Gene Expression Signature Predicts Recurrence in Lung Adenocarcinoma

Abstract

Purpose: Improving outcomes for early-stage lung cancer is a major research focus at present because a significant proportion of stage I patients develop recurrent disease within 5 years of curative-intent lung resection. Within tumor stage groups, conventional prognostic indicators currently fail to predict relapse accurately. Experimental Design: To identify a gene signature predictive of recurrence in primary lung adenocarcinoma, we analyzed gene expression profiles in a training set of 48 node-negative tumors (stage I-II), comparing tumors from cases who remained disease-free for a minimum of 36 months with those from cases whose disease recurred within 18 months of complete resection. Results: Cox proportional hazards modeling with leave-one-out cross-validation identified a 54-gene signature capable of predicting risk of recurrence in two independent validation cohorts of 55 adenocarcinomas [log-rank P = 0.039; hazard ratio (HR), 2.2; 95% confidence interval (95% CI), 1.1-4.7] and 40 adenocarcinomas (log-rank P = 0.044; HR, 3.3; 95% CI, 1.4-7.9). Kaplan-Meier log-rank analysis found that predicted poor-outcome groups had significantly shorter survival, and furthermore, the signature predicted outcome independently of conventional indicators of tumor stage and node stage. In a subset of earliest stage adenocarcinomas, generally expected to have good outcome, the signature predicted samples with significantly poorer survival. Conclusions: We describe a 54-gene signature that predicts the risk of recurrent disease independently of tumor stage and which therefore has potential to refine clinical prognosis for patients undergoing resection for primary adenocarcinoma of the lung.

Biomarker Discovery in Non–Small Cell Lung Cancer: Integrating Gene Expression Profiling, Meta-analysis, and Tissue Microarray Validation

“…Previous studies have described prognostic gene expression signatures with varying discriminating prognostic power in independent datasets (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(27)(28)(29)(30)(31). A clinical relevance of single genes included in these signatures has not yet been shown, and assays dependent on fresh-frozen tissue are difficult to introduce into routine diagnostics (18).…”

“…[7][8][9][10][11][12][13][14][15][16][17]. However, a recent review critically evaluated the suggested signatures and concluded that in general they do not provide additional prognostic information compared with traditional clinical parameters (18).…”

Relationship of Deregulated Signaling Converging onto mTOR with Prognosis and Classification of Lung Adenocarcinoma Shown by Two Independent In silico Analyses

“…However, cancer cells are thought to be capable of surviving in spite of such constraints, which conceivably leads to disease progression and ultimately confers poor prognosis in affected patients. A number of studies have aimed at identifying a list of genes related to lung cancer prognosis (2,3,10,(18)(19)(20)(21); however, criticism is frequently raised noting that the gene lists used in such prognosis prediction classifiers have minimal overlaps and provide little biological insight into the underlying mechanisms. In a related study, Bild and colleagues analyzed artificially generated cells with overexpression of various oncogenes in vitro and identified oncogenic pathway signatures with a potential to separate lung cancer patients into subgroups in relation to outcome, including drug sensitivity (22).…”

MicroRNA Classifiers for Predicting Prognosis of Squamous Cell Lung Cancer

“…Despite potentially curative surgery, f40% of patients will relapse within 5 years (2). Genomic profiling of NSCLC has recently provided insight into predicting the prognosis of patients with this disease (3)(4)(5)(6)(7)(8). These classifiers can contain up to several hundred genes for the identification of patients with early-stage NSCLC, who might benefit from chemotherapy in addition to surgical resection.…”