A diagnostic test for prostate cancer from gene expression profiling data.

Abstract

PURPOSE: Multiple recent studies show excellent classification accuracy using bioinformatics tools applied to expression profiling data on various tumors. However, the clinical applicability of these techniques remains unfulfilled because of difficulty in translating complex multigene mathematical algorithms into reproducible, platform independent tests. We recently developed a broadly applicable platform independent method based on simple ratios of gene expression to diagnose and predict outcome in cancer. In the current study we applied this technique to the diagnosis of prostate cancer. MATERIALS AND METHODS: We developed a ratio based predictive model using a training set of 32 samples with previously published gene profiling data. We then tested and refined the model using additional independent samples with previously published microarray data from another source (that is the test set of 34 samples). Finally, the optimal ratio based test was examined with quantitative reverse transcriptase-polymerase chain reaction for data acquisition in a third cohort of samples consisting of 10 frozen normal and 10 tumor prostate tissues. RESULTS: A 3-ratio test using 4 genes was 90% accurate (18 of 20 samples) for distinguishing normal prostate and prostate cancer samples obtained at surgery (Fisher's exact test p = 0.0007). This test did not result in any false-negative findings. CONCLUSIONS: We describe and validate a new gene ratio based test for the diagnosis of prostate cancer, which was developed from the analysis of extensive gene profiling data for the diagnosis of prostate cancer. This test can be easily adapted to the clinical arena without the need for complex computer software or hardware. We anticipate that the gene ratio based diagnosis of prostate cancer using fine needle aspirations could serve as a useful adjunct to standard histopathological techniques.