High-Resolution Bisulfite-Sequencing of Peripheral Blood DNA Methylation in Early-Onset and Familial Risk Breast Cancer Patients

Author Manuscript Published OnlineFirst on June 7, 2019; DOI: 10.1158/1078-0432.CCR-18-2423 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

High-Resolution Bisulfite-Sequencing of Peripheral Blood DNA Methylation in Early- Onset and Familial Risk Breast Cancer Patients

Justin Chen1*, Maria K. Haanpää2*, Joshua J. Gruber1,2, Natalie Jäger1, James M. Ford1,2ǂ, and Michael P. Snyder1ǂ

1Department of Genetics 2Department of Medicine, Oncology Division Stanford University, Stanford, CA 94305, USA.

* These authors contributed equally to this work. ǂ Co-corresponding authors

Running Title: DNA Methylation and High-Risk Breast Cancer

Keywords: DNA Methylation; Breast Cancer; Epigenetics; Cancer Predisposition; Allelic Methylation

Additional Information: Financial Support: This work used the Genome Sequencing Service Center by Stanford Center for Genomics and Personalized Medicine Sequencing Center, supported by the grant award NIH S10OD020141. MKH is supported by grants from Sigrid Juselius Foundation, Orion Research Foundation, Päivikki ja Sakari Sohlberg Foundation and Instrumentarium Science Foundation. JJG was supported by fellowships from the Jane Coffin Childs Memorial Fund for Medical Research, Stanford Cancer Institute and Susan G. Komen Foundation, as well as funding from ASCO, the Conquer Cancer Foundation and the Breast Cancer Research Foundation. NJ was supported by an EMBO Long-Term Fellowship (ALTF 325-2014). JMF is supported by the BRCA Foundation and the Breast Cancer Research Foundation. MPS is supported by grants from the NIH including a Centers of Excellence in Genomic Science award (5P50HG00773504).

Correspondence: Michael P. Snyder James M. Ford Department of Genetics 269 Campus Dr. Stanford University School of Medicine CCSR Room 1115 300 Pasteur Dr. M344 Stanford University School of Medicine Stanford, CA 94305 Stanford, CA 94305 650-736-8099 650-721-1503 [email protected] [email protected]

Disclosure of Interests: MPS is cofounder and scientific advisory board member of Personalis, SensOmics, Qbio, January, Akna, Filtricine, and Tailai. MPS is on the scientific advisory board of Genapsys and Jupiter. MPS owns stock in Abcam and Epinomics.

Word count (Introduction – Disclosure of Interests): 5,371 Total Pages: 37 Total Figures and Tables: 8 Main, 7 Supplemental

Downloaded from clincancerres.aacrjournals.org on September 30, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on June 7, 2019; DOI: 10.1158/1078-0432.CCR-18-2423 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Statement of Translational Relevance Personalized genomics not only provides vital information about what drives specific tumors, but can also be informative about the likelihood of developing cancer; hereditary forms constitute 5-

10% of all cancers, including breast cancer. By performing deep bisulfite sequencing of 99 early-onset, familial risk breast cancer patients, we demonstrate proof-of-principle that broader epigenetic states, in the form of allelic methylation at known cancer genes, exist in somatic DNA derived from peripheral blood in up to 9% of cancer patients. Additional studies will be required to identify the functional consequences of such epigenetic states and whether screening for such epi-alleles may help clarify unexplained risk for developing hereditary breast cancer.

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Abstract

Purpose: Understanding and explaining hereditary predisposition to cancer has focused on the genetic etiology of the disease. However, mutations in known genes associated with breast cancer, such as BRCA1 and BRCA2, account for less than 25% of familial cases of breast cancer. Recently, specific epigenetic modifications at BRCA1 have been shown to promote hereditary breast cancer, but the broader potential for epigenetic contribution to hereditary breast cancer is not yet well understood.

Experimental Design: We examined DNA methylation through deep bisulfite sequencing of

CpG islands and known promoter or regulatory regions in peripheral blood DNA from 99 familial or early-onset breast or ovarian cancer patients, 6 unaffected BRCA-mutation carriers, and 49 unaffected controls.

