Microarray CGH Profiling Of Serous Ovarian Tumours Identifies Novel Copy Number Alterations Associated with Patient Survival
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE29503 | GEO | 2012/06/01
SECONDARY ACCESSION(S): PRJNA141387
REPOSITORIES: GEO
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Project description:We used microarray CGH analysis with a tiling path BAC DNA microarray to profile DNA copy number alterations in 164 serous ovarian adenocarcinomas. Survival probabilities modelled by proportional hazards were used to stratify cases into good, intermediate or poor survival groups. Comparison of aCGH data from these groups was used to identify genomic alterations associated with patient survival. A total of 984 cases of serous ovarian adenocarcinoma from three different studies were combined and Cox proportional hazards used to model survival using patient age, tumour stage and residual disease status as covariates. Survival probabilities (Pr) were extracted for all cases and used to stratify 384 cases for which microarray CGH data was available. From these, we identified 70 cases with poor survival (Pr>0.784) and 70 cases with good survival (Pr<0.35). aCGH data from these cases was provided to an iterative Support Vector Machine (SVM) routine to detect copy number changes associated with survival. Candidate regions were then validated by survival analysis in all 384 cases for which aCGH data was available. This series contains aCGH data from 164 tumours from the MALOVA collection.
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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
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2013-12-22 | GSE50081 | GEO
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