Differential diagnosis of solitary lung nodules with gene expression ratios

We have developed a new technique that uses the ratios of select gene expression levels to translate complex genomic data into simple clinically relevant tests for the diagnosis and prognosis of cancer. We determined whether select gene pair ratio combinations can be used to detect and diagnose lung cancer with high accuracy and sensitivity.

We used gene expression profiling data to train a ratio-based predictor model to discriminate among a set of samples (n = 145 total) composed of normal lung, small cell lung cancer, adenocarcinoma, squamous cell carcinoma, and pulmonary carcinoid (the training set). We then examined the optimal test in an independent set of samples (the test set, n = 122). Finally, we used one aspect of the test to determine whether the gene ratio technique was capable of detecting cancer in specimens from fine-needle aspirations performed ex vivo with normal lung (n = 14) and suspected tumor nodules (n = 15) acquired at our institution.

We found that a ratio-based test with 23 genes could be used to classify training set samples with 90% accuracy. This same test was similarly accurate (88%) when applied to the test set of samples. We also found that this test was 87% and 100% accurate at detecting cancer in normal and tumorous fine-needle aspiration specimens, respectively.

The gene expression ratio diagnostic technique is likely to aid in the differential diagnosis of solitary lung nodules in patients with suspected cancer and may also prove useful in developing lung cancer screening strategies that incorporate analysis of fine-needle aspiration specimens.