Abstract

BackgroundThe emergence of immune checkpoint inhibitors (ICIs) marks the beginning of a new era of immunotherapy for hepatocellular carcinoma (HCC), however, not all patients respond successfully to this treatment. A major challenge for HCC immunotherapy is the development of ways to screen for those patients that would benefit from this type of treatment and determine the optimal treatment plan for individual patients. Therefore, it is important to find a biomarker which allows for the stratification of HCC patients, which distinguishes responders from non-responders, thereby further improving the clinical benefits for those undergoing immunotherapy.MethodsWe used univariate and multivariate Cox risk proportional regression models to evaluate the relationship between non-synonymous mutations with a mutation frequency greater than 10%. We made a prognosis of an immunotherapy HCC cohort using mutation and prognosis data. An additional three HCC queues from the cbioportal webtool were used for further verification. The CIBERSORT, IPS, quanTIseq, and MCPcounter algorithms were used to evaluate the immune cells. PCA and z-score algorithm were used to calculate immune-related signature with published gene sets. Gene set enrichment analysis (GSEA) was used to compare the differences in the pathway-based enrichment scores of candidate genes between mutant and wild types.ResultsUnivariate and multivariate Cox results showed that only CTNNB1-Mutant(CTNNB1-MUT) was associated with progression-free survival (PFS) of HCC patients in the immunotherapy cohort. After excluding the potential bias introduced by other clinical features, it was found that CTNNB1-MUT served as an independent predictor of the prognosis of HCC patients after immunotherapy (P < 0.05; HR > 1). The results of the tumor immune microenvironment (TIME) analysis showed that patients with CTNNB1-MUT had significantly reduced activated immune cells [such as T cells, B cells, M1-type macrophages, and dendritic cells (DCs)], significantly increased M2-type macrophages, a significantly decreased expression of immunostimulating molecules, low activity of the immune activation pathways (cytokine pathway, immune cell activation and recruitment) and highly active immune depletion pathways (fatty acid metabolism, cholesterol metabolism, and Wnt pathway).ConclusionsIn this study, we found CTNNB1-MUT to be a potential biomarker for HCC immunotherapy patients, because it identified those patients are less likely to benefit from ICIs.

Highlights

  • Hepatocellular carcinoma (HCC) accounts for 75-85% of primary liver cancers, ranking sixth among the most common cancers, globally

  • The results showed that only CTNNB1-Mutant (CTNNB1-MUT) was related to a less favorable prognosis for HCC patients (HR = 6.51, P = 0.0006; Figure 2A)

  • The results showed that, in both the univariate and multivariate Cox risk ratio model, only CTNNB1-MUT is significantly associated with a worse clinical prognosis for HCC patients

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Summary

Introduction

Hepatocellular carcinoma (HCC) accounts for 75-85% of primary liver cancers, ranking sixth among the most common cancers, globally. It ranks fourth among cancer-related deaths, and has a morbidity and mortality of 4.7% and 8.2% [1], respectively. The ability to screen for the patients that would benefit and determine the best combination therapy for individual patients has become a major challenge for the treatment of HCC. The emergence of immune checkpoint inhibitors (ICIs) marks the beginning of a new era of immunotherapy for hepatocellular carcinoma (HCC), not all patients respond successfully to this treatment. It is important to find a biomarker which allows for the stratification of HCC patients, which distinguishes responders from non-responders, thereby further improving the clinical benefits for those undergoing immunotherapy

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