Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm

Increasing incidence of skin cancer combined with a shortage of dermatopathologists has increased the workload of pathology departments worldwide. In addition, the high intraobserver and interobserver variability in the assessment of melanocytic skin lesions can result in underestimated or overestimated diagnosis of melanoma. Thus, the development of new techniques for skin tumor diagnosis is essential to assist pathologists to standardize diagnoses and plan accurate patient treatment. Here, we describe the development of an artificial intelligence (AI) system that recognizes cutaneous melanoma from histopathological digitalized slides with clinically acceptable accuracy. Whole-slide digital images from 100 formalin-fixed paraffin-embedded primary cutaneous melanoma were used to train a convolutional neural network (CNN) based on a pretrained Inception-ResNet-v2 to accurately and automatically differentiate tumoral areas from healthy tissue. The CNN was trained by using 60 digital slides in which regions of interest (ROIs) of tumoral and healthy tissue were extracted by experienced dermatopathologists, while the other 40 slides were used as test datasets. A total of 1377 patches of healthy tissue and 2141 patches of melanoma were assessed in the training/validation set, while 791 patches of healthy tissue and 1122 patches of pathological tissue were evaluated in the test dataset. Considering the classification by expert dermatopathologists as reference, the trained deep net showed high accuracy (96.5%), sensitivity (95.7%), specificity (97.7%), F1 score (96.5%), and a Cohen’s kappa of 0.929. Our data show that a deep learning system can be trained to recognize melanoma samples, achieving accuracies comparable to experienced dermatopathologists. Such an approach can offer a valuable aid in improving diagnostic efficiency when expert consultation is not available, as well as reducing interobserver variability. Further studies in larger data sets are necessary to verify whether the deep learning algorithm allows subclassification of different melanoma subtypes

Trpa1 expression in synovial sarcoma may support neural origin

Synovial sarcoma (SS) is a malignant mesenchymal soft tissue neoplasm. Despite its name, the cells of origin are not synovial cells, but rather neural, myogenic, or multipotent mesenchymal stem cells have been proposed as possible cells originators. Unlike other sarcomas, an unusual presentation of long-term pain at the tumor site has been documented, but the exact mechanisms have not been fully clarified yet. The transient receptor potential ankyrin 1 (TRPA1) is a nonselective cation channel mainly expressed in primary sensory neurons, where it functions as a pain sensor. TRPA1 have also been described in multiple non-excitable cells, including those derived from neural crest stem cells such as glial cells and, in particular, Schwann cell oligodendrocytes and astrocytes. We evaluated TRPA1 expression in SS. We selected a cohort of 41 SSs, and by immunohistochemistry, we studied TRPA1 expression. TRPA1 was found in 92.6% of cases. Triple TRPA1/pS100/SOX10 and TRPA1/SLUG/SNAIL staining strongly supports a neural origin of SS. TRPA1 positivity was also observed in a subset of cases negative with pS100, SOX10 and/or SLUG/SNAIL, and these divergent phenotypes may reflect a process of tumor plasticity and dedifferentiation of neural-derived SSs. Given the functional diversity of TRPA1 and its expression in neuronal and non-neuronal multipotent neural crest stem cells, it remains to be determined whether TRPA1 expression in SSs neoplastic cells plays a role in the molecular mechanism associated with premonitory pain symptoms and tumor progression

β3-Adrenoreceptor Blockade Induces Stem Cells Differentiation in Melanoma Microenvironment

Although there is an increasing evidence that cancer stem cell (CSC) niches in the tumor microenvironment (TME) plays a crucial role in sustaining solid tumors progression, several molecular players involved in this regulation still remain unknown. The role of β-adrenergic signaling in enhancing tumor growth through β2-adrenoreceptors (β2-ARs) has been confirmed in different cancer models, but the role played by the β3-adrenergic receptor (β3-AR) has recently emerged. Previous studies showed that β3-AR promotes cancer growth through the activation of different stromal cells in the TME, and leads to melanoma malignancy progression through inflammation, angiogenesis, and immunotolerance. Here we show that in B16 melanoma-bearing mice, the pharmacological β3-AR blockade is able to reduce the expression of CSC markers, and to induce a differentiated phenotype of hematopoietic subpopulations in TME. In particular, cytofluorimetric analysis (FACS) of the tumor mass shows that β3-AR antagonist SR59230A promotes hematopoietic differentiation as indicated by increased ratios of lymphoid/hematopoietic stem cells (HSCs) and of myeloid progenitor cells/HSCs, and increases the number of Ter119 and natural killer (NK) precursor cells, and of granulocyte precursors, indicating active hematopoiesis within the tumor tissue. Moreover, pharmacological antagonism of β3-AR induces mesenchymal stem cell (MSC) differentiation into adipocytes subtracting a potential renewal of the stem compartment by these cells. Here we demonstrate that β3-AR blockade in the TME by inducing the differentiation of different stromal cells at the expense of stemness traits could possibly have a favorable effect on the control of melanoma progression

Spatial Proximity and Relative Distribution of Tumor-Infiltrating Lymphocytes and Macrophages Predict Survival in Melanoma

