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

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.

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 .