Single nucleotide polymorphism profiles of canine T-cell and null-cell lymphomas
Ontology highlight
ABSTRACT:
SUBMITTER: Sirivisoot S
PROVIDER: S-EPMC11339873 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The study of gene regulatory network and protein-protein interaction network is believed to be fundamental to the understanding of molecular processes and functions in systems biology. In this study, the authors are interested in single nucleotide polymorphism (SNP) level and construct SNP-SNP interaction network to understand genetic characters and pathogenetic mechanisms of complex diseases. The authors employ existing methods to mine, model and evaluate a SNP sub-network from SNP-SNP interactions. In the study, the authors employ the two SNP datasets: Parkinson disease and coronary artery disease to demonstrate the procedure of construction and analysis of SNP-SNP interaction networks. Experimental results are reported to demonstrate the procedure of construction and analysis of such SNP-SNP interaction networks can recover some existing biological results and related disease genes.
| S-EPMC8687305 | biostudies-literature
Project description:Primary Objective:
Correlation of the skin and/or eye toxicity grade secondary to Cetuximab or Panitumumab and the SNP profile of the Epidermal Growth Factor Receptor (EGFR) domain III region.
Secondary Objectives:
Correlation of SNP profile with indicators of tumour response parameters, such as radiological response, duration of response, time to progression (TTP), overall survival (OS) time, incidence of non-dermatological adverse events.
| 2135070 | ecrin-mdr-crc
Project description:Tumour cellularity, the relative proportion of tumour and normal cells in a sample, affects the sensitivity of mutation detection, copy number analysis, cancer gene expression and methylation profiling. Tumour cellularity is traditionally estimated by pathological review of sectioned specimens; however this method is both subjective and prone to error due to heterogeneity within lesions and cellularity differences between the sample viewed during pathological review and tissue used for research purposes. In this paper we describe a statistical model to estimate tumour cellularity from SNP array profiles of paired tumour and normal samples using shifts in SNP allele frequency at regions of loss of heterozygosity (LOH) in the tumour. We also provide qpure, a software implementation of the method. Our experiments showed that there is a medium correlation 0.42 ([Formula: see text]-value=0.0001) between tumor cellularity estimated by qpure and pathology review. Interestingly there is a high correlation 0.87 ([Formula: see text]-value [Formula: see text] 2.2e-16) between cellularity estimates by qpure and deep Ion Torrent sequencing of known somatic KRAS mutations; and a weaker correlation 0.32 ([Formula: see text]-value=0.004) between IonTorrent sequencing and pathology review. This suggests that qpure may be a more accurate predictor of tumour cellularity than pathology review. qpure can be downloaded from https://sourceforge.net/projects/qpure/.
| S-EPMC3457972 | biostudies-literature
Project description:Establishing low-cost, high-throughput, simple, and accurate single nucleotide polymorphism (SNP) genotyping techniques is beneficial for understanding the intrinsic relationship between individual genetic variations and their biological functions on a genomic scale. Here, a straightforward and reliable single-molecule approach is demonstrated for precise SNP authentication by directly measuring the fluctuations in electrical signals in an electronic circuit, which is fabricated from a high-gain field-effect silicon nanowire decorated with a single hairpin DNA, in the presence of different target DNAs. By simply comparing the proportion difference of a probe-target duplex structure throughout the process, this study implements allele-specific and accurate SNP detection. These results are supported by the statistical analyses of different dynamic parameters such as the mean lifetime and the unwinding probability of the duplex conformation. In comparison with conventional polymerase chain reaction and optical methods, this convenient and label-free method is complementary to existing optical methods and also shows several advantages, such as simple operation and no requirement for fluorescent labeling, thus promising a futuristic route toward the next-generation genotyping technique.
| S-EPMC5700462 | biostudies-literature
Project description:We describe a rapid and easily automated phylogenetic grouping technique based on analysis of bacterial genome single-nucleotide polymorphisms (SNPs). We selected 13 SNPs derived from a complete sequence analysis of 11 essential genes previously used for multilocus sequence typing (MLST) of 30 Escherichia coli strains representing the genetic diversity of the species. The 13 SNPs were localized in five genes, trpA, trpB, putP, icdA, and polB, and were selected to allow recovery of the main phylogenetic groups (groups A, B1, E, D, and B2) and subgroups of the species. In the first step, we validated the SNP approach in silico by extracting SNP data from the complete sequences of the five genes for a panel of 65 pathogenic strains belonging to different E. coli pathovars, which were previously analyzed by MLST. In the second step, we determined these SNPs by dideoxy single-base extension of unlabeled oligonucleotide primers for a collection of 183 commensal and extraintestinal clinical E. coli isolates and compared the SNP phylotyping method to previous well-established typing methods. This SNP phylotyping method proved to be consistent with the other methods for assigning phylogenetic groups to the different E. coli strains. In contrast to the other typing methods, such as multilocus enzyme electrophoresis, ribotyping, or PCR phylotyping using the presence/absence of three genomic DNA fragments, the SNP typing method described here is derived from a solid phylogenetic analysis, and the results obtained by this method are more meaningful. Our results indicate that similar approaches may be used for a wide variety of bacterial species.
