND

Considerable interobserver variability exists in diagnosis of ovarian high-grade endometrioid carcinoma (HGEC) and high-grade serous carcinoma (HGSC) due to histopathological similarities. While homologous recombination deficiency (HRD) correlates with drug sensitivity in HGSC, the molecular features of HGEC are unclear.

METHODS

Fresh-frozen samples from 15 ovarian HGECs and 274 ovarian HGSCs in the JGOG-TR2 cohort were submitted to targeted DNA sequencing, RNA sequencing, DNA methylation array, and SNP array. We additionally analyzed 555 ovarian HGSCs from TCGA-OV and 287 endometrial high-grade carcinomas from TCGA-UCEC.

RESULTS

Unsupervised clustering using copy number signatures identified four distinct tumor groups (C1, C2, C3 and C4). C1 (n = 41) showed CCNE1 amplification and poor survival. C2 (n = 160) and C3 (n = 59) showed high BRCA1/2 alteration frequency with low and moderate ploidy, respectively. C4 (n = 22) was characterized by favorable outcome, higher HGEC proportion, no BRCA1/2 alteration or CCNE1 amplification, and low levels of HRD score, ploidy, intra-tumoral heterogeneity, cell proliferation rate, and WT1 gene expression. Notably, C4 exhibited a normal endometrium-like DNA methylation profile, thus, defined as "HGEC-type" tumors, which were also identified in TCGA-OV and TCGA-UCEC.

CONCLUSIONS

Ovarian "HGEC-type" tumors present a non-HRD status, favorable prognosis, and endometrial differentiation, possibly constituting a subset of clinically diagnosed HGSCs.

Recent Advances in Genomic Approaches for the Detection of Homologous Recombination Deficiency

Accurate detection of homologous recombination deficiency (HRD) in cancer patients is paramount in clinical applications, as HRD confers sensitivity to poly(ADP-ribose) polymerase (PARP) inhibitors. With the advances in genome sequencing technology, mutational profiling on a genome-wide scale has become readily accessible, and our knowledge of the genomic consequences of HRD has been greatly expanded and refined. Here, we review the recent advances in HRD detection methods. We examine the copy number and structural alterations that often accompany the genome instability that results from HRD, describe the advantages of mutational signature-based methods that do not rely on specific gene mutations, and review some of the existing algorithms used for HRD detection. We also discuss the choice of sequencing platforms (panel, exome, or whole-genome) and catalog the HRD detection assays used in key PARP inhibitor trials.

ONCOLINER: A new solution for monitoring, improving, and harmonizing somatic variant calling across genomic oncology centers

The characterization of somatic genomic variation associated with the biology of tumors is fundamental for cancer research and personalized medicine, as it guides the reliability and impact of cancer studies and genomic-based decisions in clinical oncology. However, the quality and scope of tumor genome analysis across cancer research centers and hospitals are currently highly heterogeneous, limiting the consistency of tumor diagnoses across hospitals and the possibilities of data sharing and data integration across studies. With the aim of providing users with actionable and personalized recommendations for the overall enhancement and harmonization of somatic variant identification across research and clinical environments, we have developed ONCOLINER. Using specifically designed mosaic and tumorized genomes for the analysis of recall and precision across somatic SNVs, insertions or deletions (indels), and structural variants (SVs), we demonstrate that ONCOLINER is capable of improving and harmonizing genome analysis across three state-of-the-art variant discovery pipelines in genomic oncology.

Bioinformatics tools and resources for cancer and application

ABSTRACT

Tumor bioinformatics plays an important role in cancer research and precision medicine. The primary focus of traditional cancer research has been molecular and clinical studies of a number of fundamental pathways and genes. In recent years, driven by breakthroughs in high-throughput technologies, large-scale cancer omics data have accumulated rapidly. How to effectively utilize and share these data is particularly important. To address this crucial task, many computational tools and databases have been developed over the past few years. To help researchers quickly learn and understand the functions of these tools, in this review, we summarize publicly available bioinformatics tools and resources for pan-cancer multi-omics analysis, regulatory analysis of tumorigenesis, tumor treatment and prognosis, immune infiltration analysis, immune repertoire analysis, cancer driver gene and driver mutation analysis, and cancer single-cell analysis, which may further help researchers find more suitable tools for their research.

ALLSTAR: Inference of ReliAble CausaL RuLes between Somatic MuTAtions and CanceR Phenotypes

MOTIVATION

Recent advances in DNA sequencing technologies have allowed the detailed characterization of genomes in large cohorts of tumors, highlighting their extreme heterogeneity, with no two tumors sharing the same complement of somatic mutations. Such heterogeneity hinders our ability to identify somatic mutations important for the disease, including mutations that determine clinically relevant phenotypes (e.g., cancer subtypes). Several tools have been developed to identify somatic mutations related to cancer phenotypes. However, such tools identify correlations between somatic mutations and cancer phenotypes, with no guarantee of highlighting causal relations.

RESULTS

We describe ALLSTAR, a novel tool to infer reliable causal relations between somatic mutations and cancer phenotypes. ALLSTAR identifies reliable causal rules highlighting combinations of somatic mutations with the highest impact in terms of average effect on the phenotype. While we prove that the underlying computational problem is NP-hard, we develop a branch-and-bound approach that employs protein-protein interaction networks and novel bounds for pruning the search space, while properly correcting for multiple hypothesis testing. Our extensive experimental evaluation on synthetic data shows that our tool is able to identify reliable causal relations in large cancer cohorts. Moreover, the reliable causal rules identified by our tool in cancer data show that our approach identifies several somatic mutations known to be relevant for cancer phenotypes as well as novel biologically meaningful relations.

