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

• Published in: bioRxiv
• Main Authors: Delamarre, Adele, Decraecker, Marie, Blanc, Jean-Frederic, Di-Tommaso, Sylvaine, Dourthe, Cyril, Jean-William Dupuy, Moreau, Melanie, Allain, Nathalie, Mahouche, Isabelle, Giraud, Julie, Benard, Giovanni, Lalou, Claude, Pinson, Benoit, Bioulac-Sage, Paulette, Toulouse, Caroline, Morisset, Audrey, Boursier, Jerome, Brigitte Le Bail, Anne-Aurelie Raymond, Saltel, Frederic
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 04.01.2025; Cold Spring Harbor Laboratory
• Edition: 1.1
• Subjects: Bevacizumab; Biopsy; Cancer Biology; Hepatocellular carcinoma; Immunotherapy; Liver cancer; Mass spectroscopy; Proteomes; Proteomics; Response rates; Tumors
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/2025.01.03.631224

Purpose: Hepatocellular carcinoma (HCC) is the most common form of liver cancer with a bad prognosis in case of advanced HCC, only eligible for palliative systemic therapies. After a decade of exclusive sorafenib monotherapy, with a response rate of <10%, the advent of immunotherapies represents a revolution in HCC. The combination of atezolizumab/bevacizumab is recommended as the first-line systemic treatment, with a response rate around 30%. However, there are currently no predictive factors for response to these treatment options. Experimental Design: We profiled, by high-resolution mass spectrometry-based proteomics combined with machine learning analysis, a selected cohort of fixed biopsies of advanced HCC. We grouped subjects according to their objective response to treatments, corresponded to a tumor regression vs tumor progression at 4 months after treatment. 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. The clear distinction of these two groups for each treatment is based on deregulation for 141 protein or 87 for atezolizumab/bevacizumab and sorafenib treatment, respectively. These specific proteomic signatures were sufficient to predict the response to treatment, and revealed biological pathways involved in treatments resistance. Particularly, we validated a shift in tumor cell metabolism with an immunosuppressive environment involved in the resistance to atezolizumab/bevacizumab combination. Conclusions: We performed an in-depth analysis of quantitative proteomic data from HCC biopsies to predict the treatment response to advanced HCC giving the ability to optimize patient management.Competing Interest StatementJFB: Bayer, ESAI, IPSEN, ROCHE, ASTRA-ZENECA, BMS MD: Roche, Servier