| Literature DB >> 34968251 |
Maria Fortunata Lofiego1,2, Sara Cannito1,3, Carolina Fazio1, Francesca Piazzini1, Ornella Cutaia1, Laura Solmonese2,3, Francesco Marzani1, Carla Chiarucci1, Anna Maria Di Giacomo1,3, Luana Calabrò1, Sandra Coral1,2, Michele Maio1,3, Alessia Covre1,3.
Abstract
Malignant pleural mesothelioma (MPM) is an aggressive malignancy with a severe prognosis, and with a long-standing need for more effective therapeutic approaches. However, treatment with immune checkpoint inhibitors is becoming an increasingly effective strategy for MPM patients. In this scenario, epigenetic modifications may negatively regulate the interplay between immune and malignant cells within the tumor microenvironment, thus contributing to the highly immunosuppressive contexture of MPM that may limit the efficacy of immunotherapy. Aiming to further improve prospectively the clinical efficacy of immunotherapeutic approaches in MPM, we investigated the immunomodulatory potential of different classes of epigenetic drugs (i.e., DNA hypomethylating agent (DHA) guadecitabine, histone deacetylase inhibitors VPA and SAHA, or EZH2 inhibitors EPZ-6438) in epithelioid, biphasic, and sarcomatoid MPM cell lines, by cytofluorimetric and real-time PCR analyses. We also characterized the effects of the DHA, guadecitabine, on the gene expression profiles (GEP) of the investigated MPM cell lines by the nCounter platform. Among investigated drugs, exposure of MPM cells to guadecitabine, either alone or in combination with VPA, SAHA and EPZ-6438 demonstrated to be the main driver of the induction/upregulation of immune molecules functionally crucial in host-tumor interaction (i.e., HLA class I, ICAM-1 and cancer testis antigens) in all three MPM subtypes investigated. Additionally, GEP demonstrated that treatment with guadecitabine led to the activation of genes involved in several immune-related functional classes mainly in the sarcomatoid subtype. Furthermore, among investigated MPM subtypes, DHA-induced CDH1 expression that contributes to restoring the epithelial phenotype was highest in sarcomatoid cells. Altogether, our results contribute to providing the rationale to develop new epigenetically-based immunotherapeutic approaches for MPM patients, potentially tailored to the specific histologic subtypes.Entities:
Keywords: DNA methylation; epigenetic drugs; immunotherapy; malignant pleural mesothelioma
Year: 2021 PMID: 34968251 PMCID: PMC8715476 DOI: 10.3390/epigenomes5040027
Source DB: PubMed Journal: Epigenomes ISSN: 2075-4655
Figure 1Flow cytometry analysis of changes induced by different epigenetic drugs in HLA class I and ICAM-1 molecules expression. MPM cell lines untreated or treated with guadecitabine, VPA, SAHA, EPZ-6438, or guadecitabine-based combinations were incubated with (A) anti-human HLA class I or (B) anti-human ICAM-1 mAbs and studied by flow cytometry. Data obtained were analyzed by Kaluza software. Values reported correspond to MFI in treated vs. untreated cells. Each data point represents mean value of MFI obtained in 3 independent experiments for each single cell line.
Figure 2Quantitative real-time PCR analysis of changes induced by different epigenetic drugs in CTA expression. Total RNA was extracted from MPM cell lines, untreated or treated with guadecitabine, VPA, SAHA, EPZ-6438, or guadecitabine-based combinations. Quantitative real-time PCR analyses were performed on retrotranscribed total RNA, utilizing CTA- and β-actin-specific primers. Scatter plots report mean number of molecules of (A) NY-ESO-1, (B) MAGE-A1, and (C) MAGE-A3 in treated and untreated MPM cell lines. Values are reported as specific CTA/β-actin mRNA molecules. Each data point represents mean value of molecules obtained in 3 independent experiments for each single cell line. Dotted line represents the cut-off of positive gene expression value ≥ 10 × 10−4. Full shape, p-Value ≤ 0.05 (calculated by Student t) vs. untreated cells; empty shape, p-Value >0.05.
