Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA234769 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The prognostic and predictive value of a five-microRNA-based classifier in breast cancer
| PRJNA248377 | ENA
Project description:The prognostic and predictive value of a six-microRNA-based classifier in stage II colon cancer
| PRJNA213381 | ENA
Project description:Background: Patients with early stage non-small cell lung carcinoma (NSCLC) may benefit from treatments based on more accurate prognosis. A 15-gene prognostic classifier for NSCLC was identified from mRNA expression profiling of tumor samples from the NCIC CTG JBR.10 trial. Here, we assessed its value in an independent set of cases. Methods: Expression profiling was performed on RNA from frozen, resected tumor tissues corresponding to 181 Stage I and II NSCLC cases collected at University Health Network (UHN181). Kaplan-Meier methodology was used to estimate three year overall survival probabilities and the prognostic effect of the classifier was assessed using log-rank testing. Cox proportional hazards model evaluated the signature's effect adjusting for clinical prognostic factors. Results: Expression data of the 15-gene classifier stratified UHN181 cases into high and low-risk subgroups with significantly different overall survival (HR=1.92, 95% CI: 1.15-3.23, p=0.012). Its strength as a prognostic classifier was superior to stage alone (HR=1.52, 95% CI: 0.90-2.55, p-value=0.11). In subgroup analysis, this classifier predicted survival in 127 Stage I patients (HR=2.17, 95% CI: 1.12-4.20, p=0.018) and the smaller subgroup of 48 Stage IA patients (HR=5.61, 95% CI: 1.19-26.45, p=0.014. The signature was prognostic for both adenocarcinoma and squamous cell carcinoma cases (HR= 1.76, p-value=0.058; HR= 4.19, p-value=0.045, respectively). Conclusions: The prognostic accuracy of a 15-gene classifier was validated in an independent cohort of 181 early stage NSCLC samples including Stage IA cases and in different NSCLC histologic subtypes.
2013-12-22 | GSE50081 | GEO
Project description:Background: Patients with early stage non-small cell lung carcinoma (NSCLC) may benefit from treatments based on more accurate prognosis. A 15-gene prognostic classifier for NSCLC was identified from mRNA expression profiling of tumor samples from the NCIC CTG JBR.10 trial. Here, we assessed its value in an independent set of cases. Methods: Expression profiling was performed on RNA from frozen, resected tumor tissues corresponding to 181 Stage I and II NSCLC cases collected at University Health Network (UHN181). Kaplan-Meier methodology was used to estimate three year overall survival probabilities and the prognostic effect of the classifier was assessed using log-rank testing. Cox proportional hazards model evaluated the signature's effect adjusting for clinical prognostic factors. Results: Expression data of the 15-gene classifier stratified UHN181 cases into high and low-risk subgroups with significantly different overall survival (HR=1.92, 95% CI: 1.15-3.23, p=0.012). Its strength as a prognostic classifier was superior to stage alone (HR=1.52, 95% CI: 0.90-2.55, p-value=0.11). In subgroup analysis, this classifier predicted survival in 127 Stage I patients (HR=2.17, 95% CI: 1.12-4.20, p=0.018) and the smaller subgroup of 48 Stage IA patients (HR=5.61, 95% CI: 1.19-26.45, p=0.014. The signature was prognostic for both adenocarcinoma and squamous cell carcinoma cases (HR= 1.76, p-value=0.058; HR= 4.19, p-value=0.045, respectively). Conclusions: The prognostic accuracy of a 15-gene classifier was validated in an independent cohort of 181 early stage NSCLC samples including Stage IA cases and in different NSCLC histologic subtypes. Expression profiling was performed on RNA from frozen, resected tumor tissues corresponding to 181 Stage I and II NSCLC cases collected at University Health Network (UHN181). !Series_contributor = Sandy,D,Der
2013-12-22 | E-GEOD-50081 | biostudies-arrayexpress
Project description:Medullary breast cancers (MBC) display a basal profile, but a favorable prognosis. We hypothesized that a previously published 368-gene expression signature associated with MBC might serve to define a prognostic classifier in basal cancers. We collected public gene expression and histoclinical data of 2145 invasive early breast adenocarcinomas. We developed a Support Vector Machine (SVM) classifier based on this 368-gene list in a learning set, and tested its predictive performances in an independent validation set. Then, we assessed its prognostic value and that of six prognostic signatures for disease-free survival (DFS) in the remaining 2034 samples. The SVM model accurately classified all MBC samples in the learning and validation sets. A total of 466 cases were basal across other sets. The SVM classifier separated them into two subgroups, subgroup 1 (resembling MBC) and subgroup 2 (not resembling MBC). Subgroup 1 exhibited 71% 5-year DFS, whereas subgroup 2 exhibited 50% (p=9.93E-05). The classifier outperformed the classical prognostic variables in multivariate analysis, conferring lesser risk for relapse in subgroup 1 (HR=0.52, p=3.9E-04). This prognostic value was specific to the basal subtype, in which none of the other prognostic signatures was informative.
