Tumor cells evade the immune surveillance by up-regulating surface expression of PD-L1, which interacts with PD-1 on T cells to elicit the immune checkpoint response1,2.,Anti-PD-1 antibodies have shown remarkable promise in treating tumors, including metastatic melanoma2-4.,However, patient response rate is low4,5.,A better understanding of PD-L1-mediated immune evasion is needed to predict patient response and improve treatment efficacy.,Here we report that metastatic melanoma releases a high level of extracellular vesicles (EVs), mostly in the form of exosomes, that carry PD-L1 on their surface.,Interferon-γ (IFN-γ) up-regulates PD-L1 on these vesicles, which suppresses the function of CD8 T cells and facilitates tumor growth.,In patients with metastatic melanoma, the level of circulating exosomal PD-L1 positively correlates with that of IFN-γ, and changes during the course of anti-PD-1 therapy.,The magnitudes of the early on-treatment increase in circulating exosomal PD-L1, as an indicator of the adaptive response of the tumor cells to T cell re-invigoration, stratifies clinical responders from non-responders.,Our study unveils a mechanism by which tumor cells systemically suppress the immune system, and provides a rationale for the application of exosomal PD-L1 as a predictor for anti-PD-1 therapy.
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.