Metagenomic next-generation sequencing applied in pathogen detection
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PROVIDER: PRJEB48135 | ENA |
REPOSITORIES: ENA
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Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
2024-01-08 | GSE252118 | GEO
Project description:The assignment of diffuse large B-cell lymphoma into cell-of-origin (COO) groups is becoming increasingly important with the emergence of novel therapies that have selective biological activity in germinal center B-cell-like (GCB) or activated B-cell-like (ABC) groups. The LLMPP's Lymph2Cx assay is a parsimonious digital gene-expression (NanoString) based test for COO assignment in formalin-fixed paraffin-embedded tissue (FFPET) routinely produced in standard diagnostic processes. The 20-gene assay was trained using 51 FFPET biopsies; the locked assay was then validated using an independent cohort of 68 FFPET biopsies. Comparisons were made with COO assignment using the original COO model on matched frozen tissue. In the validation cohort the assay was accurate, with only one case with definitive COO being incorrectly assigned, and robust, with >95% concordance of COO assignment between 2 independent laboratories. These qualities, along with the rapid turn-around-time, make Lymph2Cx attractive for implementation in clinical trials and, ultimately, patient management. The retrospective study included RNA extracted from 119 clinical samples.
2014-01-14 | E-GEOD-53786 | biostudies-arrayexpress
Project description:The assignment of diffuse large B-cell lymphoma into cell-of-origin (COO) groups is becoming increasingly important with the emergence of novel therapies that have selective biological activity in germinal center B-cell-like (GCB) or activated B-cell-like (ABC) groups. The LLMPP's Lymph2Cx assay is a parsimonious digital gene-expression (NanoString) based test for COO assignment in formalin-fixed paraffin-embedded tissue (FFPET) routinely produced in standard diagnostic processes. The 20-gene assay was trained using 51 FFPET biopsies; the locked assay was then validated using an independent cohort of 68 FFPET biopsies. Comparisons were made with COO assignment using the original COO model on matched frozen tissue. In the validation cohort the assay was accurate, with only one case with definitive COO being incorrectly assigned, and robust, with >95% concordance of COO assignment between 2 independent laboratories. These qualities, along with the rapid turn-around-time, make Lymph2Cx attractive for implementation in clinical trials and, ultimately, patient management.
2014-01-14 | GSE53786 | GEO
Project description:A common technique used for sensitive and specific diagnostic virus detection in clinical samples is PCR. However, an unbiased diagnostic microarray containing probes for all human pathogens could replace hundreds of individual PCR-reactions and remove the need for a clear clinical hypothesis regarding a suspected pathogen. We have established such a diagnostic platform for unbiased random amplification and subsequent microarray identification of viral pathogens in clinical samples. We show that Phi29 polymerase-amplification of a diverse set of clinical samples generates enough viral material for successful identification by the Microbial Detection Array developed at the Lawrence Livermore National Laboratory, California, USA, demonstrating the potential of the microarray technique for broad-spectrum pathogen detection. We conclude that this method detects both DNA and RNA virus, present in the same sample, as well as differentiates between different virus subtypes. We propose this assay for unbiased diagnostic analysis of all viruses in clinical samples. 19 clinical samples were analyzed for presence of virus using the MDA microarray. One of the samples is a negative control (water). One HCV-positive serum sample is included twice (HCV+1 and HCV+2).
