Clinical trial · Observational
Multi-omics Based Prediction of Treatment Response to Immunotherapy Combined with Chemotherapy in Advanced Gastric/Gastroesophageal Junction Cancer.
Predicting Treatment Response to Immunotherapy Combined with Chemotherapy in Advanced Gastric/gastroesophageal Junction Cancer Based on the Multi-omics Information During Tumor Evolution.
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Advanced Gastric Carcinoma | Gastric Carcinoma | CURATED_BROADER | 0.78 |
| Advanced Gastroesophageal Junction Adenocarcinoma | Gastroesophageal Junction Adenocarcinoma | CURATED_BROADER | 0.78 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Peripheral blood, tougue coating, saliva, and feces | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Patients with advanced gastric cancer
- description
- Advanced gastric cancer patients receiving chemotherapy combined with immunotherapy
- interventionNames
- Other: Peripheral blood, tougue coating, saliva, and feces
Primary outcomes (2)
- measure
- Objective Best Tumor Response
- timeFrame
- 12 months
- description
- 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.
- measure
- Overall Survival
- timeFrame
- 60 months
- description
- Overall survival is the duration from diagnosis to death. For patients who are alive, overall survival is censored at the last contact.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 75 Years
Show eligibility criteria text
Inclusion criteria： * Patients with gastric or gastroesophageal junction adenocarcinoma confirmed by pathology and with advanced or metastatic disease that cannot be resected * 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. * Expected survival time is greater than or equal to 3 months. Exclusion criteria： * Patients with malignant tumors other than gastric cancer or those with tumors metastasized to the stomach from other sites. * 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.
References
Publications (3)
- BACKGROUNDYuan 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. PMID 36825238
- BACKGROUNDLi 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. PMID 34840700
- BACKGROUNDSiegel 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. PMID 36633525