Author(s): Smith SH, Goldschmidt MH, McManus PM
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Abstract Melanoma is a devastating disease frequently encountered within both veterinary and human medicine. Molecular changes linked with neoplastic transformation of melanocytes include mutations in genes that encode proteins intrinsic to the regulatory pathways of two tumor suppressor proteins (retinoblastoma protein and p53), proto-oncogene mutation to oncogenes, altered expression of epithelial cadherin and CD44 adhesion molecules, and upregulation of angiogenic factors and other growth factors. Histologic evaluation of the primary mass is the most common means of diagnosis, with cytology used more frequently to document metastasis. Melanoma's highly variable histologic and cytologic patterns can make diagnosis by either method problematic. Adherent epithelioid morphology, including signet ring forms, and nonadherent round and spindle forms are recognized, with pigmentation an inconsistent finding. The site of the tumor, the thickness of the primary tumor or depth of invasion, and the number of mitotic figures per high-power field or per millimeter are used histologically to predict biologic behavior, whereas site and degree of pleomorphism are typically used for cytologic preparations. Diagnosis of amelanotic melanoma can be aided by ancillary diagnostic techniques. Tumor cells are usually positive for vimentin, S100, neuron-specific enolase, and Melan-A, and negative for cytokeratin. Melan-A as a positive marker is not as sensitive as the others are but is likely more specific. Monoclonal antibodies to human melanosome-specific antigens 1 and 5 cross-react with canine antigens for a combined sensitivity rate of 83\%. Mouse monoclonal antibody IBF9 specifically recognizes canine melanoma antigen and also has good sensitivity. Serologic markers, including cytokines, cell adhesion molecules, and melanoma-inhibitory activity, are being investigated as potential sentinels of melanoma. Currently, there is no single diagnostic technique capable of differentiating benign from malignant melanocytic neoplasms or predicting survival time.
This article was published in Vet Pathol
and referenced in Journal of Computer Science & Systems Biology