Results: In 9% of patients, we observed altered methylation in the promoter regions of genes known to be involved in cancer including hypermethylation at the tumor suppressor PTEN and hypomethylation at the proto-oncogene TEX14. These alterations occur in the form of allelic methylation that span up to hundreds of base-pairs in length.

Conclusions: Our observations suggest a broader role for DNA methylation in early-onset, familial risk breast cancer. Further studies are warranted to clarify these mechanisms and the benefits of DNA methylation screening for early risk prediction of familial cancers.

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Introduction

Breast cancer is the leading type of cancer in women world-wide, representing nearly 25% of all cases (1). Approximately 15% of invasive breast cancer can be traced to a hereditary origin, with patients having at least one first-degree female relative with the disease. The landmark discoveries of the first two major breast cancer susceptibility genes, Breast cancer 1 (BRCA1)

(2) and Breast cancer 2 (BRCA2) (3), over twenty years ago revealed the power of genetic testing; over 60% of women with heterozygous germline mutations in either of these genes ultimately develop breast cancer by the age of 70. However, there have been diminishing returns with each subsequent tumor suppressor identified. Whereas mutations in BRCA1 and

BRCA2 together account for around 15% of hereditary cases, deleterious genetic alterations in other genes such as Cadherin-1 (CDH1), Phosphatase and tensin homolog (PTEN), and

Partner and localizer of BRCA2 (PALB2) account for less than 7% of hereditary cases combined

(4). After taking into account the cumulative and putative effects of SNPs on this disease, 50% of hereditary breast cancer may still remain unexplained from a genetic perspective (4).

Tumor suppressor inactivation by hypermethylation has been well described, mainly as a

“second hit” in tumors (5). Since sporadic breast cancer tumors with BRCA1 hypermethylation remain histologically and molecularly similar to hereditary breast cancer tumors with BRCA1 mutations (6,7), we investigated whether somatic epigenetic alterations could exist more broadly in familial breast cancer. This hypothesis follows multiple epigenome-wide association studies that have used methylation arrays to suggest that heritable methylated marks can contribute to hereditary cancers, including breast cancer (8–11). Controversy has surrounded the potential for transgenerational epigenetic inheritance of methylated alleles that contribute to disease ever since the concept has been postulated (12,13). While the bar for irrefutable proof is beyond the experimental conditions that can be performed in human studies, observational support for this phenomenon has come from heritable hypermethylated MutL homolog 1 (MLH1) alleles that

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lead to Lynch syndrome (14,15), a familial predisposition to colorectal cancer (16). More recently, a study of 49 families looking for BRCA1 epimutations has identified two instances of a dominantly inherited, constitutively methylated allele at the BRCA1 promoter that leads to breast cancer (17).

Despite establishing the contribution of heritable BRCA1 epimutations to breast cancer, the prominence of this occurrence across the genome remains unknown. Here we utilize an unbiased approach to investigate breast cancer predisposition by DNA methylation. Using a cohort of 99 patients with familial and early-onset breast or ovarian cancer, we assessed putative germline DNA methylation from whole blood using bisulfite sequencing at known gene promoters and CpG islands. Although we did not observe promoter epialterations at BRCA1 or

BRCA2 in cancer patients, we did find other instances of allelic methylation with the potential to contribute to disease in 9% of our cohort. This includes allelic hypermethylation at known tumor suppressor loci such as PTEN as well as allelic hypomethylation at known proto-oncogenic loci such as Testis expressed 14 (TEX14). We also identify sites of recurrent allelic methylation specific to breast cancer patients, relative to a control cohort of unaffected women. These results, which are independent from chemotherapy treatment, provide proof of principle that allelic DNA methylation is also frequently present in individuals with cancer predisposition.

Further studies are warranted to determine if systemic DNA methylation may able to help explain hereditary breast cancer with unknown origins.

Materials and Methods

Patient Sample Selection and Collection

The cohort was selected from patients who visited Stanford Cancer Genetics Clinic between

2004 and 2013. Out of a total of 333 patients, 248 were affected. Samples were classified by the likelihood of hereditary breast cancer predisposition based on the age of first cancer

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diagnosis, number of cancer diagnoses, type of cancer, and family history of breast or ovarian cancer (Supplemental Figure S1). 99 affected patients were selected for this study, of which 87 had history of breast cancer only, 6 were ovarian cancer patients and 4 had both breast and ovarian cancers, 2 had other cancers with history of breast and ovarian cancers. 59% of the cohort had family history of breast or ovarian cancers. Earlier cancer onset, multiple cancers in the same individual, and greater family history of cancer were indicators of hereditary disease.

