Analysis of heterogeneity within genetic murine models of HCC

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 accumulated progressive driver mutations3, with hepatocytes the most likely cell of origin2. However, the landscape of driver mutations in HCC is 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. We generated over twenty-five new genetically-driven in vivo and in vitro HCC models. Our models represent multiple features of human HCC, including clonal origin, histopathological appearance, and metastasis to distant organs. 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 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 been linked to HCC treatment before. Cladribine acts in a highly effective subtype-specific manner in combination with standard of care therapy. Overall design: To investigate the gene expression profile of liver tumors in mouse models containing heterozygous Ctnnb1 tm1Mmt alleles and the human MYC gene, we employed an adeno-associated viral (AAV) vector system. This system was used to express the human MYC gene in Cre-expressing cells, thereby inducing liver tumors. Additionally, we examined gene expression variation in response to high AAV concentrations by comparing control and tumor tissues. Tumor samples (13) and control non-tumor tissue samples (6) were collected. The control non-tumor tissue samples included three from standard AAV levels and three from high AAV levels. We then preformmed bulk RNA-Seq on the tumour and non-tumour samples to determine differential gene expression.