The Deep Learning Framework iCanTCR Enables Early Cancer Detection Using the T-cell Receptor Repertoire in Peripheral Blood.

T cells recognize tumor antigens and initiate an anticancer immune response in the very early stages of tumor development, and the antigen specificity of T cells is determined by the T-cell receptor (TCR). Therefore, monitoring changes in the TCR repertoire in peripheral blood may offer a strategy to detect various cancers at a relatively early stage. Here, we developed the deep learning framework iCanTCR to identify patients with cancer based on the TCR repertoire. The iCanTCR framework uses TCRß sequences from an individual as an input and outputs the predicted cancer probability. The model was trained on over 2,000 publicly available TCR repertoires from 11 types of cancer and healthy controls. Analysis of several additional publicly available datasets validated the ability of iCanTCR to distinguish patients with cancer from noncancer individuals and demonstrated the capability of iCanTCR for the accurate classification of multiple cancers. Importantly, iCanTCR precisely identified individuals with early-stage cancer with an AUC of 86%. Altogether, this work provides a liquid biopsy approach to capture immune signals from peripheral blood for noninvasive cancer diagnosis. SIGNIFICANCE: Development of a deep learning-based method for multicancer detection using the TCR repertoire in the peripheral blood establishes the potential of evaluating circulating immune signals for noninvasive early cancer detection.

DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.

MOTIVATION: Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity. RESULTS: Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI.

Identification of shared characteristics in tumor-infiltrating T cells across 15 cancers.

Tumor-infiltrating T cells are essential players in tumor immunotherapy. Great progress has been achieved in the investigation of T cell heterogeneity. However, little is well known about the shared characteristics of tumor-infiltrating T cells across cancers. In this study, we conduct a pan-cancer analysis of 349,799 T cells across 15 cancers. The results show that the same T cell types had similar expression patterns regulated by specific transcription factor (TF) regulons across cancers. Multiple T cell type transition paths were consistent in cancers. We found that TF regulons associated with CD8+ T cells transitioned to terminally differentiated effector memory (Temra) or exhausted (Tex) states were associated with patient clinical classification. We also observed universal activated cell-cell interaction pathways of tumor-infiltrating T cells in all cancers, some of which specifically mediated crosstalk in certain cell types. Moreover, consistent characteristics of TCRs in the aspect of variable and joining region genes were found across cancers. Overall, our study reveals common features of tumor-infiltrating T cells in different cancers and suggests future avenues for rational, targeted immunotherapies.