Association of DNA-Methylation Profiles With Immune Responses Elicited in Breast Cancer Patients Immunized With a Carbohydrate-Mimicking Peptide: A Pilot Study

Cinthia Violeta Hernandez Puente, Ping-Ching Hsu, Lora J Rogers, Fariba Jousheghany, Eric Siegel, Susan A Kadlubar, J Thaddeus Beck, Issam Makhoul, Laura F Hutchins, Thomas Kieber-Emmons, Behjatolah Monzavi-Karbassi, Cinthia Violeta Hernandez Puente, Ping-Ching Hsu, Lora J Rogers, Fariba Jousheghany, Eric Siegel, Susan A Kadlubar, J Thaddeus Beck, Issam Makhoul, Laura F Hutchins, Thomas Kieber-Emmons, Behjatolah Monzavi-Karbassi

Abstract

Immune response to a given antigen, particularly in cancer patients, is complex and is controlled by various genetic and environmental factors. Identifying biomarkers that can predict robust response to immunization is an urgent need in clinical cancer vaccine development. Given the involvement of DNA methylation in the development of lymphocytes, tumorigenicity and tumor progression, we aimed to analyze pre-vaccination DNA methylation profiles of peripheral blood mononuclear cells (PBMCs) from breast cancer subjects vaccinated with a novel peptide-based vaccine referred to as P10s-PADRE. This pilot study was performed to evaluate whether signatures of differentially methylated (DM) loci can be developed as potential predictive biomarkers for prescreening subjects with cancer who will most likely generate an immune response to the vaccine. Genomic DNA was isolated from PBMCs of eight vaccinated subjects, and their DNA methylation profiles were determined using Infinium® MethylationEPIC BeadChip array from Illumina. A linear regression model was applied to identify loci that were differentially methylated with respect to anti-peptide antibody titers and with IFN-γ production. The data were summarized using unsupervised-learning methods: hierarchical clustering and principal-component analysis. Pathways and networks involved were predicted by Ingenuity Pathway Analysis. We observed that the profile of DM loci separated subjects in regards to the levels of immune responses. Canonical pathways and networks related to metabolic and immunological functions were found to be involved. The data suggest that it is feasible to correlate methylation signatures in pre-treatment PBMCs with immune responses post-treatment in cancer patients going through standard-of-care chemotherapy. Larger and prospective studies that focus on DM loci in PBMCs is warranted to develop pre-screening biomarkers before BC vaccination. Clinical Trial Registration: www.ClinicalTrials.gov, Identifier: NCT02229084.

Keywords: DNA methylation; breast cancer; cancer vaccine; immune response; pilot study.

Copyright © 2020 Hernandez Puente, Hsu, Rogers, Jousheghany, Siegel, Kadlubar, Beck, Makhoul, Hutchins, Kieber-Emmons and Monzavi-Karbassi.

Figures

Figure 1
Figure 1
PCA plot and heatmap based on the 182 differentially methylated CpG loci (p ≤ 0.001) from the regression model considering IgG titers. (A) PCA scatter plot demonstrates separation according to the anti-peptide IgG levels. (B) Heatmap representing the similarities of DNA methylomes clustered according to their IgG levels (in fold change). The columns represent the CpG probes and rows the samples taken from indicated subjects. CpG probes with no changes in methylation have a value of zero and colored in black. Hypermethylated probes are displayed in red and hypomethylated probes in green. Subjects' study-numbers and fold increases in anti-peptide antibody titers are shown.
Figure 2
Figure 2
Potential biological roles of the differentially methylated genes (p ≤ 0.001) from the regression model considering IgG titers. (A) Affected canonical pathways based on the differentially methylated genes in the study. The line defining threshold with a score of 1.3 represents the -log (p-value 0.05) on the y-axis of the bar chart. The height of the bar is related to the significance on the overlap of the analyzed genes with the pathway, and the ratio indicates the number of uploaded genes over the total number of genes involved in each of the pathway. (B) Top network and overlay of two related canonical pathways. The network is related to cell death and survival, cell cycle, and cellular development. Nodes (genes) and edges (gene relationships) are described in the legend. The intensity of the node color (red) denotes the degree of upregulation (redder is more significant according to the p-value). Uncolored nodes were integrated into the network based on evidence stored in the Ingenuity Knowledge Base.
Figure 3
Figure 3
PCA plot and heatmap based on the 398 differentially methylated CpG loci (p ≤ 0.001) from the regression model considering IFN-γ levels. (A) PCA scatter plot demonstrates that DNA methylation signatures of the subjects are different according to their IFN-γ levels. (B) Heatmap representing the similarities of DNA methylomes clustered according to their cytokine levels. The columns represent the CpG probes and rows the samples taken from indicated subjects. CpG probes with no changes in methylation have a value of zero and colored in black. Hypermethylated probes are displayed in red and hypomethylated probes in green. Subjects' study-numbers and fold increases in IFN-γ levels are shown.
Figure 4
Figure 4
Potential biological roles of the differentially methylated genes (p ≤ 0.001) from the regression model considering IFN-γ levels. (A) Coloring and display are as in Figure 2A. (B) Top network and overlay of four related canonical pathways. The network is related to cellular movement, cell death and survival, cell-to-cell signaling and interaction. Coloring and display are as in Figure 2B.