AVAILABILITY AND IMPLEMENTATION

Code, data, and scripts to reproduce the experiments available at https://github.com/VandinLab/ALLSTAR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of KRAS mutation-associated gut microbiota in colorectal cancer and construction of predictive machine learning model

Gut microbiota has demonstrated an increasingly important role in the onset and development of colorectal cancer (CRC). Nonetheless, the association between gut microbiota and KRAS mutation in CRC remains enigmatic. We conducted 16S rRNA sequencing on stool samples from 94 CRC patients and employed the linear discriminant analysis effect size algorithm to identify distinct gut microbiota between KRAS mutant and KRAS wild-type CRC patients. Transcriptome sequencing data from nine CRC patients were transformed into a matrix of immune infiltrating cells, which was then utilized to explore KRAS mutation-associated biological functions, including Gene Ontology items and Kyoto Encyclopedia of Genes and Genomes pathways. Subsequently, we analyzed the correlations among these KRAS mutation-associated gut microbiota, host immunity, and KRAS mutation-associated biological functions. At last, we developed a predictive random forest (RF) machine learning model to predict the KRAS mutation status in CRC patients, based on the gut microbiota associated with KRAS mutation. We identified a total of 26 differential gut microbiota between both groups. Intriguingly, a significant positive correlation was observed between Bifidobacterium spp. and mast cells, as well as between Bifidobacterium longum and chemokine receptor CX3CR1. Additionally, we also observed a notable negative correlation between Bifidobacterium and GOMF:proteasome binding. The RF model constructed using the KRAS mutation-associated gut microbiota demonstrated qualified efficacy in predicting the KRAS phenotype in CRC. Our study ascertained the presence of 26 KRAS mutation-associated gut microbiota in CRC and speculated that Bifidobacterium may exert an essential role in preventing CRC progression, which appeared to correlate with the upregulation of mast cells and CX3CR1 expression, as well as the downregulation of GOMF:proteasome binding. Furthermore, the RF model constructed on the basis of KRAS mutation-associated gut microbiota exhibited substantial potential in predicting KRAS mutation status in CRC patients.IMPORTANCEGut microbiota has emerged as an essential player in the onset and development of colorectal cancer (CRC). However, the relationship between gut microbiota and KRAS mutation in CRC remains elusive. Our study not only identified a total of 26 gut microbiota associated with KRAS mutation in CRC but also unveiled their significant correlations with tumor-infiltrating immune cells, immune-related genes, and biological pathways (Gene Ontology items and Kyoto Encyclopedia of Genes and Genomes pathways). We speculated that Bifidobacterium may play a crucial role in impeding CRC progression, potentially linked to the upregulation of mast cells and CX3CR1 expression, as well as the downregulation of GOMF:Proteasome binding. Furthermore, based on the KRAS mutation-associated gut microbiota, the RF model exhibited promising potential in the prediction of KRAS mutation status for CRC patients. Overall, the findings of our study offered fresh insights into microbiological research and clinical prediction of KRAS mutation status for CRC patients.

Single-mitosis dissection of acute and chronic DNA mutagenesis and repair

How chronic mutational processes and punctuated bursts of DNA damage drive evolution of the cancer genome is poorly understood. Here, we demonstrate a strategy to disentangle and quantify distinct mechanisms underlying genome evolution in single cells, during single mitoses and at single-strand resolution. To distinguish between chronic (reactive oxygen species (ROS)) and acute (ultraviolet light (UV)) mutagenesis, we microfluidically separate pairs of sister cells from the first mitosis following burst UV damage. Strikingly, UV mutations manifest as sister-specific events, revealing mirror-image mutation phasing genome-wide. In contrast, ROS mutagenesis in transcribed regions is reduced strand agnostically. Successive rounds of genome replication over persisting UV damage drives multiallelic variation at CC dinucleotides. Finally, we show that mutation phasing can be resolved to single strands across the entire genome of liver tumors from F1 mice. This strategy can be broadly used to distinguish the contributions of overlapping cancer relevant mutational processes.

No evidence that a transmissible cancer has shifted from emergence to endemism in Tasmanian devils

Tasmanian devils are endangered by a transmissible cancer known as Tasmanian devil facial tumour 1 (DFT1). A 2020 study by Patton et al. (Science 370, eabb9772 (doi:10.1126/science.abb9772)) used genome data from DFT1 tumours to produce a dated phylogenetic tree for this transmissible cancer lineage, and thence, using phylodynamics models, to estimate its epidemiological parameters and predict its future trajectory. It concluded that the effective reproduction number for DFT1 had declined to a value of one, and that the disease had shifted from emergence to endemism. We show that the study is based on erroneous mutation calls and flawed methodology, and that its conclusions cannot be substantiated.

A comprehensive performance evaluation, comparison, and integration of computational methods for detecting and estimating cross-contamination of human samples in cancer next-generation sequencing analysis

Cross-sample contamination is one of the major issues in next-generation sequencing (NGS)-based molecular assays. This type of contamination, even at very low levels, can significantly impact the results of an analysis, especially in the detection of somatic alterations in tumor samples. Several contamination identification tools have been developed and implemented as a crucial quality-control step in the routine NGS bioinformatic pipeline. However, no study has been published to comprehensively and systematically investigate, evaluate, and compare these computational methods in the cancer NGS analysis. In this study, we comprehensively investigated nine state-of-the-art computational methods for detecting cross-sample contamination. To explore their application in cancer NGS analysis, we further compared the performance of five representative tools by qualitative and quantitative analyses using in silico and simulated experimental NGS data. The results showed that Conpair achieved the best performance for identifying contamination and predicting the level of contamination in solid tumors NGS analysis. Moreover, based on Conpair, we developed a Python script, Contamination Source Predictor (ConSPr), to identify the source of contamination. We anticipate that this comprehensive survey and the proposed tool for predicting the source of contamination will assist researchers in selecting appropriate cross-contamination detection tools in cancer NGS analysis and inspire the development of computational methods for detecting sample cross-contamination and identifying its source in the future.

