Uveal melanoma (UM) is a highly metastatic cancer that, in contrast to cutaneous melanoma, is largely unresponsive to checkpoint immunotherapy.,Here, we interrogate the tumor microenvironment at single-cell resolution using scRNA-seq of 59,915 tumor and non-neoplastic cells from 8 primary and 3 metastatic samples.,Tumor cells reveal novel subclonal genomic complexity and transcriptional states.,Tumor-infiltrating immune cells comprise a previously unrecognized diversity of cell types, including CD8+ T cells predominantly expressing the checkpoint marker LAG3, rather than PD1 or CTLA4.,V(D)J analysis shows clonally expanded T cells, indicating that they are capable of mounting an immune response.,An indolent liver metastasis from a class 1B UM is infiltrated with clonally expanded plasma cells, indicative of antibody-mediated immunity.,This complex ecosystem of tumor and immune cells provides new insights into UM biology, and LAG3 is identified as a potential candidate for immune checkpoint blockade in patients with high risk UM.,Uveal melanoma is highly metastatic and unresponsive to checkpoint immunotherapy.,Here, the authors present single-cell transcriptomics of 59,915 cells in 8 primary and 3 metastatic samples, highlighting the diversity of the tumour microenvironment.
Skin lesions are a severe disease globally.,Early detection of melanoma in dermoscopy images significantly increases the survival rate.,However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc.,Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists.,In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3).,A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result.,A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map.,A straight-forward CNN is proposed for the dermoscopic feature extraction task.,The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset.,Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.