References

    1. Hutchins LF, Makhoul I, Emanuel PD, Pennisi A, Siegel ER, Jousheghany F, et al. . Targeting tumor-associated carbohydrate antigens: a phase I study of a carbohydrate mimetic-peptide vaccine in stage IV breast cancer subjects. Oncotarget. (2017) 8:99161–78. 10.18632/oncotarget.21959
    1. Makhoul I, Hutchins L, Emanuel PD, Pennisi A, Siegel E, Jousheghany F, et al. . Moving a carbohydrate mimetic peptide into the clinic. Hum Vaccin Immunother. (2015) 11:37–44. 10.4161/hv.34300
    1. Klinger M, Farhan H, Just H, Drobny H, Himmler G, Loibner H, et al. . Antibodies directed against Lewis-Y antigen inhibit signaling of Lewis-Y modified ErbB receptors. Cancer Res. (2004) 64:1087–93. 10.1158/0008-5472.CAN-03-2435
    1. Liu J, Lin B, Hao Y, Qi Y, Zhu L, Li F, et al. . Lewis y antigen promotes the proliferation of ovarian carcinoma-derived RMG-I cells through the PI3K/Akt signaling pathway. J Exp Clin Cancer Res. (2009) 28:154. 10.1186/1756-9966-28-154
    1. Durbas M, Horwacik I, Boratyn E, Kamycka E, Rokita H. GD2 ganglioside specific antibody treatment downregulates PI3K/Akt/mTOR signaling network in human neuroblastoma cell lines. Int J Oncol. (2015) 47:1143–59. 10.3892/ijo.2015.3070
    1. Tsao CY, Sabbatino F, Cheung NK, Hsu JC, Villani V, Wang X, et al. . Anti-proliferative and pro-apoptotic activity of GD2 ganglioside-specific monoclonal antibody 3F8 in human melanoma cells. Oncoimmunology. (2015) 4:e1023975. 10.1080/2162402X.2015.1023975
    1. Feinberg AP. Phenotypic plasticity and the epigenetics of human disease. Nature. (2007) 447:433–40. 10.1038/nature05919
    1. Herranz M, Esteller M. DNA methylation and histone modifications in patients with cancer: potential prognostic and therapeutic targets. Methods Mol Biol. (2007) 361:25–62. 10.1385/1-59745-208-4:25
    1. Soares J, Pinto AE, Cunha CV, Andre S, Barao I, Sousa JM, et al. . Global DNA hypomethylation in breast carcinoma: correlation with prognostic factors and tumor progression. Cancer. (1999) 85:112–8. 10.1002/(SICI)1097-0142(19990101)85:1<112::AID-CNCR16>;2-T
    1. Pakneshan P, Szyf M, Farias-Eisner R, Rabbani SA. Reversal of the hypomethylation status of urokinase. (uPA) promoter blocks breast cancer growth and metastasis. J Biol Chem. (2004) 279:31735–44. 10.1074/jbc.M401669200
    1. Chekhun VF, Kulik GI, Yurchenko OV, Tryndyak VP, Todor IN, Luniv LS, et al. . Role of DNA hypomethylation in the development of the resistance to doxorubicin in human MCF-7 breast adenocarcinoma cells. Cancer Lett. (2006) 231:87–93. 10.1016/j.canlet.2005.01.038
    1. Feng W, Shen L, Wen S, Rosen DG, Jelinek J, Hu X, et al. . Correlation between CpG methylation profiles and hormone receptor status in breast cancers. Breast Cancer Res. (2007) 9:R57. 10.1186/bcr1762
    1. Chan TA, Glockner S, Yi JM, Chen W, Van Neste L, Cope L, et al. . Convergence of mutation and epigenetic alterations identifies common genes in cancer that predict for poor prognosis. PLoS Med. (2008) 5:e114. 10.1371/journal.pmed.0050114
    1. Hill VK, Hesson LB, Dansranjavin T, Dallol A, Bieche I, Vacher S, et al. . Identification of 5 novel genes methylated in breast and other epithelial cancers. Mol Cancer. (2010) 9:51. 10.1186/1476-4598-9-51
    1. Suarez-Alvarez B, Rodriguez RM, Fraga MF, Lopez-Larrea C. DNA methylation: a promising landscape for immune system-related diseases. Trends Genet. (2012) 28:506–14. 10.1016/j.tig.2012.06.005