COSAP: Comparative Sequencing Analysis Platform

BACKGROUND

Recent improvements in sequencing technologies enabled detailed profiling of genomic features. These technologies mostly rely on short reads which are merged and compared to reference genome for variant identification. These operations should be done with computers due to the size and complexity of the data. The need for analysis software resulted in many programs for mapping, variant calling and annotation steps. Currently, most programs are either expensive enterprise software with proprietary code which makes access and verification very difficult or open-access programs that are mostly based on command-line operations without user interfaces and extensive documentation. Moreover, a high level of disagreement is observed among popular mapping and variant calling algorithms in multiple studies, which makes relying on a single algorithm unreliable. User-friendly open-source software tools that offer comparative analysis are an important need considering the growth of sequencing technologies.

RESULTS

Here, we propose Comparative Sequencing Analysis Platform (COSAP), an open-source platform that provides popular sequencing algorithms for SNV, indel, structural variant calling, copy number variation, microsatellite instability and fusion analysis and their annotations. COSAP is packed with a fully functional user-friendly web interface and a backend server which allows full independent deployment for both individual and institutional scales. COSAP is developed as a workflow management system and designed to enhance cooperation among scientists with different backgrounds. It is publicly available at https://cosap.bio and https://github.com/MBaysanLab/cosap/ . The source code of the frontend and backend services can be found at https://github.com/MBaysanLab/cosap-webapi/ and https://github.com/MBaysanLab/cosap_frontend/ respectively. All services are packed as Docker containers as well. Pipelines that combine algorithms can be customized and new algorithms can be added with minimal coding through modular structure.

CONCLUSIONS

COSAP simplifies and speeds up the process of DNA sequencing analyses providing commonly used algorithms for SNV, indel, structural variant calling, copy number variation, microsatellite instability and fusion analysis as well as their annotations. COSAP is packed with a fully functional user-friendly web interface and a backend server which allows full independent deployment for both individual and institutional scales. Standardized implementations of popular algorithms in a modular platform make comparisons much easier to assess the impact of alternative pipelines which is crucial in establishing reproducibility of sequencing analyses.

Accurate and sensitive mutational signature analysis with MuSiCal

Mutational signature analysis is a recent computational approach for interpreting somatic mutations in the genome. Its application to cancer data has enhanced our understanding of mutational forces driving tumorigenesis and demonstrated its potential to inform prognosis and treatment decisions. However, methodological challenges remain for discovering new signatures and assigning proper weights to existing signatures, thereby hindering broader clinical applications. Here we present Mutational Signature Calculator (MuSiCal), a rigorous analytical framework with algorithms that solve major problems in the standard workflow. Our simulation studies demonstrate that MuSiCal outperforms state-of-the-art algorithms for both signature discovery and assignment. By reanalyzing more than 2,700 cancer genomes, we provide an improved catalog of signatures and their assignments, discover nine indel signatures absent in the current catalog, resolve long-standing issues with the ambiguous 'flat' signatures and give insights into signatures with unknown etiologies. We expect MuSiCal and the improved catalog to be a step towards establishing best practices for mutational signature analysis.

An immunoinformatics and structural vaccinology approach to design a novel and potent multi-epitope base vaccine targeting Zika virus

Zika virus is an infectious virus, that belongs to Flaviviridae family, which is transferred to humans through mosquito vectors and severely threatens human health; but, apart from available resources, no effective and secure vaccine is present against Zika virus, to prevent such infections. In current study, we employed structural vaccinology approach to design an epitope-based vaccine against Zika virus, which is biocompatible, and secure and might trigger an adaptive and innate immune response by using computational approaches. We first retrieved the protein sequence from National Center for Biotechnology Information (NCBI) database and carried out for BLAST P. After BLAST P, predicted protein sequences were shortlisted and checked for allergic features and antigenic properties. Final sequence of Zika virus, with accession number (APO40588.1) was selected based on high antigenic score and non-allergenicity. Final protein sequence used various computational approaches including antigenicity testing, toxicity evaluation, allergenicity, and conservancy assessment to identify superior B-cell and T-cell epitopes. Two B-cell epitopes, five MHC-six MHC-II epitopes and I were used to construct an immunogenic multi-epitope-based vaccine by using suitable linkers. A 50S ribosomal protein was added at N terminal to improve the immunogenicity of vaccine. In molecular docking, strong interactions were presented between constructed vaccine and Toll-like receptor 9 (- 1100.6 kcal/mol), suggesting their possible relevance in the immunological response to vaccine. The molecular dynamics simulations ensure the dynamic and structural stability of constructed vaccine. The results of C-immune simulation revealed that constructed vaccine activate B and T lymphocytes which induce high level of antibodies and cytokines to combat Zika infection. The constructed vaccine is an effective biomarker with non-sensitization, nontoxicity; nonallergic, good immunogenicity, and antigenicity, however, experimental assays are required to verify the results of present study.

A method to improve the prediction performance of cancer-gene association by screening negative training samples through gene network data

This work focuses on data sampling in cancer-gene association prediction. Currently, researchers are using machine learning methods to predict genes that are more likely to produce cancer-causing mutations. To improve the performance of machine learning models, methods have been proposed, one of which is to improve the quality of the training data. Existing methods focus mainly on positive data, i.e. cancer driver genes, for screening selection. This paper proposes a low-cancer-related gene screening method based on gene network and graph theory algorithms to improve the negative samples selection. Genetic data with low cancer correlation is used as negative training samples. After experimental verification, using the negative samples screened by this method to train the cancer gene classification model can improve prediction performance. The biggest advantage of this method is that it can be easily combined with other methods that focus on enhancing the quality of positive training samples. It has been demonstrated that significant improvement is achieved by combining this method with three state-of-the-arts cancer gene prediction methods.

