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Whole genome DNA methylation signature of HER2positive breast cancer a

a

b

Breezy M Lindqvist , Sten Wingren , Parviz B Motlagh & Torbjörn K Nilsson a

b

School of Health and Medical Sciences; Örebro University; Örebro, Sweden

b

Department of Medical Biosciences/Clinical Chemistry; Umeå University; Umeå, Sweden Published online: 08 Jul 2014.

Click for updates To cite this article: Breezy M Lindqvist, Sten Wingren, Parviz B Motlagh & Torbjörn K Nilsson (2014) Whole genome DNA methylation signature of HER2-positive breast cancer, Epigenetics, 9:8, 1149-1162, DOI: 10.4161/epi.29632 To link to this article: http://dx.doi.org/10.4161/epi.29632

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Research Paper

Research Paper

Epigenetics 9:8, 1149–1162; August 2014; © 2014 Landes Bioscience

Whole genome DNA methylation signature of HER2-positive breast cancer Breezy M Lindqvist1,*, Sten Wingren1, Parviz B Motlagh2, and Torbjörn K Nilsson2,* School of Health and Medical Sciences; Örebro University; Örebro, Sweden; 2Department of Medical Biosciences/Clinical Chemistry; Umeå University; Umeå, Sweden

1

In order to obtain a comprehensive DNA methylation signature of HER2-positive breast cancer (HER2+ breast cancer), we performed a genome-wide methylation analysis on 17 HER2+ breast cancer and compared with ten normal breast tissue samples using the Illumina Infinium HumanMethylation450 BeadChip (450K). In HER2+ breast cancer, we found altered DNA methylation in genes involved in multicellular development, differentiation and transcription. Within these genes, we observed an overrepresentation of homeobox family genes, including several genes that have not been previously reported in relation to cancer (DBX1, NKX2–6, SIX6). Other affected genes included several belonging to the PI3K and Wnt signaling pathways. Notably, HER2, AKT3, HK1, and PFKP, genes for which altered methylation has not been previously reported, were also identified in this analysis. In total, we report 69 candidate biomarker genes with maximum differential methylation in HER2+ breast cancer. External validation of gene expression in a selected group of these genes (n = 13) revealed lowered mean gene expression in HER2+ breast cancer. We analyzed DNA methylation in six top candidate genes (AKR1B1, INA, FOXC2, NEUROD1, CDKL2, IRF4) using EpiTect Methyl II Custom PCR Array and confirmed the 450K array findings. Future clinical studies focusing on these genes, as well as on homeobox-containing genes and HER2, AKT3, HK1, and PFKP, are warranted which could provide further insights into the biology of HER2+ breast cancer.

Introduction Breast cancer is the most frequently diagnosed type of cancer in women, with an estimated 1.38 million new cases per year worldwide.1 Approximately 15–30% of breast cancers are associated with human epidermal growth factor receptor 2 (HER2) gene amplification or overexpression (henceforth termed HER2+ breast cancer), which is associated with poor prognosis.2,3 HER receptors, when activated, dimerize and activate the downstream signaling through multiple pathways, including phosphotidylinositol 3-kinase (PI3K)-activated protein kinase B (AKT) pathway, resulting in induction of cell cycle progression/survival, inhibition of apoptosis, promotion of angiogenesis, and invasion.4,5 Although the HER2 receptor has no known natural ligand, it is the preferred dimerization partner for other HER family members.6 Slower endocytosis and faster recycling of HER2-containing heterodimers, following lysosomal internalization, have been reported to promote a more potent and sustained signaling.7 HER2+ breast cancers are treated with targeted drugs (trastuzumab, lapatinib) along with chemotherapy. Resistance to trastuzumab8,9 has been found to be due to increased signaling through the PI3K/AKT pathway. Genetic alterations in the PI3K pathway are also implicated in reduced response to lapatinib treatment.10 Methylation changes in CpG islands (CGI) and CpG shores (low CpG density areas ~2 Kb close to CGI) affect gene expression.11

In addition to such local changes, global hypomethylation and chromosomal instabilities have been found in cancers.12-14 Altered methylation is thought to be an early event in breast cancer and the number of genes affected increases with progression.15-17 Epigenetic changes like methylation may also affect key players in the PI3K/ AKT pathway.18-20 Although genome-wide copy number alterations have been previously studied,21 little is known so far about genome-wide methylation changes in HER2+ breast cancer. Terada et al. reported an association between increased number of methylated genes and HER2 amplification in breast cancer.22 Also, we have recently identified methylation changes in the SLC25A43 gene and found relations to clinico-pathological features in HER2+ breast cancer.23 This motivated us to explore whole-genome DNA methylation in HER2+ breast tumor tissues using the Illumina Infinium HumanMethylation450 BeadChip. The aim was to obtain a comprehensive epigenetic signature of the HER2+ breast cancer tissue compared with normal breast tissue.

