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

Lung cancer is one of the malignant tumors with the highest incidence and mortality rates globally, with small cell lung cancer (SCLC) accounting for approximately 15%. SCLC is characterized by high malignancy, propensity for metastasis and drug resistance, and a 5-year survival rate below 7%. Despite partial progress in chemotherapy and immunotherapy, SCLC patients generally have extremely poor prognosis, and there is a lack of precise therapeutic efficacy prediction and dynamic monitoring approaches. Existing biomarkers (such as TP53/RB1 mutations) are inadequate for clinical needs due to high heterogeneity and insufficient dynamic characteristics. The rapid development of multi-omics technologies provides new opportunities for analyzing SCLC molecular features; however, previous studies have predominantly focused on single omics approaches with insufficient systematic integration, limiting clinical translation. This study aims to systematically integrate multiple omics technologies to construct predictive and dynamic monitoring models for SCLC therapeutic efficacy, providing new methods and evidence for SCLC clinical treatment and dynamic monitoring.

Study Objectives To comprehensively analyze the molecular characteristics of small cell lung cancer (SCLC) through multi-omics technologies based on peripheral blood and paraffin-embedded samples, and establish and validate multi-omics data-based models for therapeutic efficacy prediction and dynamic monitoring. Primary Objectives 1. To collect blood and paraffin-embedded samples from SCLC patients before treatment and analyze multi-omics sequencing characteristics of these patients. 2. To establish and validate SCLC therapeutic efficacy prediction and dynamic monitoring models based on multi-omics detection, constructing SCLC scoring models and molecular subtypes. Secondary Objectives To investigate the sensitivity and specificity of SCLC therapeutic efficacy prediction and dynamic monitoring models in patients with different stages of SCLC. Exploratory Objectives To analyze potential biomarkers and therapeutic targets in SCLC based on multi-omics data, and conduct in-depth analysis of dynamic changes in peripheral blood multi-omics data during SCLC treatment efficacy processes. Study Design This is a prospective, single-center study aimed at establishing SCLC therapeutic efficacy prediction and dynamic monitoring models based on multi-omics detection of peripheral blood and paraffin-embedded samples. Sample Collection Time Points 1. Collection of 20ml peripheral blood (EDTA tubes×2) and 20 unstained paraffin tissue sections before first-line first cycle treatment; 2. Collection of 20ml peripheral blood (EDTA tubes×2) before third cycle treatment; 3. Collection of 20ml peripheral blood (EDTA tubes×2) at disease progression. Sample Size and Omics Detection This study plans to enroll 40 SCLC patients, collecting unstained paraffin tissue sections before treatment and dynamically collecting peripheral blood specimens. Patient Information Collection The study requires collection of patients' demographic information before blood collection, imaging data related to disease diagnosis, hospital laboratory biochemical test results, tumor marker test results, pathological diagnosis results or other information providing diagnostic evidence, and underlying disease information. Specific information collected includes: Information to be collected for all patients includes but is not limited to: General demographic data: age, gender, race, etc.; Vital signs: blood pressure, pulse, heart rate, etc.; Previous major disease history and corresponding medication history; Tumor history and corresponding treatment history; Family genetic history; Smoking and drinking history; Multi-omics detection results.