Classification and Grading of Melanocytic Lesions in a Mouse Model of NRAS-driven Melanomagenesis. (March 2021)

• Record Type: Journal Article
• Authors: Assenmacher, Charles-Antoine; Santagostino, Sara F.; Oyama, Mark A.; Marine, Jean-Christophe; Bonvin, Elise; Radaelli, Enrico
• Is Part Of: Journal of histochemistry and cytochemistry. Volume 69:Number 3(2021)
• Page Start: 203
• Page End: 218
• Publication Date: 2021-03
• Subjects: immunohistochemistry -- lymph nodes -- melanoma -- mice -- NRAS -- skin; Histochemistry -- Periodicals; Cytochemistry -- Periodicals; Histocytochemistry -- methods; Histological Techniques; 572.05
• DOI: 10.1369/0022155420977970
• Languages: English
• ISSNs: 0022-1554

Abstract

The mouse line carrying the Tg(Tyr-NRAS*Q61K)1Bee transgene is widely used to model in vivo NRAS -driven melanomagenesis. Although the pathological features of this model are well described, classification and interpretation of the resulting proliferative lesions—including their origin, evolution, grading, and pathobiological significance—are still unclear and not supported by molecular and biological evidence. Focusing on their classification and grading, this work combines histopathology and expression analysis (using both immunohistochemistry [IHC] and quantitative PCR) of selected biomarkers to study the full spectrum of cutaneous and lymph nodal melanocytic proliferations in the Tg(Tyr-NRAS*Q61K)1Bee mouse. The analysis of cutaneous and lymph nodal melanocytic proliferations has demonstrated that a linear correlation exists between tumor grade and Ki-67, microphthalmia-associated transcription factor (MITF), gp100, and nestin IHC, with a significantly increased expression in high-grade lesions compared with low-grade lesions. The accuracy of the assessment of MITF IHC in melanomas was also confirmed by quantitative PCR analysis. In conclusion, we believe the incorporation of MITF, Ki-67, gp100, and nestin analysis into the histopathological classification/grading scheme of melanocytic proliferations described for this model will help to assess with accuracy the nature and evolution of the phenotype, monitor disease progression, and predict response to experimental treatment or other preclinical manipulations.