    1. Schoenborn JR, Dorschner MO, Sekimata M, Santer DM, Shnyreva M, Fitzpatrick DR, et al. . Comprehensive epigenetic profiling identifies multiple distal regulatory elements directing transcription of the gene encoding interferon-gamma. Nat Immunol. (2007) 8:732–42. 10.1038/ni1474
    1. Mukasa R, Balasubramani A, Lee YK, Whitley SK, Weaver BT, Shibata Y, et al. . Epigenetic instability of cytokine and transcription factor gene loci underlies plasticity of the T helper 17 cell lineage. Immunity. (2010) 32:616–27. 10.1016/j.immuni.2010.04.016
    1. Murphy KM, Stockinger B. Effector T cell plasticity: flexibility in the face of changing circumstances. Nat Immunol. (2010) 11:674–80. 10.1038/ni.1899
    1. Lu KT, Kanno Y, Cannons JL, Handon R, Bible P, Elkahloun AG, et al. . Functional and epigenetic studies reveal multistep differentiation and plasticity of in vitro-generated and in vivo-derived follicular T helper cells. Immunity. (2011) 35:622–32. 10.1016/j.immuni.2011.07.015
    1. Thomas RM, Gamper CJ, Ladle BH, Powell JD, Wells AD. de novo DNA methylation is required to restrict T helper lineage plasticity. J Biol Chem. (2012) 287:22900–9. 10.1074/jbc.M111.312785
    1. Chan HW, Kurago ZB, Stewart CA, Wilson MJ, Martin MP, Mace BE, et al. . DNA methylation maintains allele-specific KIR gene expression in human natural killer cells. J Exp Med. (2003) 197:245–55. 10.1084/jem.20021127
    1. Gomez-Lozano N, Trompeter HI, de Pablo R, Estefania E, Uhrberg M, Vilches C. Epigenetic silencing of potentially functional KIR2DL5 alleles: Implications for the acquisition of KIR repertoires by NK cells. Eur J Immunol. (2007) 37:1954–65. 10.1002/eji.200737277
    1. Santos P, Arumemi F, Park KS, Borghesi L, Milcarek C. Transcriptional and epigenetic regulation of B cell development. Immunol Res. (2011) 50:105–12. 10.1007/s12026-011-8225-y
    1. Xu CR, Feeney AJ. The epigenetic profile of Ig genes is dynamically regulated during B cell differentiation and is modulated by pre-B cell receptor signaling. J Immunol. (2009) 182:1362–9. 10.4049/jimmunol.182.3.1362
    1. McCartney DL, Walker RM, Morris SW, McIntosh AM, Porteous DJ, Evans KL. Identification of polymorphic and off-target probe binding sites on the Illumina Infinium MethylationEPIC BeadChip. Genom Data. (2016) 9:22–4. 10.1016/j.gdata.2016.05.012
    1. Kramer A, Green J, Pollard J, Jr, Tugendreich S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics. (2014) 30:523–30. 10.1093/bioinformatics/btt703
    1. Castro F, Cardoso AP, Goncalves RM, Serre K, Oliveira MJ. Interferon-gamma at the crossroads of tumor immune surveillance or evasion. Front Immunol. (2018) 9:847 10.3389/fimmu.2018.00847
    1. Zimmermann MT, Oberg AL, Grill DE, Ovsyannikova IG, Haralambieva IH, Kennedy RB, et al. . System-wide associations between DNA-methylation, gene expression, and humoral immune response to influenza vaccination. PLoS One. (2016) 11:e0152034. 10.1371/journal.pone.0152034
    1. Zimmermann MT, Kennedy RB, Grill DE, Oberg AL, Goergen KM, Ovsyannikova IG, et al. . Integration of immune cell populations, mRNA-seq, and CpG methylation to better predict humoral immunity to influenza vaccination: dependence of mRNA-Seq/CpG methylation on immune cell populations. Front Immunol. (2017) 8:445. 10.3389/fimmu.2017.00445
