T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging.

Background: T-cell receptor (TCR) repertoires provide insights into tumor immunology, yet their variations across digestive system cancers are not well understood. Characterizing TCR differences between colorectal cancer (CRC) and gastric cancer (GC), as well as developing machine learning models to distinguish cancer types, metastatic status, and disease stages are crucial for guiding clinical practices. Methods: A cohort study of 143 tumor patients (96 CRC, 47 GC) was conducted. High-throughput TCR sequencing was performed to capture TCR beta (TRB), delta (TRD), and gamma (TRG) chain data. Tissue-specific patterns in TCR repertoire features, such as V-J gene recombination, complementarity-determining region 3 (CDR3) sequences, and motif distributions, were analyzed. Multi-layer machine learning-based diagnostic models were developed by leveraging motif-based feature and deep learning-based feature extraction using ProteinBERT from the 100 most abundant CDR3 sequences per sample. These models were used to differentiate CRC from GC, distinguish between primary and metastatic CRC lesions, and predict disease stages in CRC. Results: Tissue-specific differences in TCR repertoires were observed across CRC, GC, and between primary and metastatic lesions, as well as across disease stages in CRC. Distinct V-J gene recombination patterns were identified, with CRC showing enrichment in Conclusions: Our investigation provides novel insights into TCR repertoire variations in digestive system tumors, and highlight the potential of immune repertoire features as powerful diagnostic tools for understanding cancer progression and potentially improving clinical decision-making.