This prospective study aims to develop a multi-omics-based predictive model for radiation pneumonitis in lung cancer patients receiving sequential immunotherapy and thoracic radiotherapy.
The combination of immune checkpoint inhibitors with thoracic radiotherapy has yielded substantial survival gains in lung cancer, yet this dual-modality strategy confers a markedly elevated risk of radiation pneumonitis, particularly when radiotherapy follows immunotherapy. To date, no validated biomarkers exist to stratify patients by this risk, constraining both individualized treatment planning and proactive surveillance. This prospective study addresses this unmet need by systematically collecting blood, urine, and stool samples from patients receiving sequential immunotherapy and thoracic radiotherapy. Employing integrative multi-omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, we aim to discover novel molecular signatures capable of accurately predicting radiation pneumonitis susceptibility. Ultimately, by correlating multi-omic profiles with clinical outcomes, we seek to construct a clinically actionable prediction model to inform risk-adapted monitoring, facilitate patient-clinician shared decision-making, and enhance therapeutic safety and quality of life in this expanding patient population.
Study Type
OBSERVATIONAL
Enrollment
160
This study involves collection of peripheral blood, urine, and stool samples from lung cancer patients who have received immunotherapy followed by thoracic radiotherapy. Samples are obtained at baseline prior to the initiation of radiotherapy for comprehensive multi-omics profiling, including genomic, transcriptomic, proteomic, and metabolomic analyses. No therapeutic intervention is administered as part of this study.
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, China
Area Under the Receiver Operating Characteristic Curve (AUC) of the Multi-Omics Prediction Model for Radiation Pneumonitis
The area under the ROC curve (AUC) will be calculated to evaluate the predictive performance of a multi-omics-based model-integrating genomic, transcriptomic, proteomic, and metabolomic data-for distinguishing lung cancer patients who develop grade ≥2 radiation pneumonitis from those who do not, following sequential immunotherapy and thoracic radiotherapy. Model performance will be assessed using bootstrap internal validation with 1,000 resamples to estimate optimism-corrected AUC.
Time frame: At 6 months post-radiotherapy
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