The Establishment and Clinical Application of a Prediction Model of Lung Cancer Distant Metastasis Based on the Genomic Characteristics of Circulating Tumor Cells
September 24, 2020 updated by: Chinese Medical Association
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
Study Type
Observational
Enrollment (Anticipated)
100
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
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
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Observational Models: Case-Crossover
- Time Perspectives: Prospective
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
|
Shanghai General Hospital
|
Isolated the blood sample and detected the CTC
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
relapse
Time Frame: 6months
|
6months
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
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
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
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.