蛋白质基因组学
类有机物
精密医学
计算生物学
生物标志物
个性化医疗
医学
癌症
药物开发
生物标志物发现
生物
癌症研究
药品
生物信息学
转录组
病理
内科学
药理学
蛋白质组学
基因
神经科学
基因表达
生物化学
作者
Shuyi Ji,Feng Li,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,Shuang‐Jian Qiu,Xiaoming Zhang,Guoqiang Hua
标识
DOI:10.1126/scitranslmed.adg3358
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI