Pharmaco-Proteogenomic Characterization of Liver Cancer Organoids for Precision Oncology

Authors

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 Hua
Junqiang 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

Publication Date

7-26-2023

Journal

Science Translational Medicine

DOI

10.1126/scitranslmed.adg3358

PMID

37494474

PMCID

PMC10949980

PubMedCentral® Posted Date

3-19-2024

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes

Keywords

Humans, Proteogenomics, Proteomics, Precision Medicine, Liver Neoplasms, Organoids

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.