    1. Gensous N, Franceschi C, Blomberg BB, Pirazzini C, Ravaioli F, Gentilini D, et a. Responders and non-responders to influenza vaccination: a DNA methylation approach on blood cells. Exp Gerontol. (2018) 105:94–100. 10.1016/j.exger.2018.01.019
    1. Verma D, Parasa VR, Raffetseder J, Martis M, Mehta RB, Netea M, et al. . Anti-mycobacterial activity correlates with altered DNA methylation pattern in immune cells from BCG-vaccinated subjects. Sci Rep. (2017) 7:12305. 10.1038/s41598-017-12110-2
    1. Hasso-Agopsowicz M, Scriba TJ, Hanekom WA, Dockrell HM, Smith SG. Differential DNA methylation of potassium channel KCa3.1 and immune signalling pathways is associated with infant immune responses following BCG vaccination. Sci Rep. (2018) 8:13086. 10.1038/s41598-018-31537-9
    1. Bhasin JM, Lee BH, Matkin L, Taylor MG, Hu B, Xu Y, et al. . Methylome-wide sequencing detects DNA hypermethylation distinguishing indolent from aggressive prostate cancer. Cell Rep. (2015) 13:2135–46. 10.1016/j.celrep.2015.10.078
    1. Hao X, Luo H, Krawczyk M, Wei W, Wang W, Wang J, et al. . DNA methylation markers for diagnosis and prognosis of common cancers. Proc Natl Acad Sci USA. (2017) 114:7414–9. 10.1073/pnas.1703577114
    1. Parashar S, Cheishvili D, Mahmood N, Arakelian A, Tanvir I, Khan HA, et al. . DNA methylation signatures of breast cancer in peripheral T-cells. BMC Cancer. (2018) 18:574. 10.1186/s12885-018-4482-7
    1. Pearce EL, Pearce EJ. Metabolic pathways in immune cell activation and quiescence. Immunity. (2013) 38:633–43. 10.1016/j.immuni.2013.04.005
    1. Pulendran B. Systems vaccinology: probing humanity's diverse immune systems with vaccines. Proc Natl Acad Sci USA. (2014) 111:12300–6. 10.1073/pnas.1400476111
    1. Le Bras GF, Taubenslag KJ, Andl CD. The regulation of cell-cell adhesion during epithelial-mesenchymal transition, motility and tumor progression. Cell Adh Migr. (2012) 6:365–73. 10.4161/cam.21326
    1. Heroux MS, Chesnik MA, Halligan BD, Al-Gizawiy M, Connelly JM, Mueller WM, et al. . Comprehensive characterization of glioblastoma tumor tissues for biomarker identification using mass spectrometry-based label-free quantitative proteomics. Physiol Genomics. (2014) 46:467–81. 10.1152/physiolgenomics.00034.2014
    1. Fu C, Jiang A. Generation of tolerogenic dendritic cells via the E-cadherin/beta-catenin-signaling pathway. Immunol Res. (2010) 46:72–8. 10.1007/s12026-009-8126-5
    1. van den Bossche J, Malissen B, Mantovani A, de Baetselier P, van Ginderachter JA. Regulation and function of the E-cadherin/catenin complex in cells of the monocyte-macrophage lineage and DCs. Blood. (2012) 119:1623–33. 10.1182/blood-2011-10-384289
    1. Schwartzkopff S, Grundemann C, Schweier O, Rosshart S, Karjalainen KE, Becker KF, et al. . Tumor-associated E-cadherin mutations affect binding to the killer cell lectin-like receptor G1 in humans. J Immunol. (2007) 179:1022–9. 10.4049/jimmunol.179.2.1022
    1. Mami-Chouaib F, Blanc C, Corgnac S, Hans S, Malenica I, Granier C, et al. . Resident memory T cells, critical components in tumor immunology. J Immunother Cancer. (2018) 6:87. 10.1186/s40425-018-0399-6
    1. Terry S, Savagner P, Ortiz-Cuaran S, Mahjoubi L, Saintigny P, Thiery JP, et al. . New insights into the role of EMT in tumor immune escape. Mol Oncol. (2017) 11:824–46. 10.1002/1878-0261.12093
    1. Chuang E, Alegre ML, Duckett CS, Noel PJ, Vander Heiden MG, Thompson CB. Interaction of CTLA-4 with the clathrin-associated protein AP50 results in ligand-independent endocytosis that limits cell surface expression. J Immunol. (1997) 159:144–51.