All of the patients received clinical multi gene panel testing. 76 patients had VUS or benign alterations, and 23 patients had pathogenic or likely pathogenic mutation in one of these genes:

Ataxia-telangiectasia mutated (ATM), BRCA1, BRCA2, Checkpoint kinase 2 (CHEK2), MLH1,

MutS homolog 2 (MSH2), MutY DNA glycosylase (MUTYH), PALB2, PMS1 homolog 2 (PMS2), or RAD51 homolog C (RAD51C). 13 of these patients were specific to BRCA1 or BRCA2 mutations. Patients were classified in three different categories according their exposure to chemotherapy treatment: active treatment (including patients who had completed their treatment within two months of collection), prior treatment, and never treated with chemotherapy (Table 1).

The control cohort was recruited from non-smoking female Stanford employees (graduate students, laboratory personnel, nurses, physicians, research scientists, medical assistants, residents, and fellows) on a volunteer basis as previously described (18). Healthy controls were women between the ages of 30 and 65 and were unaffected by breast cancer and had no personal or first-degree relative with a history of any type of cancer except for basal cell carcinoma. Only data about age, race, family history, and menopausal status were available for the healthy controls (Table 2). There were no significant statistical differences between the cohorts based on age of disease onset and healthy controls (p>0.05, Student’s t), race (χ2 p>0.05), or menopausal status (χ2 p>0.05, combining pre-menopause and treatment-induced menopause). However, there was a significant statistical difference between the age of

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collection between both groups (p=7.7x10-5, Student’s t) and menopausal status when excluding treatment-induced menopause (χ2 p=0.023).

This study was carried out with informed consent from all the participating individuals. The study was approved by the Institutional Review Board (IRB) of Stanford University and was conducted in accordance with the approved methods and guidelines of the U.S. Common Rule.

Genomic DNA was extracted from 3 ml peripheral whole blood using the Gentra Puregene

Blood Kit (Qiagen Sciences) according to the manufacturer’s protocol.

Bisulfite Library Construction and Sequencing

Sequencing libraries were constructed using the MethylSeqXT kit (Agilent Technologies) according to manufacturer’s instructions using 1 µg of gDNA. This target enrichment system captures 84 Mb of the genome focused on known cancer and developmental differentially methylated regions (DMRs) and CpG islands and provided a cost-effective means to sequence at a high depth-of-coverage (averaging 71x depth across all samples), providing highly quantitative methylation data at base-pair resolution (Supplemental Figures S2A and S2B).

Briefly, sheared DNA was repaired, A-tailed, and ligated to adaptors prior to overnight hybridization with biotinylated baits. Target enrichment was performed using Streptavidin T1 magnetic beads (Life Technologies), and the purified DNA was bisulfite converted and desulphonated using the EZ DNA Methylation Gold kit (Zymo Technologies). Inserts were PCR amplified and indexed using 8-bp methylation barcodes. All DNA purifications steps were performed using AMPureXP magnetic beads (Beckman Coulter), and the libraries were sequenced on an Illumina HiSeq4000 at the Stanford Center for Genomics and Personalized

Medicine.

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Data Analysis

Bisulfite HTS data was analyzed on the Stanford’s SCG compute cluster. FASTQ files were trimmed and mapped to hg19 using BSMAP (v. 2.89) (19). PCR duplicates were removed using

Picard Tools. Reads mapping to the reference sense strand were filtered out to minimize off- target, spurious results to the (+)-stranded target enrichment baits. Methylation ratios, CpG read coverage, and estimated bisulfite conversion were all calculated using MOABS (v 1.3.0)

(20). Publicly available WGBS data for three breast tumors, BT089, BT126, and BT198 were obtained as raw FASTQ files from ArrayExpress under accession E-MTAB-2014 and processed in the same pipeline, with reads accepted from both strands.