Results Data from a total of 466 255 CpGs was obtained using the HumanMethylation450 BeadChip from a study group of 17 HER2+ breast tumors and ten normal breast tissues (probe call

*Correspondence to Breezy M Lindqvist; Email: [email protected]; Torbjörn K Nilsson, Email: [email protected] Submitted: 11/18/2013; Revised: 06/16/2014; Accepted: 06/18/2014; Published Online: 07/08/2014 http://dx.doi.org/10.4161/epi.29632 www.landesbioscience.com Epigenetics 1149

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Keywords: DNA methylation, HER2-positive breast cancer, Illumina Infinium HumanMethylation450 BeadChip

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Figure 1. Unsupervised hierarchical clustering of samples based on the β values from the CpG loci. Columns represent the samples (17 cancer tissues and 10 normal tissues) and the rows represent CpGs. Color represents the β value (green = low and red = high).

rate > 99% for all samples). Principal component analysis (PCA) showed separation of breast cancer tissues and normal tissues and indicated differences in DNA methylation between these two groups (Fig. S1). Unsupervised hierarchical clustering confirmed distinctly different patterns of DNA methylation between cancer and normal breast tissues in about 3000 CpGs (Fig. 1). Overview of cancer related DNA methylation changes We have found 144 530 CpGs (30.1%) to be differentially methylated in HER2+ breast tumor tissues compared with normal breast tissues. Applying the criteria Δβ > |0.5|, we found 1294 hypomethylated (Table S1) and 5519 hypermethylated CpGs (Table S2). Figure 2 is an overview of all hypo- and hypermethylated CpGs in HER2+ breast cancer tissues. Hypo- and hyper-methylation in breast cancer tissues affected functional genomic regions, RNA transcripts, CpG shores and CpG shelves in a similar pattern. However, hypermethylation was overrepresented in CpG islands. Hypo- and hyper-methylation in breast cancer tissues was distributed in all chromosomes, with chromosome 1 harboring the highest number of these CpGs. Homeobox family of genes undergoes altered DNA methylation in HER2+ breast cancer In order to understand the functional role of aberrantly methylated genes, Database for Annotation, Visualization and Integrated Discovery (DAVID)24 was used. Functional analysis was performed on 650 hypomethylated and 1505 hypermethylated UCSC reference genes (as described in the data analysis section). For the hypomethylated genes, the most enriched cluster of genes was involved in intracellular signaling cascades, but did not reach statistical significance after correcting for multiple testing. For hypermethylated genes, the top three enriched clusters were involved in multicellular development, neurogenesis/differentiation, and transcription and are given in Table 1 (details are given in Table S3). These genes were overrepresented by the homeobox family of genes and site-wise analysis revealed hypermethylation in 106 homeobox genes (474 CpG sites), of which 50 genes had their CpGs located mostly on the islands in traditional promoter regions. Regional analysis revealed that hypermethylation affected traditional promoter region in 22 homeobox genes (Table S4). Hypomethylation in six homeobox genes (seven CpG sites) were located exclusively in the gene body, mostly in N_shore and N_shelves and did not affect traditional promoter regions. Site-wise analyses of hypoand hyper-methylated homeobox genes are given in Table S5. Thus, both regional and site-wise analyses strongly implicate homeobox genes as being differentially methylated in HER2+ breast cancer. Candidate biomarker genes in HER2 + breast cancer In order to identify genes that might be involved in HER2+ breast carcinogenesis, hypo- or hyper-methylated genes were selected, if aberrant DNA methylation was found in at least four of the interrogated CpG probes (detailed criteria for selection is given in the data analysis section). This resulted in one hypomethylated gene, TSTD1, and 68 hypermethylated candidate genes (Table S6). Regional analysis showed that methylation affected traditional promoter region in most of these candidate

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Figure 2. Methylation profile of the HER2+ breast cancer tissues. (A) Percentage of differentially methylated CpGs (adjusted P value < 0.05, Δβ ≠ 0.00) in cancer tissues when compared with normal breast tissues. (B) Percentage of hyper-/hypo-methylated CpGs (Δβ > |0.5|) within the differentially methylated CpGs. Percentage distribution of hyper-/hypo-methylated CpGs based on, (C) functional genomic distribution in the traditional promoter region (sum of the total number of CpGs located within 200 bp or 1500 bp upstream of the TSS, at 5′ UTR and in the 1st exon), gene body and 3′ UTR; (D) related RNA transcripts; (E) and CpG location. (F) Chromosomal distribution of hyper-/hypo-methylated CpGs. TSS - transcription start site; UTR – untranslated region; N_shore- upstream CpG shore; N_shelf – upstream CpG shelf; S_shore – downstream CpG shore; S_shelf – downstream CpG shelf; NA- Not available. *Percentages do not add up to 100 due to the exclusion of CpGs that do not fulfill the criteria, Δβ > |0.5|.

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Table 1. Functional annotation clustering of hypermethylated genes in HER2+ breast cancer using DAVID bioinformatics tool Number of genes

P value

Adjusted P value

GO:0007275~multicellular organismal development

483

< 0.001

< 0.001

GO:0048731~system development

421

< 0.001

< 0.001

GO:0048856~anatomical structure development

442

< 0.001

< 0.001

GO:0032502~developmental process

500

< 0.001

< 0.001

GO:0032501~multicellular organismal process

583

< 0.001

< 0.001

GO:0022008~neurogenesis

153

< 0.001

< 0.001

GO:0030182~neuron differentiation

124

< 0.001

< 0.001

GO:0030182~neuron differentiation

124

< 0.001

< 0.001

GO:0048699~generation of neurons

143

< 0.001

< 0.001

GO:0003700~transcription factor activity

209

< 0.001

< 0.001

GO:0003700~transcription factor activity

209

< 0.001

< 0.001

GO:0030528~transcription regulator activity

248

< 0.001

< 0.001

GO:0030528~transcription regulator activity

248

< 0.001

< 0.001

GO:0006355~regulation of transcription, DNA-dependent

267

< 0.001

< 0.001

GO:0006355~regulation of transcription, DNA-dependent

267

< 0.001

< 0.001

GO:0051252~regulation of RNA metabolic process

270

< 0.001

< 0.001

GO:0051252~regulation of RNA metabolic process

270

< 0.001

< 0.001

GO Term Multicellular development (Enrichment score 65.48)

