Multicenter Clinical Research for Early Diagnosis of Lung Cancer Using Blood Plasma Derived Exosome

Lung Cancer Clinical Trial

Official title:

Multicenter Clinical Research for Early Diagnosis of Lung Cancer Using Blood Plasma

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT04529915
• Other study ID #: 2020GR0176
• Status: Active, not recruiting
• Start date: April 9, 2020
• Est. completion date: December 29, 2023

Study information

• Verified date: December 2021
• Source: Korea University Guro Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Lung cancer is a leading cause of cancer death worldwide. Early diagnosis is linked to a better prognosis. Further, surgical resection at the early stages of non-small cell lung cancer (NSCLC) results in markedly improved survival rates. Computed tomography (CT)- or bronchoscopy-guided needle biopsies are standard definitive diagnostic procedures for lung cancer and are used to obtain tissue for pathological examination. However, these procedures are invasive, difficult to repeat, expensive, and risk exposure to radiation. Conversely, liquid biopsies, such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and extracellular vesicles (EVs), are simple and less invasive procedures that can be repeated more frequently than tissue biopsies.

To analyze the exosomes abundantly present in the blood and to conduct clinical studies to determine whether it is possible to diagnose lung cancer. To this end, blood samples from normal people (n = 150) and lung cancer patients (n = 320) are obtained from the Human biobank of five hospitals participating in the study.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 470
• Est. completion date: December 29, 2023
• Est. primary completion date: December 29, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 40 Years and older

Eligibility

Inclusion Criteria:

1. Patients with primary adenocarcinoma of lung with permanent pathology of N0 or N1

2. Patients with T1mi, Tsi, T1a, T1b, T1c, T2a, and T2b stage

3. An adult of Korean nationality

4. Patients without prior chemo/radiation treatment prior to lung cancer surgery

5. Patients who have not been diagnosed with other cancers prior to lung cancer surgery

Exclusion Criteria:

• Patients who do not meet the inclusion criteria

Related Conditions & MeSH terms

• Lung Cancer
• Lung Neoplasms

Intervention

Diagnostic Test:
• Exosome sampling
Centrifugation of blood plasma Size exclusion chromatography ELISA assay, Western blotting Deep-learning analysis

Locations

Country	Name	City	State
Korea, Republic of	Korea University Guro Hospital	Seoul	Guro-gu

Sponsors (1)

Lead Sponsor	Collaborator
Korea University Guro Hospital

Country where clinical trial is conducted

Korea, Republic of, 

References & Publications (5)

Bidard FC, Pierga JY, Vincent-Salomon A, Poupon MF. A "class action" against the microenvironment: do cancer cells cooperate in metastasis? Cancer Metastasis Rev. 2008 Mar;27(1):5-10. Review. — View Citation

Kaplan RN, Rafii S, Lyden D. Preparing the "soil": the premetastatic niche. Cancer Res. 2006 Dec 1;66(23):11089-93. Review. — View Citation

Psaila B, Lyden D. The metastatic niche: adapting the foreign soil. Nat Rev Cancer. 2009 Apr;9(4):285-93. doi: 10.1038/nrc2621. Review. — View Citation

Talmadge JE, Fidler IJ. AACR centennial series: the biology of cancer metastasis: historical perspective. Cancer Res. 2010 Jul 15;70(14):5649-69. doi: 10.1158/0008-5472.CAN-10-1040. Epub 2010 Jul 7. Review. — View Citation

van Akkooi AC, Verhoef C, Eggermont AM. Importance of tumor load in the sentinel node in melanoma: clinical dilemmas. Nat Rev Clin Oncol. 2010 Aug;7(8):446-54. doi: 10.1038/nrclinonc.2010.100. Epub 2010 Jun 22. Review. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Evaluation of the distinction between healthy controls and lung cancer patients through deep-learning analysis of exosomes	Comparative evaluation of whether it is possible to distinguish between healthy controls and lung cancer patients through deep-learning analysis of exosomes	3 years
Primary	Evaluating the possibility of distinguishing between normal and lung cancer patients through the analysis of lung cancer-specific exosomal protein	Quantitative analysis using lung cancer-specific exosomal protein evaluated the possibility of distinguishing between healthy controls and lung cancer patients.	3 years
Secondary	Evaluation of the possibility of distinguishing the early pathological stages in lung cancer patients through deep-learning analysis of exosomes	Evaluating whether the early stages of lung cancer patients can be distinguished using deep-learning analysis of exosomes	3 years
Secondary	Evaluation of the possibility of distinguishing the early pathological stages in lung cancer patients through quantitative analysis of lung cancer specific exosomal proteins	Evaluating whether the early stages of lung cancer patients can be distinguished using quantitative analysis of lung cancer specific exosomal proteins	3 years