Immunotyping and Quantification of Melanoma Tumor-Infiltrating Lymphocytes

Nanocarrier-Mediated Immunogenic Cell Death for Melanoma Treatment

Melanoma, a highly aggressive skin tumor, exhibits notable features including heterogeneity, a high mutational load, and innate immune escape. Despite advancements in melanoma treatment, current immunotherapies fail to fully exploit the immune system's maximum potential. Activating immunogenic cell death (ICD) holds promise in enhancing tumor cell immunogenicity, stimulating immune amplification response, improving drug sensitivity, and eliminating tumors. Nanotechnology-enabled ICD has emerged as a compelling therapeutic strategy for augmenting cancer immunotherapy. Nanoparticles possess versatile attributes, such as prolonged blood circulation, stability, and tumor-targeting capabilities, rendering them ideal for drug delivery. In this review, we elucidate the mechanisms underlying ICD induction and associated therapeutic strategies. Additionally, we provide a concise overview of the immune stress response associated with ICD and explore the potential synergistic benefits of combining ICD induction methods with the utilization of nanocarriers.

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 primary 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 independent 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.