Immunoinformatics-based potential multi-peptide vaccine designing against Jamestown Canyon Virus (JCV) capable of eliciting cellular and humoral immune responses

Jamestown Canyon virus (JCV) is a deadly viral infection transmitted by various mosquito species. This mosquito-borne virus belongs to Bunyaviridae family, posing a high public health threat in the in tropical regions of the United States causing encephalitis in humans. Common symptoms of JCV include fever, headache, stiff neck, photophobia, nausea, vomiting, and seizures. Despite the availability of resources, there is currently no vaccine or drug available to combat JCV. The purpose of this study was to develop an epitope-based vaccine using immunoinformatics approaches. The vaccine aimed to be secure, efficient, bio-compatible, and capable of stimulating both innate and adaptive immune responses. In this study, the protein sequence of JCV was obtained from the NCBI database. Various bioinformatics methods, including toxicity evaluation, antigenicity testing, conservancy analysis, and allergenicity assessment were utilized to identify the most promising epitopes. Suitable linkers and adjuvant sequences were used in the design of vaccine construct. 50s ribosomal protein sequence was used as an adjuvant at the N-terminus of the construct. A total of 5 CTL, 5 HTL, and 5 linear B cell epitopes were selected based on non-allergenicity, immunological potential, and antigenicity scores to design a highly immunogenic multi-peptide vaccine construct. Strong interactions between the proposed vaccine and human immune receptors, i.e., TLR-2 and TLR-4, were revealed in a docking study using ClusPro software, suggesting their possible relevance in the immunological response to the vaccine. Immunological and physicochemical properties assessment ensured that the proposed vaccine demonstrated high immunogenicity, solubility and thermostability. Molecular dynamics simulations confirmed the strong binding affinities, as well as dynamic and structural stability of the proposed vaccine. Immune simulation suggest that the vaccine has the potential to effectively stimulate cellular and humoral immune responses to combat JCV infection. Experimental and clinical assays are required to validate the results of this study.

Most large structural variants in cancer genomes can be detected without long reads

Short-read sequencing is the workhorse of cancer genomics yet is thought to miss many structural variants (SVs), particularly large chromosomal alterations. To characterize missing SVs in short-read whole genomes, we analyzed 'loose ends'-local violations of mass balance between adjacent DNA segments. In the landscape of loose ends across 1,330 high-purity cancer whole genomes, most large (>10-kb) clonal SVs were fully resolved by short reads in the 87% of the human genome where copy number could be reliably measured. Some loose ends represent neotelomeres, which we propose as a hallmark of the alternative lengthening of telomeres phenotype. These pan-cancer findings were confirmed by long-molecule profiles of 38 breast cancer and melanoma cases. Our results indicate that aberrant homologous recombination is unlikely to drive the majority of large cancer SVs. Furthermore, analysis of mass balance in short-read whole genome data provides a surprisingly complete picture of cancer chromosomal structure.

ReConPlot: an R package for the visualization and interpretation of genomic rearrangements

MOTIVATION

Whole-genome sequencing studies of human tumours have revealed that complex forms of structural variation, collectively known as complex genome rearrangements (CGRs), are pervasive across diverse cancer types. Detection, classification, and mechanistic interpretation of CGRs requires the visualization of complex patterns of somatic copy number aberrations (SCNAs) and structural variants (SVs). However, there is a lack of tools specifically designed to facilitate the visualization and study of CGRs.

RESULTS

We present ReConPlot (REarrangement and COpy Number PLOT), an R package that provides functionalities for the joint visualization of SCNAs and SVs across one or multiple chromosomes. ReConPlot is based on the popular ggplot2 package, thus allowing customization of plots and the generation of publication-quality figures with minimal effort. Overall, ReConPlot facilitates the exploration, interpretation, and reporting of CGR patterns.

AVAILABILITY AND IMPLEMENTATION

The R package ReConPlot is available at https://github.com/cortes-ciriano-lab/ReConPlot. Detailed documentation and a tutorial with examples are provided with the package.

Discovery adductomics provides a comprehensive portrait of tissue-, age- and sex-specific DNA modifications in rodents and humans

DNA damage causes genomic instability underlying many diseases, with traditional analytical approaches providing minimal insight into the spectrum of DNA lesions in vivo. Here we used untargeted chromatography-coupled tandem mass spectrometry-based adductomics (LC-MS/MS) to begin to define the landscape of DNA modifications in rat and human tissues. A basis set of 114 putative DNA adducts was identified in heart, liver, brain, and kidney in 1-26-month-old rats and 111 in human heart and brain by 'stepped MRM' LC-MS/MS. Subsequent targeted analysis of these species revealed species-, tissue-, age- and sex-biases. Structural characterization of 10 selected adductomic signals as known DNA modifications validated the method and established confidence in the DNA origins of the signals. Along with strong tissue biases, we observed significant age-dependence for 36 adducts, including N2-CMdG, 5-HMdC and 8-Oxo-dG in rats and 1,N6-ϵdA in human heart, as well as sex biases for 67 adducts in rat tissues. These results demonstrate the potential of adductomics for discovering the true spectrum of disease-driving DNA adducts. Our dataset of 114 putative adducts serves as a resource for characterizing dozens of new forms of DNA damage, defining mechanisms of their formation and repair, and developing them as biomarkers of aging and disease.