Transcription (Enrichment score 26.12)

Functional annotation clustering was performed under high stringency; *P values obtained using modified Fisher’s exact test and after adjusting for multiple testing using Benjamini method.

genes. The list of candidate genes with their site-wise and regionwise methylation profile is given in Table 2. STRING functional protein interaction network (9.1)25 analyses of hypermethylated candidate genes identified a large protein network. Most of the proteins in this network were coded by homeobox genes (Fig. 3). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis generated by STRING functional protein interaction network showed that hypermethylated candidate genes were involved in signaling pathways important in cancer, such as neural signaling, metabolic pathways, Wnt signaling, leukocyte transendothelial migration, and tight junction (Table 4). When DNA methylation was analyzed using methylation-sensitive RFLP analysis in six of the top candidate genes (AKR1B1, INA, FOXC2, NEUROD1, CDKL2, IRF4), frequent higher methylation was observed in an independent set of HER2+ breast tumors in comparison to normal breast tissues, as had been found using the 450K array (Fig. 4). External validation of gene expression of top candidate biomarker genes In the final step of data reduction (as shown in the Data analysis section), we selected top candidate biomarker genes, a group dominated by genes involved in transcription (HIST1H4, NEUROD1, IRF4, MIR129–2, FOXC2, POU4F1) and cell differentiation (NEUROD1, INA, IRF4, WDR69). Top candidate biomarker genes in HER2+ breast cancer and their cancer-related references are given in Table S7. Gene expression analysis of these

genes using a data set from Pau Ni I.B. et al.26 revealed lowered mean expression of most of these genes in HER2+ breast tumors in comparison to normal breast tissues, of which INA and FOXC2 were statistically significant (crude *P value ≤ 0.05). In contrast, when the total cohort of patients was analyzed (HER2+ and HER2 negative) there was no such consistent pattern between the total cohort and normal breast tissues (Table 3). Aberrant DNA methylation affects PI3K/AKT and Wnt signaling pathways in HER2+ breast cancer Hypo-or hyper-methylated genes were further verified for their involvement in HER2 related signaling pathways by deductive analysis. Alteration of methylation affected the genes of the PI3K/ AKT pathway and the Wnt signaling pathway and is graphically represented in Figure S2. The methylation status of these genes is given in Tables S1 and S2. Though HER2, AKT3, HK1, and PFKP have not been reported to show altered methylation in cancer, site-wise analysis revealed hypomethylation of CpGs in the gene bodies of these genes. Interestingly,in the case of HER2 hypomethylation affected CpGs on the 5′UTR and TSS1500 in the gene.

Discussion In this study we describe the methylation pattern of 466 255 CpGs in HER2+ breast cancer tissues and normal breast tissues

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Neurogenesis/differentiation (Enrichment score 36.68)

Table 2. Complete list of candidate biomarker genes and their methylation profile in HER2+ breast cancer (continued)

Gene name

Methylation profile at traditional promoter b

Region

Adjusted P value

Median Δβ

HIST1H4F

Histone cluster 1, H4a

5/9 (55.55%)

TSS200 Exon1

< 0.001 < 0.001

0.61 0.53

INA c

internexin neuronal intermediate filament protein, α

6/14 (42.9%)

TSS1500 TSS200 Exon1

< 0.001 < 0.001 < 0.001

0.69 0.62 0.53

NEUROD1 c

neuronal differentiation 1

7/18 (38.9%)

UTR5

< 0.001

0.52

IRF4

Interferon regulatory factor 4

7/18 (38.88%)

UTR5 Exon1

< 0.001 < 0.001

0.64 0.76

AKR1B1

Aldo-ketoreductase

7/19 (36.8%)

TSS1500 TSS200

< 0.001 < 0.001

0.58 0.54

MIR129–2

microRNA 129–2

4/11 (36.36%)

TSS200

< 0.001

0.72

FOXC2

forkhead box C2 (MFH-1, mesenchyme forkhead)

5/14 (35.7%)

NA

NA

NA

WDR69

dynein assembly factor with WDR repeat domains 1

5/14 (35.7%)

UTR5 TSS200

< 0.001 < 0.001

0.61 0.57

C1orf114

chromosome 1 open reading frame 114

7/21 (33.3%)

TSS200 Exon1

< 0.001 < 0.001

0.76 0.89

CPXM1

carboxy peptidase X (M14 family), member 1

5/15 (33.3%)

UTR5 TSS200 Exon1

< 0.001 < 0.001 < 0.001

0.60 0.59 0.64

POU4F1 c

POU class 4 transcription factor 1

6/19 (31.57%)

TSS200

< 0.001

0.53

CDKL2

cyclin-dependent kinase-like 2

5/16 (31.25%)

TSS200

< 0.001

0.54

GABRA4

Gamma-aminobutyric acid receptor A 4

5/15 (30%)

TSS200

< 0.001

0.61

TTYH1

tweety homolog 1 (Drosophila)

5/17 (29.4%)

TSS200

< 0.001

0.54

CDO1

cysteine dioxygenase, type 1

5/17 (29%)

TSS200

< 0.001

0.64

DDX25

DEAD (Asp-G×lu-AlaAsp) box helicase 25

4/14 (28.57%)