| S-EPMC1183324 | biostudies-literature
Project description:Angioimmunoblastic T-cell lymphoma (AILT) represents a subset of T-cell lymphomas but resembles an autoimmune disease in many of its clinical aspects. Despite the phenotype of effector T-cells and high expression of FAS and CTLA-4 receptor molecules, tumor cells fail to undergo apoptosis. We investigated single nucleotide polymorphisms (SNPs) of the FAS and CTLA-4 genes in 94 peripheral T-cell lymphomas. Although allelic frequencies of some FAS SNPs were enriched in AILT cases, none of these occurred at a different frequency compared to healthy individuals. Therefore, SNPs in these genes are not associated with the apoptotic defect and autoimmune phenomena in AILT.
| S-EPMC2712330 | biostudies-literature
Project description:Diffuse large B-cell lymphomas (DLBCLs) are the most common lymphoproliferative diseases in dogs. DLBCL diagnosis to date has relied on histopathological analysis; however liquid biopsies have gained attention in recent years as a source of diagnostic and prognostic information. Liquid biopsies can be a source of circulating DNA, miRNA, circulating tumour cells or extracellular vesicles (EVs). In this study EVs were isolated from the plasma of healthy dogs, and dogs with lymphoma, and adenocarcinoma by iodixanol density gradient centrifugation. These EVs were positive for the EV markers CD63 and TSG101 and the pan-B cell markers CD79a, CD21, CD45, CD20. NTA analysis revealed that the DLBCL and adenocarcinoma dogs had elevated plasma EVs relative to the healthy dogs. Furthermore, the modal size of lymphoma EVs had decreased relative to healthy dogs while adenocarcinoma EVs were unchanged. This study demonstrates that the plasma EV population is altered in canine lymphoma patients in a manner similar to previous studies on human lymphomas. The similar changes to the EV population in dogs, together with the similar pathological features and treatment protocols in canine and human non-Hodgkin lymphomas would make dogs a good comparative model for studying the role of EVs in DLBCL development and progression.
| S-EPMC9488776 | biostudies-literature
Project description:The genomic landscape in human B-cell lymphoma has revealed several somatic mutations and potentially relevant germline alterations affecting therapy and prognosis. Also, mutations originally described as somatic aberrations have been shown to confer cancer predisposition when occurring in the germline. The relevance of mutations in canine B-cell lymphoma is scarcely known and gene expression profiling has shown similar molecular signatures among different B-cell histotypes, suggesting other biological mechanisms underlining differences. Here, we present a highly accurate approach to identify single nucleotide variants (SNVs) in RNA-seq data obtained from 62 completely staged canine B-cell lymphomas and 11 normal B-cells used as controls. A customized variant discovery pipeline was applied and SNVs were found in tumors and differentiated for histotype. A number of known and not previously identified SNVs were significantly associated to MAPK signaling pathway, negative regulation of apoptotic process and cell death, B-cell activation, NF-kB and JAK-STAT signaling. Interestingly, no significant genetic fingerprints were found separating diffuse large B-cell lymphoma from indolent lymphomas suggesting that differences of genetic landscape are not the pivotal causative factor of indolent behavior. We also detected several variants in expressed regions of canine B-cell lymphoma and identified SNVs having a direct impact on genes. Using this brand-new approach the consequence of a gene variant is directly associated to expression. Further investigations are in progress to deeply elucidate the mechanisms by which altered genes pathways may drive lymphomagenesis and a higher number of cases is also demanded to confirm this evidence.
| S-EPMC6481796 | biostudies-literature
Project description:BackgroundHypertension is a prevalent condition linked to major cardiovascular conditions and multiple other comorbidities. Genetic information can offer a deeper understanding about susceptibility and the underlying disease mechanisms. The Genetic Analysis Workshop 18 (GAW18) provides abundant genotype data to determine genetic associations for being hypertensive and for the underlying trait of systolic blood pressure (SBP). The high-dimensional nature of this data promotes dimension reduction techniques to remove excess noise and also synthesize genetic information for complex, polygenic traits.MethodsFor both measured and simulated phenotype data from GAW18, we use sparse principal component analysis to obtain sparse genetic profiles that represent the underlying data structures. We then detect associations between the obtained sparse principal components (PCs) and SBP, a major indicator of hypertension, following up by investigating the sparse PCs for genetic structure to gain insight into new patterns.ResultsAfter adjusting for multiple testing, 27 of 122 PCs were significantly associated with measured SBP, offering a large number of components to investigate. Considering the top 3 PCs, linked genetic regions have been identified; these may act in unison while associated with SBP. Simulated data offered similar results.ConclusionsSparse PCs can offer a new data-driven approach to structuring genotype data and understanding the genetic mechanics behind complex, polygenic traits such as hypertension.
| S-EPMC4143624 | biostudies-literature
Project description:MotivationRecently, single-cell DNA sequencing (scDNA-seq) and multi-modal profiling with the addition of cell-surface antibodies (scDAb-seq) have provided key insights into cancer heterogeneity. Scaling these technologies across large patient cohorts, however, is cost and time prohibitive. Multiplexing, in which cells from unique patients are pooled into a single experiment, offers a possible solution. While multiplexing methods exist for scRNAseq, accurate demultiplexing in scDNAseq remains an unmet need.ResultsHere, we introduce SNACS: Single-Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting. SNACS relies on a combination of patient-level cell-surface identifiers and natural variation in genetic polymorphisms to demultiplex scDNAseq data. We demonstrated the performance of SNACS on a dataset consisting of multi-sample experiments from patients with leukemia where we knew truth from single-sample experiments from the same patients. Using SNACS, accuracy ranged from 0.948 - 0.991 vs 0.552 - 0.934 using demultiplexing methods from the single-cell literature.
| S-EPMC10871358 | biostudies-literature