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

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

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Lung Cancer
• Intervention / Treatment: Diagnostic test: Exosome sampling

• Study Type: Observational
• Enrollment (Anticipated): 470

Contacts and Locations

Study Locations

• Korea, Republic of
  • Guro-gu
    • Seoul, Guro-gu, Korea, Republic of, 08308
      • Korea University Guro Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 38 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

For this study, Koreans, regardless of gender, were studied for adults over 40 years of age.

Participating institutions for this clinical research are Korea University Guro Hospital, Gangnam Severance Hospital, Bundang Seoul National University Hospital, Seoul Asan Hospital, and Samsung Seoul Hospital.

Blood plasma samples is taken from stored in the biobank of each hospital.

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Retrospective

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Lung cancer	Diagnostic test: Exosome sampling; Centrifugation of blood plasma; Size exclusion chromatography; ELISA assay, Western blotting; Deep-learning analysis
Healthy	Diagnostic test: Exosome sampling; Centrifugation of blood plasma; Size exclusion chromatography; ELISA assay, Western blotting; Deep-learning analysis

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
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

Collaborators and Investigators

• Sponsor: Korea University Guro Hospital
• Investigators: Principal Investigator: Hyun Koo MD, PhD, MD, PhD, Professor

Publications and helpful links

General Publications

• Psaila B, Lyden D. The metastatic niche: adapting the foreign soil. Nat Rev Cancer. 2009 Apr;9(4):285-93. doi: 10.1038/nrc2621.
• Kaplan RN, Rafii S, Lyden D. Preparing the "soil": the premetastatic niche. Cancer Res. 2006 Dec 1;66(23):11089-93. doi: 10.1158/0008-5472.CAN-06-2407.
• 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.
• 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. doi: 10.1007/s10555-007-9103-x.
• 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.

Study record dates

Study Major Dates

• Study Start (Actual): April 9, 2020
• Primary Completion (Anticipated): December 29, 2023
• Study Completion (Anticipated): December 29, 2023

Study Registration Dates

• First Submitted: August 2, 2020
• First Submitted That Met QC Criteria: August 24, 2020
• First Posted (Actual): August 28, 2020

Study Record Updates

• Last Update Posted (Actual): December 30, 2021
• Last Update Submitted That Met QC Criteria: December 10, 2021
• Last Verified: December 1, 2021

More Information

Terms related to this study

• Keywords: Biomarker; Lung cancer; Liquid biopsy; Exosome; Blood plasma; Deep-learning analysis
• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Neoplasms; Lung Diseases; Neoplasms by Site; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: 2020GR0176

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: Undecided

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No