Authors
Jinwei Zhang, Yideng Cai, Jinhao Que, Zuxiang Wang, Jing Ma, Yilin Wang, Xiyun Jin, Wenyi Yang, Meng Luo, Zheng Wei, Guangfu Xue, Rongrong Yang, Fenglan Pang, Yi Hui, Wenyang Zhou, Renjie Tan, Pingping Wang, Haoxiu Sun, Zhaochun Xu, Qinghua Jiang
Published in
Cancer research. Oct 02, 2026. Epub Oct 02, 2026.
Abstract
T cells initiate anticancer immune responses at an early stage of tumorigenesis, thus enabling dynamic monitoring of peripheral blood T-cell receptor (TCR) repertoire changes as a promising strategy for early cancer detection. However, current TCR-based cancer detection methods focus on limited high-frequency TCRs instead of repertoire-scale representation, overlooking numerous crucial cancer-associated TCRs (caTCRs). Herein, we developed ScanTCR, a computational framework for early cancer detection that introduces a reference-guided repertoire-scale encoding method capable of incorporating all TCRs. ScanTCR exhibited remarkable precision in distinguishing cancer patients from non-cancer individuals using peripheral blood TCR. In a pan-cancer analysis encompassing seven cancer types, ScanTCR demonstrated robust performance in detecting both cancer types included in the training data and unseen cancer types, surpassing state-of-the-art TCR-based methods. Furthermore, ScanTCR identified caTCRs whose corresponding T cells exhibit enhanced cellular immune capacity. Collectively, this work proposes a TCR-based approach enabling repertoire-scale representation of immune receptors for noninvasive early cancer detection.
PMID:
42825518
Bibliographic data and abstract were imported from PubMed on 02 Oct 2026.
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