Differential methylation analysis was performed using Metilene (v. 0.2-6) (21), using the provided input script and filtering for at least 5 reads per CpG. DMRs were filtered for an absolute difference of 0.1 and at least 3 CpGs. Quantifying manually observed DMRs was performed by metilene analysis of a subset of the genome containing the region of interest comparing the patient against the healthy control cohort. P-values reported are products of

Mann-Whitney U tests. Unbiased DMR screening in individuals was performed by iterating through patients compared to the healthy control cohort. Candidate regions were identified where the healthy control cohort averaged > 90% methylation or < 10% methylation and the affected patient averaged 50 ± 10% methylation. Regions were filtered statistically for FDR values < 0.05. Candidate allelic methylation regions in the healthy controls were identified similarly by iterating across each healthy individual compared to the remaining 48 control cohort. As a final quality control check, potential DMRs were inspected visually to filter out regions that were subject to artificial read saturation introduced by the target enrichment platform.

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CpG methylation status of individual reads were assessed using the tanghulu function of cgmaptools (0.0.1) (22) using samtools (v. 1.4). Kernel densities were calculated and plotted in

R (v 3.3.1) using the density function.

Methylation boxplots were calculated by taking the mean of methylation ratios across a window starting 1.5 kb upstream of the most 5’-TSS and ending 500 bp downstream of the most 3’-TSS

(if any other proximal, alternative TSS exist). Boxplots were displayed in R using the ggplot2 package. Statistical significance was determined with ANOVA, using the aov and TukeyHSD functions. FDR values were obtained using the p.adjust function (Bonferroni).

Methylation heatmaps were constructed by creating TDF files using IGVTools (v. 2.3.3).

Genomic regions were visualized using the IGV Browser (v. 2.3.90) and exported.

Enrichment of cancer predisposition genes was determined using the hypergeometric test using the phyper function in R. The significance of the overlap of genes (114) and gene-associated epialterations (184) was determined against the genes included in the MethylSeqXT panel

(22160).

Principal component analysis was performed in R using the prcomp function on CpGs that had a standard deviation > 0.2 across all samples (n=7,587). CpGs were required to have at least 5 reads to be included. PCA results were displayed using the ggplot2 and ggfortify packages.

TCGA level 3 data was downloaded from the Broad Institute’s Firebrowse GDAC (v. 20160128)

(23) and analyzed in R. Log2 transformed RSEM values are presented with a Welch’s p-value.

All genomic positions reported are referenced to hg19.

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Raw sequencing data files are available through dbGaP under data access control guidelines in accordance with informed consent under the study accession phs001699.

Results

Patient/Study Cohort Description

We selected a cohort of 99 patients with early onset breast or ovarian cancer who visited the

Stanford Cancer Genetics Clinic between 2004 and 2013. Selection was designed to capture samples with the greatest likelihood of hereditary breast cancer predisposition (see Methods).

These patients participated in clinical genetic screening across a panel of breast cancer genes and could be divided into two subgroups: 76 patients without any known deleterious mutations in cancer related genes (Affected Non-carriers), and 23 patients with known or likely pathogenic mutations in cancer-related genes such as ATM, BRCA1, BRCA2, CHEK2, MLH1, MSH2,

MUTYH, PALB2, PMS2, and RAD51C (Affected Carriers). We also included 6 healthy BRCA1 or BRCA2 carriers who have never had cancer (Unaffected Carriers), and 49 healthy individuals who never had cancer served (except for basal-cell cancer) as Healthy Controls (Table 1).

The most common cancer type was invasive ductal breast cancer with ER+, PR+ and Her2 negative receptor status (16%). The mean age of the cancer diagnosis was 41 years, varying between 18 and 61 years. 33% of the patients had 2 or more cancers. Patients were mostly

Caucasian (61%) with 2 children (34%). Their BMI was normal at the time of the blood collection

(mean of 25.7), and 72% were either premenopausal or treatment had induced the menopause.

Patients had varying exposures to chemotherapy treatment: 39 patients had previously completed chemotherapy and 20 patients were undergoing active chemotherapy at the time of collection (Tables 1 and 2).

