Serum Autoantibody Profiling Using a Natural Glycoprotein Microarray for the Prognosis of Early Melanoma

Abstract

The poor prognosis of melanoma and the high-cost of lymph node biopsy for melanoma patients have led to an urgent need for the discovery of convenient and accurate prognostic indicators. Here, we have developed a natural glycoprotein microarray to discover serum autoantibodies to distinguish between patients with node negative melanoma and node positive melanoma. Duallectin affinity chromatography was used to extract glycoproteins from a melanoma cell line. Liquid-based reverse phase separation and microarray platforms were then applied to separate and spot these natural proteins on nitrocellulose slides. The serum autoantibodies were investigated by exposing these proteins to sera from 43 patients which have already been diagnosed to have different stages of early melanoma. The combination of 9 fractions provides a 55% sensitivity with 100% specificity for the detection of node positive against node negative and a 62% sensitivity with 100% specificity for the detection of node negative against node positive. Recombinant proteins were used to confirm the results using a sample set with 79 patients with diagnosed melanoma. The response of sera against recombinant 94 kD glucose-regulated protein (GRP94), acid ceramidase (ASAH1), cathepsin D (CTSD), and lactate dehydrogenase B (LDHB) shared a similar pattern to the fractions where they were identified. The glycoarray platform provides a convenient and highly-reproducible method to profile autoantibodies which could be used as serum biomarkers for prognosis of melanoma.

Acid Ceramidase in Melanoma

“…Quantitative immunohistochemical analysis of AC on human biopsies supported this hypothesis and revealed a possible relation between AC levels and node-negative Stage II melanomas. In line with our observation, work from Liu et al (9) has shown that AC is one of several proteins whose autoantibodies are expressed in 40% of node-negative patients (19 of 48) but in none of the node-positive patients (0 of 31).…”

“…A bioinformatic survey of the National Cancer Institute (NCI) gene expression database conducted in 2003 identified AC as a potential candidate for the development of new biomarkers for the prognosis of melanoma (8). Supporting this possibility, subsequent studies showed that serum of melanoma patients contains AC autoantibodies (9). Moreover, experiments with melanoma cell lines in cultures have demonstrated that dacarbazine, a standard of care for the treatment in melanoma patients, causes a time-and concentration-dependent decrease in cellular AC levels (10).…”

Inhibitors of Ceramidases

“…Elevated aCDase expression has been reported in various cancer types (French et al, 2006), including head and neck cancer (Mehta et al, 2000), colon adenocarcinoma (Realini et al, 2013), prostate cancer (Norris et al, 2006;Saad et al, 2007;Liu et al, 2009;Seelan et al, 2000;Turner et al, 2011;Morad et al, 2013;Mahdy et al, 2009) and melanoma (Liu et al, 2010;Musumarra et al, 2003;Bedia et al, 2011). Indeed, aCDase over-expression renders tumor cells resistant to chemo-and radiotherapy, while inhibition of aCDase activity sensitizes tumor cells to the effects of chemo-and radio-therapy, reduce tumor growth and prevent cancer relapse (Elojeimy et al, 2007;Gouaze-Andersson et al, 2011;Szulc et al, 2008;Bielawska et al, 2008;Liu et al, 2006;Cheng et al, 2013).…”

Outlier-Based Differential Expression Analysis in Proteomics Studies

“…A melanoma dataset was used to explore the utility of serum auto-antibodies as biomarkers to distinguish between “node-negative” and “node-positive” melanoma as described by Liu et al [11]. Node-positive status indicates metastasis to a sentinel lymph node, while node-negative status indicates the absence of metastasis, and thus an earlier stage of cancer.…”

“…This diagnostic can be used to reveal the relative amounts of outlier-like versus “central” differential expression in a dataset. Finally, we used a proteomics dataset of serum samples from melanoma patients [11] to contrast the evidence for differential expression obtained using outlier-based versus mean-based approaches to differential expression analysis.…”