NCT04558255

A Preliminary Study on the Detection of Plasma Markers in Early Diagnosis for Lung Cancer

Status unknown Last updated 22 September 2020

What this trial tests

trial testing A machine-learning method which can robustly discriminate early-stage lung cancer patients from controls in Lung Cancer in 1,000 participants. Status unknown.

Timeline

1 January 2020

Primary endpoint
1 December 2020

1 December 2021

Quick facts

Lead sponsor	Peking University People's Hospital
Status	Status unknown
Study type	OBSERVATIONAL
Enrollment	1,000
Start date	1 January 2020
Primary completion	1 December 2020
Estimated completion	1 December 2021
Sites	1 location across China

Drugs / interventions tested

• A machine-learning method which can robustly discriminate early-stage lung cancer patients from controls

Conditions studied

• Lung Cancer — all drugs for Lung Cancer →

Sponsor

Peking University People's Hospital

Who can join

Adults 20 to 75, any sex, with Lung Cancer. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Lung cancer is the most common cancer with the highest morbidity and mortality in the world. Stagement is closely related to the 5 years of survival rate of patients. The postoperative 5-year survival rate is above 90% for stage ⅠA lung cancer patients, while the 5-year survival rate of stage IV lung cancer patients is less than 5%. Therefore, early screening and diagnosis for lung cancer is a key method to reduce lung cancer mortality and prolong survival for patients.

At present, low-dose computed tomography (LDCT) is the most effective method for early detection of lung cancer. In addition to imaging examination, plasma tumor markers detection is also a common clinical detection method for tumor screening and postoperative monitoring.

Liquid biopsy is a non-invasive or minimally invasive method for testing blood or other liquid samples to analyze tumor-related markers including nucleic acids and proteins. Several studies have explored the detection of hot spot gene mutations, methylation and methylation changes of DNA, protein markers and autoantibodies in peripheral blood in lung cancer patients. Liquid biopsy has generally become the most popular field for early diagnosis of lung cancer.

Based above, it is necessary to combine multi-omics methods to improve the detection of early stage lung cancer. In our study, we intend to integrate molecular features obtained through liquid biopsy and clinical data of lung cancer patients, and develop and prospectively validate a machine-learning method which can robustly discriminate early-stage lung cancer patients from controls.

Publications & conference data

3 peer-reviewed publications reference this trial (live from Europe PMC):

1. Multi-omics integrated circulating cell-free DNA genomic signatures enhanced the diagnostic performance of early-stage lung cancer and postoperative minimal residual disease.
   Li Y, Jiang G, Wu W, Yang H, et al · · 2023 · cited 34× · PMID 37027928 · DOI 10.1016/j.ebiom.2023.104553
2. Translational Bioinformatics Applied to the Study of Complex Diseases.
   Casotti MC, Meira DD, Alves LNR, Bessa BGO, et al · · 2023 · cited 12× · PMID 36833346 · DOI 10.3390/genes14020419
3. Exploring the Potential of Non-Coding RNAs as Liquid Biopsy Biomarkers for Lung Cancer Screening: A Literature Review.
   Garbo E, Del Rio B, Ferrari G, Cani M, et al · · 2023 · cited 11× · PMID 37835468 · DOI 10.3390/cancers15194774