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Peripheral Blood DNA Methylation Ratios Remain Homogenous Across the Cohort

Using genomic DNA extracted from whole-blood, we performed high-depth bisulfite sequencing across selected regions of the genome focused on known cancer and developmental differentially methylated regions (DMRs) and CpG islands. As opposed to dramatic shifts in global methylation profiles in studies that have compared tumor tissue to normal tissue (24), in the blood DNA we observed only subtle differences in global methylation between affected patients and unaffected patients or healthy controls. Similarly, patients with known mutations in cancer-related genes did not have major differences when compared to patients without cancer.

Global peripheral blood methylation ratios remained similar across the cohort (Supplemental

Figure S2C). Principal component analysis did not clearly cluster these subgroups, and only

5.615% and 1.677% of the variance could be accounted for by principal components 1 and 2, respectively (Supplemental Figure S2D). Despite inter-individual differences in methylation ratios across the genome, we did not identify any statistically significant DMRs that passed quality control between healthy controls and affected patients or unaffected carriers.

BRCA1 and BRCA2 Promoter Methylation is Absent

Previous studies employing probe-based and array-based strategies have observed low-level hypermethylation at the BRCA1 promoter (9,10,25), suggesting that germline DNA methylation at this locus could contribute to breast cancer predisposition. Coupled with the fact that inactivating mutations in either BRCA1 or BRCA2 are found in 15% of hereditary breast and ovarian cancer, representing the greatest risk factors for disease (4,26–28), and that promoter hypermethylation at BRCA1 in breast tumors is associated with poor outcome (29), we examined if similar promoter hypermethylation existed in our cohort. We examined these promoters using approximate 2 kb windows encompassing the transcriptional start site (TSS,

1.5 kb upstream and 500 bp downstream). This led to the identification of only modest changes (< 2%) in average methylation state across these regulatory regions (Figure 1A). Since

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one of the main advantages of our study is the increased resolution afforded by bisulfite sequencing, we looked directly at the methylation states for individual CpGs surrounding the

TSS of BRCA1 and BRCA2. At both of these loci, methylation ratios remained similar across all groups (Figures 1B and 1C).

Allelic Methylation at Known Cancer-Related Genes

We extended this approach to examine approximate 2 kb windows encompassing the TSS of other genes known to be frequently inactivated in hereditary breast and ovarian cancer (30).

This panel of 24 additional genes includes BRCA1-A complex subunit (ABRAXAS1), ATM,

BRCA1 associated RING domain 1 (BARD1), Bloom syndrome protein (BLM), BRCA1 interacting protein 1 (BRIP1), CDH1, CHECK2, FA complementation group C (FANCC), FA complementation group M (FANCM), MLH1, Meiotic recombination 11 homolog (MRE11),

MSH2, Nibrin (NBN), Neurofibromin (NF1), PALB2, PMS2, PTEN, RAD51 homolog B

(RAD51B), RAD51C, RAD51 homolog D (RAD51D), RecQ protein-like (RECQL), RAD50 interactor 1 (RINT1), Serine/threonine kinase 11 (STK11), and Tumor protein p53 (TP53). Akin to our observations at BRCA1 and BRCA2, the average methylation ratios at these gene promoters were similar across the cohort, with few significant differences between subgroups

(Supplemental Figure S3).

Despite the statistically significant changes in methylation ratios between healthy controls and an affected subgroup at ABRAXAS1, MSH2, STK11, and TP53, a closer inspection at base-pair resolution at these loci indicated that these results were likely due to noise at the terminal end of target enrichment regions and low read coverage, and not reflective of true biological differences (Supplemental Figures S4A-D). Similarly, the average hypermethylation observed at the RAD51C promoter in both affected cancer cohorts when compared to healthy controls was not driven by any obvious changes at the individual CpG or sample level. Instead, we

