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

  1. Adèle Delamarre
  2. Marie Decraecker
  3. Jean-Frédéric Blanc
  4. Sylvaine Di Tommaso
  5. Cyril Dourthe
  6. Jean-William Dupuy
  7. Mélanie Moreau
  8. Nathalie Allain
  9. Isabelle Mahouche
  10. Julie Giraud
  11. Giovanni Bénard
  12. Claude Lalou
  13. Benoît Pinson
  14. Paulette Bioulac-Sage
  15. Caroline Toulouse
  16. Audrey Morisset
  17. Jérôme Boursier
  18. Brigitte Le Bail
  19. Anne-Aurélie Raymond
  20. Frédéric Saltel

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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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  1. PREreview

    This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17164693.

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

    (bioRxiv preprint, DOI: 10.1101/2025.01.03.631224)

    Reviewer: Grifton Tafadzwa Muchovu, MSc Student in Medical Biotechnology, University of Piemonte Orientale

    Summary

    This paper explored how the protein composition of liver cancer tumours (HCC) can serve to predict patients likely to respond to systemic drugs like atezolizumab/bevacizumab or sorafenib. The authors revealed protein signatures associated with treatment response in diagnostic biopsy specimens using high-resolution mass spectrometry and emphasized the possibility of an important role of mitochondrial oxidative phosphorylation and immune infiltration in the resistance.

    Key Strengths

    • Clinical significance: The ability to predict the response to treatment is one of the key unmet clinical needs in the advanced HCC. The demonstration of the ability of diagnostic biopsies to be employed in proteomic profiling is a good start on the road to practical implementation.

    • Technical method: Deep proteomics and machine-learning can be used effectively to discover biomarkers, and more importantly, they can be used to show how advanced analytics can find significant patterns in a small amount of clinical data.

    • Mechanistic understanding: The relationship between metabolic reprogramming and immune exclusion is interesting and offers a possible biological rationale of treatment refusal.

    Points to Improve or Clarify

    • Cohort size and validation: The sample of patients (e.g. 9 and 15 non-responders in atezolizumab/bevacizumab group) is small. Such protein signatures would be useful in confirming whether they have a predictive value when pertaining to an independent and larger cohort that would be external validation.

    • Etiopathogenesis of liver disease: Since HCC has many etiologies (e.g. metabolic dysfunction like MASH, alcohol-related liver disease), it would be appropriate to examine whether the proteomic signatures are etiology-specific or, at least, comment on this as a possible source of variability.

    • Machine-learning description: The choice of model, feature ranking, and cross-validation information would be better disclosed and the results could be easier reproducible and evaluated (or access to code).

    • Follow-up: The experiments with the 3D spheroid are a good beginning and further data on in-vivo experiments would help to make the point that oxidative phosphorylation has a direct effect on immune infiltration and treatment response.

    Minor Comments

    • Elaborate in the discussion on the issue of whether proteins that are common in treatment groups are indicative of general HCC biology or common resistance pathways.

    Overall Assessment

    It is a good and clinically applicable proteomic research. It generates promising biomarker candidates and mechanistic hypotheses which may eventually inform the selection of therapy in advanced HCC. This will be followed by more important steps of larger, independent validation and more open machine-learning reporting before clinical implementation.

    Competing interests

    The author declares that they have no competing interests.

  2. Version published to 10.1101/2025.01.03.631224 on bioRxiv