    1. Valk E, Rudd CE, Schneider H. CTLA-4 trafficking and surface expression. Trends Immunol. (2008) 29:272–9. 10.1016/j.it.2008.02.011
    1. Kwek SS, Cha E, Fong L. Unmasking the immune recognition of prostate cancer with CTLA4 blockade. Nat Rev Cancer. (2012) 12:289–97. 10.1038/nrc3223
    1. Sullivan SA, Zhu M, Bao S, Lewis CA, Ou-Yang CW, Zhang W. The role of LAT-PLCgamma1 interaction in gammadelta T cell development and homeostasis. J Immunol. (2014) 192:2865–74. 10.4049/jimmunol.1302493
    1. Perchet T, Petit M, Banchi EG, Meunier S, Cumano A, Golub R. The notch signaling pathway is balancing type 1 innate lymphoid cell immune functions. Front Immunol. (2018) 9:1252. 10.3389/fimmu.2018.01252
    1. Meng L, Bai Z, He S, Mochizuki K, Liu Y, Purushe J, et al. . The notch ligand DLL4 defines a capability of human dendritic cells in regulating Th1 and Th17 differentiation. J Immunol. (2016) 196:1070–80. 10.4049/jimmunol.1501310
    1. Meng L, Hu S, Wang J, He S, Zhang Y. DLL4(+) dendritic cells: key regulators of notch signaling in effector T cell responses. Pharmacol Res. (2016) 113:449–57. 10.1016/j.phrs.2016.09.001
    1. Janghorban M, Xin L, Rosen JM, Zhang XH. Notch signaling as a regulator of the tumor immune response: to target or not to target? Front Immunol. (2018) 9:1649 10.3389/fimmu.2018.01649
    1. Dehkhoda F, Lee CMM, Medina J, Brooks AJ. The growth hormone receptor: mechanism of receptor activation, cell signaling, and physiological aspects. Front Endocrinol. (Lausanne). (2018) 9:35. 10.3389/fendo.2018.00035
    1. Denduluri SK, Idowu O, Wang Z, Liao Z, Yan Z, Mohammed MK, et al. . Insulin-like growth factor. (IGF) signaling in tumorigenesis and the development of cancer drug resistance. Genes Dis. (2015) 2:13–25. 10.1016/j.gendis.2014.10.004
    1. Zong CS, Chan J, Levy DE, Horvath C, Sadowski HB, Wang LH. Mechanism of STAT3 activation by insulin-like growth factor I receptor. J Biol Chem. (2000) 275:15099–105. 10.1074/jbc.M000089200
    1. Terry MB, Delgado-Cruzata L, Vin-Raviv N, Wu HC, Santella RM. DNA methylation in white blood cells: association with risk factors in epidemiologic studies. Epigenetics. (2011) 6:828–37. 10.4161/epi.6.7.16500
    1. Reyngold M, Turcan S, Giri D, Kannan K, Walsh LA, Viale A, et al. . Remodeling of the methylation landscape in breast cancer metastasis. PLoS One. (2014) 9:e103896. 10.1371/journal.pone.0103896
    1. Flanagan JM, Wilson A, Koo C, Masrour N, Gallon J, Loomis E, et al. . Platinum-based chemotherapy induces methylation changes in blood DNA associated with overall survival in patients with ovarian cancer. Clin Cancer Res. (2017) 23:2213–22. 10.1158/1078-0432.CCR-16-1754
    1. Lund RJ, Huhtinen K, Salmi J, Rantala J, Nguyen EV, Moulder R, et al. . DNA methylation and transcriptome changes associated with cisplatin resistance in ovarian cancer. Sci Rep. (2017) 7:1469. 10.1038/s41598-017-01624-4
    1. Jung M, Pfeifer GP. Aging and DNA methylation. BMC Biol. (2015) 13:7. 10.1186/s12915-015-0118-4
    1. Xu X, Su S, Barnes VA, De Miguel C, Pollock J, Ownby D, et al. . A genome-wide methylation study on obesity: differential variability and differential methylation. Epigenetics. (2013) 8:522–33. 10.4161/epi.24506

Source: PubMed

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

3
Abonnieren