A gene expression signature predicts survival of patients with stage I non-small cell lung cancer

Abstract

Background: Lung cancer is the leading cause of cancer-related death in the United States. Nearly 50% of patients with stages I and II non-small cell lung cancer (NSCLC) will die from recurrent disease despite surgical resection. No reliable clinical or molecular predictors are currently available for identifying those at high risk for developing recurrent disease. As a consequence, it is not possible to select those high-risk patients for more aggressive therapies and assign less aggressive treatments to patients at low risk for recurrence.

Methods and findings: In this study, we applied a meta-analysis of datasets from seven different microarray studies on NSCLC for differentially expressed genes related to survival time (under 2 y and over 5 y). A consensus set of 4,905 genes from these studies was selected, and systematic bias adjustment in the datasets was performed by distance-weighted discrimination (DWD). We identified a gene expression signature consisting of 64 genes that is highly predictive of which stage I lung cancer patients may benefit from more aggressive therapy. Kaplan-Meier analysis of the overall survival of stage I NSCLC patients with the 64-gene expression signature demonstrated that the high- and low-risk groups are significantly different in their overall survival. Of the 64 genes, 11 are related to cancer metastasis (APC, CDH8, IL8RB, LY6D, PCDHGA12, DSP, NID, ENPP2, CCR2, CASP8, and CASP10) and eight are involved in apoptosis (CASP8, CASP10, PIK3R1, BCL2, SON, INHA, PSEN1, and BIK).

Conclusions: Our results indicate that gene expression signatures from several datasets can be reconciled. The resulting signature is useful in predicting survival of stage I NSCLC and might be useful in informing treatment decisions.

Figure 1. Validation Analyses of Gene Expression Profiling

(A) QRT-PCR validations of several candidate survival-related genes. Bars represent fold changes for the selected genes with differential expression between long- (>5 y) and short-term survival (<2 y) patients. Positive fold change represents up-regulated, and negative fold change represents down-regulated in short-term survival patients. * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.005. (B and C) Immunostaining analysis of CRABP1 and ABCC1 expression in long- and short- term survival lung cancer patients. Low magnification (B) and 40× (C). Positive CRABP1 immunoreactivity was observed in cytoplasm of an acinar ADC (lower left photomicrographs of B and C) from short-term survival patients, and no CRABP1 reactivity was seen in a lung ADC from a long-term survival patient (upper left). Strong ABCC1 membranous staining (lower right) in tumor cells from short-term survival patients was observed, and weak ABCC1 reactivity was seen in a lung ADC from a long-term survival patient (upper right). (D) Distribution of CRABP1 and ABCC1 protein levels in short- and long-term survival patients.

Figure 2. Survival Analyses of Stage I NSCLC

(A) Kaplan-Meier survival curves for patients with stage IA and with IB NSCLC. (B) Kaplan-Meier survival curves for stage IA and IB patients defined by having positive (high-risk) or negative (low-risk) risk scores of overall survival. The risk scores were estimated with seven principle components based on the model built by 64 survival-related genes identified in five datasets. (C) Area under the ROC curve for survival models based on stage information or expression data, respectively.

Figure 3. Gene Expression Patterns of 64 Top Survival Genes for 197 NSCLC Patients from Datasets 1 to 5

Patients were generally classified into two groups (short-term versus long-term survival) with distinct expression patterns. The first column on the left represents patient status: 0, alive; 1, dead; the second column on the left represents follow-up time (days).

Figure 4. Comparison of the Prediction Accuracy of Lung Cancer Survival Using Our 64-Gene Signature and a Different 50-Gene Signature

(A and B) Kaplan-Meier survival curves for dataset 6 under our 64-gene signature (A) and the 50-gene signature from Beer et al. [5] (B). Scores were estimated using two principle components. (C and D) Kaplan-Meier survival curves for dataset 7 using our 64-gene signature (C) and the 50-gene signature from Beer et al. [5] (D). Scores were estimated using eight principle components.