TSS200 Exon1

< 0.001 < 0.001

0.54 0.52

DOK5

Docking protein 5

4/14 (28.57%)

TSS200

< 0.001

0.50

POU4F2 c

POU class 4 transcription factor 2

4/14 (28.57%)

NA

NA

NA

RASL10A

RAS-like, family 10, member A

4/14 (28.57%)

TSS200

< 0.001

0.61

PUS3

pseudouridylate synthase 3

4/15 (26.7%)

TSS1500

< 0.001

0.52

CHST2

carbohydrate (keratan sulfate Gal-6) sulfotransferase 1

5/19 (26.3%)

UTR5

< 0.001

0.50

Based on site-wise analysis; bbased on regional analysis, adjusted *P value (Benjamini-Hochberg method); cpresent in the large protein network identified by STRING; hypomethylated gene given in bold letters; NA – not available. Detailed site-wise analysis of candidate biomarker genes is presented in Table S6. a

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Gene symbol

Number of significantly aberrant methylated sites/Total number of sites (%)a

HOXA9

homeobox A9

7/27 (25.9%)

TSS200

< 0.001

0.69

VWC2

von Willebrand factor C domain containing 2

7/27 (25.9%)

TSS200 Exon1

< 0.001 < 0.001

0.64 0.56

TSTD1

thiosulfurtransferase (rhodanase)-like domain containing 1

4/16 (25%)

TSS200

< 0.001

-0.52

CLIP4

CAP-GLY domain containing linker protein family, member 4

5/20 (25%)

TSS1500 TSS200

< 0.001 < 0.001

0.51 0.59

MSC c

activated B-cell factor 1

6/24 (25%)

TSS200 Exon1

< 0.001 < 0.001

0.50 0.60

TMEM155

transmembrane protein 155

4/17 (23.5%)

TSS200

< 0.001

0.57

HOXA1 c

homeobox A1

4/18 (22.2%)

TSS1500

< 0.001

0.56

PHOX2A c

Paired-like homeobox 2a

5/23, 21.7%

TSS1500

< 0.001

0.56

USP44

ubiquitin specific peptidase 44

4/19, 21.05%

UTR5 TSS1500

< 0.001 < 0.001

0.66 0.56

NEFM

neurofilament, medium polypeptide

5/24, 20.8%

TSS200 Exon1

< 0.001 < 0.001

0.51 0.51

CLD10

claudin 10

6/30, 20%

NA

NA

NA

OTX2 c

orthodenticle homeobox 2

6/30, 20%

NA

NA

NA

SIX6 c

SIX homeobox 6

4/20, 20%

Exon1

< 0.001

0.54

DNM3

dynamin 3

9/46, 19.56%

UTR5 TSS1500 Exon1

0.001 < 0.001 0.001

0.54 0.74 0.56

ALDH1L1

aldehyde dehydrogenase 1 family, member L1

6/31, 19.35%

TSS200

0.001

0.52

DMRTA2

doublesex-and MB3related transcription factor A2

6/31, 19.35%

TSS200 Exon1

< 0.001 < 0.001

0.54 0.56

GALR1

galnin receptor 1

6/31, 19.35%

UTR5 TSS200 Exon1

< 0.001 < 0.001 < 0.001

0.58 0.54 0.50

PCDH8

protocadherin 8

5/26, 19.23%

UTR5 TSS200

< 0.001 < 0.001

0.60 0.55

LYPD5

LY6/PLAUR domain containing 5

5/26, 19.2%

NA

NA

NA

NID2

nidogen 2

5/26, 19.2%

UTR5 TSS1500 Exon1

< 0.001 < 0.001 < 0.001

0.52 0.58 0.52

NKX2–6

NK2 homeobox 6

4/21, 19.04%

TSS1500 TSS200 Exon1

< 0.001 < 0.001 < 0.001

0.52 0.59 0.55

PDPN

Podoplanin glycoprotein

4/21, 19.04%

NA

NA

NA

DBX1 c

developing brain homeobox 1

5/27, 18.5%

TSS200 Exon1

< 0.001 < 0.001

0.59 0.83

Based on site-wise analysis; bbased on regional analysis, adjusted *P value (Benjamini-Hochberg method); cpresent in the large protein network identified by STRING; hypomethylated gene given in bold letters; NA – not available. Detailed site-wise analysis of candidate biomarker genes is presented in Table S6. a

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Table 2. Complete list of candidate biomarker genes and their methylation profile in HER2+ breast cancer (continued)

APC

adenomatous polyposis coli

7/39, 17.9%

UTR5

< 0.001

0.50

FEZF2 c

FEZ family, zinc finger 2

5/28, 17.85%

NA

NA

NA

TOX2

TOX high mobility group family member 2

5/30, 16.7%

NA

NA

NA

SFRP2 c

secreted frizzled protein 2

7/43, 16.27%

TSS200

< 0.001

0.57

SALL3

sal-like 3 (Drosophila)

