Proteomics in melanoma biomarker discovery: great potential, many obstacles

Beyond Nicotinamide Metabolism: Potential Role of Nicotinamide N-Methyltransferase as a Biomarker in Skin Cancers

Skin cancers (SC) collectively represent the most common type of malignancy in white populations. SC includes two main forms: malignant melanoma and non-melanoma skin cancer (NMSC). NMSC includes different subtypes, namely, basal cell carcinoma (BCC), squamous cell carcinoma (SCC), Merkel cell carcinoma (MCC), and keratoacanthoma (KA), together with the two pre-neoplastic conditions Bowen disease (BD) and actinic keratosis (AK). Both malignant melanoma and NMSC are showing an increasing incidence rate worldwide, thus representing an important challenge for health care systems, also because, with some exceptions, SC are generally characterized by an aggressive behavior and are often diagnosed late. Thus, identifying new biomarkers suitable for diagnosis, as well as for prognosis and targeted therapy is mandatory. Nicotinamide N-methyltransferase (NNMT) is an enzyme that is emerging as a crucial player in the progression of several malignancies, while its substrate, nicotinamide, is known to exert chemopreventive effects. Since there is increasing evidence regarding the involvement of this enzyme in the malignant behavior of SC, the current review aims to summarize the state of the art as concerns NNMT role in SC and to support future studies focused on exploring the diagnostic and prognostic potential of NNMT in skin malignancies and its suitability for targeted therapy.

Precision Medicine and Melanoma: Multi-Omics Approaches to Monitoring the Immunotherapy Response

The treatment and management of patients with metastatic melanoma have evolved considerably in the "era" of personalized medicine. Melanoma was one of the first solid tumors to benefit from immunotherapy; life expectancy for patients in advanced stage of disease has improved. However, many progresses have yet to be made considering the (still) high number of patients who do not respond to therapies or who suffer adverse events. In this scenario, precision medicine appears fundamental to direct the most appropriate treatment to the single patient and to guide towards treatment decisions. The recent multi-omics analyses (genomics, transcriptomics, proteomics, metabolomics, radiomics, etc.) and the technological evolution of data interpretation have allowed to identify and understand several processes underlying the biology of cancer; therefore, improving the tumor clinical management. Specifically, these approaches have identified new pharmacological targets and potential biomarkers used to predict the response or adverse events to treatments. In this review, we will analyze and describe the most important omics approaches, by evaluating the methodological aspects and progress in melanoma precision medicine.

Decoding Melanoma Development and Progression: Identification of Therapeutic Vulnerabilities

Melanoma, a cancer of the skin, arises from transformed melanocytes. Melanoma has the highest mutational burden of any cancer partially attributed to UV induced DNA damage. Localized melanoma is “curable” by surgical resection and is followed by radiation therapy to eliminate any remaining cancer cells. Targeted therapies against components of the MAPK signaling cascade and immunotherapies which block immune checkpoints have shown remarkable clinical responses, however with the onset of resistance in most patients, and, disease relapse, these patients eventually become refractory to treatments. Although great advances have been made in our understanding of the metastatic process in cancers including melanoma, therapy failure suggests that much remains to be learned and understood about the multi-step process of tumor metastasis. In this review we provide an overview of melanocytic transformation into malignant melanoma and key molecular events that occur during this evolution. A better understanding of the complex processes entailing cancer cell dissemination will improve the mechanistic driven design of therapies that target specific steps involved in cancer metastasis to improve clinical response rates and overall survival in all cancer patients.

Concurrent Targeting of Glutaminolysis and Metabotropic Glutamate Receptor 1 (GRM1) Reduces Glutamate Bioavailability in GRM1+ Melanoma

Aberrant glutamatergic signaling has been implicated in altered metabolic activity in many cancer types, including malignant melanoma. Previously, we have illustrated the role of metabotropic glutamate receptor 1 (GRM1) in neoplastic transformation of melanocytes in vitro and spontaneous metastatic melanoma in vivo. In this study, we showed that autocrine stimulation constitutively activates the GRM1 receptor and its downstream mitogenic signaling. GRM1-activated (GRM1+) melanomas exhibited significantly increased expression of glutaminase (GLS), which catalyzes the first step in the conversion of glutamine to glutamate. In cultured GRM1+ melanoma cell lines, CB-839, a potent, selective, and orally bioavailable inhibitor of GLS, suppressed cell proliferation, while riluzole, an inhibitor of glutamate release, promoted apoptotic cell death in vitro and in vivo. Combined treatment with CB-839 and riluzole treatment proved to be superior to single-agent treatment, restricting glutamate bioavailability and leading to effective suppression of tumor cell proliferation in vitro and tumor progression in vivo. Hyperactivation of GRM1 in malignant melanoma is an oncogenic driver, which acts independently of canonical melanoma proto-oncogenes, BRAF or NRAS. Overall, these results indicate that expression of GRM1 promotes a metabolic phenotype that supports increased glutamate production and autocrine glutamatergic signaling, which can be pharmacologically targeted by decreasing glutamate bioavailability and the GLS-dependent glutamine to glutamate conversion. SIGNIFICANCE: These findings demonstrate that targeting glutaminolytic glutamate bioavailability is an effective therapeutic strategy for GRM1-activated tumors.

