Molecular stool testing for colorectal cancer surveillance
Ontology highlight
ABSTRACT:
DISEASE(S): Colorectal Cancer, Surveillance, Molecular Stool Testingcolorectaal Carcinoom, Surveillance, Moleculaire Ontlastingstest
PROVIDER: 19571 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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Project description:Rationale: Since January 2014 the Dutch screening programme for bowel cancer has been implemented. Screening will increase the demand for surveillance. Although patients in whom adenomas have been removed are at increased risk of progressing to cancer, solid evidence on the reduction of death from CRC through the current colonoscopy-based surveillance is lacking. Furthermore, colonoscopy-based surveillance leads to high logistic demands, high individual burden and high costs. Therefore, there is need for new surveillance strategies. Stool-based molecular testing (Cologuard, consisting of a stool DNA test and an immunochemical assay for human hemoglobin) or Faecal Immunochemical Testing (FIT) may serve as an alternative for colonoscopy surveillance.
The aim of this study is to compare the accuracy of an established molecular stool test (Cologuard) and FIT to colonoscopy for detection of advanced adenomas or CRC (advanced neoplasia) in a surveillance population. These outcomes will be used to model various strategies of stool-based molecular surveillance to inform health policy decisions.
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Project description:Sub-clinical acute rejection (subAR) in kidney transplant recipients (KTR) leads to chronic rejection and graft loss. Non-invasive biomarkers are needed to detect subAR. 307 KTR were enrolled into a multi-center observational study. Precise clinical phenotypes (CP) were used to define subAR. Differential gene expression (DGE) data from peripheral blood samples paired with surveillance biopsies were used to train a Random Forests (RF) model to develop a gene expression profile (GEP) for subAR. A separate cohort of paired samples was used to validate the GEP. Clinical endpoints and gene pathway mapping were used to assess clinical validity and biologic relevance. DGE data from 530 samples (130 subAR) collected from 250 KTR yielded a RF model: AUC 0.85; 0.84 after internal validation with bootstrap resampling. We selected a predicted probability threshold favoring specificity and NPV (87% and 88%) over sensitivity and PPV (64% and 61%, respectively). We tested the locked model/threshold on a separate cohort of 138 KTR undergoing surveillance biopsies at our institution (rejection 42; no rejection 96): NPV 78%; PPV 51%; AUC 0.66. Both the CP and GEP of subAR within the first 12 months following transplantation were independently associated with worse graft outcomes at 24 months, including de novo donor-specific antibody (DSA). Serial GEP tracked with response to treatment of subAR. DGE data from both cohorts mapped to gene pathways indicative of allograft rejection.
2018-07-24 | GSE107503 | GEO
Project description:Sub-clinical acute rejection (subAR) in kidney transplant recipients (KTR) leads to chronic rejection and graft loss. Non-invasive biomarkers are needed to detect subAR. 307 KTR were enrolled into a multi-center observational study. Precise clinical phenotypes (CP) were used to define subAR. Differential gene expression (DGE) data from peripheral blood samples paired with surveillance biopsies were used to train a Random Forests (RF) model to develop a gene expression profile (GEP) for subAR. A separate cohort of paired samples was used to validate the GEP. Clinical endpoints and gene pathway mapping were used to assess clinical validity and biologic relevance. DGE data from 530 samples (130 subAR) collected from 250 KTR yielded a RF model: AUC 0.85; 0.84 after internal validation with bootstrap resampling. We selected a predicted probability threshold favoring specificity and NPV (87% and 88%) over sensitivity and PPV (64% and 61%, respectively). We tested the locked model/threshold on a separate cohort of 138 KTR undergoing surveillance biopsies at our institution (rejection 42; no rejection 96): NPV 78%; PPV 51%; AUC 0.66. Both the CP and GEP of subAR within the first 12 months following transplantation were independently associated with worse graft outcomes at 24 months, including de novo donor-specific antibody (DSA). Serial GEP tracked with response to treatment of subAR. DGE data from both cohorts mapped to gene pathways indicative of allograft rejection.
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Project description:Longitudinal analysis of Salmonella typhimurium mRNA from superspeader mouse cecal content and stool compared to in vitro Salmonella typhimurium mRNA.
