Establishing a novel colorectal cancer predictive model based on unique gut microbial single nucleotide variant markers.
Ontology highlight
ABSTRACT:
SUBMITTER: Ma C
PROVIDER: S-EPMC7808391 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Surrogate biomarkers for metastatic colorectal cancer (mCRC) are urgently needed to achieve the best outcomes for targeted therapy.A clinical association analysis was performed to examine the three single-nucleotide polymorphisms (SNPs) that were previously proposed as markers of chemosensitivity to the cetuximab (124 patients) and bevacizumab regimens (100 patients) in mCRC patients. In addition, biological correlations were examined for the candidate SNPs in terms of their regulatory pathway.For cetuximab regimens, patients homozygous for the wild-type alleles (GG) of LIFR rs3729740 exhibited a 1.9 times greater overall response rate (ORR) and 1.4 months longer progression-free survival (PFS) than those homozygous or heterozygous for the mutant allele (GA and AA; P=0.022 and 0.027, respectively). For bevacizumab regimens, patients homozygous for the minor alleles (TT) of ANXA11 rs1049550 exhibited an ORR twice as high as those homozygous or heterozygous for the ancestral allele (CC and CT; P=0.031). Overall response rate gain was achieved up to 10% in patients with wild-type LIFR rs3729740 patients either with wild-type KRAS or skin toxicity (P=0.001) respectively. Specifically in clones treated with cetuximab and bevacizumab regimens, active p-ERK and MMP-9 expressions were significantly reduced in clones expressing wild-type LIFR rs3729740 (P=0.044) and in those expressing minor-type ANXA11 rs1049550 (P=0.007), respectively.LIFR rs3729740 and possibly ANXA11 rs1049550 may be useful as biomarkers for predicting whether mCRC patients are sensitive to relevant target regimens, although further validation in large cohorts is needed.
| S-EPMC3658526 | biostudies-literature
Project description:Globally, colorectal cancer (CRC) is the second most common cause of mortality worldwide. Considerable evidence indicates that dysbiosis of the gut microbial community and its metabolite secretions play a fundamental role in advanced adenoma (ADA) and CRC development and progression. This study is a systematic review that aims to assess the clinical association between gut microbial markers and/or gut and circulating metabolites with ADA and CRC. Five electronic databases were searched by four independent reviewers. Only controlled trials that compared ADA and/or CRC with healthy control (HC) using either untargeted (16s rRNA gene or whole genome sequencing) or targeted (gene-based real-time PCR) identification methods for gut microbiome profile, or untargeted or targeted metabolite profiling approaches from the gut or serum/plasma, were eligible. Three independent reviewers evaluated the quality of the studies using the Cochrane Handbook for Systematic Reviews of Interventions. Twenty-four studies were eligible. We identified strong evidence of two microbial markers Fusobacterium and Porphyromonas for ADA vs. CRC, and nine microbial markers Lachnospiraceae-Lachnoclostridium, Ruminococcaceae-Ruminococcus, Parvimonas spp., Parvimonas micra, Enterobacteriaceae, Fusobacterium spp., Bacteroides, Peptostreptococcus-Peptostreptococcus stomatis, Clostridia spp.-Clostridium hylemonae, Clostridium symbiosum, and Porphyromonas-Porphyromonas asaccharolytica for CRC vs. HC. The remaining metabolite marker evidence between the various groups, including ADA vs. HC, ADA vs. HC, and CRC vs. HC, was not of sufficient quality to support additional findings. The identified gut microbial markers can be used in a panel for diagnosing ADA and/or CRC. Further research in the metabolite markers area is needed to evaluate the possibility to use in diagnostic or prognostic markers for colorectal cancer.
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Project description:The recent widespread application of whole-genome sequencing (WGS) for microbial disease investigations has spurred the development of new bioinformatics tools, including a notable proliferation of phylogenomics pipelines designed for infectious disease surveillance and outbreak investigation. Transitioning the use of WGS data out of the research laboratory and into the front lines of surveillance and outbreak response requires user-friendly, reproducible and scalable pipelines that have been well validated. Single Nucleotide Variant Phylogenomics (SNVPhyl) is a bioinformatics pipeline for identifying high-quality single-nucleotide variants (SNVs) and constructing a whole-genome phylogeny from a collection of WGS reads and a reference genome. Individual pipeline components are integrated into the Galaxy bioinformatics framework, enabling data analysis in a user-friendly, reproducible and scalable environment. We show that SNVPhyl can detect SNVs with high sensitivity and specificity, and identify and remove regions of high SNV density (indicative of recombination). SNVPhyl is able to correctly distinguish outbreak from non-outbreak isolates across a range of variant-calling settings, sequencing-coverage thresholds or in the presence of contamination. SNVPhyl is available as a Galaxy workflow, Docker and virtual machine images, and a Unix-based command-line application. SNVPhyl is released under the Apache 2.0 license and available at http://snvphyl.readthedocs.io/ or at https://github.com/phac-nml/snvphyl-galaxy.
