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Liquid Biopsy-Based Novel Modality for Postoperative Management of Lung Cancer

P

Peking University

Status

Enrolling

Conditions

Adaptive Treatment
Non-small Cell Lung Cancer
Minimal Residual Disease

Treatments

Other: Adaptive postoperative management based on minimal residual disease

Study type

Interventional

Funder types

Other
Other U.S. Federal agency

Identifiers

NCT06930807
2023ZD0501900

Details and patient eligibility

About

The goal of this study is to develop new techniques for minimal residual disease(MRD) monitoring and to confirm the efficacy and safety of MRD-guided postoperative management for early stage non-small cell lung cancer. The main questions this study aims to answer are:

  • How to develop multi-omics-based high-sensitivity detection methods to accurately capture MRD and monitor postoperative recurrence in lung cancer?
  • Is adaptive treatment guided by ctDNA-MRD for lung cancer patients superior to traditional clinical management and effectively improves survival?
  • Do heterogeneous patient populations (grouped by stages, histopathological subtypes, driver mutations, and treatment histories) show differences in effects under ctDNA-MRD guided postoperative management strategies?

Full description

Approximately 30% of early-stage lung cancer patients experience recurrence after curative surgery. However, the clinical utility of routine chest CT surveillance remains limited. Emerging evidence has demonstrated that liquid biopsy-based minimal residual disease (MRD) detection may serve as a more sensitive monitoring strategy. Longitudinal ctDNA dynamics analysis further enables real-time assessment of tumor progression and early detection of molecular relapse.

This study aims to develop a novel multidimensional approach for non-invasive postoperative recurrence monitoring in lung cancer and establish an MRD-guided adjuvant therapy model to optimize precision treatment and MRD-stratified follow-up protocols for lung cancer patients. This study will evaluate the sensitivity and specificity of novel monitoring methods and further compare emerging non-invasive recurrence monitoring approaches with conventional ctDNA-based techniques, driving continuous advancements in lung cancer research.

Enrollment

1,000 estimated patients

Sex

All

Ages

18+ years old

Volunteers

No Healthy Volunteers

Inclusion criteria

  • Non-small cell lung cancer with stage IA-IIIA (8th edition TNM classification) planning to undergo curative surgery or to undergo neoadjuvant therapy
  • Solid nodules >1 cm or ground-glass nodules >1.5 cm on imaging
  • No history of malignancies other than non-small cell lung cancer in the past 5 years
  • Specimens are well preserved and imaging documents are accessible.

Exclusion criteria

  • Age<18 years old
  • Non-small cell lung cancer with pathologic stage IIIB-IV (8th edition TNM classification)
  • Pathology results confirmed not to be non-small cell lung cancer
  • History of malignancies other than non-small cell lung cancer in the past 5 years

Trial design

Primary purpose

Treatment

Allocation

Non-Randomized

Interventional model

Parallel Assignment

Masking

None (Open label)

1,000 participants in 2 patient groups

adaptive postoperative management group
Experimental group
Description:
For those who don't receive neoadjuvant therapy, if the MRD testing results after surgery keep negative, postoperative adjuvant therapy will be waived. For those who receive neoadjuvant therapy, if the efficacy reaches pathological complete response(pCR) and MRD testing results keep negative, postoperative adjuvant therapy will be waived. Otherwise, participants will receive a customized treatment plan based on Multi-disciplinary Treatment (MDT).
Treatment:
Other: Adaptive postoperative management based on minimal residual disease
standard clinical postoperative management group
No Intervention group
Description:
These participants will receive standard postoperative adjuvant therapy and routine follow-up.

Trial contacts and locations

1

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Central trial contact

Xiaoqiu Yuan; Kezhong Chen, MD

Data sourced from clinicaltrials.gov

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