Classification of node-positive melanomas into prognostic subgroups using keratin, immune, and melanogenesis expression patterns.

Affiliation

Netanely D(1), Leibou S(2), Parikh R(2), Stern N(1), Vaknine H(3), Brenner R(3), Amar S(3), Factor RH(4), Perluk T(4), Frand J(4), Nizri E(2)(5), Hershkovitz D(2)(6), Zemser-Werner V(6), Levy C(2), Shamir R(7).
Author information:
(1)Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
(2)Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
(3)Department of Oncology, Edith Wolfson Medical Center, Holon, Israel.
(4)Department of Plastic and Reconstructive Surgery, Edith Wolfson Medical Center, Holon, Israel.
(5)Department of Surgery A, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
(6)Institute of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
(7)Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. [Email]

Abstract

Cutaneous melanoma tumors are heterogeneous and show diverse responses to treatment. Identification of robust molecular biomarkers for classifying melanoma tumors into clinically distinct and homogenous subtypes is crucial for improving the diagnosis and treatment of the disease. In this study, we present a classification of melanoma tumors into four subtypes with different survival profiles based on three distinct gene expression signatures: keratin, immune, and melanogenesis. The melanogenesis expression pattern includes several genes that are characteristic of the melanosome organelle and correlates with worse survival, suggesting the involvement of melanosomes in melanoma aggression. We experimentally validated the secretion of melanosomes into surrounding tissues by melanoma tumors, which potentially affects the lethality of metastasis. We propose a simple molecular decision tree classifier for predicting a tumor's subtype based on representative genes from the three identified signatures. Key predictor genes were experimentally validated on melanoma samples taken from patients with varying survival outcomes. Our three-pattern approach for classifying melanoma tumors can contribute to advancing the understanding of melanoma variability and promote accurate diagnosis, prognostication, and treatment.