The gene expression profiles of primary and metastatic melanoma yields a transition point of tumor progression and metastasis

<p>Abstract</p> <p>Background</p> <p>The process of malignant transformation, progression and metastasis of melanoma is poorly understood. Gene expression profiling of human cancer has allowed for a unique insight into the genes that are involved in these processes. Thus, we have attempted to utilize this approach through the analysis of a series of primary, non-metastatic cutaneous tumors and metastatic melanoma samples.</p> <p>Methods</p> <p>We have utilized gene microarray analysis and a variety of molecular techniques to compare 40 metastatic melanoma (MM) samples, composed of 22 bulky, macroscopic (replaced) lymph node metastases, 16 subcutaneous and 2 distant metastases (adrenal and brain), to 42 primary cutaneous cancers, comprised of 16 melanoma, 11 squamous cell, 15 basal cell skin cancers. A Human Genome U133 Plus 2.0 array from Affymetrix, Inc. was utilized for each sample. A variety of statistical software, including the Affymetrix MAS 5.0 analysis software, was utilized to compare primary cancers to metastatic melanomas. Separate analyses were performed to directly compare only primary melanoma to metastatic melanoma samples. The expression levels of putative oncogenes and tumor suppressor genes were analyzed by semi- and real-time quantitative RT-PCR (qPCR) and Western blot analysis was performed on select genes.</p> <p>Results</p> <p>We find that primary basal cell carcinomas, squamous cell carcinomas and thin melanomas express dramatically higher levels of many genes, including <it>SPRR1A/B</it>, <it>KRT16/17</it>, <it>CD24</it>, <it>LOR</it>, <it>GATA3</it>, <it>MUC15</it>, and <it>TMPRSS4</it>, than metastatic melanoma. In contrast, the metastatic melanomas express higher levels of genes such as <it>MAGE</it>, <it>GPR19</it>, <it>BCL2A1</it>, <it>MMP14</it>, <it>SOX5</it>, <it>BUB1</it>, <it>RGS20</it>, and more. The transition from non-metastatic expression levels to metastatic expression levels occurs as melanoma tumors thicken. We further evaluated primary melanomas of varying Breslow's tumor thickness to determine that the transition in expression occurs at different thicknesses for different genes suggesting that the "transition zone" represents a critical time for the emergence of the metastatic phenotype. Several putative tumor oncogenes (<it>SPP-1</it>, <it>MITF</it>, <it>CITED-1</it>, <it>GDF-15</it>, <it>c-Met</it>, <it>HOX </it>loci) and suppressor genes (<it>PITX-1</it>, <it>CST-6</it>, <it>PDGFRL</it>, <it>DSC-3</it>, <it>POU2F3</it>, <it>CLCA2</it>, <it>ST7L</it>), were identified and validated by quantitative PCR as changing expression during this transition period. These are strong candidates for genes involved in the progression or suppression of the metastatic phenotype.</p> <p>Conclusion</p> <p>The gene expression profiling of primary, non-metastatic cutaneous tumors and metastatic melanoma has resulted in the identification of several genes that may be centrally involved in the progression and metastatic potential of melanoma. This has very important implications as we continue to develop an improved understanding of the metastatic process, allowing us to identify specific genes for prognostic markers and possibly for targeted therapeutic approaches.</p

Effective knowledge management in translational medicine

<p>Abstract</p> <p>Background</p> <p>The growing consensus that most valuable data source for biomedical discoveries is derived from human samples is clearly reflected in the growing number of translational medicine and translational sciences departments across pharma as well as academic and government supported initiatives such as Clinical and Translational Science Awards (CTSA) in the US and the Seventh Framework Programme (FP7) of EU with emphasis on translating research for human health.</p> <p>Methods</p> <p>The pharmaceutical companies of Johnson and Johnson have established translational and biomarker departments and implemented an effective knowledge management framework including building a data warehouse and the associated data mining applications. The implemented resource is built from open source systems such as i2b2 and GenePattern.</p> <p>Results</p> <p>The system has been deployed across multiple therapeutic areas within the pharmaceutical companies of Johnson and Johnsons and being used actively to integrate and mine internal and public data to support drug discovery and development decisions such as indication selection and trial design in a translational medicine setting. Our results show that the established system allows scientist to quickly re-validate hypotheses or generate new ones with the use of an intuitive graphical interface.</p> <p>Conclusions</p> <p>The implemented resource can serve as the basis of precompetitive sharing and mining of studies involving samples from human subjects thus enhancing our understanding of human biology and pathophysiology and ultimately leading to more effective treatment of diseases which represent unmet medical needs.</p

