Lung cancer is the leading cause of cancer deaths worldwide. In 2008, 1.61 million new cases, and 1.38 million deaths due to lung cancer were recorded. This high mortality rate is mainly due to the late stage at which lung cancer is diagnosed. While early diagnosis has been successfully implemented through tomography-based population screenings in high-risk individuals, there is a need for simpler, non-invasive and more accessible methodologies for effective early cancer detection programs. Circulating microRNA (miRNA) profiles have been suggested as promising diagnostic and prognostic biomarkers for cancer, including lung cancer. However, the results have so far been inconsistent between studies. The objective of this study was to explore the potential of circulating miRNAs in plasma for early detection of lung cancer using global profiling approach.
Plasma samples were collected from 100 early stage (I to IIIA) non-small-cell lung cancer patients (35 lung adenocarcinoma and 65 squamous cell carcinoma patients) and 100 age- and gender-matched healthy controls. 754 circulating miRNAs were analyzed via quantitative RT-PCR using TaqMan Low Density Arrays. Data were quantile normalized and limma analysis with adjustment for multiple testing to control for false discovery rate (FDR, Benjamini-Hochberg method) was performed to identify differentially regulated miRNA between cases and controls. Penalized Lasso logistic regression model (with penalty parameter tuning conducted by 10-fold cross-validation) was used to compute the least redundant panel of miRNAs for discriminating between cases and controls. The area under the receiver operating characteristic curve (AUC) was calculated to assess the discriminatory power of the model. Internal validation was conducted by calculating the bootstrap optimism-corrected AUC for the selected model.
Sixty one plasma miRNAs were found to be significantly differentially expressed between lung cancer cases and controls including 33 upregulated and 28 downregulated miRNAs (p-value < 0.05). Penalized Lasso logistic regression identified a panel of 24 miRNA having the optimum classification performance. Multiple logistic regression analysis showed that combination of the 24 miRNAs alone could discriminate lung cancer cases from healthy controls (AUC of 0.92). This classification improved to AUC of 0.94 following addition of gender, age at interview and smoking status to the model. Internal validation of the model using the bootstrap optimism corrected AUC suggests that the discriminatory power of the panel will be high when applied to independent samples (corrected AUC of 0.86 and for the 24-miRNA panel alone, 0.87 for the model including gender, age and smoking status). These results highlight the potential of circulating miRNA as biomarkers for lung cancer. Independent prospective validation of the clinical potential of the panel within a cohort with pre-diagnostic samples is warranted.
Citation Format: Magdalena B. Wozniak, Ghislaine Scelo, David Muller, Anush Moukeria, David Zaridze, Paul Brennan. Circulating microRNAs as non-invasive biomarkers for early detection of lung cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 884. doi:10.1158/1538-7445.AM2014-884