2023-03-22 | GSE207764 | GEO
Project description:Background and Aims: RNA biomarkers derived from sloughed enterocytes would provide an ideal, non-invasive method for early detection of colorectal cancer (CRC) and precancerous adenomas. To realize this goal, a highly reliable method to isolate preserved human RNA from stool samples is needed. Here we develop a protocol to identify RNA biomarkers associated with CRC to assess the use of these biomarkers for noninvasive screening of disease. Methods: Stool samples were collected from 454 patients prior to a colonoscopy. A nucleic acid extraction protocol was developed to isolate human RNA from 330 stool samples and transcript abundances were estimated by microarray analysis. This 330-patient cohort was split into a training set of 265 individuals to develop a machine learning model and a testing set of 65 individuals to determine the model’s ability to detect colorectal neoplasms. Results: Analysis of the transcriptome from 265 individuals identified 200 transcript clusters as differentially expressed (p<0.03). These transcripts were used to build a Support Vector Machine (SVM) based model to classify 65 individuals within the testing set. This SVM algorithm attained a 95% sensitivity for precancerous adenomas and a 65% sensitivity for CRC (stage I-IV). The machine learning algorithm attained a specificity of 59% for healthy individuals and an overall accuracy of 72.3%. Conclusions: We developed an RNA-based neoplasm detection model that is sensitive for CRC and precancerous adenomas. The model allows for non-invasive assessment of tumors and could potentially be used to provide clinical guidance for individuals within the screening population for colorectal cancer.
2019-02-25 | GSE99573 | GEO
Project description:Cardioviruses are a genus of picornaviruses that cause severe illnesses in rodents, but little is known about the prevalence, diversity, or spectrum of disease of such agents among humans. We report the identification of a group of human cardioviruses that have been detected and cloned directly from patient specimens (Chiu and DeRisi, et al, PNAS, 2008). This series includes 9 arrays (both raw and normalized data) used to detect cardioviruses in human respiratory and stool specimens. The arrays employed here are capable of pan-viral detection (Wang and DeRisi, et al., PNAS, 2002). Keywords: viral detection, cardiovirus, TMEV, gastroenteritis The series includes 3 arrays from respiratory samples and 6 arrays from stool samples. Among the 3 arrays from respiratory sample, 1 array has a signature for an adenovirus, 1 array has a signature for human metapneumovirus, and 1 array has a signature for cardiovirus UC1 (see Chiu and DeRisi, et al., PNAS, in 2008). All 6 arrays from stool samples are cardiovirus-positive; some show evidence of dual infection with other gastroenteritis viruses (i.e. norovirus, rotavirus, etc.). Data in Sample records fed to E-Predict (Urisman, et al, Genome Biology, 2005) E-Predict normalization metrics Array Normalization: Sum E-Matrix Normalization: Quadratic Distance Metric: Pearson Uncentered
2008-09-17 | E-GEOD-11569 | biostudies-arrayexpress
Project description:Understanding gene expression by bacteria during the actual course of human infection may provide important insights into microbial pathogenesis. In this study, we evaluated the transcriptional profile of Vibrio cholerae, the causative agent of cholera, in clinical specimens from cholera patients. We collected samples of human stool and vomitus that were positive by dark-field microscopy for abundant vibrios and used a microarray to compare gene expression in organisms recovered directly from the early and late stages of human infection. Our results reveal that V. cholerae gene expression within the human host environment differs from patterns defined in in vitro models of pathogenesis. tcpA, the major subunit of the essential V. cholerae colonization factor, was significantly more highly expressed in early compared with late infection; however, the genes encoding cholera toxin were not highly expressed in either phase of human infection. Furthermore, expression of the virulence regulators, toxRS and tcpPH, was uncoupled. Interestingly, the pattern of gene expression indicates that the human upper intestine may be a uniquely suitable environment for the transfer of genetic elements that are important in the evolution of pathogenic strains of V. cholerae. These findings provide a more detailed assessment of the transcriptome of V. cholerae in the human host than previous studies of organisms in stool alone and have implications for cholera control and the design of improved vaccines. The V. cholerae microarray consists of 3,890 full-length PCR products representing the annotated open reading frames from the initial release of the V. cholerae N16961 genome. Each labeling and hybridization was performed in duplicate. Genomic DNA was used as a universal internal control for the quality of the microarray and to allow for the comparison of results across multiple experiments. Data were normalized using locally-weighted regression (Lowess) to obtain the relative abundance of each transcript as an intensity ratio with respect to that of genomic DNA. High correlation coefficients were observed between technical replicates (Pearsonâs correlation coefficient (r) > 0.80) and between results of separate clinical specimens of vomitus (r > 0.77) and of stool (r > 0.80). Hence, the results from the two clinical vomitus specimens and the five clinical stool specimens were pooled. Fold changes for the relative expression of a given gene between the two clinical specimens were calculated by dividing the normalized median intensity ratios with respect to genomic DNA.
2010-06-09 | E-GEOD-2775 | biostudies-arrayexpress