Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma.

The pathological distinction between malignant pleural mesothelioma (MPM)and adenocarcinoma (ADCA) of the lung can be cumbersome using established methods. We propose that a simple technique, based on the expression levels of a small number of genes, can be useful in the early and accurate diagnosis of MPM and lung cancer. This method is designed to accurately distinguish between genetically disparate tissues using gene expression ratios and rationally chosen thresholds. Here we have tested the fidelity of ratio-based diagnosis in differentiating between MPM and lung cancer in 181 tissue samples (31 MPM and 150 ADCA). A training set of 32 samples (16 MPM and 16 ADCA) was used to identify pairs of genes with highly significant, inversely correlated expression levels to form a total of 15 diagnostic ratios using expression profiling data. Any single ratio of the 15 examined was at least 90% accurate in predicting diagnosis for the remaining 149 samples (e.g., test set). We then examined (in the test set) the accuracy of multiple ratios combined to form a simple diagnostic tool. Using two and three expression ratios, we found that the differential diagnoses of MPM and lung ADCA were 95% and 99% accurate, respectively. We propose that using gene expression ratios is an accurate and inexpensive technique with direct clinical applicability for distinguishing between MPM and lung cancer. Furthermore, we provide evidence suggesting that this technique can be equally accurate in other clinical scenarios.