4/26, 15.4%

TSS1500

< 0.001

0.54

GRIA4

glutamate receptor, ionotropic, AMPA 4

4/26, 15.38%

NA

NA

NA

NKX6–2

NK6 homeobox 2

5/33, 15.15%

NA

NA

NA

SFTA3

surfactant associated 3

4/29, 13.8%

NA

NA

NA

PRKCB

Protein kinase C, β

5/37, 13.5%

UTR5 Exon1

< 0.001 < 0.001

0.72 0.67

FLI1

Friend leukemia virus integration 1

7/53, 13.2%

Exon1

< 0.001

0.53

FLRT2

fibronectin leucine rich transmembrane protein 2

6/49, 12.24%

Exon1

< 0.001

0.60

NRN1

neuritin 1

5/42, 11.9%

TSS200

< 0.001

0.51

RASGRF2

Ras protein-specific guanine nucleotidereleasing factor 2

4/34, 11.76%

NA

NA

NA

TLX1 c

T-cell leukemia homeobox 1

4/34, 11.76%

NA

NA

NA

TBR1 c

T-box, brain, 1

4/37, 10.8%

NA

NA

NA

MAST1

microtubule associated serine/ threonine kinase 1

5/47, 10.6%

NA

NA

NA

TXNRD1

thioredoxin reductase 1

5/49, 10.2%

TSS200

< 0.001

0.53

ALOX5

arachidonate 5-lipoxygenase

4/40, 10%

NA

NA

NA

NXPH1

neurexophilin 1

4/40, 10%

NA

NA

NA

NR2E1

nuclear receptor subfamily 2, group E, member 1

5/55, 9.09%

TSS1500

< 0.001

0.52

PAX7

paired box 7

6/82, 7,3%

TSS1500

< 0.001

0.51

EDNRB c

endothelin receptor type B

4/59, 6.7%

NA

NA

NA

PAX6 c

paired box 6

7/104, 6.7%

NA

NA

NA

CUGBP2

CUGBP, Elav-like member 2

5/87, 5.7%

NA

NA

NA

fibrosin-like 1

5/157, 3.18%

NA

NA

NA

FBRSL1

Based on site-wise analysis; based on regional analysis, adjusted *P value (Benjamini-Hochberg method); present in the large protein network identified by STRING; hypomethylated gene given in bold letters; NA – not available. Detailed site-wise analysis of candidate biomarker genes is presented in Table S6. a

b

c

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Table 2. Complete list of candidate biomarker genes and their methylation profile in HER2+ breast cancer (continued)

using a genome-wide methylation array. Our findings show extensive hypermethylation of the CpG islands in HER2+ breast cancers. Hypermethylation affected transcription factors, especially the homeobox genes in HER2+ breast cancers. Alteration in DNA methylation was also found to affect members of key signaling pathways in HER2+ breast cancers. Further, we have identified 69 candidate genes with maximum differential methylation in HER2+ breast cancers. Functional analysis of hypermethylated genes indicated that they are involved in development, differentiation and transcription, overrepresented by the homeobox-containing family of genes. This is consistent with the recent findings of Fackler et al.,27 who performed genome-wide methylation analysis using Illumina Infinium HumanMethylation27 array. Since we employed the more comprehensive 450K array, we could

ascertain the regional distribution of methylation in the homeobox genes. Homeobox genes code for homeoproteins, which are transcriptional regulatory proteins that control embryogenesis; their deregulation (loss or gain of gene expression) is associated with cancer.28,29 Homeobox genes undergo altered expression in human breast cancer30 and, currently, 11 homeobox gene classes are recognized that are further divided into 102 homeobox gene families.31 Homeobox-containing genes with hypermethylation in traditional promoter regions identified in our study were found to be overrepresented by genes of the ANTP class (HOXL and NKL subclasses). Epigenetic regulation is the most common mechanism that controls homeobox gene expression and silenced homeobox genes have methylated CpG islands in their promoter region.32,33 Similar to our findings, hypermethylation has been previously reported in HOXA1, HOXA7, HOXA9, HOXD10,

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Figure 3. Predicted interaction network for proteins coded by the hypermethylated candidate biomarker genes. Protein interaction network predicted using STRING 9.1. Different line colors, indicate different kinds of evidence for association between the proteins (deep blue for co-occurrence, black for co-expression, pink for experiments, light blue for databases, green for text mining, purple for homology) coded by the hypermethylated candidate biomarker genes (n = 68). Boxes represents proteins coded by transcription factors, * represents homeobox-containing genes in the large protein network.

TLX3, and PAX3. We found promoter hypermethylation in HOXA1, HOXD9, HOXD13, PAX3, and PHOX2A, whose higher expression have been reported in cancers. HOXA1 has been found to be overexpressed in cancers and has been proposed to have an oncogenic role in mammary carcinogenesis. Interestingly, HOXA1 inhibition promotes invasiveness in pancreatic cancer. Similar findings are also reported in other homeobox genes like HOXC8 and HOXD13. HOXC8 expression is inversely related to progression and metastasis in pancreatic ductal adenocarcinoma, while its overexpression correlates with loss of tumor differentiation in prostate cancer. HOXD13 expression ranges from low (in prostate cancers) to high (in breast cancers). These differences in methylation (hypo-/hyper-methylation), as well as expression in different cancers, indicate the complex and diverse roles that these genes may have in different cancers. Six of the promoter-hypermethylated homeobox genes (DBX1,

HOXA1, HOXA9, NKX2–6, PHOX2A, SIX6 ) are among the candidate biomarker genes identified in this study and could be important in breast carcinogenesis. This is especially the case for DBX1, NKX2–6, and SIX6, which were not previously reported in relation to cancer. (Homeobox genes with hypermethylated promoters identified in our study and its cancer-related references are provided in Table S4). Of the 69 candidate genes with maximum differential methylation in HER2+ breast cancers, TSTD1/KAT was the only gene that was hypomethylated. This candidate biomarker gene has been previously reported to be preferentially expressed in breast cancer cell lines.34 Predicted functional/biological interaction of the proteins coded by the 68 hypermethylated candidate biomarker genes indicated that they are involved in neural signaling (GABRA4, GRIA4, GALR1, EDNRB), metabolic pathways (CDO1, AKR1B1, ALOX5), the Wnt