2010-05-05 | GSE21653 | GEO
Project description:Medullary breast cancers (MBC) display a basal profile, but a favorable prognosis. We hypothesized that a previously published 368-gene expression signature associated with MBC might serve to define a prognostic classifier in basal cancers. We collected public gene expression and histoclinical data of 2145 invasive early breast adenocarcinomas. We developed a Support Vector Machine (SVM) classifier based on this 368-gene list in a learning set, and tested its predictive performances in an independent validation set. Then, we assessed its prognostic value and that of six prognostic signatures for disease-free survival (DFS) in the remaining 2034 samples. The SVM model accurately classified all MBC samples in the learning and validation sets. A total of 466 cases were basal across other sets. The SVM classifier separated them into two subgroups, subgroup 1 (resembling MBC) and subgroup 2 (not resembling MBC). Subgroup 1 exhibited 71% 5-year DFS, whereas subgroup 2 exhibited 50% (p=9.93E-05). The classifier outperformed the classical prognostic variables in multivariate analysis, conferring lesser risk for relapse in subgroup 1 (HR=0.52, p=3.9E-04). This prognostic value was specific to the basal subtype, in which none of the other prognostic signatures was informative. The IPC series contained frozen tumor samples obtained from 266 early breast cancer patients who underwent initial surgery in our institution between 1992 and 2004. They included 227 cases previously reported {Finetti, 2008 #1758} and 39 additional cases, all similarly profiled using Affymetrix U133 Plus 2.0 human oligonucleotide microarrays as previously described {Finetti, 2008 #1758}. The study was approved by the IPC review board, and informed consent was available for each case. Gene expression data of 266 BCs were quantified by using whole-genome DNA microarrays (HG-U133 plus 2.0, Affymetrix).
2010-05-05 | E-GEOD-21653 | biostudies-arrayexpress
Project description:Prognostic classifier based on genome-wide DNA methylation profiling in well differentiated thyroid tumors
| PRJNA381920 | ENA
Project description:Currently there is no method available to predict response to farnesyltransferase inhibitors (FTI). We analyzed gene expression profiles from the bone marrow of patients from a phase 2 study of the FTI tipifarnib, in older adults with previously untreated acute myeloid leukemia (AML). The RASGRP1:APTX gene expression ratio was found to predict response to tipifarnib with the greatest accuracy. This two-gene ratio was validated by quantitative PCR (QPCR) in the newly diagnosed AML cohort. We further demonstrated that this classifier could predict response to tipifarnib in an independent set of 54 samples from relapsed or refractory AML, with a negative predictive value (NPV) and positive predictive value (PPV) of 92% and 28%, respectively (odds ratio of 4.4). The classifier also predicted for improved overall survival (154 vs 56 days, p = 0.0001), which was shown to be independent of other prognostic factors including a previously described gene expression classifier predictive of overall survival. Therefore, these data indicate that a two-gene expression assay may have utility in categorizing a population of AML patients who are more likely to respond to tipifarnib. Experiment Overall Design: 34 samples from 34 patients
2008-06-17 | E-GEOD-8970 | biostudies-arrayexpress
Project description:This study focuses on platform comparison to assess performance variability in circulating microRNA (ct-miR) detection, agreement in assignment of a miR signature classifier (MSC) and concordance for the identification of cancer-associated miRs in plasma samples from non‐small cell lung cancer (NSCLC) patients. A plasma cohort of 10 NSCLC patients and 10 healthy donors matched for clinical features and MSC risk level was profiled for miRs expression using two sequencing- and three quantitative PCR (qPCR)-based platforms. Intra- and inter-platform variations were examined by correlation and concordance analysis. MSC risk levels were compared to those estimated using a reference method. Differentially expressed ct-miRs were identified among NSCLC patients and donors and the diagnostic value of those dysregulated in patients was assessed by receiver operating characteristic curve analysis. Downregulation of miR-150-5p was verified by qPCR. The Cancer Genome Atlas (TCGA) lung carcinoma dataset was used for validation at tissue level. Intra-platform reproducibility was consistent whereas the highest values of inter-platform correlations were among qPCR-based platforms. MSC classification concordance was >80% for four platforms. Dysregulation and discriminatory power of miR-150-5p and -210-3p were documented. Both were significantly dysregulated also on TCGA tissue-originated profiles from lung cell carcinoma in comparison to normal samples. Overall, our studies provide a large performance analysis between five different platforms for miRs quantification, indicate the solidity of MSC classifier and identify two noninvasive biomarkers for NSCLC
2022-08-10 | GSE204950 | GEO
Project description:This study focuses on platform comparison to assess performance variability in circulating microRNA (ct-miR) detection, agreement in assignment of a miR signature classifier (MSC) and concordance for the identification of cancer-associated miRs in plasma samples from non‐small cell lung cancer (NSCLC) patients. A plasma cohort of 10 NSCLC patients and 10 healthy donors matched for clinical features and MSC risk level was profiled for miRs expression using two sequencing- and three quantitative PCR (qPCR)-based platforms. Intra- and inter-platform variations were examined by correlation and concordance analysis. MSC risk levels were compared to those estimated using a reference method. Differentially expressed ct-miRs were identified among NSCLC patients and donors and the diagnostic value of those dysregulated in patients was assessed by receiver operating characteristic curve analysis. Downregulation of miR-150-5p was verified by qPCR. The Cancer Genome Atlas (TCGA) lung carcinoma dataset was used for validation at tissue level. Intra-platform reproducibility was consistent whereas the highest values of inter-platform correlations were among qPCR-based platforms. MSC classification concordance was >80% for four platforms. Dysregulation and discriminatory power of miR-150-5p and -210-3p were documented. Both were significantly dysregulated also on TCGA tissue-originated profiles from lung cell carcinoma in comparison to normal samples. Overall, our studies provide a large performance analysis between five different platforms for miRs quantification, indicate the solidity of MSC classifier and identify two noninvasive biomarkers for NSCLC
2022-08-10 | GSE204945 | GEO