Tumor microenvironment plays a crucial role in primary cutaneous melanoma (CM) progression. Although the role of tumor-infiltrating lymphocyte (TIL) density has been known for a long time, its spatial distribution and impact with or without tumor-associated macrophages (TAMs) remain controversial. Herein, we investigated spatial proximity between tumor cells and immune cells in 113 primary CM and its correlation with disease-free (DFS) and overall survival (OS). The study cohort included clinical stage II (n 1⁄4 79) and stage III (n 1⁄4 34) primary CM with a Breslow thickness of >2 mm (with a median age of 64 years, including 72 men and 41 women). In univariate models, patients with SOX10þ melanoma cells with high proximity to CD8þ TILs in a 20 mm radius showed longer DFS (hazard ratio [HR], 0.58; 95% CI, 0.36e0.93; P 1⁄4 .025) and OS (HR, 0.55; 95% CI, 0.32e0.92; P 1⁄4 .023). Furthermore, at multivariate combined analysis, patients with SOX10þ melanoma cells with high proximity to CD8þ TILs or low proximity to CD163þ TAMs in a 20 mm radius showed an increased OS (aHR, 0.37; 95% CI, 0.14e0.96; P 1⁄4 .04) compared with melanoma patients with low proximity to CD8þ TILs or high proximity to CD163þ TAMs. In a subgroup analysis including 92 patients, a significant negative impact on DFS (aHR, 4.49; 95% CI, 1.73e11.64; P 1⁄4 .002) and OS (aHR, 3.97; 95% CI, 1.37e11.49; P 1⁄4 .01) was observed in sentinel lymph node (SLN)-negative patients with a high proximity of CD163þ TAMs to CD8þ TILs. These findings could help identify high-risk patients in the context of thick melanoma and a negative SLN. Our study suggests the importance of quantifying not only the density of immune cells but also the individual and combined relative spatial distributions of tumor cells and immune cells for clinical outcomes in SLN-negative primary CM patients

Tumors carrying BRAF-mutations over-express NAMPT that is genetically amplified and possesses oncogenic properties

Background: Nicotinamide phosphoribosyltransferase (NAMPT), the rate-limiting enzyme in nicotinamide adenine dinucleotide (NAD) biosynthesis, is up-regulated in several cancers, including metastatic melanoma (MM). The BRAF oncogene is mutated in different cancer types, among which MM and thyroid carcinoma (THCA) are prominent. Drugs targeting mutant BRAF are effective, especially in MM patients, even though resistance rapidly develops. Previous data have linked NAMPT over-expression to the acquisition of BRAF resistance, paving the way for therapeutic strategies targeting the two pathways. Methods: Exploiting the TCGA database and a collection of MM and THCA tissue microarrays we studied the association between BRAF mutations and NAMPT expression. BRAF wild-type (wt) cell lines were genetically engineered to over-express the BRAF V600E construct to demonstrate a direct relationship between over-activation of the BRAF pathway and NAMPT expression. Responses of different cell line models to NAMPT (i)nhibitors were studied using dose–response proliferation assays. Analysis of NAMPT copy number variation was performed in the TCGA dataset. Lastly, growth and colony forming assays were used to study the tumorigenic functions of NAMPT itself. Results: The first finding of this work is that tumor samples carrying BRAF-mutations over-express NAMPT, as demonstrated by analyzing the TCGA dataset, and MM and THC tissue microarrays. Importantly, BRAF wt MM and THCA cell lines modified to over-express the BRAF V600E construct up-regulated NAMPT, confirming a transcriptional regulation of NAMPT following BRAF oncogenic signaling activation. Treatment of BRAF-mutated cell lines with two different NAMPTi was followed by significant reduction of tumor growth, indicating NAMPT addiction in these cells. Lastly, we found that several tumors over-expressing the enzyme, display NAMPT gene amplification. Over-expression of NAMPT in BRAF wt MM cell line and in fibroblasts resulted in increased growth capacity, arguing in favor of oncogenic properties of NAMPT. Conclusions: Overall, the association between BRAF mutations and NAMPT expression identifies a subset of tumors more sensitive to NAMPT inhibition opening the way for novel combination therapies including NAMPTi with BRAFi/MEKi, to postpone and/or overcome drug resistance. Lastly, the over-expression of NAMPT in several tumors could be a key and broad event in tumorigenesis, substantiated by the finding of NAMPT gene amplification

Tumor-Infiltrating Lymphocyte Recognition in Primary Melanoma by Deep Learning Convolutional Neural Network

The presence of tumor-infiltrating lymphocytes (TILs) is associated with a favorable prognosis of pri-mary melanoma (PM). Recently, artificial intelligence (AI)-based approach in digital pathology was proposed for the standardized assessment of TILs on hematoxylin and eosin-stained whole slide images (WSIs). Herein, the study applied a new convolution neural network (CNN) analysis of PM WSIs to automatically assess the infiltration of TILs and extract a TIL score. A CNN was trained and validated in a retrospective cohort of 307 PMs including a training set (237 WSIs, 57,758 patches) and an inde-pendent testing set (70 WSIs, 29,533 patches). An AI-based TIL density index (AI-TIL) was identified after the classification of tumor patches by the presence or absence of TILs. The proposed CNN showed high performance in recognizing TILs in PM WSIs, showing 100% specificity and sensitivity on the testing set. The AI-based TIL index correlated with conventional TIL evaluation and clinical outcome. The AI-TIL index was an independent prognostic marker associated directly with a favorable prognosis. A fully automated and standardized AI-TIL appeared to be superior to conventional methods at differentiating the PM clinical outcome. Further studies are required to develop an easy-to-use tool to assist pathologists to assess TILs in the clinical evaluation of solid tumors. (Am J Pathol 2023, 193: 2099-2110; https://doi.org/10.1016/j.ajpath.2023.08.013