Dataset Information

Validation of the 12-gene Predictive Signature for Adjuvant Chemotherapy Response in Lung Cancer.

ABSTRACT: PURPOSE:Response to adjuvant chemotherapy after tumor resection varies widely among patients with non-small cell lung cancer (NSCLC); therefore, it is of clinical importance to prospectively predict who will benefit from adjuvant chemotherapy before starting the treatment. The goal of this study is to validate a 12-gene adjuvant chemotherapy predictive signature developed from a previous study using a clinical-grade assay. EXPERIMENTAL DESIGN:We developed a clinical-grade assay for formalin-fixed, paraffin-embedded (FFPE) samples using the NanoString nCounter platform to measure the mRNA expression of the previously published 12-gene set. The predictive performance was validated in a cohort of 207 patients with early-stage resected NSCLC with matched propensity score of adjuvant chemotherapy. RESULTS:The effects of adjuvant chemotherapy were significantly different in patients from the predicted adjuvant chemotherapy benefit group and those in the predicted adjuvant chemotherapy nonbenefit group (P = 0.0056 for interaction between predicted risk group and adjuvant chemotherapy). Specifically, in the predicted adjuvant chemotherapy benefit group, the patients receiving adjuvant chemotherapy had significant recurrence-free survival (RFS) benefit (HR = 0.34; P = 0.016; adjuvant chemotherapy vs. nonadjuvant chemotherapy), while in the predicted adjuvant chemotherapy nonbenefit group, the patients receiving adjuvant chemotherapy actually had worse RFS (HR = 1.86; P = 0.14; adjuvant chemotherapy vs. nonadjuvant chemotherapy) than those who did not receive adjuvant chemotherapy. CONCLUSIONS:This study validated that the 12-gene signature and the FFPE-based clinical assay predict that patients whose resected lung adenocarcinomas exhibit an adjuvant chemotherapy benefit gene expression pattern and who then receive adjuvant chemotherapy have significant survival advantage compared with patients whose tumors exhibit the benefit pattern but do not receive adjuvant chemotherapy.

SUBMITTER: Xie Y

PROVIDER: S-EPMC7274213 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Validation of the 12-gene Predictive Signature for Adjuvant Chemotherapy Response in Lung Cancer.

Xie Yang Y Lu Wei W Wang Shidan S Tang Ximing X Tang Hao H Zhou Yunyun Y Moran Cesar C Behrens Carmen C Roth Jack A JA Zhou Qinghua Q Johnson David H DH Swisher Stephen G SG Heymach John V JV Papadimitrakopoulou Vassiliki A VA Xiao Guanghua G Minna John D JD Wistuba Ignacio I II

Clinical cancer research : an official journal of the American Association for Cancer Research 20181004 1

<h4>Purpose</h4>Response to adjuvant chemotherapy after tumor resection varies widely among patients with non-small cell lung cancer (NSCLC); therefore, it is of clinical importance to prospectively predict who will benefit from adjuvant chemotherapy before starting the treatment. The goal of this study is to validate a 12-gene adjuvant chemotherapy predictive signature developed from a previous study using a clinical-grade assay.<h4>Experimental design</h4>We developed a clinical-grade assay fo ...[more]

PMID: 30287547

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Project description:The clinical benefit of adjuvant chemotherapy for stage II colorectal cancer (CRC) is controversial. This study aimed to explore novel gene signature to predict outcome benefit of postoperative 5-Fu-based therapy in stage II CRC.Gene-expression profiles of stage II CRCs from two datasets with 5-Fu-based adjuvant chemotherapy (training dataset, n?=?212; validation dataset, n?=?85) were analyzed to identify the indicator. A systemic approach by integrating gene-expression and protein-protein interaction (PPI) network was implemented to develop the predictive signature. Kaplan-Meier curves and Cox proportional hazards model were used to determine the survival benefit of adjuvant chemotherapy. Experiments with shRNA knock-down were carried out to confirm the signature identified in this study.In the training dataset, we identified 44 PPI sub-modules, by which we separate patients into two clusters (1 and 2) having different chemotherapeutic benefit. A predictor of 11 PPI sub-modules (11-PPI-Mod) was established to discriminate the two sub-groups, with an overall accuracy of 90.1%. This signature was independently validated in an external validation dataset. Kaplan-Meier curves showed an improved outcome for patients who received adjuvant chemotherapy in Cluster 1 sub-group, but even worse survival for those in Cluster 2 sub-group. Similar results were found in both the training and the validation dataset. Multivariate Cox regression revealed an interaction effect between 11-PPI-Mod signature and adjuvant therapy treatment in the training dataset (RFS, p?=?0.007; OS, p?=?0.006) and the validation dataset (RFS, p?=?0.002). From the signature, we found that PTGES gene was up-regulated in CRC cells which were more resistant to 5-Fu. Knock-down of PTGES indicated a growth inhibition and up-regulation of apoptotic markers induced by 5-Fu in CRC cells.Only a small proportion of stage II CRC patients could benefit from adjuvant therapy. The 11-PPI-Mod as a potential predictor could be helpful to distinguish this sub-group with favorable outcome.

Project description:BackgroundWe developed and validated a prognostic and predictive computational pathology risk score (CoRiS) using H&E stained tissue images from patients with early-stage non-small cell lung cancer (ES-NSCLC).Methods1330 patients with ES-NSCLC were acquired from 3 independent sources and divided into four cohorts D1-4. D1 comprised 100 surgery treated patients and was used to identify prognostic features via an elastic-net Cox model to predict overall and disease-free survival. CoRiS was constructed using the Cox model coefficients for the top features. The prognostic performance of CoRiS was evaluated on D2 (N=331), D3 (N=657) and D4 (N=242). Patients from D2 and D3 which comprised surgery + chemotherapy were used to validate CoRiS as predictive of added benefit to adjuvant chemotherapy (ACT) by comparing survival between different CoRiS defined risk groups.FindingsCoRiS was found to be prognostic on univariable analysis, D2 (hazard ratio (HR) = 1.41, adjusted (adj.) P = .01) and D3 (HR = 1.35, adj. P < .001). Multivariable analysis showed CoRiS was independently prognostic, D2 (HR = 1.41, adj. P < .001) and D3 (HR = 1.35, adj. P < .001), after adjusting for clinico-pathologic factors. CoRiS was also able to identify high-risk patients who derived survival benefit from ACT D2 (HR = 0.42, adj. P = .006) and D3 (HR = 0.46, adj. P = .08).InterpretationCoRiS is a tissue non-destructive, quantitative and low-cost tool that could potentially help guide management of ES-NSCLC patients.FundingData collection, anlaysis, and computation resources of the research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award numbers: 1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R01 CA216579-01A1, R01 CA220581-01A1, 1U01 CA239055-01. National Center for Research Resources under award number 1 C06 RR12463-01. VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.

Dataset Information

Validation of the 12-gene Predictive Signature for Adjuvant Chemotherapy Response in Lung Cancer.

Publications

Validation of the 12-gene Predictive Signature for Adjuvant Chemotherapy Response in Lung Cancer.

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