2011-04-14 | E-GEOD-28597 | biostudies-arrayexpress
Project description:Evaluation of commercial DNA extraction kits for implementation of rapid Nanopore sequencing for routine diagnostic laboratories
| PRJNA768992 | ENA
Project description:Background Published multi-gene classifiers suggested outcome prediction for patients with stage UICC II colon cancer based on different gene expression signatures. However, there is currently no translation of these classifiers for application in routine diagnostic. Therefore, we aimed at validating own and published gene expression signatures employing methods which enable RNA and protein detection in routine diagnostic specimens. Results Immunohistochemistry was applied to 68 stage UICC II colon cancers to determine the protein expression of five selected previously published classifier genes (CDH17, LAT, CA2, EMR3, and TNFRSF11A). Correlation of protein expression data with clinical outcome within a 5-year post-surgery course failed to separate patients with a disease-free follow-up [Group DF] and relapse [Group R]). In addition, RNA from macrodissected tumor samples from 53 of these 68 patients was profiled on Affymetrix GeneChips (HG-U133 Plus 2.0). Prognostic signatures were generated by Nearest Shrunken Centroids with cross-validation. Although gene expression profiling allowed the identification of differentially expressed genes between the groups DF and R, a stable classification and prognosis signature was not discernable in our data. Furthermore, the application of previously published gene signatures consisting of 22 and 19 genes, respectively, to our gene expression data set using ‘global tests’ and leave-one-out cross-validation was unable to predict clinical outcome (prediction rate 75.5% and 64.2%; n.s.). T-stage was the only independent prognostic factor for relapse in multivariate analysis with established clinical and pathological parameters including microsatellite status. Conclusions Our protein and gene expression analyses currently do not support application of molecular classifiers for prediction of clinical outcome in routine diagnostic as a basis for patient-orientated therapy in stage UICC II colon cancer. Further studies are needed to develop prognosis signatures applicable in patient care.
2011-04-10 | GSE18088 | GEO
Project description:A common technique used for sensitive and specific diagnostic virus detection in clinical samples is PCR. However, an unbiased diagnostic microarray containing probes for all human pathogens could replace hundreds of individual PCR-reactions and remove the need for a clear clinical hypothesis regarding a suspected pathogen. We have established such a diagnostic platform for unbiased random amplification and subsequent microarray identification of viral pathogens in clinical samples. We show that Phi29 polymerase-amplification of a diverse set of clinical samples generates enough viral material for successful identification by the Microbial Detection Array developed at the Lawrence Livermore National Laboratory, California, USA, demonstrating the potential of the microarray technique for broad-spectrum pathogen detection. We conclude that this method detects both DNA and RNA virus, present in the same sample, as well as differentiates between different virus subtypes. We propose this assay for unbiased diagnostic analysis of all viruses in clinical samples.
2011-04-14 | GSE28597 | GEO
Project description:Identification of early diagnostic and prognostic biomarkers measurable in liquid biopsies remains a major challenge in oncology today. OMICs technologies cannot be applied on clinically precious human material as this is required in its integrity for routine pathology investigation. Hence all OMICs discovery today is done on residual and not clinically relevant material. In the study, we present a novel, non-destructive, procedure named EXPEL, which uses rapid, pressure-assisted, interstitial fluid extrusion, allowing for full clinical pathology and OMICs analysis (proteins, metabolites, miRNA and DNA) on the same specimen. To demonstrate this we engage in a simultaneous biomarker discovery and routine diagnostic analysis of human colorectal cancer and liver metastases. Biomarkers discovered with EXPEL methodology can be readily detected in human sera, allowing for unprecedented translation to the clinic. EXPEL method enables for the first time both clinicians and scientist to explore identical, fresh, clinical material regardless of origin and size.
2018-10-26 | PXD005693 | Pride
Project description:Identification of early diagnostic and prognostic biomarkers measurable in liquid biopsies remains a major challenge in oncology today. OMICs technologies cannot be applied on clinically precious human material as this is required in its integrity for routine pathology investigation. Hence all OMICs discovery today is done on residual and not clinically relevant material. In the study, we present a novel, non-destructive, procedure named EXPEL, which uses rapid, pressure-assisted, interstitial fluid extrusion, allowing for full clinical pathology and OMICs analysis (proteins, metabolites, miRNA and DNA) on the same specimen. To demonstrate this we engage in a simultaneous biomarker discovery and routine diagnostic analysis of human colorectal cancer and liver metastases. Biomarkers discovered with EXPEL methodology can be readily detected in human sera, allowing for unprecedented translation to the clinic. EXPEL method enables for the first time both clinicians and scientist to explore identical, fresh, clinical material regardless of origin and size.
2018-10-26 | PXD005709 | Pride