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In this project, based on the information of advanced gastric/gastroesophageal junction cancer in evolution under immunotherapy combined with chemotherapy treatment, we will integrate multi-omics dynamic data to identify essential features that correlate to therapeutic effects of immunotherapy therapy, screen potential molecular markers/dominant microbiota for predicting the efficacy of immunotherapy and establish a multimodal predictive model for patients that benefit from immunotherapy. Our project could provide evidence to predict response to immunotherapy for patients with advanced gastric/gastroesophageal junction cancer and potentially optimize the clinical decision-making about therapy for advanced gastric/gastroesophageal junction cancer.
Main objective: to extract and identify multi omics information tags related to the efficacy of immunotherapy for advanced gastric / gastroesophageal junction cancer
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
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients with advanced gastric cancer | Advanced gastric cancer patients receiving chemotherapy combined with immunotherapy |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Peripheral blood, tougue coating, saliva, and feces | Other | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Objective Best Tumor Response | Response using Response Evaluation Criteria In Solid Tumors (RECIST) criteria. Complete Response=disappearance of all target lesions; Partial Response=30% decrease in sum of longest diameter of target lesions; Progressive Disease=20% increase in sum of longest diameter of target lesions; Stable Disease=small changes that do not meet above criteria. | 12 months |
| Overall Survival | Overall survival is the duration from diagnosis to death. For patients who are alive, overall survival is censored at the last contact. | 60 months |
| Measure | Description | Time Frame |
|---|---|---|
| Progression-free Survival | The period from diagnosis until disease progression or death on study, whichever occurred first. | 36 months |
| Measure | Description | Time Frame |
|---|---|---|
| Survival rate of 12 month | The proportion of people who are still alive within 12 month from the diagnosis | 12 month from the diagnosis |
Inclusion criteria：
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Patients with advanced or metastatic gastric or gastroesophageal junction adenocarcinoma
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Xiangdong Cheng Cheng, PhD | Contact | +0086-0571-88128041 | Chengxd516@126.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) | Recruiting | Hangzhou | Zhejiang | 310000 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36825238 | Background | 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. | |
| 34840700 |
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We currently have no plans to share individual participant data (IPD) with other researchers.
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| Background |
| 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. |
| 36633525 | Background | 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. |
| ID | Term |
|---|---|
| D003672 | Defecation |
| ID | Term |
|---|---|
| D004068 | Digestive System Physiological Phenomena |
| D055688 | Digestive System and Oral Physiological Phenomena |
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