| S-EPMC5628696 | biostudies-literature
Project description:In silico methods for detecting functionally relevant genetic variants are important for identifying genetic markers of human inherited disease. Much research has focused on protein-coding variants since coding regions have well-defined physicochemical and functional properties. However, many bioinformatics tools are not applicable to variants outside coding regions. Here, we increase the classification performance of our regulatory single-nucleotide variant predictor (RSVP) for variants that cause regulatory abnormalities from an AUC of 0.90-0.97 by incorporating genomic regions identified by the ENCODE project into RSVP. RSVP is comparable to a recently published tool, Genome-Wide Annotation of Variants (GWAVA); both RSVP and GWAVA perform better on regulatory variants than a traditional variant predictor, combined annotation-dependent depletion (CADD). However, our method outperforms GWAVA on variants located at similar distances to the transcription start site as the positive set (AUC: 0.96) as compared with GWAVA (AUC: 0.71). Much of this disparity is due to RSVP's incorporation of features pertaining to the nearest gene (expression, GO terms, etc.), which are not included in GWAVA. Our findings hold out the promise of a framework for the assessment of all functional regulatory variants, providing a means to predict which rare or de novo variants are of pathogenic significance.
| S-EPMC6192032 | biostudies-literature
Project description:Innovations in sequencing technologies have allowed biologists to make incredible advances in understanding biological systems. As experience grows, researchers increasingly recognize that analyzing the wealth of data provided by these new sequencing platforms requires careful attention to detail for robust results. Thus far, much of the scientific Communit's focus for use in bacterial genomics has been on evaluating genome assembly algorithms and rigorously validating assembly program performance. Missing, however, is a focus on critical evaluation of variant callers for these genomes. Variant calling is essential for comparative genomics as it yields insights into nucleotide-level organismal differences. Variant calling is a multistep process with a host of potential error sources that may lead to incorrect variant calls. Identifying and resolving these incorrect calls is critical for bacterial genomics to advance. The goal of this review is to provide guidance on validating algorithms and pipelines used in variant calling for bacterial genomics. First, we will provide an overview of the variant calling procedures and the potential sources of error associated with the methods. We will then identify appropriate datasets for use in evaluating algorithms and describe statistical methods for evaluating algorithm performance. As variant calling moves from basic research to the applied setting, standardized methods for performance evaluation and reporting are required; it is our hope that this review provides the groundwork for the development of these standards.
| S-EPMC4493402 | biostudies-literature
Project description:Colorectal cancer (CRC) is the third most commonly diagnosed cancer in both men and women in the United States, with an estimated 153,760 new cases predicted for 2007. Since the 1960s, 5-fluorouracil (5-FU) has remained the mainstay of therapeutic options in the treatment of advanced CRC, with response rates of 20% to 25%. The introduction of newer agents such as oxaliplatin and irinotecan in combination with 5-FU has increased response rates to 40% to 50% in advanced disease and improved overall survival. The development of monoclonal antibodies targeting the epidermal growth factor receptor or vascular endothelial growth factor has demonstrated additional clinical benefit for patients with metastatic disease. However, many patients succumb to their disease, and a significant proportion will experience severe chemotherapy-associated toxicities while deriving little or no benefit. To improve the treatment of CRC, efforts must be directed toward the identification of patients who are likely to respond to a specific therapy, those who will experience severe toxicities, and those who will benefit from chemotherapy in the adjuvant setting. However, the utility of individual markers of response, toxicity, and disease recurrence remains in question. Efforts are now under way to develop multimarker profiles that can more accurately predict disease response. In this review, we discuss both predictive and prognostic markers identified in the treatment of CRC in terms of their robustness and their ability to assist the clinician in developing the most efficacious and least toxic therapeutic strategy for each patient.