Complementarity of Matrix- and Nanostructure-Assisted Laser Desorption/Ionization Approaches

In recent years, matrix-assisted laser desorption/ionization (MALDI) has become the main tool for the study of biological macromolecules, such as protein nano-machines, especially in the determination of their molecular masses, structure, and post-translational modifications. A key role in the classical process of desorption and ionization of the sample is played by a matrix, usually a low-molecular weight weak organic acid. Unfortunately, the interpretation of mass spectra in the mass range of below m/z 500 is difficult, and hence the analysis of low molecular weight compounds in a matrix-assisted system is an analytical challenge. Replacing the classical matrix with nanomaterials, e.g., silver nanoparticles, allows improvement of the selectivity and sensitivity of spectrometric measurement of biologically important small molecules. Nowadays, the nanostructure-assisted laser desorption/ionization (NALDI) approach complements the classic MALDI in the field of modern bioanalytics. In particular, the aim of this work is to review the recent advances in MALDI and NALDI approaches.

MALDI MSI of MeLiM melanoma: Searching for differences in protein profiles

Background

Treatment of advanced cutaneous melanoma remains challenging, and new data on melanoma biology are required. The most widely accepted criteria for the prognostic evaluation of melanoma are histopathological and clinical parameters, and the identification of additional tumor markers is thus of paramount importance. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI), an important tool in cancer research, is useful for unraveling the molecular profile of melanoma.

Methodology/Principal findings

In this report, we used the melanoma-bearing Libechov minipig (MeLiM), a unique animal model that allows observation of the complete spontaneous regression of invasive cutaneous melanoma, to investigate i) the differences between melanoma and healthy skin protein profiles and ii) the proteins potentially involved in spontaneous regression. The MeLiM tissues were cryosected, histologically characterized, analyzed by MALDI MSI, and immunohistologically stained. Multivariate statistical analyses of the MALDI MSI data revealed ten relevant m/z ions, of which the expression levels varied significantly among the studied MeLiM tissues. These ion peaks were used to create mass ion images/maps and visualize the differences between tumor and healthy skin specimens, as well as among histologically characterized tissue regions.

Conclusions/Significance

Protein profiles comprising ten statistically significant mass ion peaks useful for differentiating cutaneous melanoma and healthy skin tissues were determined. Peaks at m/z 3044, 6011, 6140 and 10180 were overexpressed in melanoma compared with healthy skin tissue. More specifically, m/z 6140 was expressed at significantly (p < 0.05) higher levels in normally growing melanoma regions than in regions with early and late spontaneous regression. This study demonstrates the clinical utility of MALDI MSI for the analysis of tissue cryosections at a molecular level.

A Well-Controlled Experimental System to Study Interactions of Cytotoxic T Lymphocytes with Tumor Cells

While T cell-based immunotherapies are steadily improving, there are still many patients who progress, despite T cell-infiltrated tumors. Emerging evidence suggests that T cells themselves may provoke immune escape of cancer cells. Here, we describe a well-controlled co-culture system for studying the dynamic T cell - cancer cell interplay, using human melanoma as a model. We explain starting material, controls, and culture parameters to establish reproducible and comparable cultures with highly heterogeneous tumor cells. Low passage melanoma cell lines and melanoma-specific CD8+ T cell clones generated from patient blood were cultured together for up to 3 days. Living melanoma cells were isolated from the co-culture system by fluorescence-activated cell sorting. We demonstrate that the characterization of isolated melanoma cells is feasible using flow cytometry for protein expression analysis as well as an Agilent whole human genome microarray and the NanoString technology for differential gene expression analysis. In addition, we identify five genes (ALG12, GUSB, RPLP0, KRBA2, and ADAT2) that are stably expressed in melanoma cells independent of the presence of T cells or the T cell-derived cytokines IFNγ and TNFα. These genes are essential for correct normalization of gene expression data by NanoString. Further to the characterization of melanoma cells after exposure to CTLs, this experimental system might be suitable to answer a series of questions, including how the affinity of CTLs for their target antigen influences the melanoma cell response and whether CTL-induced gene expression changes in melanoma cells are reversible. Taken together, our human T cell - melanoma cell culture system is well suited to characterize immune-related mechanisms in cancer cells.