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Figure  4. Methylation-sensitive RFLP analysis of candidate biomarker genes in an independent set of HER2+ breast cancers. DNA methylation was analyzed in six HER2+ breast cancer tissues (BrC) and six normal breast tissues (NB) using EpiTect Methyl II Custom PCR Array in the candidate biomarker genes AKR1B1, CDKL2, FOXC2, INA, IRF4, and NEUROD1. The difference between NB and BrC was statistically significant for all six genes, with P values of 0.001 (AKR1B1), 0.019 (CDKL2), 0.00028 (FOXC2), 3 × 10 –10 (INA), 0.006 (IRF4), and 1 × 10 –6 (NEUROD1). The mean value of % methylation for each group of samples per gene is shown as a horizontal bar. AKR, AKR1B1; CDK, CDKL2; FOX, FOXC2; IRF, IRF4; NEU, NEUROD1.

Table 3. External validation of gene expression of the top candidate biomarker genes Probe ID

HIST1H4F

208026_at

Breast tumors vs. normal tissues a crude P value

Log-2fold change

0.936

0.00947397

HER2+ breast tumors vs. normal tissues b crude P value

Log-2fold change

Fold change

1.006588465

0.492

-0.1354257

-1.098416879

Fold change

INA

204465_s_at

0.897

-0.01674665

-1.011675526

0.028

-0.581906

-1.496825457

NEUROD1

206282_at

0.759

0.04650875

1.032762663

0.502

-0.1321135

-1.09589798

IRF4

216986_s_at 216987_at 204562_at

0.016 0.930 1.000

-0.33264184 0.02020397 -0.0000727

-1.253917306 1.01410285 -1.00050393

0.164 0.118 0.934

-0.2763732 -0.6597849 0.0160854

-1.211146346 -1.579847057 1.011211938

AKR1B1

201272_at

0.942

0.0153619

1.01070495

0.733

-0.0889985

-1.063631567

MIR129–2

NA

-

-

FOXC2

214520_at

0.467

0.14228558

WDR69

NA

-

-

C1orf114

206721_at

0.881

0.02071239

CPXM1

NA

-

-

POU4F1

211341_at 206940_s_at

0.062 0.673

0.48678423 0.10045951

1.103652185 1.014460288 1.401317852 1.072114885

-

-

-

0.001

-0.6582823

-1.578202467

-

-

-

0.453

-0.1132462

-1.081659336

-

-

-

0.159 0.795

0.5653085 0.0608507

1.479703889 1.043080643

CDKL2

207073_at

0.507

-0.07778702

-1.055397901

0.217

-0.2148565

-1.160588469

GABRA4

208463_at

0.603

0.05713343

1.040396485

0.376

-0.1790787

-1.132160661

Breast tumors (n = 19) and normal tissues (n = 23) were used for the analysis; bOnly HER2+ breast tumors (n = 10) and normal tissues (n = 15) were used for the analysis; Statistically significant crude *P values are given in bold letters; NA- not available. Data set from Pau Ni I.B. et al. 26

a

signaling pathway (APC, SFRP2, PRKCB), and leukocyte transendothelial migration and tight junctions (CLDN10, PRKCB). With the exception of GABRA4, aberrant methylation or expression has been reported in these genes identified by the STRING functional protein interaction network. Top candidate biomarker genes are involved in transcription (HIST1H4, NEUROD1, IRF4, mir129–2, FOXC2, POU4F1), neural signaling/development/differentiation (GABRA4, INA, NEUROD1, WDR69, POU4F1), glucose metabolism (AKR1B1), and epidermal growth factor signaling (CDKL2). Five of these genes, HIST1H4F, GABRA4, WDR69, C1orf114, and CPXM1, have not been previously identified to be involved in any cancer. External validation of the top candidate biomarker genes using a publically available data set26 showed lowered mean expression in HER2+ breast cancers, statistically significant in INA and FOXC2, consistent with the findings of transcriptional repression as result of hypermethylated gene promoters.35 A recent 450K based study showed that only 15% of the significantly differentially methylated genes showed a correlated change in mRNA expression,36 similar to our external validation findings. When DNA methylation was analyzed by an alternate method in six of these top candidate biomarker genes (AKR1B1, INA, FOXC2, NEUROD1, CDKL2, and IRF4), significantly higher methylation was found in HER2+ breast tumors in relation to normal breast tissues. In addition to the above-mentioned genes, altered methylation was found in genes of the PI3K/AKT pathway and the Wnt signaling pathway in HER2+ breast cancers. Most notable among these are HER2, AKT3, HK1, and PFKP, which have not been reported to undergo altered methylation in cancer.