Cystatin E/M suppresses legumain activity and invasion of human melanoma

<p>Abstract</p> <p>Background</p> <p>High activity of cysteine proteases such as legumain and the cathepsins have been shown to facilitate growth and invasion of a variety of tumor types. In breast cancer, several recent studies have indicated that loss of the cysteine protease inhibitor cystatin E/M leads to increased growth and metastasis. Although cystatin E/M is normally expressed in the skin, its role in cysteine protease regulation and progression of malignant melanoma has not been studied.</p> <p>Methods</p> <p>A panel of various non-melanoma and melanoma cell lines was used. Cystatin E/M and C were analyzed in cell media by immunoblotting and ELISA. Legumain, cathepsin B and L were analyzed in cell lysates by immunoblotting and their enzymatic activities were analyzed by peptide substrates. Two melanoma cell lines lacking detectable secretion of cystatin E/M were transfected with a cystatin E/M expression plasmid (pCST6), and migration and invasiveness were studied by a Matrigel invasion assay.</p> <p>Results</p> <p>Cystatin E/M was undetectable in media from all established melanoma cell lines examined, whereas strong immunobands were detected in two of five primary melanoma lines and in two of six lines derived from patients with metastatic disease. Among the four melanoma lines secreting cystatin E/M, the glycosylated form (17 kD) was predominant compared to the non-glycosylated form (14 kD). Legumain, cathepsin B and L were expressed and active in most of the cell lines, although at low levels in the melanomas expressing cystatin E/M. In the melanoma lines where cystatin E/M was secreted, cystatin C was generally absent or expressed at a very low level. When melanoma cells lacking secretion of cystatin E/M were transfected with pCST6, their intracellular legumain activity was significantly inhibited. In contrast, cathepsin B activity was not affected. Furthermore, invasion was suppressed in cystatin E/M over-expressing melanoma cell lines as measured by the transwell Matrigel assay.</p> <p>Conclusions</p> <p>These results suggest that the level of cystatin E/M regulates legumain activity and hence the invasive potential of human melanoma cells.</p

Immunological network signatures of cancer progression and survival

<p>Abstract</p> <p>Background</p> <p>The immune contribution to cancer progression is complex and difficult to characterize. For example in tumors, immune gene expression is detected from the combination of normal, tumor and immune cells in the tumor microenvironment. Profiling the immune component of tumors may facilitate the characterization of the poorly understood roles immunity plays in cancer progression. However, the current approaches to analyze the immune component of a tumor rely on incomplete identification of immune factors.</p> <p>Methods</p> <p>To facilitate a more comprehensive approach, we created a ranked immunological relevance score for all human genes, developed using a novel strategy that combines text mining and information theory. We used this score to assign an immunological grade to gene expression profiles, and thereby quantify the immunological component of tumors. This immunological relevance score was benchmarked against existing manually curated immune resources as well as high-throughput studies. To further characterize immunological relevance for genes, the relevance score was charted against both the human interactome and cancer information, forming an expanded interactome landscape of tumor immunity. We applied this approach to expression profiles in melanomas, thus identifying and grading their immunological components, followed by identification of their associated protein interactions.</p> <p>Results</p> <p>The power of this strategy was demonstrated by the observation of early activation of the adaptive immune response and the diversity of the immune component during melanoma progression. Furthermore, the genome-wide immunological relevance score classified melanoma patient groups, whose immunological grade correlated with clinical features, such as immune phenotypes and survival.</p> <p>Conclusions</p> <p>The assignment of a ranked immunological relevance score to all human genes extends the content of existing immune gene resources and enriches our understanding of immune involvement in complex biological networks. The application of this approach to tumor immunity represents an automated systems strategy that quantifies the immunological component in complex disease. In so doing, it stratifies patients according to their immune profiles, which may lead to effective computational prognostic and clinical guides.</p

IL-21 induces in vivo immune activation of NK cells and CD8+ T cells in patients with metastatic melanoma and renal cell carcinoma

PURPOSE: Human interleukin-21 (IL-21) is a class I cytokine previously reported in clinical studies on immune responsive cancers. Here we report the effects of systemic IL-21 therapy on the immune system in two phase 1 trials with this novel cytokine. EXPERIMENTAL DESIGN: Recombinant IL-21 was administered by intravenous bolus injection at dose levels from 1 to 100 microg/kg using two planned treatment regimens: thrice weekly for 6 weeks (3/week); or once daily for five consecutive days followed by nine dose-free days (5 + 9). The following biomarkers were studied in peripheral blood mononuclear cells (PBMC) during treatment: phosphorylation of STAT3, alterations in the composition of leukocyte subsets, ex vivo cytotoxicity, expression of effector molecules in enriched CD8(+) T cells and CD56(+) NK cells by quantitative RT-PCR, and gene array profiling of CD8(+) T cells. RESULTS: Effects of IL-21 were observed at all dose levels. In the 5 + 9 regimen IL-21 induced a dose dependent decrease in circulating NK cells and T cells followed by a return to baseline in resting periods. In both CD8(+) T cells and CD56(+) NK cells we found up-regulation of perforin and granzyme B mRNA. In addition, full transcriptome analysis of CD8(+) T cells displayed changes in several transcripts associated with increased cell cycle progression, cellular motility, and immune activation. Finally, cytotoxicity assays showed that IL-21 enhanced the ability of NK cells to kill sensitive targets ex vivo. CONCLUSIONS: IL-21 was biologically active at all dose levels administered with evidence of in vivo NK cell and CD8(+) T cell activation

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach

Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 × 10−4) alone remained predictive after adjusting for clinical predictors