Genomic profiling of melanocytic lesions representing the spectrum of benign melanocytic lesion to metastatic melanoma.
The Mel38 signature was examined using total RNA from 308 FFPE tissue specimens with a melanocytic cellular content of 10% or greater. Patient and specimen details are summarised in Table 1. To visualise the relationship between the 38 microRNAs and specimen details, two-dimensional hierarchical clustering of the complete cohort was performed (Figure 2). This resulted in an ordering of samples corresponding to an increasing degree of cancer progression (left to right). The gradual transition in levels of relative up and down microRNA expression reflects the continuum of naevi to melanoma progression which, as commonly seen in clinical practice.
Notably, two microRNAs (hsa-mir-205 and hsa-mir-497) appear to have similarly low expression in the benign naevi and invasive/metastatic melanomas, but high expression in the early-stage invasive melanomas. These microRNAs may therefore have a specific role in aiding a melanoma cell to develop early invasive characteristics and may warrant further investigation.
The Mel38 score is positively correlated with increasing melanoma stage and is statistically significant independent to other variables.
Mel38 scores for each specimen were calculated using support vector machine derived gene weights, as previously described. The scores range from 0 to 10 and are positivity correlated with the degree of malignancy, as shown by box plots of the scores vs AJCC stage (all samples) and vs MPath-Dx classes (primary lesions only), in figure Figure 3A and 3B respectfully. The intraclass correlation of the score vs specimen status (naevi to metastatic melanoma) was 0.85.
To verify that the Mel38 score is a continuous predictor of malignancy, independent to other clinicopathological variables, general linear models (GLM) were computed using was using patient age, gender, histological subtype, and AJCC Stage or MPATH-Dx class[12]. In both models, the genomic score was significantly different between AJCC stages and MPATH-Dx classes, independent to the other variables in the model (P<0.001).
A separate multivariate analysis of Mel38 scores from invasive melanoma specimens only (n=128) was performed, including, Breslow depth, tumour cell content, patient age, gender, and biopsy site. In this subset, the Mel38 score remained statistically significantly between disease stages (P=0.012), independent of the other variables included in the statistical model, indicating robustness to other factors known to influence microRNA expression.
The Mel38 score is a binary classifier of clinically higher- vs. lower-risk melanocytic lesions.
Whilst the Mel38 score demonstrates a statistically significant continuous association with malignancy, inspection of the MPATH-Dx vs Mel38 box plot (Figure 3B) shows that the largest difference in genomic scores occurs between MPATH-Dx Class IV and V specimens. Notable differences in suggested clinical actions and patient outcome (i.e. 5- and 10-year disease specific survival rates) are also observed between these two classes, as summarised in Table 2. Further assessment of using the Mel38 score as a binary classification tool to assign identify higher-risk (M-PATH Dx Class V) vs lower-risk (Classes I-IV) lesions was then performed.
Receiver operator curve (ROC) analysis on Mel38 scores from 74 MPATH-Dx Class V vs. 181 Class I-IV specimens resulted in area under the curve (AUC) was 0.96 (95% CI 0.92 to 0.98, P<0.001). Inspection of the ROC data showed that a Mel38 score of ≥2.3 was the optimal classification threshold for classifying a specimen as higher-risk. This threshold corresponded to a true positive rate of 95% (i.e.. Sensitivity; 95 CI: 87% to 99%) and true negative rate of 83% (i.e. specificity; CI: 77% to 89%). These performance data suggest that using Mel38 with a threshold of ≥2.3 would result in a underdiagnosis rate of 8.8% (10/113), compared to the observed rate of 27% for conventional pathology alone[14].
Mel38 profiling of Spitz naevi shows similarity to both benign and invasive melanoma.
Spitz naevi are an uncommon type of melanocytic naevus, histologically similar to melanoma and regarded as a challenging subcategory of melanocytic skin lesions to diagnose. Mel38-FFPE analysis was performed on twenty specimens of Spitz naevi submitted for routine histopathologic analysis from individuals ranging from two to thirty years old. As shown in Figure 1, the expression profile of the spitz naevi exhibits similarity to both benign and malignant lesions. Inspection of individual microRNAs in this figure reveals several that appear to have spitz-specific patterns of expression, i.e., hsa-mir-424-5p, hsa-mir-301a-3p and hsa-mir-1537-3p.
One way ANOVA of the Mel38 scores in spitz naevi vs other naevi subtypes revealed statistically significance (P<0.001), with mean scores of 2.7 (95% CI: 2.5 to 3.0) vs 1.5 (95% CI: 1.4 to 1.7), respectively, and significant pairwise differences to all naevi subtypes present. When analysed using a multivariate GLM, incorporating patient age, gender, and naevi subtype, the Mel38 scores of the spitz naevi were only significantly higher than the compound naevi subtype (P=0.015). This suggests that the younger age of the spitz naevi patients compared to the rest of the cohort may be an influence on the Mel38 expression profile.
The Mel38 score exhibits low technical variability between replicates and in longitudinal control sample analysis.
RNA from 30 naevi and 30 melanoma samples was pooled, aliquoted into 3ul volumes and analysed over a period of 7 (non-sequential) weeks. The Mel38 scores of each pool are shown longitudinally in Figure 5A. The mean score for the naevi control pool is 2.1 (standard deviation: 0.25) and 6.5 (standard deviation: 0.15) for the invasive melanoma pool. Additional control sample data will be generated over time and monitored to assess technical noise using Levey Jennings plots.
To assess the reproducibility of the genomic score throughout its dynamic range (i.e., 0 to 10), RNA from 89 samples was re-analysed and compared to the original Mel38 score. As shown in Figure 5B, there is high consistency between replicate scores, with an intraclass correlation coefficient of 0.96, (95% CI 0.94 to 0.98) and no deviation from linearity (Cusum test, P=0.62). These results demonstrate that the Mel38 signature exhibits a high degree of longitudinal stability and technical reproducibility.