Abstract
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.