Multi-omics Based Prediction of Treatment Response to Immunotherapy Combined with Chemotherapy in Advanced Gastric/Gastroesophageal Junction Cancer.
NCT ID: NCT06642857
Last Updated: 2024-10-15
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
150 participants
OBSERVATIONAL
2024-03-25
2026-02-01
Brief Summary
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Detailed Description
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Secondary objective: to construct and validate the efficacy prediction model of chemotherapy combined with immunotherapy for gastric cancer, in order to optimize the scheme decision of advanced gastric cancer treatment
Exploratory purpose: to screen potential molecular markers / dominant flora for predicting the efficacy of immunotherapy in patients with advanced gastric / gastroesophageal junction cancer
Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Patients with advanced gastric cancer
Advanced gastric cancer patients receiving chemotherapy combined with immunotherapy
Peripheral blood, tougue coating, saliva, and feces
Peripheral blood, coating, saliva, and feces on the tongue and clinical data of patients with advanced gastric cancer patients who received chemotherapy combined with immunotherapy will be collected.
Interventions
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Peripheral blood, tougue coating, saliva, and feces
Peripheral blood, coating, saliva, and feces on the tongue and clinical data of patients with advanced gastric cancer patients who received chemotherapy combined with immunotherapy will be collected.
Eligibility Criteria
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Inclusion Criteria
* HER2 negative
* Not received any anti-tumor treatment before.
* After evaluation, the treatment plan is chemotherapy combined with immunotherapy.
* Aged 18 to 75 years old, gender is not limited.
Exclusion Criteria
* Patients who have previously received anti-tumor treatments such as surgery, radiotherapy and chemotherapy, targeted therapy or immunotherapy.
* Patients with severe infections.
* Those with a history of mental illness cannot cooperate with the research.
* Patients with severe heart, liver, kidney and other diseases.
* Pregnant or lactating patients.
* HER2 positive.
18 Years
75 Years
ALL
No
Sponsors
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Xiangdong Cheng
OTHER
Responsible Party
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Xiangdong Cheng
Professor
Locations
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Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)
Hangzhou, Zhejiang, China
Countries
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Central Contacts
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Facility Contacts
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References
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Yuan L, Yang L, Zhang S, Xu Z, Qin J, Shi Y, Yu P, Wang Y, Bao Z, Xia Y, Sun J, He W, Chen T, Chen X, Hu C, Zhang Y, Dong C, Zhao P, Wang Y, Jiang N, Lv B, Xue Y, Jiao B, Gao H, Chai K, Li J, Wang H, Wang X, Guan X, Liu X, Zhao G, Zheng Z, Yan J, Yu H, Chen L, Ye Z, You H, Bao Y, Cheng X, Zhao P, Wang L, Zeng W, Tian Y, Chen M, You Y, Yuan G, Ruan H, Gao X, Xu J, Xu H, Du L, Zhang S, Fu H, Cheng X. Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study. EClinicalMedicine. 2023 Feb 6;57:101834. doi: 10.1016/j.eclinm.2023.101834. eCollection 2023 Mar.
Li MY, Zhu DJ, Xu W, Lin YJ, Yung KL, Ip AWH. Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis. J Healthc Eng. 2021 Nov 18;2021:5853128. doi: 10.1155/2021/5853128. eCollection 2021.
Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023 Jan;73(1):17-48. doi: 10.3322/caac.21763.
Other Identifiers
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IRB-2024-175
Identifier Type: -
Identifier Source: org_study_id
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