Pharmaco-proteogenomic characterization of liver cancer organoids for precision oncology

Shuyi Ji, Li Feng, Zile Fu, Gaohua Wu, Yingcheng Wu, Youpei Lin, Dayun Lu, Yuanli Song, Peng Cui, Zijian Yang, Chen Sang, Guohe Song, Shangli Cai, Yuanchuang Li, Hanqing Lin, Shu Zhang, Xiaoying Wang, Shuangjian Qiu, Xiaoming Zhang, Guoqiang HuaJunqiang Li, Jian Zhou, Zhi Dai, Xiangdong Wang, Li Ding, Pei Wang, Daming Gao, Bing Zhang, Henry Rodriguez, Jia Fan, Hans Clevers, Hu Zhou, Yidi Sun, Qiang Gao

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Organoid models have the potential to recapitulate the biological and pharmacotypic features of parental tumors. Nevertheless, integrative pharmaco-proteogenomics analysis for drug response features and biomarker investigation for precision therapy of patients with liver cancer are still lacking. We established a patient-derived liver cancer organoid biobank (LICOB) that comprehensively represents the histological and molecular characteristics of various liver cancer types as determined by multiomics profiling, including genomic, epigenomic, transcriptomic, and proteomic analysis. Proteogenomic profiling of LICOB identified proliferative and metabolic organoid subtypes linked to patient prognosis. High-throughput drug screening revealed distinct response patterns of each subtype that were associated with specific multiomics signatures. Through integrative analyses of LICOB pharmaco-proteogenomics data, we identified the molecular features associated with drug responses and predicted potential drug combinations for personalized patient treatment. The synergistic inhibition effect of mTOR inhibitor temsirolimus and the multitargeted tyrosine kinase inhibitor lenvatinib was validated in organoids and patient-derived xenografts models. We also provide a user-friendly web portal to help serve the biomedical research community. Our study is a rich resource for investigation of liver cancer biology and pharmacological dependencies and may help enable functional precision medicine.

Original languageEnglish
Article numbereadg3358
JournalScience Translational Medicine
Volume15
Issue number706
DOIs
StatePublished - 26 Jul 2023

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