A gut-on-a-chip incorporating human faecal samples and peristalsis predicts responses to immune checkpoint inhibitors for melanoma

蠕动 黑色素瘤 免疫系统 免疫检查点 生物 癌症研究 免疫学 医学 内科学 免疫疗法
作者
Mattia Ballerini,Serena Galié,Punit Tyagi,Carlotta Catozzi,Hariam Raji,Amir Nabinejad,Angeli Dominique Macandog,Alessandro Cordiale,Bianca Ionela Slivinschi,Karol K. Kugiejko,Martina Freisa,Paola Occhetta,Jennifer A. Wargo,Pier Francesco Ferrucci,Emilia Cocorocchio,Nicola Segata,Andrea Vignati,Andrey Morgun,Michela Deleidi,Teresa Manzo
出处
期刊:Nature Biomedical Engineering [Nature Portfolio]
被引量:4
标识
DOI:10.1038/s41551-024-01318-z
摘要

Patient responses to immune checkpoint inhibitors can be influenced by the gastrointestinal microbiome. Mouse models can be used to study microbiome–host crosstalk, yet their utility is constrained by substantial anatomical, functional, immunological and microbial differences between mice and humans. Here we show that a gut-on-a-chip system mimicking the architecture and functionality of the human intestine by including faecal microbiome and peristaltic-like movements recapitulates microbiome–host interactions and predicts responses to immune checkpoint inhibitors in patients with melanoma. The system is composed of a vascular channel seeded with human microvascular endothelial cells and an intestinal channel with intestinal organoids derived from human induced pluripotent stem cells, with the two channels separated by a collagen matrix. By incorporating faecal samples from patients with melanoma into the intestinal channel and by performing multiomic analyses, we uncovered epithelium-specific biomarkers and microbial factors that correlate with clinical outcomes in patients with melanoma and that the microbiome of non-responders has a reduced ability to buffer cellular stress and self-renew. The gut-on-a-chip model may help identify prognostic biomarkers and therapeutic targets. A gut-on-a-chip incorporating peristaltic-like movements and faecal samples from patients with melanoma recapitulates microbiome–host interactions and predicts the responses of the patients to immune checkpoint inhibitors.
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