dataset
posted on 2024-06-04, 07:22 authored by Yideng Cai, Meng Luo, Wenyi Yang, Chang Xu, Pingping Wang, Guangfu Xue, Xiyun Jin, Rui Cheng, Jinhao Que, Wenyang Zhou, Boran Pang, Shouping Xu, Yu Li, Qinghua Jiang, Zhaochun Xu Details of the original TCR-seq datasets used in this study
Funding
National Science and Technology Major Project (国家科技重大专项)
National Natural Science Foundation of China (NSFC)
Science, Technology & Innovation Project of Xiongan New Area in China
History
ARTICLE ABSTRACT
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 TCRβ 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.
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.
