Multi-institutional prospective validation of prognostic mRNA signatures in early stage squamous lung cancer (Alliance) [cohort I]
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA659855 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Multi-institutional prospective validation of prognostic mRNA signatures in early stage squamous lung cancer (Alliance) [Cohort II]
| PRJNA659803 | ENA
Project description:Multi-institutional prospective validation of prognostic mRNA signatures in early stage squamous lung cancer (Alliance)
| PRJNA659802 | ENA
Project description:Purpose: The primary objective of the current study was to validate biomarkers to identify the 10% to 27% of patients with stage I and 35% of patients with stage IIA squamous cell carcinoma of lung (SC) who are likely to recur following surgical resection, so that these patients may be offered enrollment in clinical trials evaluating directed ACT. A secondary objective was to identify patients with stage IIB SC who are unlikely to develop recurrences and might thereby be spared the potential significant toxicity and expense of ACT. Methods: Two-stage validation used independent core laboratories, objective quality control standards, locked test parameters, and large multi-institutional specimen/data sets. First stage validation confirmed a signature’s ability to stratify patient survival. Second stage validation determined which signature(s) optimally improved risk discrimination when added to baseline clinical predictors. Participants were prospectively enrolled on institutional (Cohort I) or cooperative group (Cohort II) biospecimen/data collection protocols. All cases underwent central review of clinical, pathologic and biospecimen parameters using objective criteria to determine final inclusion (Cohort I: n=249; Cohort II: n=234). Primary selection required that a signature significantly predict 3-years survival after surgery in Cohort I. Signatures meeting this criterion were further tested in Cohort II, comparing risk prediction using baseline risk factors alone versus in combination with the signature. Results: Male sex, advanced age, and higher stage were associated with shorter survival in Cohort I and established a baseline clinical model. Of three signatures validated in Cohort I, one signature was validated in Cohort II and statistically significantly enhanced prognosis relative to the baseline model (C-index difference 0.122; p<0.05). Conclusions: These results represent the first rigorous validation of a test appropriate to direct adjuvant treatment or clinical trials for patients with lung squamous cell carcinoma.
2020-08-28 | GSE157010 | GEO
Project description:Purpose: The primary objective of the current study was to validate biomarkers to identify the 10% to 27% of patients with stage I and 35% of patients with stage IIA squamous cell carcinoma of lung (SC) who are likely to recur following surgical resection, so that these patients may be offered enrollment in clinical trials evaluating directed ACT. A secondary objective was to identify patients with stage IIB SC who are unlikely to develop recurrences and might thereby be spared the potential significant toxicity and expense of ACT. Methods: Two-stage validation used independent core laboratories, objective quality control standards, locked test parameters, and large multi-institutional specimen/data sets. First stage validation confirmed a signature’s ability to stratify patient survival. Second stage validation determined which signature(s) optimally improved risk discrimination when added to baseline clinical predictors. Participants were prospectively enrolled on institutional (Cohort I) or cooperative group (Cohort II) biospecimen/data collection protocols. All cases underwent central review of clinical, pathologic and biospecimen parameters using objective criteria to determine final inclusion (Cohort I: n=249; Cohort II: n=234). Primary selection required that a signature significantly predict 3-years survival after surgery in Cohort I. Signatures meeting this criterion were further tested in Cohort II, comparing risk prediction using baseline risk factors alone versus in combination with the signature. Results: Male sex, advanced age, and higher stage were associated with shorter survival in Cohort I and established a baseline clinical model. Of three signatures validated in Cohort I, one signature was validated in Cohort II and statistically significantly enhanced prognosis relative to the baseline model (C-index difference 0.122; p<0.05). Conclusions: These results represent the first rigorous validation of a test appropriate to direct adjuvant treatment or clinical trials for patients with lung squamous cell carcinoma.
2020-08-28 | GSE157009 | GEO
Project description:Prospective cohort carriage study
| PRJEB53187 | ENA
Project description:Tuberculosis biomarker validation cohort
| PRJNA616117 | ENA
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:PURPOSE: The IRSN-23 model uses DNA microarray analysis of tumor tissue to personalize patients into highly sensitive chemotherapy (Gp-R) or less sensitive (Gp-NR) based on the expression of immune-related genes. This study assessed the reproducibility of the IRSN-23 in prospective independent validation sets and investigated its clinical significance and impact on breast cancer subtype classification. METHODS: Tumour tissues were collected from 146 breast cancer patients undergoing preoperative chemotherapy (paclitaxel-FEC) ± trastuzumab in Osaka University Hospital (OUH). Patients were classified into Gp-R or Gp-NR using IRSN-23, and the model was examined for its ability to predict the pathological complete response (pCR). RESULTS: In the OUH prospective dataset, the pCR rate was significantly higher in the Gp-R group (29·3% (11/41)) than in the Gp-NR group (1·4% (1/71)) not using trastuzumab (P = 1·70E-5). CONCLUSION: This study provides prospective validation of the IRSN-23 in predicting chemotherapy efficacy, demonstrating a high degree of reproducibility.
2023-05-19 | GSE230881 | GEO
Project description:We assayed leukocyte global gene expression for a prospective validation cohort of 106 adult patients admitted to UK intensive care units with severe sepsis due to community acquired pneumonia.
2016-02-25 | E-MTAB-4451 | biostudies-arrayexpress
Project description:Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath [Park, Sunho et al. “An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types.” Bioinformatics (2016): 1643-51. doi:10.1093/bioinformatics/btv692] to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated in a prospective manner.
2022-01-01 | GSE183136 | GEO