Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA215955 | ENA |
REPOSITORIES: ENA
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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.
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:The analytical validation of a 15 gene prognostic signature for early-stage, completely resected, non-small-cell lung carcinoma that distinguishes between patients with good and poor prognoses.
2014-11-06 | GSE63074 | GEO
Project description:Background: The JBR.10 trial demonstrated significant survival benefit from adjuvant cisplatin/vinorelbine (ACT) in stage IB-II NSCLC (HR 0.69, p=0.04), but stage IB patients did not derive significant benefit (HR: 0.94, p= 0.79). We hypothesized that expression profiling could identify stage-independent subgroups of patients who might benefit from adjuvant chemotherapy. Methods: Gene expression profiling was conducted on mRNA isolated from frozen JBR.10 tumor samples (either from patients under observation [OBS], or treated with ACT). The minimum gene set that selected for the greatest separation of good and poor prognosis patient subgroups in OBS patients was identified and this gene signature was used to classify patients into high and low risk for death after surgery, and predict ACT effect. The prognostic gene signature was additionally tested on ACT patients and publicly available microarray datasets. Results: A 15-gene signature separated OBS patients into equal high and low risk subgroups with significantly different prognoses (HR 15.02, 95% CI 5.12-44.04, p=0.0001). The signature was prognostic in both stage IB and II. It was also predictive of improved survival following ACT treatment in high-risk patients (HR 0.33, 95% CI 0.17-0.63, p=0.0005), but not in low risk patients (HR 3.67, 95% CI 1.22-11.06, p=0.0133; interaction p=0.0001). The prognostic effect of the signature was validated in two independent gene expression datasets of 169 stage I-II adenocarcinoma and 106 squamous cell carcinoma patients. Conclusions: This microarray-based 15-gene prognostic expression signature is stage and histology independent and may select early stage NSCLC patients who are most likely to benefit from adjuvant chemotherapy with cisplatin/vinorelbine. Keywords: Expression profiling by microarray; prognosis prediction 90 samples
2010-09-09 | E-GEOD-14814 | biostudies-arrayexpress
Project description:Background: Accurate survival stratification in early-stage NSCLC could inform the use of adjuvant therapy. We developed a clinically-implementable mortality risk score incorporating distinct tumor microenvironmental gene expression signatures and clinical variables. Methods: Gene expression profiles from 1106 non-squamous NSCLCs were used for generation and internal validation of a 9-gene molecular prognostic index (MPI). Expression of the MPI genes was determined within sorted tumor cell subpopulations. A quantitative PCR (qPCR) assay was developed and validated on an independent cohort of FFPE tissues. A prognostic score using clinical variables was generated using Surveillance Epidemiology and End Results (SEER) data and combined with the MPI. Results: The MPI stratified stage I patients into prognostic categories in four independent validation datasets, including three microarray and one FFPE qPCR cohorts (HR=2.4, 95% CI, 1.8-3.3, P=7x10-9 in the largest microarray cohort; and HR=2.5, 95% CI 1.1-6.0, P=.03 in stage I patients of the qPCR validation cohort). Prognostic genes were expressed in distinct tumor cell subpopulations and expression of genes implicated in cellular proliferation and stem cells portended poor outcomes, while expression of genes involved in normal lung differentiation and immune infiltration was associated with superior survival. Integrating the MPI with clinical variables conferred greatest prognostic power (HR=3.3, 95% CI 2.4-4.6; P=2x10-15 in the largest microarray cohort; and HR=3.6, 95% CI 1.5-8.8, P=.003 in stage I patients of the qPCR validation cohort). Finally, the MPI was prognostic irrespective of somatic alterations in EGFR, KRAS, TP53, and ALK. Conclusion: The MPI incorporates genes expressed in the tumor and its microenvironment, and designates risk of death for patients with early-stage non-squamous NSCLC. The MPI can be implemented clinically using qPCR assays on FFPE tissues and a composite model integrating the MPI with clinical variables provides the most accurate risk stratification.
