Tuesday 16th August 2022

Molecular Signature, Immune Landscape of HCV-Associated HCC | JHC

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Introduction

Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third leading cause of cancer-related death worldwide.1,2 The incidence is increasing worldwide, with an estimated global incidence rate of 9.3 per 100,000 person-year in 2018 and a corresponding mortality rate of 8.5.2 Thus, the incidence and mortality of HCC are roughly equivalent. Moreover, HCC has a poor 5-year survival,1 with a dramatic increase in mortality over the past decades in contrast with the decreasing mortality reported for several tumors.3 Although the pathogenesis of HCC remains to be elucidated, chronic infection with hepatitis C virus (HCV) and hepatitis B virus (HBV) account for 71% of all HCC cases worldwide.4,5 Both of these viruses are included in the list of carcinogenic viruses.6 Cirrhosis is the single most important risk factor for HCC, being present in more than 80% of the cases.7 The use of long-term antiviral therapy in patients with chronic hepatitis B and the success of direct-acting antivirals in eradicating HCV in over 95% of patients have significantly decreased but not eliminated the risk of HCC.8

HCV is an enveloped RNA virus belonging to the Flaviviridae family.9 HCV does not integrate into the host genome, and therefore it likely promotes hepatocarcinogenesis through chronic inflammation, liver regeneration and fibrosis rather than through a direct oncogenic effect.10,11 However, experiments in mouse models suggested that overexpression of the HCV core protein may promote hepatocarcinogenesis.12 HBV was the first virus associated with the development of HCC13 and is the leading cause of HCC worldwide.5 Although the availability of an effective vaccine against HBV holds promise for the elimination of HBV-associated HCC, more than 257 million chronic HBsAg carriers in the world are still at increased risk of developing cirrhosis and HCC.14 Of note, approximately 20% of HBV-related HCC cases do not present with cirrhosis, suggesting a direct oncogenic effect of HBV.15 HBV is the prototype member of the Hepadnaviridae family. It contains in its interior a nucleocapsid with the viral genome, a 3.2 Kb relaxed circular double-stranded DNA (dsDNA). After entry into hepatocytes, the nucleocapsid is released into the cytoplasm and migrates to the nucleus,16 where the viral genome can integrate into the host DNA, causing insertional mutagenesis and activation of cancer driver genes.17

HCC is one of the most heterogenous cancers comprising distinct molecular and clinical subgroups.18,19 Interestingly, it has been shown that only about 20% of HCC cases, regardless of the tumor etiology, show a high to moderate levels of immune cell infiltration while the vast majority displays a low degree of immune-cell infiltration.18,20,21 The immune microenvironment of HCC has been studied in recent years,20–24 but there is limited information on whether the immune-cell infiltration varies according to the different viral etiology. Given the involvement of the immune system in the pathogenesis of chronic viral hepatitis22–24 and the increasing use of immune-targeted therapies for solid tumors, it is important to investigate the immune landscape of HCC in order to identify differences and similarities among HCCs of different viral etiology. Access to a unique collection of paired liver specimens (tumor and surrounding nontumorous tissue) from 20 well-characterized Caucasian patients with HCV- and HBV-HCC, seen at a single center in Italy, gave us the unique opportunity to define the molecular signature and the immunologic landscape of these tumors to better understand their differences and similarities.

Materials and Methods

Patients

The study included liver specimens from 9 patients with HCV-associated HCC, 1 female and 8 males, aged 60 ± 8 years (mean ± SD), and 11 patients with HBV-associated HCC, 1 female and 10 males, aged 57.7 ± 7.7 years. The clinical, virologic, and histopathological features of the patients were previously reported.25,26 All patients were followed at the Liver Transplantation Center of the Brotzu Hospital in Cagliari, Italy. Informed consent was obtained from all subjects involved in the study. The protocol was approved by the ethical Committee of the Hospital Brotzu (Cagliari, Italy), and by the Office of Human Subjects Research of the NIH, granted on the condition that all samples were deidentified.

