Abstract 1893: Proteomic profiling of advanced hepatocellular carcinoma identifies predictive signatures of response to treatments

Background and Aims: Hepatocellular carcinoma (HCC) is the most common form of liver cancer but, due to late diagnosis, the prognosis is extremely poor and the majority of cases are advanced HCC, which are only eligible for palliative systemic therapies. After a decade of exclusive sorafenib monotherapy, with a response rate of < 10%, the advent of new combinations including immunotherapies represents a revolution in the management of HCC. The combination of atezolizumab/bevacizumab is recommended as the first-line systemic treatment for HCC, with a response rate of no more than 30%. Additionally, less than 25% of non-responding patients are eligible for second-line treatment. However, there are currently no predictive factors for response to these different treatment options. Method: We profiled, by high-resolution mass spectrometry-based proteomics combined with machine learning analysis, a selected cohort of formalin-fixed, paraffin-embedded biopsies of advanced HCC adenomas with clinical follow-up collected from several French hospitals. We grouped subject according to their objective response to treatments, corresponded to a tumor regression > 30% (G1) and tumor progression > 20% (G2) at 6 months after the beginning for atezolizumab/bevacizumab and sorafenib treatments. Results: We generated a proteome database of 50 selected HCC samples. We compared the relative protein abundance between tumoral and non-tumoral liver tissues from advanced HCC patients treated with atezolizumab/bevacizumab or sorafenib. The clear distinction of these two groups for each treatment using principal component analysis with a deregulation for 141 protein or 87 for atezolizumab /bevacizumab and sorafenib treatment, respectively. These signatures were sufficient to predict the response. Moreover, these signatures reveal the biological pathways involved in the resistance to treatments. Particularly, we revealed and validated a shift in tumor cell energy metabolism inducing an immunosuppressive environment involved in the resistance to atezolizumab/bevacizumab. Conclusion: We performed an in-depth analysis of quantitative proteomic data from HCC biopsies using statistical packages and found that their proteome includes information to predict the treatment response to advanced HCC giving the ability to optimize patient management. Citation Format: Adèle Delamarre, Marie Decraecker, Sylvaine Di-Tommaso, Cyril Dourthe, Jean-William Dupuy, Mélanie Moreau, Nathalie Allain, Isabelle Mahouche, Julie Giraud, Giovanni Benard, Claude Lalou, Paulette Bioulac-Sage, Jérome Boursier, Jean-Frédéric Blanc, Brigitte LeBail, Anne-Aurélie Raymond, Frederic Saltel. Proteomic profiling of advanced hepatocellular carcinoma identifies predictive signatures of response to treatments [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1893.