Proteomic profiling of advanced hepatocellular carcinoma identifies predictive signatures of response to treatments
Abstract
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 treatment’s 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.
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