Use of Optical Genome Mapping to Detect Structural Variants in Neuroblastoma

BACKGROUND

Neuroblastoma is the most common extracranial solid tumour in children, accounting for 15% of paediatric cancer deaths. Multiple genetic abnormalities have been identified as prognostically significant in neuroblastoma patients. Optical genome mapping (OGM) is a novel cytogenetic technique used to detect structural variants, which has not previously been tested in neuroblastoma. We used OGM to identify copy number and structural variants (SVs) in neuroblastoma which may have been missed by standard cytogenetic techniques.

METHODS

Five neuroblastoma cell lines (SH-SY5Y, NBLW, GI-ME-N, NB1691 and SK-N-BE2(C)) and two neuroblastoma tumours were analysed using OGM with the Bionano Saphyr® instrument. The results were analysed using Bionano Access software and compared to previous genetic analyses including G-band karyotyping, FISH (fluorescent in situ hybridisation), single-nucleotide polymorphism (SNP) array and RNA fusion panels for cell lines, and SNP arrays and whole genome sequencing (WGS) for tumours.

RESULTS

OGM detected copy number abnormalities found using previous methods and provided estimates for absolute copy numbers of amplified genes. OGM identified novel SVs, including fusion genes in two cell lines of potential clinical significance.

CONCLUSIONS

OGM can reliably detect clinically significant structural and copy number variations in a single test. OGM may prove to be more time- and cost-effective than current standard cytogenetic techniques for neuroblastoma.

Identification of biomarkers related to immune and inflammation in membranous nephropathy: comprehensive bioinformatic analysis and validation

Background

Membranous nephropathy (MN) is an autoimmune glomerular disease that is predominantly mediated by immune complex deposition and complement activation. The aim of this study was to identify key biomarkers of MN and investigate their association with immune-related mechanisms, inflammatory cytokines, chemokines and chemokine receptors (CCRs).

Methods

MN cohort microarray expression data were downloaded from the GEO database. Differentially expressed genes (DEGs) in MN were identified, and hub genes were determined using a protein-protein interaction (PPI) network. The relationships between immune-related hub genes, immune cells, CCRs, and inflammatory cytokines were examined using immune infiltration analysis, gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA). Finally, the immune-related hub genes in MN were validated using ELISA.

Results

In total, 501 DEGs were identified. Enrichment analysis revealed the involvement of immune- and cytokine-related pathways in MN progression. Using WGCNA and immune infiltration analysis, 2 immune-related hub genes (CYBB and CSF1R) were identified. These genes exhibited significant correlations with a wide range of immune cells and were found to participate in B cell/T cell receptor and chemokine signaling pathways. In addition, the expressions of 2 immune-related hub genes were positively correlated with the expression of CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2.

Conclusion

Our study identified CSF1 and CYBB as immune-related hub genes that potentially influence the expression of CCRs and pro-inflammatory cytokines (CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2). CSF1 and CYBB may be potential biomarkers for MN progression, providing a perspective for diagnostic and immunotherapeutic targets of MN.

Proteogenomics of different urothelial bladder cancer stages reveals distinct molecular features for papillary cancer and carcinoma in situ

The progression of urothelial bladder cancer (UC) is a complicated multi-step process. We perform a comprehensive multi-omics analysis of 448 samples from 190 UC patients, covering the whole spectrum of disease stages and grades. Proteogenomic integration analysis indicates the mutations of HRAS regulated mTOR signaling to form urothelial papilloma rather than papillary urothelial cancer (PUC). DNA damage is a key signaling pathway in the progression of carcinoma in situ (CIS) and related to APOBEC signature. Glucolipid metabolism increase and lower immune cell infiltration are associated with PUC compared to CIS. Proteomic analysis distinguishes the origins of invasive tumors (PUC-derived and CIS-derived), related to distinct clinical prognosis and molecular features. Additionally, loss of RBPMS, associated with CIS-derived tumors, is validated to increase the activity of AP-1 and promote metastasis. This study reveals the characteristics of two distinct branches (PUC and CIS) of UC progression and may eventually benefit clinical practice.

Computational Quantification of Cancer Immunoediting

The remarkable success of cancer immunotherapy has revolutionized cancer treatment, emphasizing the importance of tumor-immune interactions in cancer evolution and treatment. Cancer immunoediting describes the dual effect of tumor-immune interactions: inhibiting tumor growth by destroying tumor cells and facilitating tumor escape by shaping tumor immunogenicity. To better understand tumor-immune interactions, it is critical to develop computational methods to measure the extent of cancer immunoediting. In this review, we provide a comprehensive overview of the computational methods for quantifying cancer immunoediting. We focus on describing the basic ideas, computational processes, advantages, limitations, and influential factors. We also summarize recent advances in quantifying cancer immunoediting studies and highlight future research directions. As the methods for quantifying cancer immunoediting are continuously improved, future research will further help define the role of immunity in tumorigenesis and hopefully provide a basis for the design of new personalized cancer immunotherapy strategies.

Construction of stemness gene score by bulk and single-cell transcriptome to characterize the prognosis of breast cancer

Breast cancer (BC) is a heterogeneous disease characterized by significant differences in prognosis and therapy response. Numerous prognostic tools have been developed for breast cancer. Usually these tools are based on bulk RNA-sequencing (RNA-Seq) and ignore tumor heterogeneity. Consequently, the goal of this study was to construct a single-cell level tool for predicting the prognosis of BC patients. In this study, we constructed a stemness-risk gene score (SGS) model based on single-sample gene set enrichment analysis (ssGSEA). Patients were divided into two groups based on the median SGS. Patients with a high SGS scores had a significantly worse prognosis than those with a low SGS, and these groups exhibited differences in several tumor characteristics, such as immune infiltration, gene mutations, and copy number variants. Our results indicate that the SGS is a reliable tool for predicting prognosis and response to immunotherapy in BC patients.

