Large-Scale T-cell Receptor Repertoire Profiling Unveils Tumor-Specific Signals for Diagnosing Indeterminate Pulmonary Nodules.

Indeterminate pulmonary nodules (IPN) are increasingly detected due to increasing health awareness and widespread lung cancer screening, yet distinguishing benign from malignant nodules remains a critical challenge. Emerging evidence suggests that recognizing cancer-associated immune signatures represents a powerful approach for early-stage cancer detection. This study explored the clinical utility of T-cell receptor (TCR) repertoire analysis in IPN evaluation. By conducting large-scale TCR sequencing (6,059 blood and 988 tumor samples), we established LungTCR (https://www.lungtcr.com/), a comprehensive TCR repertoire database, and proposed a method for the quantitative assessment of tumor-related immune responses. LungTCR was leveraged to develop TCRnodseek plus, a diagnostic model integrating clinical data, CT imaging, and TCR features. A multicenter prospective study (ChiCTR2200055761) involving 1,107 patients with IPN validated the superior diagnostic performance of TCRnodseek plus over existing approaches. Mechanistic analyses revealed that the identified lung cancer-related TCR clones are enriched in non-small cell lung cancer and are predominantly present in malignant nodules and tumor tissues. This study provides a robust TCR database and an advanced diagnostic model, offering a framework for precise IPN differentiation. SIGNIFICANCE: Construction of the largest TCR database of lung nodules enabled identification of lung cancer-specific TCR sequences and development of an advanced machine learning model to distinguish benign from malignant pulmonary nodules. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .