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Acral melanoma is a dismal subtype of melanoma occurring in glabrous acral skin, and has a higher incidence in East Asians. We perform single-cell RNA sequencing for 63,394 cells obtained from 5 acral and 3 cutaneous melanoma samples to investigate tumor heterogeneity and immune environment. We define 5 orthogonal functional cell clusters that are involved in TGF-beta signaling, Type I interferon, Wnt signaling, Cell cycle, and Cholesterol efflux signaling. Signatures of enriched TGF-beta, Type I interferon, and cholesterol efflux signaling are significantly associated with good prognosis of melanoma. Compared with cutaneous melanoma, acral melanoma samples have significantly severe immunosuppressive state including depletion of cytotoxic CD8+ T cells, enrichment of Treg cells, and exhausted CD8+ T cells. PD1 and TIM-3 have higher expression in the exhaustive CD8+ T cells of acral melanoma. Key findings are verified in two independent validation sets. This study contributes to our better understanding of acral melanoma.

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Exponential accumulation of single-cell transcriptomes poses great challenge for efficient assimilation. Here, we present an approach entitled tGPT towards integration of 22.3 million single-cell transcriptomes by modeling gene expression rankings as generative pretraining task. tGPT is conceptually simple in that it autoregressively models the ranking of a gene in the context of its preceding neighbors. We demonstrated the high performance of tGPT on a range of fundamental single-cell analysis tasks and novel applications on bulk tissues. The single-cell clusters and cell lineage trajectories derived from tGPT are highly aligned with known cell labels and states. The feature patterns of tumor bulk tissues learned by tGPT are associated with a wide range of genomic alteration events, prognosis and treatment outcome of immunotherapy. tGPT represents a new analytical paradigm for integrating and deciphering massive amount of transcriptome data and it will facilitate the interpretation and clinical translation of single-cell transcriptomes.

Hashimoto’s thyroiditis (HT) is the main cause of hypothyroidism. We develop a deep learning model called HTNet for diagnosis of HT by training on 106,513 thyroid ultrasound images from 17,934 patients and test its performance on 5051 patients from 2 datasets of static images and 1 dataset of video data. HTNet achieves an area under the receiver operating curve (AUC) of 0.905 (95% CI: 0.894 to 0.915), 0.888 (0.836–0.939) and 0.895 (0.862–0.927). HTNet exceeds radiologists’ performance on accuracy (83.2% versus 79.8%; binomial test, p < 0.001) and sensitivity (82.6% versus 68.1%; p < 0.001). By integrating serologic markers with imaging data, the performance of HTNet was significantly and marginally improved on the video (AUC, 0.949 versus 0.888; DeLong’s test, p = 0.004) and static-image (AUC, 0.914 versus 0.901; p = 0.08) testing sets, respectively. HTNet may be helpful as a tool for the management of HT.