Structural underpinnings of mutation rate variations in the human genome

Single nucleotide mutation rates have critical implications for human evolution and genetic diseases. Importantly, the rates vary substantially across the genome and the principles underlying such variations remain poorly understood. A recent model explained much of this variation by considering higher-order nucleotide interactions in the 7-mer sequence context around mutated nucleotides. This model's success implicates a connection between DNA shape and mutation rates. DNA shape, i.e. structural properties like helical twist and tilt, is known to capture interactions between nucleotides within a local context. Thus, we hypothesized that changes in DNA shape features at and around mutated positions can explain mutation rate variations in the human genome. Indeed, DNA shape-based models of mutation rates showed similar or improved performance over current nucleotide sequence-based models. These models accurately characterized mutation hotspots in the human genome and revealed the shape features whose interactions underlie mutation rate variations. DNA shape also impacts mutation rates within putative functional regions like transcription factor binding sites where we find a strong association between DNA shape and position-specific mutation rates. This work demonstrates the structural underpinnings of nucleotide mutations in the human genome and lays the groundwork for future models of genetic variations to incorporate DNA shape.

Chromothripsis detection with multiple myeloma patients based on deep graph learning

MOTIVATION

Chromothripsis, associated with poor clinical outcomes, is prognostically vital in multiple myeloma. The catastrophic event is reported to be detectable prior to the progression of multiple myeloma. As a result, chromothripsis detection can contribute to risk estimation and early treatment guidelines for multiple myeloma patients. However, manual diagnosis remains the gold standard approach to detect chromothripsis events with the whole-genome sequencing technology to retrieve both copy number variation (CNV) and structural variation data. Meanwhile, CNV data are much easier to obtain than structural variation data. Hence, in order to reduce the reliance on human experts' efforts and structural variation data extraction, it is necessary to establish a reliable and accurate chromothripsis detection method based on CNV data.

RESULTS

To address those issues, we propose a method to detect chromothripsis solely based on CNV data. With the help of structure learning, the intrinsic relationship-directed acyclic graph of CNV features is inferred to derive a CNV embedding graph (i.e. CNV-DAG). Subsequently, a neural network based on Graph Transformer, local feature extraction, and non-linear feature interaction, is proposed with the embedding graph as the input to distinguish whether the chromothripsis event occurs. Ablation experiments, clustering, and feature importance analysis are also conducted to enable the proposed model to be explained by capturing mechanistic insights.

AVAILABILITY AND IMPLEMENTATION

The source code and data are freely available at https://github.com/luvyfdawnYu/CNV_chromothripsis.

Detecting gene breakpoints in noisy genome sequences using position-annotated colored de-Bruijn graphs

BACKGROUND

Identifying the locations of gene breakpoints between species of different taxonomic groups can provide useful insights into the underlying evolutionary processes. Given the exact locations of their genes, the breakpoints can be computed without much effort. However, often, existing gene annotations are erroneous, or only nucleotide sequences are available. Especially in mitochondrial genomes, high variations in gene orders are usually accompanied by a high degree of sequence inconsistencies. This makes accurately locating breakpoints in mitogenomic nucleotide sequences a challenging task.

RESULTS

This contribution presents a novel method for detecting gene breakpoints in the nucleotide sequences of complete mitochondrial genomes, taking into account possible high substitution rates. The method is implemented in the software package DeBBI. DeBBI allows to analyze transposition- and inversion-based breakpoints independently and uses a parallel program design, allowing to make use of modern multi-processor systems. Extensive tests on synthetic data sets, covering a broad range of sequence dissimilarities and different numbers of introduced breakpoints, demonstrate DeBBI 's ability to produce accurate results. Case studies using species of various taxonomic groups further show DeBBI 's applicability to real-life data. While (some) multiple sequence alignment tools can also be used for the task at hand, we demonstrate that especially gene breaks between short, poorly conserved tRNA genes can be detected more frequently with the proposed approach.

CONCLUSION

The proposed method constructs a position-annotated de-Bruijn graph of the input sequences. Using a heuristic algorithm, this graph is searched for particular structures, called bulges, which may be associated with the breakpoint locations. Despite the large size of these structures, the algorithm only requires a small number of graph traversal steps.

Identification of Driver Epistatic Gene Pairs Combining Germline and Somatic Mutations in Cancer

Cancer arises from the complex interplay of various factors. Traditionally, the identification of driver genes focuses primarily on the analysis of somatic mutations. We describe a new method for the detection of driver gene pairs based on an epistasis analysis that considers both germline and somatic variations. Specifically, the identification of significantly mutated gene pairs entails the calculation of a contingency table, wherein one of the co-mutated genes can exhibit a germline variant. By adopting this approach, it is possible to select gene pairs in which the individual genes do not exhibit significant associations with cancer. Finally, a survival analysis is used to select clinically relevant gene pairs. To test the efficacy of the new algorithm, we analyzed the colon adenocarcinoma (COAD) and lung adenocarcinoma (LUAD) samples available at The Cancer Genome Atlas (TCGA). In the analysis of the COAD and LUAD samples, we identify epistatic gene pairs significantly mutated in tumor tissue with respect to normal tissue. We believe that further analysis of the gene pairs detected by our method will unveil new biological insights, enhancing a better description of the cancer mechanism.

Prediction and Verification of Potential Therapeutic Targets for Non-Responders to Infliximab in Ulcerative Colitis

Background

Infliximab (IFX) has been widely used in ulcerative colitis (UC) patients. However, the subsequent effective treatment of IFX non-response in UC patients remains a challenge. This study aims to predict potential therapeutic targets for non-responders by performing a bioinformatic analysis of the data in the Gene Expression Omnibus (GEO) database and validation by biopsies.

