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.

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.

Study Overview

• Status: Recruiting
• Conditions: Advanced Gastric Carcinoma; Advanced Gastroesophageal Junction Adenocarcinoma
• Intervention / Treatment: Other: Peripheral blood, tougue coating, saliva, and feces

Detailed Description

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

• Study Type: Observational
• Enrollment (Estimated): 150

Contacts and Locations

• Study Contact: Name: Xiangdong Cheng Cheng, PhD; Phone Number: +0086-0571-88128041; Email: Chengxd516@126.com

Study Locations

• China
  • Zhejiang
    • Hangzhou, Zhejiang, China, 310000
      • Recruiting
      • Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)
      • Contact: Xiangdong Cheng Xiangdong Cheng, PhD; Phone Number: +0086-0571-88128041; Email: chengxd516@126.com
      • Contact: Nannan Zhang Nannan Zhang, PhD; Phone Number: 18920166859; Email: natilen@163.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Patients with advanced or metastatic gastric or gastroesophageal junction adenocarcinoma

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Patients with advanced gastric cancer; Advanced gastric cancer patients receiving chemotherapy combined with immunotherapy	Other: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	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

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Progression-free Survival	The period from diagnosis until disease progression or death on study, whichever occurred first.	36 months

Other Outcome Measures

Outcome Measure	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

Collaborators and Investigators

• Sponsor: Xiangdong Cheng

Publications and helpful links

General Publications

• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Actual): March 25, 2024
• Primary Completion (Estimated): February 1, 2026
• Study Completion (Estimated): February 1, 2026

Study Registration Dates

• First Submitted: September 19, 2024
• First Submitted That Met QC Criteria: October 14, 2024
• First Posted (Actual): October 15, 2024

Study Record Updates

• Last Update Posted (Actual): October 15, 2024
• Last Update Submitted That Met QC Criteria: October 14, 2024
• Last Verified: September 1, 2024

More Information

Terms related to this study

• Keywords: efficacy prediction; Advanced Gastric Carcinoma; Advanced Gastroesophageal Junction Adenocarcinoma; Chemotherapy combined with immunotherapy
• Additional Relevant MeSH Terms: Digestive System Diseases; Neoplasms by Histologic Type; Neoplasms; Neoplasms by Site; Carcinoma; Neoplasms, Glandular and Epithelial; Gastrointestinal Neoplasms; Digestive System Neoplasms; Gastrointestinal Diseases; Stomach Diseases; Head and Neck Neoplasms; Esophageal Diseases; Stomach Neoplasms; Adenocarcinoma; Esophageal Neoplasms
• Other Study ID Numbers: IRB-2024-175

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: We currently have no plans to share individual participant data (IPD) with other researchers.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No