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Lookup NU author(s): Dr Jack LeslieORCiD, Misti McCainORCiD, Professor Helen ReevesORCiD, Professor Derek MannORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2025.Hepatocellular carcinoma (HCC), the most common form of primary liver cancer, is a leading cause of cancer-related mortality worldwide1,2. HCC occurs typically from a background of chronic liver disease, caused by a spectrum of predisposing conditions. Tumour development is driven by the expansion of clones that accumulate progressive driver mutations3, with hepatocytes the most likely cell of origin2. However, the landscape of driver mutations in HCC is broadly independent of the underlying aetiologies4. Despite an increasing range of systemic treatment options for advanced HCC, outcomes remain heterogeneous and typically poor. Emerging data suggest that drug efficacies depend on disease aetiology and genetic alterations5,6. Exploring subtypes in preclinical models with human relevance will therefore be essential to advance precision medicine in HCC7. Here we generated a suite of genetically driven immunocompetent in vivo and matched in vitro HCC models. Our models represent multiple features of human HCC, including clonal origin, histopathological appearance and metastasis. We integrated transcriptomic data from the mouse models with human HCC data and identified four common human–mouse subtype clusters. The subtype clusters had distinct transcriptomic characteristics that aligned with the human histopathology. In a proof-of-principle analysis, we verified response to standard-of-care treatment and used a linked in vitro–in vivo pipeline to identify a promising therapeutic candidate, cladribine, that has not previously been linked to HCC treatment. Cladribine acts in a highly effective subtype-specific manner in combination with standard-of-care therapy.
Author(s): Muller M, May S, Hall H, Kendall TJ, McGarry L, Blukacz L, Nuciforo S, Georgakopoulou A, Jamieson T, Phinichkusolchit N, Dhayade S, Suzuki T, Huguet-Pradell J, Powley IR, Officer-Jones L, Pennie RL, Esteban-Fabro R, Gris-Oliver A, Pinyol R, Skalka GL, Leslie J, Hoare M, Sprangers J, Malviya G, Mackintosh A, Johnson E, McCain M, Halpin J, Kiourtis C, Nixon C, Clark G, Clark W, Shaw R, Hedley A, Drake TM, Tan EH, Neilson M, Murphy DJ, Lewis DY, Reeves HL, Le Quesne J, Mann DA, Carlin LM, Blyth K, Llovet JM, Heim MH, Sansom OJ, Miller CJ, Bird TG
Publication type: Article
Publication status: Published
Journal: Nature
Year: 2025
Pages: epub ahead of print
Online publication date: 19/02/2025
Acceptance date: 02/01/2025
Date deposited: 25/03/2025
ISSN (print): 0028-0836
ISSN (electronic): 1476-4687
Publisher: Nature Research
URL: https://doi.org/10.1038/s41586-025-08585-z
DOI: 10.1038/s41586-025-08585-z
Data Access Statement: Data files for transcriptomic analyses are available at the Gene Expression Omnibus (GEO) under accession numbers GSE275444 and GSE273806 (mouse models) and GSE275443 (organoids). Our transcriptomic data are freely available to browse through a user-friendly interactive browser online, enabling HuMo classification of external transcriptomic datasets (http://shinyapps.crukscotlandinstitute.ac.uk/humo_app/). Immunohistochemical and H&E staining of the GEMMs is publicly available at the BioImage Archive under accession number S-BIAD1365. Montironi cohort data were provided on request to the original authors25 and the TCGA data were accessed from publicly accessible databases4. The mouse genome (GRCm39.103) was accessed from Ensembl (https://www.ensembl.org). All data generated and/or analysed during the current study are also available from the corresponding authors on reasonable request. Source data are provided with this paper.
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