Though HER2 gene amplification or overexpression is reported in HER2+ breast cancers2,3 and HER2 mediated downstream signaling through PI3K/AKT pathway4 is well documented, methylation status of HER2 or AKT has not been reported so far. In addition to HER2 and AKT3, two important checkpoints of the glycolytic pathway, HK1 and PFKP, were also found to be hypomethylated. Increased glycolysis in the presence of oxygen is a feature of cancer37 and is favored by increased expression of hexokinases.38 Recently Krüppel-like factor 4 (KLF4) has been shown to increase glycolytic activity in breast cancer cells via upregulation of PFKP gene.39 However, alteration of methylation in HK1 and PFKP has not been previously reported. In addition, HIF3A, inhibitor of HIF-mediated gene expression was also found to be hypermethylated in our study consistent with a recent report.40 Wnt signaling pathway was affected by both hypomethylation of Bcl9, a Wnt activator41 and hypermethylation of Wnt suppressors, such as, SOX17, APC, SFRP2, SFRP5, and DKK3.42 Epigenetic alteration has been previously described in SOX17, APC, SFRP2, SFRP5, and DKK3 in human cancers and may result in deregulated Wnt signaling (primary references available at http://www.stanford.edu/group/nusselab/cgi-bin/wnt/). In addition, hypermethylation also was found in RASSF1 and RASSF10, members of the Ras-Association Domain Family, which is epigenetically deregulated in cancers.43 Both RASSF1 and RASSF10 have tumor suppressive roles, and promoter methylation in RASSF1 is associated with poor survival in breast cancer.44 RASSF10 epigenetic deregulation is also reported in other cancers.45 We also found hypomethylation in cyclin D1 (CCDN1), which is amplified or overexpressed in cancers and

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Gene symbol

Annotated Pathway

Genes involved

Neuroactive ligand-receptor interaction

GABRA4, GRIA4, GALR1, EDNRB

Metabolic pathways

CDO1, AKR1B1, ALOX5

Wnt signaling

APC, SFRP2, PRKCB

Leukocyte transendothelial migration, tight junction

CLDN10, PRKCB

Calcium signaling pathway

PRKCB, EDNRB

Maturity onset diabetes of the young

NEUROD1, PAX6

MAPK signaling pathway

RASGRF2, PRKCB

Pathways in cancer

APC, PRKCB

Fc gamma R-mediated phagocytosis

PRKCB, DNM3

Melanogenesis

PRKCB, EDNRB

KEGG pathway analysis generated by the STRING functional protein interaction network showing that hypermethylated candidate genes are involved in important signaling pathways.

associated with tumor progression in breast cancer.46 Together, altered methylation may contribute to increased cell proliferation through afore mentioned key signaling pathway genes aided by HIF-mediated gene expression and altered glucose metabolism. In conclusion, our study revealed extensive hypermethylation in HER2+ breast cancers mostly affecting CpG islands. Aberrant methylation affected homeobox-containing genes and members of key signaling pathways in HER2+ breast cancers. Applying stringent selection criterion we have identified candidate biomarker genes with maximum differential methylation in HER2+ breast tumors. External validation of gene expression in a selected group of these genes revealed lowered mean gene expression in HER2+ breast cancers when compared with normal breast tissues. When DNA methylation was analyzed by an alternate method, six of the top candidate biomarker genes showed frequent higher methylation in an independent set of HER2+ breast tumors, similar to the 450K array findings. Small sample size and lack of clinical data are limitations of the present study and, therefore, future studies in larger HER2+ breast cancer cohorts with clinical data are necessary. The relevance of altered methylation in candidate biomarker genes, as well as homeobox-containing genes. HER2, AKT3, HK1, and PFKP, should be further explored in HER2+ breast cancer.

Materials and Methods Tissue samples, genomic DNA extraction and quality check DNA from 17 HER2+ breast tumors and ten unrelated normal breast tissues (~10 mg) were isolated from fresh frozen tissues using QIAamp DNA Mini Kit (QIAGEN®) according to the standard protocol. Clinical data was not used in the present study. DNA quantification was performed using NanoDrop® ND-1000 UV-Vis Spectrophotometer (NanoDrop Technologies).

Electrophoresis of the normalized DNA (50 ng/µl using TE buffer [pH 8.0]) was performed using 1% (w/v) agarose gel in 1× Tris-Borate-EDTA buffer and stained using GelRed™ (Biotium Inc.). Serva 1 kB ladder was used and the gel was run for 90 min. at 100V followed by visualization in a standard transilluminator (ChemiDoc XRS, Bio-Rad). Only those samples that showed one distinct band (≥10 000 bp) and no smears were included in the study. To ensure high DNA purity, samples with a 260/280 ratio > 2 and / or 260/230 ratio < 1 were not used in the study. Bisulfite conversion and Infinium methylation assay A total of 500 ng ds-DNA from each sample was used for bisulfite conversion using EZ-96 DNA Methylation-Gold kit (Zymo Research) as per standard recommendations. Bisulfite treatment of the gDNA results in the conversion of unmethylated cytosines to uracils, while the methylated cytosines remain the same. This C/T polymorphism generated by the bisulfite treatment is genotyped and used to evaluate CpG methylation in the Infinium methylation assay that covers 96% of CGIs and 99% of human RefSeq genes.47 A total of 200 ng of bisulfite converted DNA (Bs-DNA) was then used for methylation assay using Infinium HumanMethylation450 BeadChip kit according Illumina Infinium HD Methylation protocol. Briefly, this included whole genome amplification (WGA) where Bs-DNA is denatured, neutralized and incubated at 37 °C followed by enzymatic fragmentation, precipitation and resuspension in hybridization buffer. Resuspended samples were then hybridized on the HumanMethylation450 BeadChip 48 during which the WGA-DNA molecules hybridize to site-specific DNA probes bound to individual bead types. Following a washing step to remove the non-hybridized DNA, singlebase extension using labeled ddNTPs (biotin labeled ddCTP, ddGTP; DNP labeled ddATP, ddTTP) was performed using Bs-hybridized DNA as the template. The array was then scanned using Illumina iScan Scanner, which simultaneously scans the BeadChips at two wavelengths, and an image file was created for each channel. iScan Control software determined intensity values for each bead type and data files were created for each channel. GenomeStudio Methylation module (v1.0) ensured that the built in controls (sample independent, sample dependent, negative controls) were satisfactory. The software then used the data files to obtain the percentage of methylation for each cytosine of a given CpG locus. This is given by a β value, which is the ratio of the methylated signal divided by the sum of the methylated and unmethylated signals. It is expressed as a continuous variable that ranges from 0 (unmethylated) to 1 (fully methylated). Validation study using EpiTect Methyl II custom PCR array DNA from an independent set of well characterized HER2positive breast cancer tumors23 (n = 6) and unrelated normal breast tissues (n = 6, same as those used in the 450K array) were used for validating array findings. DNA methylation was analyzed by an alternative method, methylation-sensitive RFLP using EpiTect Methyl II Custom PCR Array from Qiagen (Valencia, CA). The genes selected for validation were AKR1B1, INA, FOXC2, NEUROD1, CDKL2, IRF4, and the assay

