title: A prognostic test for adenocarcinoma of the lung from gene expression profiling data.
abstract: Until recently, it has been impossible to determine which patients with resected stage I lung cancer are among the 30% who will die of metastatic cancer within 5 years of surgery. Bioinformatics tools applied to lung cancer expression profiling data have identified prognostic genes that have been used to develop predictor models, but thus far, these models have not been incorporated into routine clinical use because of their inherent complexity and requirement for relatively large numbers of genes. We have used ratios of gene expression to overcome these limitations. Here, we evaluate the ability of this technique to identify patients with stage I lung adenocarcinoma at risk for recurrence. We derived candidate ratio-based tests from analysis of 36 stage I lung adenocarcinoma samples using previously published expression profiling data. Eleven of these tests were identified for additional study and assessed for classification accuracy in an independent set of 60 stage I lung adenocarcinoma samples. We then evaluated the optimal ratio-based test in the independent samples using Kaplan-Meier survival analysis. Finally, we examined the ability of this test to predict outcome in a set of 97 stage I breast adenocarcinoma. We found that subsets of the independent lung cancer samples predicted to be associated with either good or poor outcome using the optimal ratio-based test differed significantly (P=0.0056) in terms of survival with a classification accuracy of 74% (P=0.0043, Fisher's exact test). When this test was applied to stage II and III lung cancers, most specimens were classified as poor outcome cancers. Interestingly, we found that the same test significantly (P=0.0417) predicted recurrence of stage I breast tumors, suggesting that at least some of the marker genes we identified may have generalized prognostic significance for adenocarcinoma. Our results provide additional evidence that expression ratios are highly accurate in predicting cancer recurrence and may be used in a simple test to predict response to surgical therapy in early-stage lung adenocarcinoma.
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics* / Q000401:mortality / Q000473:pathology; D014408:Biomarkers, Tumor; D001943:Breast Neoplasms / Q000235:genetics / Q000401:mortality / Q000473:pathology; D020869:Gene Expression Profiling; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics* / Q000401:mortality / Q000473:pathology; D009367:Neoplasm Staging; D020411:Oligonucleotide Array Sequence Analysis; D011237:Predictive Value of Tests; D011379:Prognosis; D012008:Recurrence; D015203:Reproducibility of Results; D012306:Risk; D015996:Survival Rate
chemical_list: D014408:Biomarkers, Tumor