Using Multi-omics Sequencing to Predict Treatment Response and Monitor Progress in Small Cell Lung Cancer A Prospective Study

What this Trial Is About

Small cell lung cancer SCLC is a highly malignant type of lung cancer with a low 5-year survival rate below 7%. It often spreads quickly and resists treatment, making prognosis poor. Researchers are exploring new ways to predict how well treatments will work and to monitor disease progress more effectively by integrating multiple molecular data types from blood and tissue samples. This approach aims to provide better tools for managing SCLC treatment and monitoring. The study collects blood and paraffin-embedded tissue samples from patients before and during treatment to analyze multiple molecular features using advanced sequencing technologies. Samples are taken before the first treatment cycle, before the third cycle, and at disease progression. Researchers will develop and test models that use these molecular data to predict treatment response and track disease changes over time. Participants provide demographic, clinical, imaging, laboratory, and tumor marker information. The study evaluates molecular subtypes and scoring models related to SCLC treatment effectiveness and monitors changes in blood molecular data during therapy. The monitoring period lasts up to 3 years or until disease progression. This observational study involves 40 patients and aims to support dynamic treatment decisions and improve understanding of SCLC molecular changes during therapy.

A Multi-omics Sequencing-based Model for Predicting Efficacy and Dynamic Monitoring of Treatment in Small Cell Lung Cancer