RNA-Sequencing

Total RNA from liver tissue was extracted from frozen liver specimens as described previously27 using TRIzol reagent (Invitrogen) according to the manufacturer’s recommendations. Total RNA quality and integrity were assessed using the Agilent 2100 Bioanalyzer. The mRNA libraries were constructed from 0.5 to 1 µg mRNA using the Illumina TruSeq RNA Sample Prep Kits, version 2. The resulting cDNA was fragmented using a Covaris E210. Library amplification was performed using 10 cycles to minimize the risk of over-amplification. Unique barcode adapters were applied to each library. Libraries were pooled in equimolar ratio and sequenced together on a HiSeq 2500 with ver 4 flow cells and sequencing reagents. At least 47 million 125-base read pairs were generated for each individual library. Data were processed using RTA 1.18.64 and CASAVA 1.8.2.

All RNA-sequencing (RNA-seq) samples had a raw yield of at least 60 million paired-end 125 base-pair reads. Data were processed using the Pipeliner workflow (https://github.com/CCBR/Pipeliner). Reads were trimmed to remove contaminating adapter sequences and low-quality bases using cutadapt28 and aligned to the human hg38 reference genome and Gencode release 28 using STAR v2.5.2b run in 2-pass mode.29 RSEM v1.3.030 was used for gene-level expression quantification, and limma v 3.40.231 was used for voom quantile normalization and paired differential expression analysis. Only genes passing the following cutoff were used for downstream analyses: more than 0.5 counts per million across at least nine or ten samples for HBV and HCV respectively. Selection criteria for transcriptomic analysis require genes to have fold-change greater than +1.0 or lower than −1.0, and FDR-adjusted P value less than 0.05. RNA-seq data have been deposited in the NCBI Sequence Read Archive (SRA) (accession number PRNJA719288).

Functional Immune Categories

We first selected the genes involved in the immune response from the bulk gene expression data (expressed as fold change between tumor and non-tumor tissue) based on a curated list of 828 genes encompassing cell surface markers, secreted proteins, and intracellular factors. This list was generated by combining gene ontology with two databases, Reactome32 and Ingenuity [QIAGEN Inc.]. To identify pathways among the differentially expressed immune genes, we utilized the WEB-based Gene SeT AnaLysis Toolkit (WebGestalt, version 2019) analysis using our curated list of immune genes as reference.33 In this web application, we selected the overrepresentation enrichment analysis (ORA), followed by pathway analysis using Reactome as database.32 We selected a multiple test adjustment with Benjamini & Hochberg (BH) correction and set a P-value < 0.05.

Immunohistochemistry

Immunohistochemical staining of formalin fixed paraffin-embedded liver sections was performed using a panel of antibodies including CD3, CD8, CD20, CD68 (Dako), alpha-SMA (Abcam), and CD163 (Novus Biologicals). Briefly, sections of 3 to 5 μm were deparaffinized through graded alcohols and xylene. Immunohistochemical staining was performed after antigen retrieval using either citrate buffer (10 mmol, pH 6.0) or EDTA (1 mmol, pH 9.0). Slides were incubated in Tris-goat serum (3%) for 15 min and then incubated at room temperature with primary antibodies. Detection was carried out on the automated system BenchMark XT autostainer (Ventana Medical Systems) according to the manufacturer’s instructions. Images were taken using an Olympus BX41 microscope, objective UPlanFI 20×/0.75, with an adaptor U-TV0.5xC using a digital camera Q-imaging Micropublisher 5.0 RTV. The images were captured using Q-Capture version 3.1.

Statistical Analysis

Pathway analysis was performed using Ingenuity Pathway Analysis (IPA, version 01–19-00, Qiagen Redwood City, www.qiagen.com/ingenuity). The association of genes to pathways was evaluated as the ratio between the number of genes present in the dataset and the total number of genes that map to the same pathway. The Fisher’s exact test was also used to…

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