Original Article
Translational Oncology
A Two-Gene Prognostic Classifier for Early-Stage Lung Squamous Cell Carcinoma in Multiple Large-Scale and Geographically Diverse Cohorts

https://doi.org/10.1016/j.jtho.2016.08.141Get rights and content
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Abstract

Introduction

There are no validated molecular methods that prospectively identify patients with surgically resected lung squamous cell carcinoma (SCC) at high risk for recurrence. By focusing on the expression of genes with known functions in development of lung SCC and prognosis, we sought to develop a robust prognostic classifier of early-stage lung SCC.

Methods

The expression of 253 genes selected by literature search was evaluated in microarrays from 107 stage I/II tumors. Associations with survival were evaluated by Cox regression and Kaplan-Meier survival analyses in two independent cohorts of 121 and 91 patients with SCC, respectively. A classifier score based on multivariable Cox regression was derived and examined in six additional publicly available data sets of stage I/II lung SCC expression profiles (n = 358). The prognostic value of this classifier was evaluated in meta-analysis of patients with stage I/II (n = 479) and stage I (n = 326) lung SCC.

Results

Dual specificity phosphatase 6 gene (DUSP6) and actinin alpha 4 gene (ACTN4) were associated with prognostic outcome in two independent patient cohorts. Their expression values were utilized to develop a classifier that identified patients with stage I/II lung SCC at high risk for recurrence (hazard ratio [HR] = 4.7, p = 0.018) or cancer-specific mortality (HR = 3.5, p = 0.016). This classifier also identified patients at high risk for recurrence (HR = 2.7, p = 0.008) or death (HR = 2.2, p = 0.001) in publicly available data sets of stage I/II and in meta-analysis of stage I patients.

Conclusions

We have established and validated a prognostic classifier to inform clinical management of patients with lung SCC after surgical resection.

Keywords

Lung squamous cell carcinoma
Prognostic classifier
Biomarker
Microarray
Gene expression

Cited by (0)

Drs. Noro, Ishigame, and Walsh equally contributed to this work.

Disclosure: The authors declare no conflict of interest.