Abstract

BackgroundThe tumor immunological microenvironment (TIME) has a prominent impact on prognosis and immunotherapy. However, the heterogeneous TIME and the mechanisms by which TIME affects immunotherapy have not been elucidated in hepatocellular carcinoma (HCC).MethodsA total of 2195 eligible HCC patients from TCGA and GEO database were collected. We comprehensively explored the different heterogeneous TIME phenotypes and its clinical significance. The potential immune escape mechanisms and what genomic alterations may drive the formation of different phenotypes were further investigated.ResultsWe identified three phenotypes in HCC: TIME-1, the “immune-deficiency” phenotype, with immune cell depletion and proliferation; TIME-2, the “immune-suppressed” phenotype, with enrichment of immunosuppressive cells; TIME-3, the “immune-activated phenotype”, with abundant leukocytes infiltration and immune activation. The prognosis and sensitivity to both sorafenib and immunotherapy differed among the three phenotypes. We also underlined the potential immune escape mechanisms: lack of leukocytes and defective tumor antigen presentation capacity in TIME-1, increased immunosuppressive cells in TIME-2, and rich in immunoinhibitory molecules in TIME-3. The different phenotypes also demonstrated specific genomic events: TIME-1 characterized by TP53, CDKN2A, CTNNB1, AXIN1 and FOXD4 alterations; TIME-2 characterized by significant alteration patterns in the PI3K pathway; TIME-3 characterized by ARID1A mutation. Besides, the TIME index (TI) was proposed to quantify TIME infiltration pattern, and it was a superior prognostic and immunotherapy predictor. A pipeline was developed to classify single patient into one of these three subtypes and calculated the TI.ConclusionsWe identified three TIME phenotypes with different clinical outcomes, immune escape mechanisms and genomic alterations in HCC, which could present strategies for improving the efficacy of immunotherapy. TI as a novel prognostic and immunotherapeutic signature that could guide personalized immunotherapy and clinical management of HCC.

Highlights

  • The tumor immunological microenvironment (TIME) has a prominent impact on prognosis and immunotherapy

  • We identified the epigenetically silenced genes (ESGs) using the following criteria: (1) excluding the CpG sites methylated in normal tissues or less than 10% of the tumor samples; (2) the DNA methylation data was divided into the methylation group and unmethylation group, according to the cutoff (β-value = 0.3); (3) for each probe, if the difference between the corresponding gene mean expression in the unmethylated group and that in the methylated group was > 1.64 standard deviations of the unmethylated group, the probe would be labeled as epigenetically silenced; (4) when multiple probes were assigned to the same gene, the gene with more than half of the corresponding probes were labeled as epigenetically silenced, and identified as ESG

  • Based on the infiltration profiles of 24 immune cells in TIME, 1,821 hepatocellular carcinoma (HCC) samples were classified into three TIME phenotypes (TIME-1 = 721, TIME-2 = 530, TIME-3 = 570)

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Summary

Introduction

The tumor immunological microenvironment (TIME) has a prominent impact on prognosis and immunotherapy. Hepatocellular carcinoma (HCC) is the dominant histologic type of primary liver cancer, with a high incidence and mortality rate [1]. There are various therapeutic modalities for HCC, including surgical resection, chemotherapy, radiofrequency ablation and liver transplantation, its recurrence rate and prognosis remains unsatisfactory [2, 3]. Immunotherapy has made great progress as a new treatment method in HCC. To date, this only benefited a subset of patients [4, 5]. The insufficient understanding of the tumor immunological microenvironment (TIME) may be the main reason for disappointing results. HCC has significant TIME heterogeneity, and the comprehensive understanding of the heterogeneity was crucial for clinical diagnosis, personalized treatment and prognosis prediction in HCC [6]

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