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

Official title

A Multi-omics Sequencing-based Model for Predicting Efficacy and Dynamic Monitoring of Treatment in Small Cell Lung Cancer: A Prospective, Non-interventional Study

Overview

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.

Detailed description

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.

Primary outcome measures

• SCLC scoring models and molecular subtypes [Time frame: From enrollment to the end of monitoring at 3 years or the occurrence of disease progression.]

Secondary outcome measures (2)

• Sensitivity and specificity of SCLC therapeutic efficacy prediction and dynamic monitoring models [Time frame: From enrollment to the end of monitoring at 3 years or the occurrence of disease progression.]
• Dynamic changes in peripheral blood multi-omics data during SCLC treatment efficacy processes [Time frame: From enrollment to the end of monitoring at 3 years or the occurrence of disease progression.]

Eligibility criteria

Inclusion criteria

• Patients meeting the following criteria may have samples collected:

• Voluntary signing of informed consent;
• Age ≥18 years;
• Expected survival time ≥3 months;
• Eastern Cooperative Oncology Group (ECOG) performance status score of 0 or 1;
• Treatment-naïve limited-stage or extensive-stage SCLC confirmed by histology or cytology;
• Agreement to provide blood samples and paraffin-embedded samples;
• Measurable target lesions for efficacy evaluation.

Exclusion criteria

• Patients with any of the following conditions will be excluded from sample collection:

• Archived tumor tissue or pre-treatment tumor biopsy or histological examination showing previous histological or cytological evidence of non-small cell or small cell/non-small cell mixed components;
• Investigator-determined unsuitability for peripheral blood collection due to complications or other conditions;
• Active, known, or suspected autoimmune disease (excluding vitiligo, type I diabetes, residual hypothyroidism caused by autoimmune thyroiditis requiring only hormone replacement therapy, or conditions not expected to recur without external stimulation);
• Active tuberculosis (TB) infection based on chest X-ray, sputum examination, and clinical examination. Patients with active pulmonary TB infection history within the previous year should be excluded even if treated. Patients with active pulmonary TB infection history more than one year ago should also be excluded unless previous anti-TB treatment can be proven adequately effective;
• Comorbidities requiring immunosuppressive drug treatment, or requiring systemic or local corticosteroid use at immunosuppressive doses;
• Pregnancy or lactation;
• Positive human immunodeficiency virus antibody (HIVAb), active hepatitis B virus infection (HBsAg positive and HBV-DNA >10³ copies/ml), or hepatitis C virus infection (HCV antibody positive and HCV-RNA > lower limit of detection at study center);
• History of severe neurological or psychiatric disorders, including but not limited to: dementia, depression, seizures, bipolar disorder, etc.;
• Use of any anti-tumor drugs before blood sample collection;
• Previous history of other malignant tumors (excluding non-melanoma skin cancer and the following carcinoma in situ: bladder, gastric, colon, endometrial, cervical/dysplasia, melanoma, or breast cancer);
• Patients receiving live vaccines within 28 days before blood sample collection.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Cohort

Study locations

China · 1 center

• National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chine — Beijing

Publications

• Rosner S, Levy B. Relapsed small-cell lung cancer: a disease of continued unmet need. Lancet Respir Med. 2023 Jan;11(1):6-8. doi: 10.1016/S2213-2600(22)00389-7. Epub 2022 Oct 14. No abstract available. PMID 36252600
• Wang Z, Liu C, Zheng S, Yao Y, Wang S, Wang X, Yin E, Zeng Q, Zhang C, Zhang G, Tang W, Zheng B, Xue L, Wang Z, Feng X, Wang Y, Ying J, Xue Q, Sun N, He J. Molecular subtypes of neuroendocrine carcinomas: A cross-tissue classification framework based on five transcriptional regulators. Cancer Cell. 2024 Jun 10;42(6):1106-1125.e8. doi: 10.1016/j.ccell.2024.05.002. Epub 2024 May 23. PMID 38788718
• Heeke S, Gay CM, Estecio MR, Tran H, Morris BB, Zhang B, Tang X, Raso MG, Rocha P, Lai S, Arriola E, Hofman P, Hofman V, Kopparapu P, Lovly CM, Concannon K, De Sousa LG, Lewis WE, Kondo K, Hu X, Tanimoto A, Vokes NI, Nilsson MB, Stewart A, Jansen M, Horvath I, Gaga M, Panagoulias V, Raviv Y, Frumkin D, Wasserstrom A, Shuali A, Schnabel CA, Xi Y, Diao L, Wang Q, Zhang J, Van Loo P, Wang J, Wistuba PMID 38278149
• Blackhall FH. Reframing recalcitrance for small-cell lung cancer. Ann Oncol. 2021 Jul;32(7):829-830. doi: 10.1016/j.annonc.2021.04.022. Epub 2021 May 3. No abstract available. PMID 33957222
• Dingemans AC, Fruh M, Ardizzoni A, Besse B, Faivre-Finn C, Hendriks LE, Lantuejoul S, Peters S, Reguart N, Rudin CM, De Ruysscher D, Van Schil PE, Vansteenkiste J, Reck M; ESMO Guidelines Committee. Electronic address: clinicalguidelines@esmo.org. Small-cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up☆. Ann Oncol. 2021 Jul;32(7):839-853. doi: 10.1016/j.ann PMID 33864941
• Chen H, Drapkin BJ, Minna JD. Proteomics: A new dimension to decode small cell lung cancer. Cell. 2024 Jan 4;187(1):14-16. doi: 10.1016/j.cell.2023.11.042. PMID 38181738
• Remon J, Aldea M, Besse B, Planchard D, Reck M, Giaccone G, Soria JC. Small cell lung cancer: a slightly less orphan disease after immunotherapy. Ann Oncol. 2021 Jun;32(6):698-709. doi: 10.1016/j.annonc.2021.02.025. Epub 2021 Mar 15. PMID 33737119
• Lu C, Wei XW, Wang Z, Zhou Z, Liu YT, Zheng D, He Y, Xie ZH, Li Y, Zhang Y, Zhang YC, Huang ZJ, Mei SQ, Liu JQ, Guan XH, Deng Y, Chen ZH, Tu HY, Xu CR, Chen HJ, Zhong WZ, Yang JJ, Zhang XC, Mok TSK, Wu YL, Zhou Q. Allelic Context of EGFR C797X-Mutant Lung Cancer Defines Four Subtypes With Heterogeneous Genomic Landscape and Distinct Clinical Outcomes. J Thorac Oncol. 2024 Apr;19(4):601-612. doi: 1 PMID 37981218

Identifiers

NCT: NCT07026669 · NCC5154

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