2015-08-24 | GSE67639 | GEO
Project description:Purpose: The JBR.10 trial demonstrated benefit from adjuvant cisplatin/vinorelbine (ACT) in early-stage non-small-cell lung cancer (NSCLC). We hypothesized that expression profiling may identify stage-independent subgroups who might benefit from ACT. Patients and Methods: Gene expression profiling was conducted on mRNA from 133 frozen JBR.10 tumor samples (62 observation [OBS], 71 ACT). The minimum gene set that was selected for the greatest separation of good and poor prognosis patient subgroups in OBS patients was identified. The prognostic value of this gene signature was tested in four independent published microarray data sets and by quantitative reverse-transcriptase polymerase chain reaction (RT-qPCR). Results: A 15-gene signature separated OBS patients into high-risk and low-risk subgroups with significantly different survival (hazard ratio [HR], 15.02; 95% CI, 5.12 to 44.04; P .001; stage I HR, 13.31; P .001; stage II HR, 13.47; P .001). The prognostic effect was verified in the same 62 OBS patients where gene expression was assessed by qPCR. Furthermore, it was validated consistently in four separate microarray data sets (total 356 stage IB to II patients without adjuvant treatment) and additional JBR.10 OBS patients by qPCR (n 19). The signature was also predictive of improved survival after ACT in JBR.10 high-risk patients (HR, 0.33; 95% CI, 0.17 to 0.63; P .0005), but not in low-risk patients (HR, 3.67; 95% CI, 1.22 to 11.06; P = .0133; interaction P .001). Significant interaction between risk groups and ACT was verified by qPCR. Conclusion: This 15-gene expression signature is an independent prognostic marker in early-stage, completely resected NSCLC, and to our knowledge, is the first signature that has demonstrated the potential to select patients with stage IB to II NSCLC most likely to benefit from adjuvant chemotherapy with cisplatin/vinorelbine.
2010-09-09 | GSE14814 | GEO
Project description:Background: Robust prognostic stratification of patients with oropharyngeal squamous cell carcinoma (OPSCC) is important for developing individualized treatment plans. This study was conducted to develop and validate a clinically feasible prognostic classifier based on transcriptome-wide gene expression profiles. Methods: Tumor tissues were collected from 208 OPSCC patients treated at Washington University in St. Louis and 115 OPSCC patients treated at Vanderbilt University, used for model training and validation, respectively. OPSCC patients (n = 70) from the TCGA cohort were also included for independent validation. Based on RNA-seq profiling data, Cox proportional hazards regression analysis was performed to identify genes associated with disease outcomes. Then, Lasso-penalized multivariate survival models were constructed to identify biomarker genes for developing a prognostic gene signature. Findings: A 60-gene signature was identified by RNA-seq profiling analysis. Computed risk score of the gene signature was significantly predictive of 5-year overall survival of the training cohort (Hazard ratio (HR) 28.32, P = 4.3E-41). Subgroup analysis stratified by HPV status revealed that the signature was prognostic in HPV-positive OPSCC patients (HR 30.55, P = 7.0E-37) and was independent of clinical features. Importantly, the gene signature was validated in two independent patient cohorts, including the TCGA cohort (HR 3.94, P = 0.0018) and the Vanderbilt cohort (HR 8.50, P = 5.7E-09) for overall survival. Conclusions: The prognostic gene signature is a robust tool for risk stratification of OPSCC patients. The signature remains prognostic among HPV-positive OPSCC patients.
2021-04-13 | GSE171898 | GEO
Project description:To determine whether early-stage ovarian high grade serous carcinoma (HGSC) represents a distinct genomic entity, we collected samples from 43 patients with stage IA-IIA HGSC to identify potential differences in short genomic variants and copy number aberrations, and compared them to a cohort of 52 late-stage (stage IIIC-IV) cases. We found no significant differences in somatic mutations or focal copy number alterations between early-stage and late-stage cohorts. There was, however, a significant difference in both ploidy and copy number signature exposure between early and late-stage samples, with higher ploidy and signature 4 exposure in late-stage cases. Unsupervised hierarchical clustering revealed three clusters, which were prognostic. Together, our data suggest that early and late-stage HGSC share fundamental genomic features, but that late- stage disease appears distinct from early-stage, with evidence of whole genome duplication that may provide evolutionary benefit.
| EGAS00001005567 | EGA
Project description:104 early-stage (IA and IB) non-small cell lung cancers were evaluated with the Illumina Human HT-12 v4 BeadChip.
2015-06-01 | E-MTAB-3448 | biostudies-arrayexpress
Project description:Early-stage epithelial ovarian cancer (eEOC) patients have a generally favorable prognosis but heterogeneous behavior at recurrence. Accurate prediction of the risk of relapse is still a major concern, essentially to avoid overtreatment. We have identified a robust miRNA-based signature named MiROvaR able to predict early disease recurrence in case materials of mostly advanced-stage EOC patients. We challenged MiROvaR in the eEOC sub-setting (stage IA-IIB) and it proved to accurately classify eEOC patients according to their risk of relapse.
2022-12-21 | GSE169314 | GEO