Immunotherapies that block inhibitory checkpoint receptors on T cells have transformed the clinical care of cancer patients 1 . However, whether the T cell response to checkpoint blockade relies on reinvigoration of pre-existing tumor infiltrating T cells (TILs) or on recruitment of novel T cells remains unclear 2 – 4 . Here, we performed paired single-cell RNA (scRNA) and T cell receptor (TCR)- sequencing on 79,046 cells from site-matched tumors from patients with basal cell carcinoma (BCC) or squamous cell carcinoma (SCC) pre- and post-anti-PD-1 therapy. Tracking TCR clones and transcriptional phenotypes revealed a coupling of tumor-recognition, clonal expansion, and T cell dysfunction marked by clonal expansions of CD8 + CD39 + T cells, which co-expressed markers of chronic T cell activation and exhaustion. However, this expansion did not derive from pre-existing TIL clones; rather, it was comprised of novel clonotypes not previously observed in the same tumor. Clonal replacement of T cells was preferentially observed in exhausted CD8 + T cells and evident in BCC and SCC patients. These results demonstrate that pre-existing tumor-specific T cells may have limited reinvigoration capacity, and that the T cell response to checkpoint blockade derives from a distinct repertoire of T cell clones that may have just recently entered the tumor.

Background: PRAME (PReferentially expressed Antigen in MElanoma) has shown utility in distinguishing melanoma from benign melanocytic lesions, but knowledge of its expression pattern in intermediate melanocytic and spitzoid proliferations is limited. Methods: Immunohistochemical expression of PRAME was examined in 112 melanocytic proliferations with intermediate histopathologic or spitzoid features. Results: Any intensity of nuclear PRAME staining in at least 60% of lesional melanocytes was determined as the best threshold for diffuse staining in this cohort. Nearly all non-spitzoid melanomas (23/24; 95.8%) demonstrated diffuse PRAME expression. PRAME was completely negative in 95.6% (43/45) of mitotically-active nevi, traumatized nevi, nevi with persistent/recurrent features, and dysplastic nevi. Most Spitz nevi (15/20) and atypical Spitz tumors (10/13) entirely lacked PRAME expression. One Spitz nevus, one atypical Spitz tumor, and one spitzoid melanoma (1/2) demonstrated diffuse PRAME expression. Conclusions: Although diffuse PRAME expression is generally limited to malignant melanoma, benign Spitz nevi and atypical Spitz tumors can infrequently express diffuse PRAME. PRAME immunohistochemistry can be useful in the evaluation of atypical melanocytic proliferations with intermediate histopathologic features but should be interpreted with caution in the setting of spitzoid neoplasms.

Artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, while their actual impact on human diagnosticians, when incorporated into clinical workflows, remains relatively unexplored. In this study, we developed a deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images (WSI), and evaluated its effect on the diagnostic performance of 11 pathologists with varying levels of expertise. Our model achieved accuracies of 0.885 on a validation set of 26 WSI, and 0.842 on an independent test set of 80 WSI. Although use of the assistant did not change the mean accuracy of the 11 pathologists (p = 0.184, OR = 1.281), it significantly improved the accuracy (p = 0.045, OR = 1.499) of a subset of nine pathologists who fell within well-defined experience levels (GI subspecialists, non-GI subspecialists, and trainees). In the assisted state, model accuracy significantly impacted the diagnostic decisions of all 11 pathologists. As expected, when the model's prediction was correct, assistance significantly improved accuracy (p = 0.000, OR = 4.289), whereas when the model's prediction was incorrect, assistance significantly decreased accuracy (p = 0.000, OR = 0.253), with both effects holding across all pathologist experience levels and case difficulty levels. Our results highlight the challenges of translating AI models into the clinical setting, and emphasize the importance of taking into account potential unintended negative consequences of model assistance when designing and testing medical AI-assistance tools.npj Digital Medicine (2020) 3:23 ; https://doi.

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