The Establishment and Clinical Application of a Prediction Model of Lung Cancer Distant Metastasis Based on the Genomic Characteristics of Circulating Tumor Cells

Lung cancer is the most common type of cancer in my country, but the 5-year survival time of lung cancer patients is only 17%. Among them, the biggest reason that affects the patient's prognosis is the metastasis of the tumor. There are very few clinical methods suitable for the treatment of metastatic lung cancer, and the curative effect is not good. Therefore, early monitoring and interventions to prevent distant colonization of metastases are the key to improving the survival of lung cancer. The preliminary research of this project found that circulating tumor cells in peripheral blood can be used as an effective means for clinical diagnosis and treatment of lung malignant tumors. Through the analysis of the difference in time and space metastasis of lung cancer patients, it is found that the genomes of different metastasis stages and metastatic organs of lung cancer are quite different , And is closely related to the patient's survival. For this reason, we propose the hypothesis that the genomic mutation characteristics of circulating tumor cells can detect tumor metastasis signals earlier than CT imaging diagnosis. To test this hypothesis, we will develop a cancer metastasis risk assessment system based on tumor genomics. First, we collect big data on the genome of primary and metastatic lung cancer from public databases, and use statistical methods to screen out genomic features that are significantly related to metastatic lung cancer and its metastatic colonization organs. Secondly, using these features to develop a set of machine learning models that can determine the risk of metastasis of a lung cancer based on its genome features. Finally, we applied the model to clinical practice. By detecting the circulating tumor cells of patients with primary lung cancer during the reexamination, we established a statistical noise reduction model to extract the genomic characteristics, and then substituted into the model to determine the circulating tumor cells carried by the patient Whether there is a risk of recurrence and metastasis. By comparing the imaging data in the review, we will verify whether the model detects early metastasis signals of lung cancer earlier than imaging methods. Ultimately, our model will aggregate genomic markers related to metastasis risk, explore their drug targeting, and provide powerful big data analysis support for early intervention in metastasis colonization and prolonging the survival of lung cancer patients. If the topic is demonstrated, it will help to clarify the use of tumor genome big data analysis to reveal the genomic driver mutations of metastatic lung cancer; demonstrate the feasibility of circulating tumor cell genome driver mutations to predict the risk of lung cancer metastasis; and finally clarify the PI3K/Akt/mTOR signal Can inhibitors of the pathway be used as a target for early intervention in lung cancer metastasis.

Study Overview

• Status: Unknown
• Conditions: Lung Cancer; CTCs
• Intervention / Treatment: Diagnostic test: blood sample

• Study Type: Observational
• Enrollment (Anticipated): 100

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 75 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

non-small cell lung cancer with any stage

Description

Inclusion Criteria:

Patients with non-small cell lung cancer 18 to 75 years old, patients of any stage, with at least one measurable lesion on chest imaging, ECOG PS score: 0 to 1 point

Exclusion Criteria:

Small cell lung cancer, including patients with mixed small cell carcinoma and non-small cell carcinoma

Study Plan

How is the study designed?

Design Details

• Observational Models: Case-Crossover
• Time Perspectives: Prospective

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Shanghai General Hospital	Diagnostic test: blood sample; Isolated the blood sample and detected the CTC

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
relapse	6months

Collaborators and Investigators

• Sponsor: Chinese Medical Association

Study record dates

Study Major Dates

• Study Start (Anticipated): December 1, 2020
• Primary Completion (Anticipated): September 30, 2022
• Study Completion (Anticipated): September 30, 2022

Study Registration Dates

• First Submitted: September 24, 2020
• First Submitted That Met QC Criteria: September 24, 2020
• First Posted (Actual): September 29, 2020

Study Record Updates

• Last Update Posted (Actual): September 29, 2020
• Last Update Submitted That Met QC Criteria: September 24, 2020
• Last Verified: September 1, 2020

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Respiratory Tract Diseases; Neoplasms; Lung Diseases; Neoplasms by Site; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplastic Processes; Neoplasm Metastasis; Lung Neoplasms; Neoplastic Cells, Circulating
• Other Study ID Numbers: 2020KY187

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