| S-EPMC2631215 | biostudies-other
Project description:Background:Colorectal cancer has significant impact on individuals and healthcare systems. Many genes have been identified to influence its pathogenesis. However, the genetic basis of mucinous tumor histology, an aggressive subtype of colorectal cancer, is currently not well-known. This study aimed to identify common and rare genetic variations that are associated with the mucinous tumor phenotype. Methods:Genome-wide single nucleotide polymorphism (SNP) data was investigated in a colorectal cancer patient cohort (n?=?505). Association analyses were performed for 729,373 common SNPs and 275,645 rare SNPs. Common SNP association analysis was performed using univariable and multivariable logistic regression under different genetic models. Rare-variant association analysis was performed using a multi-marker test. Results:No associations reached the traditional genome-wide significance. However, promising genetic associations were identified. The identified common SNPs significantly improved the discriminatory accuracy of the model for mucinous tumor phenotype. Specifically, the area under the receiver operating characteristic curve increased from 0.703 (95% CI: 0.634-0.773) to 0.916 (95% CI: 0.873-0.960) when considering the most significant SNPs. Additionally, the rare variant analysis identified a number of genetic regions that potentially contain causal rare variants associated with the mucinous tumor phenotype. Conclusions:This is the first study applying both common and rare variant analyses to identify genetic associations with mucinous tumor phenotype using a genome-wide genotype data. Our results suggested novel associations with mucinous tumors. Once confirmed, these results will not only help us understand the biological basis of mucinous histology, but may also help develop targeted treatment options for mucinous tumors.
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Project description:Current variant callers are not suitable for single-cell DNA sequencing, as they do not account for allelic dropout, false-positive errors and coverage nonuniformity. We developed Monovar (https://bitbucket.org/hamimzafar/monovar), a statistical method for detecting and genotyping single-nucleotide variants in single-cell data. Monovar exhibited superior performance over standard algorithms on benchmarks and in identifying driver mutations and delineating clonal substructure in three different human tumor data sets.
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Project description:The aim of this study was to investigate the association between the susceptibility of severe oral mucositis (OM) in Chinese nasopharyngeal carcinoma (NPC) patients treated with radiotherapy and single nucleotide polymorphisms (SNPs) across the whole genome. SNPs were screened in a total of 24 patients with NPC and an additional 6 were subjected to mRNA expression analysis. Patients were subdivided into CTC 0-2 (CTC toxicity grade 0, 1, and 2) and CTC 3+ (CTC toxicity grade 3 and above) groups according to their CTC (common toxicity criteria) scores. The GTEx dataset was used to performed eQTL analyses and in-vitro functional assays were performed for eQTL-associated genes. Our data identified 7 functional SNPs associated with the development of OM. We observed that rs11081899-A, located in the 5'-UTR of the ZNF24 gene, was significantly correlated with a higher risk of severe mucositis (OR = 14.631, 95% CI = 2.61-105.46, p = 1.2 × 10-4), and positively associated with ZNF24 mRNA expression (p = 4.1 × 10-6) from GTEx dataset. In addition, high ZNF24 mRNA expression was associated with severe OM in patients with NPC (p = 0.02). Further functional assays revealed that ZNF24 knockdown reduced p65 expression and suppressed TNF-α-induced NF-κB activation and pro-inflammatory cytokines release. These findings suggested that rs11081899-A may be a genetic susceptibility factor for radiation-induced OM in patients with NPC, although its value in clinical application needs to be further verified in a large cohort. Also, we suggested that downregulation of ZNF24 may attenuate the development of mucositis by suppressing NF-κB activation.
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Project description:The structure of the human gut microbial community is determined by host genetics and environmental factors, where alterations in its structure have been associated with the onset of different diseases. Establishing a defined human gut microbial community within inbred rodent models provides a means to study microbial-related pathologies, however, an in-depth comparison of the established human gut microbiota in the different models is lacking. We compared the efficiency of establishing the bacterial component of a defined human microbial community within germ-free (GF) rats, GF mice, and antibiotic-treated specific pathogen-free mice. Remarkable differences were observed between the different rodent models. While the majority of abundant human-donor bacterial phylotypes were established in the GF rats, only a subset was present in the GF mice. Despite the fact that members of the phylum Bacteriodetes were well established in all rodent models, mice enriched for phylotypes related to species of Bacteroides. In contrary to the efficiency of Clostridiales to populate the GF rat in relative proportions to that of the human-donor, members of Clostridia cluster IV only poorly colonize the mouse gut. Thus, the genetic background of the different recipient rodent systems (that is, rats and mice) strongly influences the nature of the populating human gut microbiota, determining each model's biological suitability.
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