Development and Validation of a Prognostic Gene-Expression Signature for Lung Adenocarcinoma

<div><p>Although several prognostic signatures have been developed in lung cancer, their application in clinical practice has been limited because they have not been validated in multiple independent data sets. Moreover, the lack of common genes between the signatures makes it difficult to know what biological process may be reflected or measured by the signature. By using classical data exploration approach with gene expression data from patients with lung adenocarcinoma (n = 186), we uncovered two distinct subgroups of lung adenocarcinoma and identified prognostic 193-gene gene expression signature associated with two subgroups. The signature was validated in 4 independent lung adenocarcinoma cohorts, including 556 patients. In multivariate analysis, the signature was an independent predictor of overall survival (hazard ratio, 2.4; 95% confidence interval, 1.2 to 4.8; <em>p</em> = 0.01). An integrated analysis of the signature revealed that <em>E2F1</em> plays key roles in regulating genes in the signature. Subset analysis demonstrated that the gene signature could identify high-risk patients in early stage (stage I disease), and patients who would have benefit of adjuvant chemotherapy. Thus, our study provided evidence for molecular basis of clinically relevant two distinct two subtypes of lung adenocarcinoma.</p> </div

Univariate and Multivariate Cox Proportional Hazard Regression Analyses of Overall Survival in the ACC Cohort (n = 117).

<p>Abbreviations: CI, confidence interval; M, male; F (sex), female; WT, wild-type; F (gene signature), fast-growing; S, slow-growing.</p

Clinical and Pathological Features of Lung Adenocarcinoma Cancer Patients.

<p>Abbreviations: TM, Toronto and Memorial Sloan-Kettering Cancer Center; HM, H. Lee Moffit Cancer Center and University of Michigan; MGH, Massachusetts General Hospital; ACC, Aichi Cancer Center; NA, Not available.</p

Significant association of the 2 gene-expression signature subtypes with adjuvant chemotherapy.

<p>(A) Kaplan-Meier plots of the overall survival (OS) of adenocarcinoma patients in the TM and HM cohorts. The data were plotted according to the prognostic gene-expression signature (subgroups F and S). Kaplan-Meier plots of patients in (B) subgroup F or (C) subgroup S with stage III disease. Data were plotted according to whether patients were treated with or without adjuvant chemotherapy (CTX).</p

Cross comparison of gene lists from 4 independent cohorts of lung adenocarcinoma patients.

<p>(A) Venn diagram of genes whose expression is significantly different between subgroups F and S. a univariate test (2-sample t-test) with multivariate permutation test (10,000 random permutations) was applied. In each comparison, we applied a cut-off P-value of less than 0.001 to retain genes whose expression was significantly different between the 2 groups of tissues examined. (B) Expression patterns of selected genes shared in 4 lung adenocarcinoma cohorts. The expression of 470 genes is commonly up- or down-regulated in all 4 cohorts. Colored bars at the top of the heat map represent samples as indicated.</p

Kaplan-Meier plots of the DFS rates of AUS patients with stage III colorectal cancer in AUS cohort.

<p>Patients were stratified by risk level according to the genomic predictors, ColoGuideEx (A), MDA114 (B), Meta163 (C), and OncoDX (D) and grouped by whether they had received adjuvant chemotherapy (CTX) or not. Int, intermediate.</p

Clinical and pathological characteristics of patients with colorectal cancer.

*<p>NA, Not Available</p

Concordance of the five genomic predictors in grouping AUS patients by risk level.

<p>Correlation was quantified using Cramer's V statistics. * Inverse correlation</p

Kaplan–Meier survival plots of the DFS rates of VI patients stratified by risk level according to the five genomic predictors (A to E).

<p><i>P</i> values are based on the log-rank test. Int, intermediate.</p

Multivariate Cox proportional hazard regression analyses of DFS with clinical variables and genomic predictors.

<p>Multivariate Cox proportional hazard regression analyses of DFS with clinical variables and genomic predictors.</p