Improving melanoma classification by integrating genetic and morphologic features

Abstract

Background: In melanoma, morphology-based classification systems have not been able to provide relevant information for selecting treatments for patients whose tumors have metastasized. The recent identification of causative genetic alterations has revealed mutations in signaling pathways that offer targets for therapy. Identifying morphologic surrogates that can identify patients whose tumors express such alterations (or functionally equivalent alterations) would be clinically useful for therapy stratification and for retrospective analysis of clinical trial data.

Methodology/principal findings: We defined and assessed a panel of histomorphologic measures and correlated them with the mutation status of the oncogenes BRAF and NRAS in a cohort of 302 archival tissues of primary cutaneous melanomas from an academic comprehensive cancer center. Melanomas with BRAF mutations showed distinct morphological features such as increased upward migration and nest formation of intraepidermal melanocytes, thickening of the involved epidermis, and sharper demarcation to the surrounding skin; and they had larger, rounder, and more pigmented tumor cells (all p-values below 0.0001). By contrast, melanomas with NRAS mutations could not be distinguished based on these morphological features. Using simple combinations of features, BRAF mutation status could be predicted with up to 90.8% accuracy in the entire cohort as well as within the categories of the current World Health Organization (WHO) classification. Among the variables routinely recorded in cancer registries, we identified age < 55 y as the single most predictive factor of BRAF mutation in our cohort. Using age < 55 y as a surrogate for BRAF mutation in an independent cohort of 4,785 patients of the Southern German Tumor Registry, we found a significant survival benefit (p < 0.0001) for patients who, based on their age, were predicted to have BRAF mutant melanomas in 69% of the cases. This group also showed a different pattern of metastasis, more frequently involving regional lymph nodes, compared to the patients predicted to have no BRAF mutation and who more frequently displayed satellite, in-transit metastasis, and visceral metastasis (p < 0.0001).

Conclusions: Refined morphological classification of primary melanomas can be used to improve existing melanoma classifications by forming subgroups that are genetically more homogeneous and likely to differ in important clinical variables such as outcome and pattern of metastasis. We expect this information to improve classification and facilitate stratification for therapy as well as retrospective analysis of existing trial data.

Figure 1. Grading of Morphological Features

(A) Scatter of intraepidermal melanocytes. (B) Nesting of intraepidermal melanocytes. (C) Cytoplasmic pigmentation of neoplastic melanocytes. (D) Cell shapes.

Figure 2. Heatmap of Features by Mutation Status (A) and by WHO Subtypes (B)

Variables significantly associated with BRAF. Samples are in columns, variables in rows. The scores range from shades of blue (low score), to gray (intermediate scores), to yellow (high scores). Samples are ordered within each category using agglomerative hierarchical clustering on morphological variables by Euclidean distance and Ward's criterion. Ac, acral; CSD, chronic sun damage as defined in [12]; H, head; LE, lower extremity; Tr, trunk; UE, upper extremity;.

Figure 3. Prediction Algorithms of BRAF Mutation

Prediction trees for BRAF mutation status with morphologic variables (A) or variables in the Southern German Tumor Registry (B). (A) Terminal nodes display heatmaps showing samples by mutation status, ordered and coded as in Figure 2A. (B) The prediction tree for BRAF mutation using the variables of age, sex, body site, and WHO type also recorded in melanoma registries identifies an age cutoff of 55 y as the single best predictor of BRAF mutation status. Kaplan Meier survival analysis from the Southern German Tumor Registry shows a significant (p < 0.0001) decrease in survival when stratified by this age cutoff.