A simple two-gene prognostic model for adenocarcinoma of the lung

Objective: We hypothesized that clinical outcome of resected early-stage adenocarcinoma of the lung can be predicted by the expression of a few critically important genes as measured by quantitative real-time reverse-transcriptase polymerase chain reaction in formalin-fixed paraffin-embedded primary tumors. Methods: Twenty-two prognostic genes for the metastatic phenotype were identified through complementary DNA microarray analysis of 4 cancer cell lines and bioinformatics analysis. Expression levels of a subset of these genes (n = 13) were measured by real-time time reverse-transcriptase polymerase chain reaction in formalin-fixed paraffin-embedded primary adenocarcinoma from patients whose disease recurred within 2 years (n = 9) and in patients who did not have a recurrence (n = 11). Receiver operating characteristic curves were analyzed to establish prognostic values of single genes. The most informative gene was combined with the remaining genes to determine whether there was a particular pair(s) that yielded high diagnostic accuracy. A small validation study was performed. Results: Receiver operating characteristic curve analysis of the single genes revealed that high expression of CK19 was associated with nonrecurrence (area under the curve = 0.859, confidence interval = 0.651-0.970). The CK19/EpCAM2 gene ratio had the most reproducible prognostic accuracy, followed by the CK19/P-cadherin ratio. A Kaplan-Meier survival analysis generated from the CK19/EpCAM2 ratio resulted in highly significant curves as a function of marker positivity (P = .0007; hazard ratio = 10.7). Significance declined but was maintained in the validation study. Conclusions: This preliminary study provides evidence that the CK19/EpCAM2 and/or CK19/P-cadherin ratio(s) may be a simple and accurate prognostic indicator of clinical outcome in early-stage adenocarcinoma of the lung. If further validation studies from large patient cohorts confirm the results, adjuvant therapy could be targeted to this high-risk group.