Dataset Information

Prediction of postoperative recurrence-free survival in non-small cell lung cancer by using an internationally validated gene expression model.

ABSTRACT:

Purpose

This study was performed to discover prognostic genomic markers associated with postoperative outcome of stage I to III non-small cell lung cancer (NSCLC) that are reproducible between geographically distant and demographically distinct patient populations.

Experimental design

American patients (n = 27) were stratified on the basis of recurrence and microarray profiling of their tumors was performed to derive a training set of 44 genes. A larger Korean patient validation cohort (n = 138) was also stratified by recurrence and screened for these genes. Four reproducible genes were identified and used to construct genomic and clinicogenomic Cox models for both cohorts.

Results

Four genomic markers, DBN1 (drebrin 1), CACNB3 (calcium channel beta 3), FLAD1 (PP591; flavin adenine dinucleotide synthetase), and CCND2 (cyclin D2), exhibited highly significant differential expression in recurrent tumors in the training set (P < 0.001). In the validation set, DBN1, FLAD1 (PP591), and CACNB3 were significant by Cox univariate analysis (P ≤ 0.035), whereas only DBN1 was significant by multivariate analysis. Genomic and clinicogenomic models for recurrence-free survival (RFS) were equally effective for risk stratification of stage I to II or I to III patients (all models P < 0.0001). For stage I to II or I to III patients, 5-year RFS of the low- and high-risk patients was approximately 70% versus 30% for both models. The genomic model for overall survival of stage I to III patients was improved by addition of pT and pN stage (P < 0.0013 vs. 0.010).

Conclusion

A 4-gene prognostic model incorporating the multivariate marker DBN1 exhibits potential clinical utility for risk stratification of stage I to III NSCLC patients.

SUBMITTER: Mitra R

PROVIDER: S-EPMC3086939 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

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Publications

Prediction of postoperative recurrence-free survival in non-small cell lung cancer by using an internationally validated gene expression model.

Mitra Ranjana R Lee Jinseon J Jo Jisuk J Milani Monica M McClintick Jeanette N JN Edenberg Howard J HJ Kesler Kenneth A KA Rieger Karen M KM Badve Sunil S Cummings Oscar W OW Mohiuddin Ahmed A Thomas Dafydd G DG Luo Xianghua X Juliar Beth E BE Li Lang L Mesaros Clementina C Blair Ian A IA Srirangam Anjaiah A Kratzke Robert A RA McDonald Clement J CJ Kim Jhingook J Potter David A DA

Clinical cancer research : an official journal of the American Association for Cancer Research 20110117 9

<h4>Purpose</h4>This study was performed to discover prognostic genomic markers associated with postoperative outcome of stage I to III non-small cell lung cancer (NSCLC) that are reproducible between geographically distant and demographically distinct patient populations.<h4>Experimental design</h4>American patients (n = 27) were stratified on the basis of recurrence and microarray profiling of their tumors was performed to derive a training set of 44 genes. A larger Korean patient validation c ...[more]

PMID: 21242119

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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.

Dataset Information

Prediction of postoperative recurrence-free survival in non-small cell lung cancer by using an internationally validated gene expression model.

Purpose

Experimental design

Results

Conclusion

Publications

Prediction of postoperative recurrence-free survival in non-small cell lung cancer by using an internationally validated gene expression model.

Similar Datasets

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