UV biomarker genes for classification and risk stratification of cutaneous actinic keratoses and squamous cell carcinoma subtypes

Abstract

Currently, there is no sensitive molecular test for identifying transformation-prone actinic keratoses (AKs) and aggressive squamous cell carcinoma (SCC) subtypes. Biomarker-based molecular testing represents a promising tool for risk stratifying these lesions. We evaluated the utility of a panel of ultraviolet (UV) radiation-biomarker genes in distinguishing between benign and transformation-prone AKs and SCCs. The expression of the UV-biomarker genes in 31 SCC and normal skin (NS) pairs and 10 AK/NS pairs was quantified using the NanoString nCounter system. Biomarker testing models were built using logistic regression models with leave-one-out cross validation in the training set. The best model to classify AKs versus SCCs (area under curve (AUC) 0.814, precision score 0.833, recall 0.714) was constructed using a top-ranked set of 13 UV-biomarker genes. Another model based on a 15-gene panel was developed to differentiate histologically concerning from less concerning SCCs (AUC 1, precision score 1, recall 0.714). Finally, 12 of the UV-biomarker genes were differentially expressed between AKs and SCCs, while 10 genes were uniquely expressed in the more concerning SCCs. UV-biomarker gene subsets demonstrate dynamic utility as molecular tools to classify and risk stratify AK and SCC lesions, which will complement histopathologic diagnosis to guide treatment of high-risk patients.

Keywords: actinic keratosis; squamous cell carcinoma; ultraviolet radiation.

Figure 1.

Histopathology of representative samples. A-C) Images of a representative AK sample at 4x, 10x, and 20x, respectively, showing epidermal hyperplasia and dysplasia and marked hyper and parakeratosis with alteration of the ortho and parakeratotic keratin. The dermis shows solar elastosis and an infiltrate of mononuclear cells. D) Matched normal skin adjacent to the AK sample. E-G) Images of a representative KA sample at 4x, 10x, and 20x, respectively. Epidermis shows a crater-like invagination filled with ortho and parakeratotic horn. The lining of the invagination is formed by proliferating dysplastic squamous epithelium. H) Matched normal skin adjacent to the KA sample. I-K) Images of a representative SCC sample at 4x, 10x, and 20x, respectively. Arising from the epidermis and extending into the dermis there are aggregates of dysplastic keratinocytes. L) Matched normal skin adjacent to the SCC sample. AK: Actinic keratosis, KA: keratoacanthoma, SCC: squamous cell carcinoma.

Figure 2.

Heatmap showing the gene expression signatures (log 2-fold change) of 77 genes in each of the UV, SCC, or AK samples versus their matched normal skin control. Red, white, and blue colors indicate over-expressed, not differentially expressed, or under-expressed genes, respectively. Black, cyan, and green colors in the sidebar represent UV, AK, and SCC samples, respectively.

Figure 3.

A) Receiver-operating curves (ROC) curves showing the training and the testing performance of the Logistic regression model using the 29 training samples with LOOCV (left) and the 12 testing samples (right), respectively. B) Heatmap showing clustering of AK samples in orange and SCC samples in green based on the 13 features selected by the model.

Figure 4.

A) Receiver-operating curves (ROC) curves showing the training and the testing performance of the logistic regression model using the 22 training SCC samples with LOOCV (left) and in the 9 testing SCC samples (right), respectively. B) Heatmap showing the clustering of more concerning SCC (purple) and less concerning SCC (cyan) signatures based on the 15 UV biomarker genes selected by this model.

Figure 5.

Differential expression of SCCs and AKs and more concerning versus less concerning SCCs. A) Boxplot of genes with lower expression values in AK signatures than in SCC signatures. B) Boxplot of genes with higher expression values in AK signatures than in SCC signatures. C) Boxplot of genes with lower expression values in more concerning SCC signatures than in less concerning SCC signatures. D) Boxplot of genes with higher expression values in more concerning SCC signatures than in less concerning SCC signatures.