Proteogenomic characterization of patient tissues has enriched our understanding of liver cancer, uncovering potential therapeutic targets, new subtypes, and offering hope for more effective treatments. However, there remains a gap in translating these findings into clinical practice. To bridge this gap, the International Cancer Proteogenomic Consortium (ICPC) team lead by researchers from China, Shanghai Institute of Material Medical of the Chinese Academy of Sciences and Fudan University, in collaboration with scientists from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) established a novel, patient-derived liver cancer organoid biobank (LICOB). The creation of this resource and the subsequent analysis presented in this paper represent crucial steps forward in our understanding of liver cancer biology.
To establish the reliability of LICOB models, the team conducted extensive comparisons with original tissues and public datasets. Notably, LICOB models retained the morphology, multiomic characteristics, and tumor heterogeneity of the original tissues. This included the proliferation and metabolism subtypes of Hepatocellular carcinoma (HCC) and the diverse prognostic subtypes of intrahepatic cholangiocarcinoma (ICC). Among other achievements, the team showcased the utility of LICOB models by identifying two subclasses of the metabolic subtype of HCC,“L-LM” and “L-DM,” and proposing G6PD as a potential therapeutic target for the later.
The study also revealed the potential of LICOB models in identifying possible drug combinations based on pharmaco-proteogenomic data. The team demonstrated that combinatorial drug treatments demonstrate superior efficacy compared to individual drug treatments, exemplified by the sensitivity of lenvatinib-resistant organoids to temsirolimus. Analysis found that HCC organoids resistant to either lenvatinib or temsirolimus as well as lenvatinib-resistant ICCs were sensitive to the combination. Apart from the flexibility in designing and testing drug combinations in vivo, LICOB models have the potential to incorporate additional levels of molecular information, such as global phosphoproteomics and longitudinal response signals for even greater precision.
In summary, the creation and rigorous assessment of LICOB marks a significant advancement in liver cancer research. This achievement, in conversation with the team’s two prior studies (1, 2), underscores the value of a proteogenomic approach--liver cancer's complexity makes multiomic data integration essential for informed drug selection and biomarker discovery. The establishment of LICOB opens new avenues for understanding the biological mechanisms underlying drug responses and informing potential drug combinations in liver cancer.