Proteomics and melanoma: a current perspective

Proteomics is the study of the protein complement of the genome, and this powerful technique complements genomic studies. Proteomic experiments result in the generation of large volumes of data requiring complicated analysis algorithms and subsequent confirmatory studies. Until recently, technological limitations of experimental protocols precluded the use of formalin-fixed tissues for these types of studies. Recent advances have allowed the use of valuable archived patient tissue samples in proteomic research, resulting in an opportunity to perform cutting edge translational research. The field of melanoma research stands to benefit greatly from collaboration between dermatopathologists and proteomic scientists. This article seeks to: 1) describe proteomics for dermatologists and pathologists, including the tools used in proteomic research, and 2) convey a historical account of proteomic studies within the field of melanoma followed by a discussion on how recent advances are informing current studies.

Vitronectin and dermcidin serum levels predict the metastatic progression of AJCC I-II early-stage melanoma

Like many cancers, an early diagnosis of melanoma is fundamental to ensure a good prognosis, although an important proportion of stage I-II patients may still develop metastasis during follow-up. The aim of this work was to discover serum biomarkers in patients diagnosed with primary melanoma that identify those at a high risk of developing metastasis during the follow-up period. Proteomic and mass spectrophotometry analysis was performed on serum obtained from patients who developed metastasis during the first years after surgery for primary tumors and compared with that from patients who remained disease-free for more than 10 years after surgery. Five proteins were selected for validation as prognostic factors in 348 melanoma patients and 100 controls by ELISA: serum amyloid A and clusterin; immune system proteins; the cell adhesion molecules plakoglobin and vitronectin and the antimicrobial protein dermcidin. Compared to healthy controls, melanoma patients have high serum levels of these proteins at the moment of melanoma diagnosis, although the specific values were not related to the histopathological stage of the tumors. However, an analysis based on classification together with multivariate statistics showed that tumor stage, vitronectin and dermcidin levels were associated with the metastatic progression of patients with early-stage melanoma. Although melanoma patients have increased serum dermcidin levels, the REPTree classifier showed that levels of dermcidin <2.98 μg/ml predict metastasis in AJCC stage II patients. These data suggest that vitronectin and dermcidin are potent biomarkers of prognosis, which may help to improve the personalized medical care of melanoma patients and their survival.

Proteomic Findings in Melanoma

Although the emergence of proteomics as an independent branch of science is fairly recent, within a short period of time it has contributed substantially in various disciplines. The tool of mass spectrometry has become indispensable in the analysis of complex biological samples. Clinical applications of proteomics include detection of predictive and diagnostic markers, understanding mechanism of action of drugs as well as resistance mechanisms against them and assessment of therapeutic efficacy and toxicity of drugs in patients. Here, we have summarized the major contributions of proteomics towards the study of melanoma, which is a deadly variety of skin cancer with a high mortality rate.

Biomarkers for Bone Tumors: Discovery from Genomics and Proteomics Studies and Their Challenges

Diagnosis of bone tumor currently relies on imaging and biopsy, and hence, the need to find less invasive ways for its accurate detection. More recently, numerous promising deoxyribonucleic acid (DNA) and protein biomarkers with significant prognostic, diagnostic and/or predictive abilities for various types of bone tumors have been identified from genomics and proteomics studies. This article reviewed the putative biomarkers for the more common types of bone tumors (that is, osteosarcoma, Ewing sarcoma, chondrosarcoma [malignant] and giant cell tumor [benign]) that were unveiled from the studies. The benefits and drawbacks of these biomarkers, as well as the technology platforms involved in the research, were also discussed. Challenges faced in the biomarker discovery studies and the problems in their translation from the bench to the clinical settings were also addressed.

A protein deep sequencing evaluation of metastatic melanoma tissues

Malignant melanoma has the highest increase of incidence of malignancies in the western world. In early stages, front line therapy is surgical excision of the primary tumor. Metastatic disease has very limited possibilities for cure. Recently, several protein kinase inhibitors and immune modifiers have shown promising clinical results but drug resistance in metastasized melanoma remains a major problem. The need for routine clinical biomarkers to follow disease progression and treatment efficacy is high. The aim of the present study was to build a protein sequence database in metastatic melanoma, searching for novel, relevant biomarkers. Ten lymph node metastases (South-Swedish Malignant Melanoma Biobank) were subjected to global protein expression analysis using two proteomics approaches (with/without orthogonal fractionation). Fractionation produced higher numbers of protein identifications (4284). Combining both methods, 5326 unique proteins were identified (2641 proteins overlapping). Deep mining proteomics may contribute to the discovery of novel biomarkers for metastatic melanoma, for example dividing the samples into two metastatic melanoma "genomic subtypes", ("pigmentation" and "high immune") revealed several proteins showing differential levels of expression. In conclusion, the present study provides an initial version of a metastatic melanoma protein sequence database producing a total of more than 5000 unique protein identifications. The raw data have been deposited to the ProteomeXchange with identifiers PXD001724 and PXD001725.