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Table 4. KEGG pathway analysis of the hypermethylated candidate biomarker genes

catalog numbers were EPHS113490–1A, EPHS101893–1A, EPHS105521–1A, EPHS108742–1A, EPHS111085–1A, and EPHS112120–1A, respectively. Assays were designed to make sure that they are in close proximity to the CpG probes that were identified as hypermethylated and fulfilled the criteria for candidate gene selection (see Table S6 and Data analysis). This technique is based on RFLP cleavage of the target gene using a pair of restriction endonucleases whose activities differ by requiring either the presence or absence of methylated cytosines in their respective recognition sequences. Real-time PCR (using RT2 SYBR Green qPCR Mastermix, on an ABI 7900 instrument) was used to quantify the relative amounts of DNA remaining after each enzyme digestion, and the methylation status of individual genes was calculated through the ratios between the different amplicons detected. The amplicons were located in CpG islands harboring the transcription start sites of the genes (except for NEUROD1), the exact positions are recorded on the manufacturer’s website (http://www.qiagen.com). All procedures were performed according to the workflow charts provided by the manufacturer.

Data analysis Data analysis was performed using R software (Illumina Methylation Analyzer package)49 which also quality controls the data material. Data analysis includes filtering of loci with missing β value, arcsine transformation of the raw β value followed by the moderated t test (empirical Bayes statistics) to obtain a P value. This P value was corrected using Benjamini-Hochberg method to obtain an adjusted P value. Principal component analysis (PCA) was performed which provide a visual overview of the methylation differences between the samples. Unsupervised hierarchical clustering of samples based on the β values from the CpG loci (CpGs) was performed using only CpGs that fulfilled the criteria, adjusted P value ≤ 0.05 and β value > 0.5. For site-wise analysis, only CpGs with an adjusted P value of |0.5| were included for downstream

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Figure 5. Main steps in the data analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Schematic representation of the main steps in the data analysis of the Illumina Infinium HumanMethylation450 BeadChip array. The complete pipeline of the analysis is given in the Data analysis under the Materials and Methods section.

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No potential conflicts of interest were disclosed. Funding

This study was supported by the grants from Swedish Cancer Society; Nyckelfonden, Örebro; Örebro County Council; and Lions Cancer Foundation, Uppsala. Acknowledgments

Methylation assay was performed by the SNP and SEQ Technology Platform, which is a part of Science for Life Laboratory, Uppsala University and is supported by the Swedish Research Council. We thank Sanja A. Farkas for her assistance with the data analysis. Supplemental Materials

Supplemental materials may be found here: www.landesbioscience.com/journals/epigenetics/article/29632

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analysis. This is a stringent criteria, considering that the cut off value of |0.2| represents the differential methylation detection limit with 99% confidence.50 Applying this criterion, CpGs were defined as hypomethylated (negative Δβ) or hypermethylated (positive Δβ) in HER2+ breast cancer tissues. We then classified these CpGs according to four criteria: (1) functional genomic distribution at traditional promoter region (sum of the total number of CpGs located within 200 bp or 1500 bp upstream of the transcription start site, at 5′ UTR and in the 1st exon) gene body and 3′ UTR; (2) related RNA transcripts; (3) CpG location; and (4) chromosomal distribution. As a next step, genes that have hypo- or hyper-methylated CpGs anywhere within the gene were defined as hypo- or hyper-methylated genes. The functional role of these genes were investigated by DAVID24 using the Gene Ontology (GO) terms, BPMFCC_ALL and BPMFCC_FAT. Gene groups with an enrichment score > 1.3 and a P value (modified Fisher exact test / EASE score) < 0.05 after adjusting for multiple testing (using Benjamini method) were considered to be statistically significant. Since homeobox genes were overrepresented in the hypermethylated gene group, regional analysis at the traditional promoter regions was performed on all the homeobox genes in the hypo- and hyper-methylated gene lists. For region analysis, all the CpGs in a region wer taken in to account and for a given gene, median β value was used to generate the methylation index of the region. Further, a stricter criterion was applied for selecting the candidate biomarker genes in HER2+ breast cancers. For this purpose, a gene was regarded as hypomethylated if the group mean β value in tumor tissues was close to zero and that it was methylated in at least four of the interrogated CpG probes in the normal tissues, while a gene was regarded as hypermethylated if the group mean β value was ≤0.1 in normal tissues but was methylated in tumor tissues in at least four of the interrogated

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