Tumor-based Gene Expression Biomarkers to Predict Survival Following Curative Intent Resection for Stage I Lung Adenocarcinoma
Background and objective: Lung cancer is the leading cause of cancer-related death. Surgical resection for early stages lung cancer remains the best hope for a cure. However, 30 to 50% of surgically treated patients will relapse. We have no marker or tool to predict relapse or remission after surgery. The objective of this study is to identify tumor-based gene expression biomarkers associated with survival after curative resection for stage 1 adenocarcinoma.
Methods: Candidate genes were selected based on literature review as well as analyses performed in public databases (PRECOG) and using our own microarray gene expression dataset of lung cancer and non-tumor lung parenchyma tissues collected at 0, 2, 4 and 6 cm away from the lesion. The selected genes were measured by quantitative real-time PCR (qPCR) in the tumors of 243 patients with stage 1 adenocarcinoma. Kaplan-Meier analyses were performed to establish the discriminatory performance of these biomarkers.
Results: Based on literature, we have selected 11 candidate genes that showed promising prognostic value. Complementary analyses with PRECOG and our own microarray dataset enabled us to choose three genes associated with poor outcome (RRM1, EZH2 and FOXM1) and three genes associated with favourable outcome (BTG2, SELENBP1 and NFIB). Pathological stages (stage 1A and 1B) were significantly associated with survival in our series of 243 patients (Kaplan-Meier log-rank; p=1.4e-03). qPCR results for FOXM1, BTG2, SELENBP1 and NFIB in the same series revealed no difference in survival curves of patients with low compared to high gene expression. On the other hand, qPCR results for EZH2 and RRM1 revealed significant difference in survival curves between patients with low compared to high gene expression (EZH2 Kaplan-Meier logrank; p=3.8e-02, RRM1 Kaplan-Meier logrank; p=3.3e-04). Combining RRM1 results with clinical data (age, sex, and pathological stages) improves our predictive model (Kaplan-Meier log-rank; p=4.7e-06). Conclusion: Our results support EZH2 and RRM1 as potential prognosis biomarkers for stage I lung adenocarcinoma. More genes and combinations of genes must be evaluated to identify biomarkers that predict survival and relapse.