Methods

Colonic mucosal biopsies expression profiles of IFX-treated UC patients (GSE73661, GSE16879) were utilized to predict potential therapeutic targets. Bioinformatics analyses were used to explore potential biological mechanisms. CytoHubba was performed to screen hub genes. We used a validation dataset and colonic mucosal biopsies of UC patients to validate hub genes.

Results

A total of 147 DEGs were identified (119 upregulated genes and 28 downregulated genes). GSEA showed that DEGs in GSE73661 were enriched in the pathways of the cytokine-cytokine receptor, the chemokine, and the adhesion molecules system. Based on the PPI network analysis, we identified four hub genes (and the transcription factor NF-κB). Then, we validate the expression of hub genes by reverse transcription-polymerase chain reaction (RT-PCR). We found higher expression of IL-6, IL1B, CXCL8, and CCL2 in non-responders compared to responders.

Conclusion

In summary, four potential targets (IL-6, IL1B, CXCL8, and CCL2) were finally identified by performing a bioinformatics analysis of the datasets in the GEO database. Their expression was confirmed in colonic mucosal biopsies of patients with UC. These results can help to further explore the mechanism of non-responders to IFX in UC and to provide potential targets for their subsequent treatment.

The non-coding competing endogenous RNAs in acute myeloid leukemia: biological and clinical implications

Acute myeloid leukemia (AML) is a hematologic carcinoma that has seen a considerable improvement in patient prognosis because of genetic diagnostics and molecularly-targeted therapies. Nevertheless, recurrence and drug resistance remain significant obstacles to leukemia treatment. It is critical to investigate the underlying molecular mechanisms and find solutions. Non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), circular RNAs, long non-coding RNAs, and pseudogenes, have been found to be crucial components in driving cancer. The competing endogenous RNA (ceRNA) mechanism has expanded the complexity of miRNA-mediated gene regulation. A great deal of literature has shown that ncRNAs are essential to the biological functions of the ceRNA network (ceRNET). NcRNAs can compete for the same miRNA response elements to influence miRNA-target RNA interactions. Recent evidence suggests that ceRNA might be a potential biomarker and therapeutic strategy. So far, however, there have been no comprehensive studies on ceRNET about AML. What is not yet clear is the clinical application of ceRNA in AML. This study attempts to summarize the development of research on the related ceRNAs in AML and the roles of ncRNAs in ceRNET. We also briefly describe the mechanisms of ceRNA and ceRNET. What's more significant is that we explore the clinical value of ceRNAs to provide accurate diagnostic and prognostic biomarkers as well as therapeutic targets. Finally, limitations and prospects are considered.

Variant calling and benchmarking in an era of complete human genome sequences

Genetic variant calling from DNA sequencing has enabled understanding of germline variation in hundreds of thousands of humans. Sequencing technologies and variant-calling methods have advanced rapidly, routinely providing reliable variant calls in most of the human genome. We describe how advances in long reads, deep learning, de novo assembly and pangenomes have expanded access to variant calls in increasingly challenging, repetitive genomic regions, including medically relevant regions, and how new benchmark sets and benchmarking methods illuminate their strengths and limitations. Finally, we explore the possible future of more complete characterization of human genome variation in light of the recent completion of a telomere-to-telomere human genome reference assembly and human pangenomes, and we consider the innovations needed to benchmark their newly accessible repetitive regions and complex variants.

Analysis of potential immune-related genes involved in the pathogenesis of ischemia-reperfusion injury following liver transplantation

Background

Hepatic ischemia-reperfusion (I/R) injury is an unavoidable pathological process that occurs after liver transplantation. However, the immune-related molecular mechanism still remains unclear. This study aims to further explore the biological mechanisms of immune-related genes in hepatic I/R injury.

Methods

Gene microarray data was downloaded from the Gene Expression Omnibus (GEO) expression profile database and the differentially expressed genes (DEGs) were taken for intersection. After identifying common DEGs, functional annotation, protein-protein interaction (PPI) network, and modular construction were performed. The immune-related hub genes were obtained, which their upstream transcription factors and non-RNAs were predicted. Validation of the hub genes expression and immune infiltration were performed in a mouse model of hepatic I/R injury.

Results

A total of 71 common DEGs were obtained from three datasets (GSE12720, GSE14951, GSE15480). The GO and KEGG enrichment analysis results indicated that immune and inflammatory response played an important role in hepatic I/R injury. Finally, 9 immune-related hub genes were identified by intersecting cytoHubba with immune-related genes, including SOCS3, JUND, CCL4, NFKBIA, CXCL8, ICAM1, IRF1, TNFAIP3, and JUN.

Conclusion

Our study revealed the importance of the immune and inflammatory response in I/R injury following liver transplantation and provided new insights into the therapeutic of hepatic I/R injury.

The hub genes and their potential regulatory mechanisms in chronic spontaneous urticaria revealed by integrated transcriptional expression analysis

