Correlation of histopathologic characteristics to protein expression and function in malignant melanoma

Abstract

Background: Metastatic melanoma is still one of the most prevalent skin cancers, which upon progression has neither a prognostic marker nor a specific and lasting treatment. Proteomic analysis is a versatile approach with high throughput data and results that can be used for characterizing tissue samples. However, such analysis is hampered by the complexity of the disease, heterogeneity of patients, tumors, and samples themselves. With the long term aim of quest for better diagnostics biomarkers, as well as predictive and prognostic markers, we focused on relating high resolution proteomics data to careful histopathological evaluation of the tumor samples and patient survival information.

Patients and methods: Regional lymph node metastases obtained from ten patients with metastatic melanoma (stage III) were analyzed by histopathology and proteomics using mass spectrometry. Out of the ten patients, six had clinical follow-up data. The protein deep mining mass spectrometry data was related to the histopathology tumor tissue sections adjacent to the area used for deep-mining. Clinical follow-up data provided information on disease progression which could be linked to protein expression aiming to identify tissue-based specific protein markers for metastatic melanoma and prognostic factors for prediction of progression of stage III disease.

Results: In this feasibility study, several proteins were identified that positively correlated to tumor tissue content including IF6, ARF4, MUC18, UBC12, CSPG4, PCNA, PMEL and MAGD2. The study also identified MYC, HNF4A and TGFB1 as top upstream regulators correlating to tumor tissue content. Other proteins were inversely correlated to tumor tissue content, the most significant being; TENX, EHD2, ZA2G, AOC3, FETUA and THRB. A number of proteins were significantly related to clinical outcome, among these, HEXB, PKM and GPNMB stood out, as hallmarks of processes involved in progression from stage III to stage IV disease and poor survival.

Conclusion: In this feasibility study, promising results show the feasibility of relating proteomics to histopathology and clinical outcome, and insight thus can be gained into the molecular processes driving the disease. The combined analysis of histological features including the sample cellular composition with protein expression of each metastasis enabled the identification of novel, differentially expressed proteins. Further studies are necessary to determine whether these putative biomarkers can be utilized in diagnostics and prognostic prediction of metastatic melanoma.

Fig 1. Location of primary tumors on the body of melanoma patients and their route of progression.

Red font represents poor prognosis (“non-survivors”), green font represents good prognosis (“survivors”).

Fig 2

(A) Overall survival of the prognostic groups, “survivors” (group 1) and “non-survivors” (group 2). (B) All ten cases were subjected to rigorous review both on histological and clinical grounds. (C) Those were omitted (lighter grey text), where the tumor content of the examined tissue was low (<10%) or the clinical follow-up data resulted in non-disease-specific outcome measures (one patient in group 2 died without evidence of malignancy). Abbreviations: l: left; r: right; LN met: lymph node metastasis; CNS: central nervous system; tx: therapy. Numbers in brackets mean lymph nodes containing metastatic melanomas / all lymph nodes dissected from either the groin or the axilla: n_metastatic/n_all.

Fig 3

(A) Volcano plot showing differences in approximate protein abundance between survivors and non-survivors. T-test p-value and mean fold difference shown (protein abundance evaluated as sum of PSMs). (B) As in (A), but only significant proteins shown (p<0.05). (C) Biological relationship network (IPA) for proteins differentiating between survivors and non-survivors (shown in B), and having literature links to melanoma or metastasis. Only significant proteins shown (T-test p-value below 0.01), excluding proteins having no IPA relationships within the presented set.

Fig 4. Identified melanoma markers and pathobiological processes in tissue samples of melanoma lymph node metastases.

The graphics displays key upstream regulators found according to analysis based on DAVID and IPA evaluation. The graph displays major regulated canonical pathways and subnetworks resulting in cellular functions and molecular fingerprints providing survival advantage for malignant cells with promotion to further progression and metastasis.