Lung Cancer (NSCLC), Lung Cancer (SCLC)
Conditions
Keywords
Radiation Pneumonitis, Multi-Omics, Immunotherapy, Predictive Biomarkers
Brief summary
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.
Detailed description
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.
Interventions
OTHERNo Intervention: Observational Cohort
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.
Sponsors
HAN GUANG
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
Patients aged 18 or older years with a life expectancy of at least 6 months. Additional inclusion criteria were a Zubrod performance status of 0-1, documentation of baseline chest computed tomography (CT) scans prior to the initiation of immunotherapy, receipt of at least 2 cycles of systemic therapy (PD-1) as either monotherapy or combination therapy within the past six months, voluntary participation with written informed consent, and appropriate baseline biochemical tests. The radiation dose for thoracic radiotherapy was ≥30Gy for all eligible patients in this study. No use of antibiotics within 1 week prior to the initiation of thoracic radiotherapy.
Exclusion criteria
Participation in other interventional clinical studies or treatments, having received investigational drugs or treatments within 4 weeks prior to the first dose, or prior receipt of a solid organ or hematopoietic stem cell transplant, having an active infection, or pre-existing/active ICI-induced pneumonitis. \-
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area Under the Receiver Operating Characteristic Curve (AUC) of the Multi-Omics Prediction Model for Radiation Pneumonitis | At 6 months post-radiotherapy | 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. |
Countries
China
Contacts
CONTACTGuang Han
Outcome results
None listed