Chronic spontaneous urticaria (CSU) is a recurrent disease characterized by wheals and or angioedema, and its pathogenesis is still unclear. The microarray datasets of skin tissue from CSU patients and healthy controls were integrated and analysed in Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified using the NetworkAnalyst tool. Then, the Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Subsequently, a protein-protein interaction (PPI) network of DEGs was constructed by STRING and the related hub genes were identified through the MOCDE tool. The potential miRNAs targeting hub genes were predicted based on the intersection of three online databases, namely TargetScanHuman, TargetBase and miRNet. Differentially expressed lncRNAs (DElncRNAs) was performed using the GEO2R tool. The potential miRNAs targeting DElncRNAs were predicted through miRNet. Finally, the shared miRNAs targeting both hub genes and DElncRNAs were used to construct an mRNA/miRNA/lncRNA regulatory network. A total of 296 DEGs were obtained, which were mainly enriched in inflammatory and immune responses. Further, 14 hub genes were identified by the PPI network of DEGs. Clinical correlation analysis showed that the mRNA expressions of S100A7, S100A8, S100A9, S100A12, IL6 and SOCS3 in CSU were positively correlated with the 7-day urticaria activity score (UAS7), and their potential diagnostic value was supported by the receiver operating characteristic curve (ROC) analysis. Five up-regulated lncRNAs in the cytoplasm were obtained by DElncRNAs analysis. The ROC analysis showed that PVT1, SNHG3 and ZBTB20 - AS1 was of potential diagnostic value for CSU. Eight shared miRNAs targeting both hub genes and DElncRNAs were identified and used to construct a competing endogenous RNA (ceRNA) network. It was found that the IL-6/miR - 149 - 5p/ZBTB20 - AS1 axis might play an important role in the activation of mast cells in CSU. IL-6 and its related regulatory molecules may be used as potential diagnostic markers and therapeutic targets for CSU.

Developing a multiepitope vaccine for the prevention of SARS-CoV-2 and monkeypox virus co-infection: A reverse vaccinology analysis

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and monkeypox virus (MPXV) severely threaten human health; however, currently, no vaccine can prevent a co-infection with both viruses.

METHODS

Five antigens were selected to predict dominant T and B cell epitopes screened for immunogenicity, antigenicity, toxicity, and sensitization. After screening, all antigens joined in the construction of a novel multiepitope vaccine. The physicochemical and immunological characteristics, and secondary and tertiary structures of the vaccine were predicted and analyzed using bio- and immunoinformatics. Finally, codon optimization and cloning in-silico were performed.

RESULTS

A new multiepitope vaccine, named S7M8, was constructed based on four helper T lymphocyte (HTL) epitopes, six cytotoxic T lymphocyte (CTL) epitopes, five B cell epitopes, as well as Toll-like receptor (TLR) agonists. The antigenicity and immunogenicity scores of the S7M8 vaccine were 0.907374 and 0.6552, respectively. The S7M8 vaccine was comprised of 26.96% α-helices, the optimized Z-value of the tertiary structure was -5.92, and the favored area after majorization in the Ramachandran plot was 84.54%. Molecular docking showed that the S7M8 vaccine could tightly bind to TLR2 (-1100.6 kcal/mol) and TLR4 (-950.3 kcal/mol). In addition, the immune stimulation prediction indicated that the S7M8 vaccine could activate T and B lymphocytes to produce high levels of Th1 cytokines and antibodies.

CONCLUSION

S7M8 is a promising biomarker with good antigenicity, immunogenicity, non-toxicity, and non-sensitization. The S7M8 vaccine can trigger significantly high levels of Th1 cytokines and antibodies and may be a potentially powerful tool in preventing SARS-CoV-2 and MPXV.

Nanotechnology-Driven Delivery Systems in Inoculation Therapies

Nanotechnology and genomics are the newest allies of inoculation design. In recent years, nucleic acids have been targeted as sources of therapeutics to stimulate immune responses, to both fight disease and create memory to trigger further responses to threat. A myriad of promising findings in cancer research and virology has been reported in the current literature. Nanosystems are demonstrating their capabilities as efficient carriers, improving the efficacy of drug delivery, including nucleic acids as therapeutics, at focal sites, in living systems. This chapter approaches major elements involved in the successful use of nanotechnology as delivery platforms to optimise the efficacy of nucleic acids-driven therapeutics, particularly mRNA vectors as coding engines for targeted viral proteins. Latest findings in nanotechnological design are highlighted, key discoveries associated with the success of nanodelivery platforms are presented, and key characteristics of nanodelivery systems in nucleic acids-based vaccine technology are discussed, to illustrate their distinct advantages and disadvantages.

Cellular barcoding to decipher clonal dynamics in disease

Cellular barcodes are distinct DNA sequences that enable one to track specific cells across time or space. Recent advances in our ability to detect natural or synthetic cellular barcodes, paired with single-cell readouts of cell state, have markedly increased our knowledge of clonal dynamics and genealogies of the cells that compose a variety of tissues and organs. These advances hold promise to redefine our view of human disease. Here, we provide an overview of cellular barcoding approaches, discuss applications to gain new insights into disease mechanisms, and provide an outlook on future applications. We discuss unanticipated insights gained through barcoding in studies of cancer and blood cell production and describe how barcoding can be applied to a growing array of medical fields, particularly with the increasing recognition of clonal contributions in human diseases.

Unraveling the Structural Variations of Early-Stage Mycosis Fungoides-CD3 Based Purification and Third Generation Sequencing as Novel Tools for the Genomic Landscape in CTCL

Mycosis fungoides (MF) is the most common cutaneous T-cell lymphoma (CTCL). At present, knowledge of genetic changes in early-stage MF is insufficient. Additionally, low tumor cell fraction renders calling of copy-number variations as the predominant mutations in MF challenging, thereby impeding further investigations. We show that enrichment of T cells from a biopsy of a stage I MF patient greatly increases tumor fraction. This improvement enables accurate calling of recurrent MF copy-number variants such as ARID1A and CDKN2A deletion and STAT5 amplification, undetected in the unprocessed biopsy. Furthermore, we demonstrate that application of long-read nanopore sequencing is especially useful for the structural variant rich CTCL. We detect the structural variants underlying recurrent MF copy-number variants and show phasing of multiple breakpoints into complex structural variant haplotypes. Additionally, we record multiple occurrences of templated insertion structural variants in this sample. Taken together, this study suggests a workflow to make the early stages of MF accessible for genetic analysis, and indicates long-read sequencing as a major tool for genetic analysis for MF.