Classification and prediction of survival in patients with the leukemic phase of cutaneous T cell lymphoma.

We have used cDNA arrays to investigate gene expression patterns in peripheral blood mononuclear cells from patients with leukemic forms of cutaneous T cell lymphoma, primarily Sezary syndrome (SS). When expression data for patients with high blood tumor burden (Sezary cells >60% of the lymphocytes) and healthy controls are compared by Student's t test, at P < 0.01, we find 385 genes to be differentially expressed. Highly overexpressed genes include Th2 cells-specific transcription factors Gata-3 and Jun B, as well as integrin beta1, proteoglycan 2, the RhoB oncogene, and dual specificity phosphatase 1. Highly underexpressed genes include CD26, Stat-4, and the IL-1 receptors. Message for plastin-T, not normally expressed in lymphoid tissue, is detected only in patient samples and may provide a new marker for diagnosis. Using penalized discriminant analysis, we have identified a panel of eight genes that can distinguish SS in patients with as few as 5% circulating tumor cells. This suggests that, even in early disease, Sezary cells produce chemokines and cytokines that induce an expression profile in the peripheral blood distinctive to SS. Finally, we show that using 10 genes, we can identify a class of patients who will succumb within six months of sampling regardless of their tumor burden.

A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT.

Low-dose CT (LDCT) is widely accepted as the preferred method for detecting pulmonary nodules. However, the determination of whether a nodule is benign or malignant involves either repeated scans or invasive procedures that sample the lung tissue. Noninvasive methods to assess these nodules are needed to reduce unnecessary invasive tests. In this study, we have developed a pulmonary nodule classifier (PNC) using RNA from whole blood collected in RNA-stabilizing PAXgene tubes that addresses this need. Samples were prospectively collected from high-risk and incidental subjects with a positive lung CT scan. A total of 821 samples from 5 clinical sites were analyzed. Malignant samples were predominantly stage 1 by pathologic diagnosis and 97% of the benign samples were confirmed by 4 years of follow-up. A panel of diagnostic biomarkers was selected from a subset of the samples assayed on Illumina microarrays that achieved a ROC-AUC of 0.847 on independent validation. The microarray data were then used to design a biomarker panel of 559 gene probes to be validated on the clinically tested NanoString nCounter platform. RNA from 583 patients was used to assess and refine the NanoString PNC (nPNC), which was then validated on 158 independent samples (ROC-AUC = 0.825). The nPNC outperformed three clinical algorithms in discriminating malignant from benign pulmonary nodules ranging from 6-20 mm using just 41 diagnostic biomarkers. Overall, this platform provides an accurate, noninvasive method for the diagnosis of pulmonary nodules in patients with non-small cell lung cancer. SIGNIFICANCE: These findings describe a minimally invasive and clinically practical pulmonary nodule classifier that has good diagnostic ability at distinguishing benign from malignant pulmonary nodules.

Gene expression profiling of melanocytic lesions.

DNA microarrays, microscopic grids of DNA, can be used to assess gene expression within a particular cell or cell population. Since DNA from thousands of genes can be hybridized and analyzed in one experiment, researchers can globally characterize genes expressed in normal and various pathologic states. To accurately assess the differences between normal and pathologic states, one derives cDNA from control and diseased tissue specimens for genes expression profiling. For these reasons, microarray technology may be of particular interest to dermatopathologists and dermatologists interested in understanding cutaneous disease because these physicians have access to tissue specimens. In addition, microarray technology is an efficient way of identifying molecules expressed in a cell population; therefore, it can be used to search for unique immunohistologic markers. To this end, we have used microarray technology to define differences in the gene expression profile of nevi and melanomas. In this manuscript, we discuss the results of our study, which confirm previously known differences in gene expression between melanoma and nevi. While a few genes appear slightly overexpressed in nevi, a number of genes involved in regulating cell proliferation were upregulated in melanoma, such as cyclin D1, cdc2-related protein kinase, c-Myc binding protein, early growth response protein 1, and pleiotrophin. "Housekeeping" genes such as glyceraldehyde 3-phosphate dehydrogenase were expressed at similar levels in melanoma and nevi. Surprisingly, a majority of genes were expressed at similar levels in both nevi and melanoma. Based on this study, DNA microarray technology appears to be a valuable tool for identifying genes that may be specifically expressed in cutaneous lesions.