RESUMO
Single-cell RNA sequencing (scRNA-seq) data are noisy and sparse. Here, we show that transfer learning across datasets remarkably improves data quality. By coupling a deep autoencoder with a Bayesian model, SAVER-X extracts transferable gene-gene relationships across data from different labs, varying conditions and divergent species, to denoise new target datasets.
Assuntos
Neoplasias da Mama/metabolismo , Biologia Computacional/métodos , Leucócitos Mononucleares/metabolismo , Análise de Sequência de RNA/normas , Análise de Célula Única/métodos , Linfócitos T/metabolismo , Transcriptoma , Animais , Teorema de Bayes , Feminino , Perfilação da Expressão Gênica , Regulação da Expressão Gênica , Humanos , Camundongos , Análise de Sequência de RNA/métodosRESUMO
In the version of this article originally published, the institution in affiliation 10 was missing. Affiliation 10 was originally listed as Department of Surgery, Royal Melbourne Hospital and Royal Womens' Hospital, Melbourne, Victoria, Australia. It should have been Department of Surgery, Royal Melbourne Hospital and Royal Womens' Hospital, University of Melbourne, Melbourne, Victoria, Australia. The error has been corrected in the HTML and PDF versions of this article.
RESUMO
The quantity of tumor-infiltrating lymphocytes (TILs) in breast cancer (BC) is a robust prognostic factor for improved patient survival, particularly in triple-negative and HER2-overexpressing BC subtypes1. Although T cells are the predominant TIL population2, the relationship between quantitative and qualitative differences in T cell subpopulations and patient prognosis remains unknown. We performed single-cell RNA sequencing (scRNA-seq) of 6,311 T cells isolated from human BCs and show that significant heterogeneity exists in the infiltrating T cell population. We demonstrate that BCs with a high number of TILs contained CD8+ T cells with features of tissue-resident memory T (TRM) cell differentiation and that these CD8+ TRM cells expressed high levels of immune checkpoint molecules and effector proteins. A CD8+ TRM gene signature developed from the scRNA-seq data was significantly associated with improved patient survival in early-stage triple-negative breast cancer (TNBC) and provided better prognostication than CD8 expression alone. Our data suggest that CD8+ TRM cells contribute to BC immunosurveillance and are the key targets of modulation by immune checkpoint inhibition. Further understanding of the development, maintenance and regulation of TRM cells will be crucial for successful immunotherapeutic development in BC.