Abstract 6395: Metabolic dysregulation as a determinant of prognosis and response to sorafenib in liver cancer

Abstract

Abstract Background & Aims: Elevated metabolic activity is a defining characteristic of liver cancer. Nevertheless, our understanding of the molecular underpinnings of high metabolic activity in liver cancer and its impact on clinical outcomes and treatment responses remains limited. We aimed to elucidate the metabolic features of liver cancer associated with clinical outcomes, particularly overall survival and responsiveness to sorafenib treatment. Methods: Utilizing cross-species comparisons of genomic data, we integrated gene expression profiles from liver tissues in metabolically challenged mice and from patients with cirrhosis and liver cancer. To categorize patients, we developed a genomic predictor and applied it to gene expression data from a cirrhosis cohort (n=216) and large liver cancer cohorts (n=1390). Comprehensive statistical and informatics analyses were conducted to evaluate clinical significance, including overall survival and responses to standard treatments. Moreover, we integrated genomic data derived from patient-derived xenograft (PDX) models into our analytical framework. Results: Our integrated data analysis unveiled three distinct metabolic subtypes of tissues: high, moderate, and low metabolic subtypes. In the cirrhosis cohort, the high metabolic subtype was significantly associated with early liver cancer development. Among liver cancer cohorts, the high metabolic subtype exhibited characteristics such as poor survival, heightened hepatic stem cell features, increased genomic instability, and elevated expression of AFP, KRT19, EPCAM, and SALL4. Notably, when liver cancer patients undergoing systemic sorafenib treatment were stratified using our genomic predictor, those with low and moderate metabolic activity derived significant benefits from sorafenib therapy. Conversely, the high metabolic subtype displayed substantial resistance to sorafenib, suggesting that metabolic activity may dictate the response of liver cancer cells to sorafenib treatment. Gene network analysis of the metabolic expression signature identified multiple signaling pathways that potentially contribute to sorafenib resistance in liver cancer cells. Our analysis predicted the activation of pathways such as the TGF-β pathway (TGFB1), consistent with previous findings of TGF-β pathway activation in highly metabolic cancer cells. Additionally, our analysis identified YAP1, HIF1A, and MAPK pathways as activated pathways in sorafenib-resistant liver cancer. Conclusions: This study identifies clinically and metabolically distinct subtypes of liver cancer, biomarkers associated with these subtypes, and mechanisms underlying metabolic-mediated resistance of liver cancer cells to sorafenib. Furthermore, the metabolic subtypes observed in PDX models provide valuable tools for selecting appropriate preclinical models for future research endeavors. Citation Format: Hyewon Park, Sowon Park, Sung-Hwan Lee, Yun Seong Jeong, Bohwa Sohn, Sun Young Yim, Ju-Seog Lee. Metabolic dysregulation as a determinant of prognosis and response to sorafenib in liver cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6395.