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LCPBert: ProtBERT-based early-stage lung cancer prediction from T cell receptor beta sequences
Xin Yang1, Yuwei Zhou1, Zixuan Zhang1
1Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
None:
Early detection of lung cancer remains challenging due to limitations of current methods. We developed LCPBert, a deep learning framework leveraging peripheral blood T cell receptor beta (TCRβ) repertoires for early detection of lung cancer. LCPBert accurately discriminated lung cancer-associated TCRs (test AUC = 0.82). Based on LCRI (lung cancer risk index), LCPBert robustly stratified lung cancer risk in the external validation cohort: healthy donors (0.111 ± 0.058), benign pulmonary nodules (0.184 ± 0.113), lung cancer (0.296 ± 0.166; p < 0.001) and showed a spatial gradient from peripheral blood (0.296) to tumor tissue (0.384, p < 0.084). At an LCRI cutoff of 0.1465, LCPBert achieved 75% sensitivity and 70% specificity in discriminating lung cancer patients from healthy individuals. In a longitudinal cohort, LCRI elevation (Δ > 0.15) exceeding 0.30 after SBRT predicted distant metastasis (DM) in 75% of patients who developed DM. In addition, LCRI predicted lung cancer independently of age, sex, and TCR diversity (D50). LCPBert provides an accurate and non-invasive approach for early-stage lung cancer detection.
