Dataset Information

Recurrence-associated gene signature in patients with stage I non-small-cell lung cancer.

ABSTRACT: Recurrent gene mutations and fusions in cancer patients are likely to be associated with cancer progression or recurrence by Vogelstein et al. (Science (80-) 340, 1546-1558 (2013)). In this study, we investigated gene mutations and fusions that recurrently occurred in early-stage cancer patients with stage I non-small-cell cancer (NSCLC). Targeted exome sequencing was performed to profile the variants and confirmed their fidelity at the gene and pathway levels through comparison with data for stage I lung cancer patients, which was obtained from The Cancer Genome Atlas (TCGA). Next, we identified prognostic gene mutations (ATR, ERBB3, KDR, and MUC6), fusions (GOPC-ROS1 and NTRK1-SH2D2A), and VEGF signaling pathway associated with cancer recurrence. To infer the functional implication of the recurrent variants in early-stage cancers, the extent of their selection pattern was investigated, and they were shown to be under positive selection, implying a selective advantage for cancer progression. Specifically, high selection scores were observed in the variants with significantly high risks for recurrence. Taken together, the results of this study enabled us to identify recurrent gene mutations and fusions in a stage I NSCLC cohort and to demonstrate positive selection, which had implications regarding cancer recurrence.

SUBMITTER: Cho SH

PROVIDER: S-EPMC8486871 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Recurrence-associated gene signature in patients with stage I non-small-cell lung cancer.

Cho Su Han SH Yoon Shinkyo S Lee Dae Ho DH Kim Sang-We SW Kim Kwoneel K

Scientific reports 20211001 1

Recurrent gene mutations and fusions in cancer patients are likely to be associated with cancer progression or recurrence by Vogelstein et al. (Science (80-) 340, 1546-1558 (2013)). In this study, we investigated gene mutations and fusions that recurrently occurred in early-stage cancer patients with stage I non-small-cell cancer (NSCLC). Targeted exome sequencing was performed to profile the variants and confirmed their fidelity at the gene and pathway levels through comparison with data for st ...[more]

PMID: 34599262

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Project description:Background: The current staging system is imprecise for prognostic prediction of early-stage non-small cell lung cancer (NSCLC). This study aimed to develop a robust prognostic signature for early-stage NSCLC, allowing classification of patients with a high risk of poor outcome and specific treatment decision. Method: In the present study, a comprehensive genome-wide profiling analysis was conducted using a retrospective pool of early-stage NSCLC patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE31210, GSE37745, and GSE50081 and The Cancer Genome Atlas (TCGA). Cox proportional hazards models were implemented to determine the association between gene expression levels and overall patient survival in each dataset. The common genes among all datasets were selected as candidate prognostic genes. A risk score model was developed and validated using four independent datasets and the entire cohort. The Kaplan-Meier with log-rank test was used to assess survival difference. Results: A univariate Cox proportional hazards regression analysis for each dataset showed that a total of 2280 genes in GSE31210, 762 genes in GSE37745, 871 genes in GSE50081, and 666 genes in TCGA were identified as candidate protective genes, while overall 2131 genes in GSE31210, 913 in GSE37745, 1107 in GSE50081, and 997 in TCGA were identified as candidate risky genes. There were 8 common genes associated with overall survival, including 7 mRNA and 1 lncRNA. By using the Step-wise multivariate Cox analysis, an 8-gene prognostic signature (CDCP1, HMMR, TPX2, CIRBP, HLF, KBTBD7, SEC24B-AS1, and SH2B1) for early-stage NSCLC was developed. Patients in the high-risk group had shorter overall survival than those in the low-risk group. Multivariate regression and stratified analysis suggested that the prognostic power of the 8-gene signature was independent of other clinical factors. Furthermore, the 8-gene signature achieved AUC values of 0.726, 0.701, 0.725 and 0.650 in GSE31210, GSE37745, GSE50081 and TCGA, respectively. Moreover, the combination of the 8-gene signature and the stage resulted to a better patient classification for survival prediction and treatment decision. Conclusion: This study developed a robust gene signature with great value for prognostic prediction in early-stage NSCLC, which may contribute to patient classification and personalized treatment decisions.

Project description:BackgroundCancer recurrence after tumor resection in early-stage non-small cell lung cancer (NSCLC) is common, yet difficult to predict. The lung microbiota and systemic immunity may be important modulators of risk for lung cancer recurrence, yet biomarkers from the lung microbiome and peripheral immune environment are understudied. Such markers may hold promise for prediction as well as improved etiologic understanding of lung cancer recurrence.MethodsIn tumor and distant normal lung samples from 46 stage II NSCLC patients with curative resection (39 tumor samples, 41 normal lung samples), we conducted 16S rRNA gene sequencing. We also measured peripheral blood immune gene expression with nanoString®. We examined associations of lung microbiota and peripheral gene expression with recurrence-free survival (RFS) and disease-free survival (DFS) using 500 × 10-fold cross-validated elastic-net penalized Cox regression, and examined predictive accuracy using time-dependent receiver operating characteristic (ROC) curves.ResultsOver a median of 4.8 years of follow-up (range 0.2-12.2 years), 43% of patients experienced a recurrence, and 50% died. In normal lung tissue, a higher abundance of classes Bacteroidia and Clostridia, and orders Bacteroidales and Clostridiales, were associated with worse RFS, while a higher abundance of classes Alphaproteobacteria and Betaproteobacteria, and orders Burkholderiales and Neisseriales, were associated with better RFS. In tumor tissue, a higher abundance of orders Actinomycetales and Pseudomonadales were associated with worse DFS. Among these taxa, normal lung Clostridiales and Bacteroidales were also related to worse survival in a previous small pilot study and an additional independent validation cohort. In peripheral blood, higher expression of genes TAP1, TAPBP, CSF2RB, and IFITM2 were associated with better DFS. Analysis of ROC curves revealed that lung microbiome and peripheral gene expression biomarkers provided significant additional recurrence risk discrimination over standard demographic and clinical covariates, with microbiome biomarkers contributing more to short-term (1-year) prediction and gene biomarkers contributing to longer-term (2-5-year) prediction.ConclusionsWe identified compelling biomarkers in under-explored data types, the lung microbiome, and peripheral blood gene expression, which may improve risk prediction of recurrence in early-stage NSCLC patients. These findings will require validation in a larger cohort.

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Recurrence-associated gene signature in patients with stage I non-small-cell lung cancer.

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Recurrence-associated gene signature in patients with stage I non-small-cell lung cancer.

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