Data from MicroRNA Expression Differentiates Histology and Predicts Survival of Lung Cancer

Abstract

<div>Abstract<p><b>Purpose:</b> The molecular drivers that determine histology in lung cancer are largely unknown. We investigated whether microRNA (miR) expression profiles can differentiate histologic subtypes and predict survival for non–small cell lung cancer.</p><p><b>Experimental Design:</b> We analyzed miR expression in 165 adenocarcinoma and 125 squamous cell carcinoma (SQ) tissue samples from the Environment And Genetics in Lung cancer Etiology (EAGLE) study using a custom oligo array with 440 human mature antisense miRs. We compared miR expression profiles using <i>t</i> tests and F tests and accounted for multiple testing using global permutation tests. We assessed the association of miR expression with tobacco smoking using Spearman correlation coefficients and linear regression models, and with clinical outcome using log-rank tests, Cox proportional hazards, and survival risk prediction models, accounting for demographic and tumor characteristics.</p><p><b>Results:</b> MiR expression profiles strongly differed between adenocarcinoma and SQ (<i>P</i><sub>global</sub> < 0.0001), particularly in the early stages, and included miRs located on chromosome loci most often altered in lung cancer (e.g., 3p21-22). Most miRs, including all members of the let-7 family, were downregulated in SQ. Major findings were confirmed by quantitative real time-polymerase chain reaction (qRT-PCR) in EAGLE samples and in an independent set of lung cancer cases. In SQ, the low expression of miRs that are downregulated in the histology comparison was associated with 1.2- to 3.6-fold increased mortality risk. A five-miR signature significantly predicted survival for SQ.</p><p><b>Conclusions:</b> We identified a miR expression profile that strongly differentiated adenocarcinoma from SQ and had prognostic implications. These findings may lead to histology-based therapeutic approaches. Clin Cancer Res; 16(2); 430–41</p></div>