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identified a significant DMR (chr17:56,769,048-56,769,761; p=4.4x10-14) upstream of the 5’UTR in Patient 2417 that averaged 56.9% methylation while all other samples displayed nearly complete methylation (Figure 2A, arrow). All healthy controls averaged 90.7% methylation within this region. As a tumor suppressor involved in the DNA damage response pathway, canonical gene activation of RAD51C by hypomethylation seemed to be a counter-intuitive mechanism for predisposition and oncogenesis. However, RAD51C shares a bidirectional promoter with TEX14, and we found that this hypomethylated region completely encompassed the TSS and first exon of TEX14. TEX14 cooperates with SCY1-Like 1 (SCYL1) and Polo-like kinase 1 (PLK1) to degrade the RE-1 silencing transcription factor (REST) tumor suppressor and is frequently overexpressed in triple negative breast cancer (31). We observed that the majority of sequencing reads within this locus were either completely methylated or entirely unmethylated (Figure 2B), suggesting the presence of a heterozygous unmethylated allele with the potential for aberrant, overexpression of TEX14. Notably, Patient 2417 presented clinically with triple negative breast cancer, and the time of collection was 8 years post-treatment with chemotherapy.

We assessed for additional DNA methylation differences in the form of aberrant, allelic methylation in this panel of known genes in hereditary breast and ovarian cancer. We identified a similar DMR within the 5’-UTR of PTEN in Patient 2282 (chr10:89,623,973-89,624,168; p=6.0x10-6) that averaged 30.9% methylation, whereas most other samples were devoid of methylation (Figure 1C, arrow); this patient also had a heterozygous PALB2 mutation. Healthy controls averaged 1.39% methylation within this region. Patient 2282 was diagnosed with ductal breast cancer at 39 years of age and was actively receiving chemotherapy at the time of collection. PTEN encodes for a tumor suppressor whose primary function is to regulate the

Phosphoinositide 3-kinase (PI3K)-Serine/threonine Kinase 1 (AKT) pathway. Inactivating mutations in PTEN are frequently found in Cowden syndrome, an inherited disorder

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characterized by pathway hyperactivity that promotes cell growth and survival, leading to predisposition to several types of cancer, including breast (32). In addition to altered PTEN methylation in Cowden syndrome (33), promoter methylation at PTEN is commonly found in breast tumors, where it associates with lower gene expression (34). Similar to what we observed at the TEX14 promoter in Patient 2417, we found that the majority of sequencing reads at this locus were either completely methylated or entirely unmethylated (Figure 2D), indicating a heterozygous methylated allele with the potential to inhibit PTEN expression and drive a haploinsufficient phenotype. Seven additional cancer patients exhibited promoter hypermethylation at the PTEN promoter, but to a lesser degree (Patients 513, 2203, 2208,

2217, 2221, 2260, and 5111; Figure 2C, arrowheads). When analyzed in aggregate, these samples share the same DMR as Patient 2282 (chr10:89,623,973-89,624,168; p=4.4x10-14), but average only 18.2% methylation and have methylated:unmethylated allele ratios that range from

0.12-0.43 (Supplemental Figure S5) compared to 0.88 in Patient 2282. Three of these patients were heterozygous carriers for BRCA1 or CHEK2 mutations (Patients 513, 2203, 5111); there were no commonalities across these tumors based on type or hormone receptor status.

Patients 513, 2217, 2221, and 2260 were never treated with chemotherapy by the time of collection. Samples from Patients 2203, 2208, and 5111 were collected at 2 years, 6 years, and

8 years post-chemotherapy, respectively, but each of these patients were not on chemotherapy at the time of collection.

We also observed an intragenic DMR within CDH1 in Patient 2980 (chr16:68,856,954-

68,857,037; p=1.3x10-4) that suggested allelic methylation (Figure 3A and B). Healthy controls averaged 94.9% methylation whereas Patient 2980 averaged 61.8% across the region. This patient is a BRCA2 carrier and had triple negative invasive ductal breast cancer when she was

38 years old and was actively receiving chemotherapy at the time of collection. CDH1 encodes for E-cadherin, a cellular adhesion molecule that cooperates with the β-catenin/Wnt signaling

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pathway, and is a common breast cancer tumor suppressor (35). Germline CDH1 mutations confer a high risk for signet ring cell diffuse gastric cancer and lobular breast cancer (36). This region of allelic methylation is located upstream of an alternative start site encoding a transcriptional variant lacking the extracellular domain.