Protein target quantification decision tree

The utility of mass spectrometry-(MS-) based proteomic platforms and their clinical applications have become an emerging field in proteomics in recent years. Owing to its selectivity and sensitivity, MS has become a key technological platform in proteomic research. Using this platform, a large number of potential biomarker candidates for specific diseases have been reported. However, due to lack of validation, none has been approved for use in clinical settings by the Food and Drug Administration (FDA). Successful candidate verification and validation will facilitate the development of potential biomarkers, leading to better strategies for disease diagnostics, prognostics, and treatment. With the recent new developments in mass spectrometers, high sensitivity, high resolution, and high mass accuracy can be achieved. This greatly enhances the capabilities of protein biomarker validation. In this paper, we describe and discuss recent developments and applications of targeted proteomics methods for biomarker validation.

Quantitative Proteomics Identifies Activation of Hallmark Pathways of Cancer in Patient Melanoma

Molecular pathways regulating melanoma initiation and progression are potential targets of therapeutic development for this aggressive cancer. Identification and molecular analysis of these pathways in patients has been primarily restricted to targeted studies on individual proteins. Here, we report the most comprehensive analysis of formalin-fixed paraffin-embedded human melanoma tissues using quantitative proteomics. From 61 patient samples, we identified 171 proteins varying in abundance among benign nevi, primary melanoma, and metastatic melanoma. Seventy-three percent of these proteins were validated by immunohistochemistry staining of malignant melanoma tissues from the Human Protein Atlas database. Our results reveal that molecular pathways involved with tumor cell proliferation, motility, and apoptosis are mis-regulated in melanoma. These data provide the most comprehensive proteome resource on patient melanoma and reveal insight into the molecular mechanisms driving melanoma progression.

Serum peptidomic biomarkers for pulmonary metastatic melanoma identified by means of a nanopore-based assay

The significant mortality rate associated with metastatic melanoma, which exceeds the number of deaths attributed to the primary tumor, is primarily due to poor diagnosis and increased resistance to systemic therapy. Early detection and treatment of invasive melanoma are therefore crucial to increase survival rates. Low molecular weight proteins and peptides have garnered significant interest as biomarker candidates as they potentially represent a snap shot of pathological condition within the body and, by extension, the organism as a whole. We have developed a nanoporous silica-based platform to segregate the low molecular weight from the high molecular weight protein fraction to aid in the detection of peptides from serum samples using mass spectrometry. The combination of sample treatment with our platform, MALDI-TOF MS and following biostatistical analysis led to the discovery and identification of 27 peptides that are potential biomarkers associated with the development of pulmonary metastatic melanoma. We strongly believe our findings can assist to discover stage-specific peptide signatures and lead to more specific and personalized treatments for patients suffering from pulmonary metastatic melanoma.

Performance reproducibility index for classification

MOTIVATION

A common practice in biomarker discovery is to decide whether a large laboratory experiment should be carried out based on the results of a preliminary study on a small set of specimens. Consideration of the efficacy of this approach motivates the introduction of a probabilistic measure, for whether a classifier showing promising results in a small-sample preliminary study will perform similarly on a large independent sample. Given the error estimate from the preliminary study, if the probability of reproducible error is low, then there is really no purpose in substantially allocating more resources to a large follow-on study. Indeed, if the probability of the preliminary study providing likely reproducible results is small, then why even perform the preliminary study?

RESULTS

This article introduces a reproducibility index for classification, measuring the probability that a sufficiently small error estimate on a small sample will motivate a large follow-on study. We provide a simulation study based on synthetic distribution models that possess known intrinsic classification difficulties and emulate real-world scenarios. We also set up similar simulations on four real datasets to show the consistency of results. The reproducibility indices for different distributional models, real datasets and classification schemes are empirically calculated. The effects of reporting and multiple-rule biases on the reproducibility index are also analyzed.

AVAILABILITY

We have implemented in C code the synthetic data distribution model, classification rules, feature selection routine and error estimation methods. The source code is available at http://gsp.tamu.edu/Publications/supplementary/yousefi12a/.