Figure 3Quantitative real-time PCR analysis of changes induced by different epigenetic drugs in the expression of cadherin-coding genes. Total RNA was extracted from MPM cell lines, untreated or treated with guadecitabine, VPA, SAHA, EPZ-6438, and guadecitabine-based combinations. Quantitative real-time PCR analyses were performed on retrotranscribed total RNA, utilizing CDH1-, CDH2- and β-actin-specific primers. Scatter plots report the mean number of molecules of (A) CDH1 and (B) CDH2 in treated and untreated MPM cell lines. Values are reported as specific cadherins/β-actin gene. Each data point represents the mean value of molecules obtained in 3 independent experiments for each single cell line. The dotted line represents the cut-off of positive gene expression value ≥10 × 10−4. Full shape, p-Value ≤0.05 (calculated by Student t) vs. untreated cells; empty shape, p-Value >0.05.
Figure 4Bar graphs of the most frequently modulated canonical pathways and upstream regulators in MPM cell lines treated with guadecitabine. The gene expression profile of 10 MPM cell lines untreated or treated with guadecitabine was evaluated by NanoString nCounter profiler. Data analysis (log2 ratio of treated vs. untreated cell lines) was elaborated through IPA software, filtered by Z-score ≥ 2, and cumulated based on the frequency of modulation (activation and inhibition) for canonical pathways (A) and upstream regulators (B).
Figure 5Modulation of specific functional classes of genes by guadecitabine treatment in MPM cell lines. A color scale was used to depict the predominant up- (red) or down- (green) regulation of genes belonging to the selected functional classes calculated as follow: (%up − %down) × (%up + %down)/100.
Figure 6Correlation among changes in the expression values of selected genes after guadecitabine treatment from nCounter and quantitative real-time PCR analyses. Quantitative real-time PCR assays were performed in 10 untreated and guadecitabine-treated MPM cell lines to quantify the expression of 9 randomly selected genes. Mean values of FC (mFC) induced by guadecitabine in the constitutive levels of gene-specific mRNA expression were correlated to the mFC values obtained by the Nanostring analysis using Pearson’s correlation coefficient.
Primer sequences sets for quantitative real-time PCR analysis.
| Forward Sequence | Reverse Sequence | |
|---|---|---|
| NY-ESO-1 | 5′-TGCTTGAGTTCTACCTCGCCA-3′ | 5′-TATGTTGCCGGACACAGTGAA-3′ |
| MAGE-A1 | 5′-GCCAAGCACCTCTTGTATCCTG-3′ | 5′-GGAGCAGAAAACCAACCAAATC-3′ |
| MAGE-A3 | 5′-TGTCGTCGGAAATTGGCAGTAT-3′ | 5′-CAAAGACCAGCTGCAAGGAACT-3′ |
| CDH1 | 5′-AGAGACTGGGTTATTCCTCC-3′ | 5′-GGATTTGATCTGAACCAGGT-3′ |
| CDH2 | 5′-CCTTTCAAACACAGCCACGG-3′ | 5′-TGTTTGGGTCGGTCTGGATG-3′ |
| IL-1β | 5′-ACTTGTTCTTTGAAGCTGATGGC-3′ | 5′-CTGTAGTGGTGGTCGGAGATTC-3′ |
| IFN-γ | 5′-CAGGTCATTCAGATGTAGCGGAT-3′ | 5′-ATGTCTTCCTTGATGGTCTCCAC-3′ |
| IFNGR | 5′-CATCACGTCATACCAGCCATTT-3′ | 5′-ATGTCTTCCTTGATGGTCTCCAC-3′ |
| IL-6 | 5′-AACCTGAACCTTCCAAAGATGG-3′ | 5′-TCTGGCTTGTTCCTCACTACT-3′ |
| IL-10 | 5′-CGGCGCTGTCATCGATTT-3′ | 5′-TTAAAGGCATTCTTCACCTGCTC-3′ |