A Preclinical Human-Derived Autologous Gastric Cancer Organoid/Immune Cell Co-Culture Model to Predict the Efficacy of Targeted Therapies

Jayati Chakrabarti, Vivien Koh, Jimmy Bok Yan So, Wei Peng Yong, Yana Zavros

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Tumors expressing programmed cell death-ligand 1 (PD-L1) interact with programmed cell death protein 1 (PD-1) on CD8+ cytotoxic T lymphocytes (CTLs) to evade immune surveillance leading to the inhibition of CTL proliferation, survival, and effector function, and subsequently cancer persistence. Approximately 40% of gastric cancers express PD-L1, yet the response rate to immunotherapy is only 30%. We present the use of human-derived autologous gastric cancer organoid/immune cell co-culture as a preclinical model that may predict the efficacy of targeted therapies to improve the outcome of cancer patients. Although cancer organoid co-cultures with immune cells have been reported, this co-culture approach uses tumor antigen to pulse the antigen-presenting dendritic cells. Dendritic cells (DCs) are then cultured with the patient's CD8+ T cells to expand the cytolytic activity and proliferation of these T lymphocytes before co-culture. In addition, the differentiation and immunosuppressive function of myeloid-derived suppressor cells (MDSCs) in culture are investigated within this co-culture system. This organoid approach may be of broad interest and appropriate to predict the efficacy of therapy and patient outcome in other cancers, including pancreatic cancer.

Original languageEnglish (US)
Article numbere61443
JournalJournal of Visualized Experiments
Volume2021
Issue number173
DOIs
StatePublished - Jul 2021

ASJC Scopus subject areas

  • General Neuroscience
  • General Chemical Engineering
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology

Fingerprint

Dive into the research topics of 'A Preclinical Human-Derived Autologous Gastric Cancer Organoid/Immune Cell Co-Culture Model to Predict the Efficacy of Targeted Therapies'. Together they form a unique fingerprint.

Cite this