Genome-wide Allelic Methylation

Our initial results indicate that the most informative epigenetic changes contributing to breast cancer predisposition may arise as allelic methylation. We next turned to an unbiased approach to identify potentially similar epialterations by searching for DMRs in individual affected patients compared to healthy controls. Potential sites for aberrant allelic gain or loss of methylation were established by searching for uniformly methylated or unmethylated regions across all healthy controls that then approach 50% methylation in individual cancer patients, at an FDR < 0.05.

Candidate epialterations were rare, as 68% of samples had one or no regions identified, and only 9% of samples had five or more regions identified in our screen (Supplemental Tables ST1 and ST2). We identified an enrichment of candidate epialterations in cancer predisposition genes in the cancer cohort: 4 instances of candidate epialterations in the cancer cohort overlapped with a curated list of 114 cancer predisposition genes (37), whereas no candidate epialterations in the healthy controls were found in the list (Figure 4A, p=2.65 x 10-3).

The unbiased nature of this screen allowed us to identify patients with other epialterations in regions of interest. Among these include Patient 2203 who had a hypermethylated DMR that encompassed microRNA-596 (miR-596; chr8:1,765,429-1,765,789; p=1.6x10-9; Figure 4B).

This patient averaged 40.1% methylation across this region whereas all healthy controls averaged just 4.01% methylation, and methylation read densities suggested this methylated region was allelic (Figure 4C). DNA methylation can also regulate miRNA expression, and disruption of this process leads to functional consequences in tumorigenesis and metastasis

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(38). miR-596 has been previously shown to induce p53-mediated apoptosis (39) and has tumor suppressive functions when expressed in melanoma and oral squamous cell carcinoma

(40,41). Patient 2203 was previously mentioned to have increased PTEN promoter methylation and is a BRCA1 carrier; her sample was collected 2 years post-chemotherapy.

We also observed a DMR near the TSS of TatD DNase domain containing 1 (TATDN1 - chr8:125,550,351-125,551,216; p=1.3x10-12) in Patient 14246 that extended through the first exon of the neighboring gene NADH:ubiquinone oxidoreductase subunit B9 (NDUFB9) upon visual inspection (Figure 5A). Patient 14246 averaged 43.4% methylation over this region, whereas healthy controls averaged 6.6% methylation. This patient had ductal breast cancer at

33 years of age and never received chemotherapy by the time of collection. TATDN1 amplifications and fusion products with Gasdermin B (GSDMB) have been reported in breast carcinomas (42,43), and NDUFB9 knockdown has been shown to promote proliferation in breast cancer cells by disrupting mitochondrial metabolism (44). The hypermethylation in this individual appeared to be allelic based on the methylation status of sequencing reads strictly over the region encompassing TATDN1 (Figure 5B), whereas the region overlapping NDUFB9 has a broader density of methylated reads, contraindicating allelic methylation (Figure 5C).

Allelic methylation overlapping TATDN1 was confirmed by the presence of a heterozygous SNP within the DMR (chr8:125,550,868; C>G; rs7009290). We identified 128 reads that covered this specific SNP, with all 64 methylated reads associated with the polymorphic guanine allele and all 64 unmethylated reads associated with the reference cytosine (Figure 5D). Since only 14 bp separates the TSS of TATDN1 and NDUFB9, this methylated allele could function to affect transcription of either or both genes.

Recurrent Promoter Allelic Methylation

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Our screen also revealed recurrent hypomethylation at the Insulin-like growth factor binding protein, acid labile subunit (IGFALS) promoter. IGFALS cooperates with Insulin Growth Factor

(IGF) binding proteins and drastically enhances the circulating half-lives of IGF-I or IGF-II when combined in a ternary complex (45). GWA studies have identified several SNPs within the IGF pathway that are associated with breast cancer (46,47), and high IGF concentrations are associated with increased cancer risk, including breast (48,49). We observed this allelic methylation (chr16:1,844,768-1,845,114, p=5.0x10-14) in four breast cancer patients who averaged 59.3% methylation (2981, 6305, 8192, and 16251; Figure 6A, arrows), but not in healthy controls where methylation ratios averaged 93.1%. One of these patients (2981) was a carrier for a BRCA1 mutation. Both Patients 2981 and 8192 were actively receiving chemotherapy at the time of collection, whereas Patients 6305 and 16251 never received chemotherapy by the time of collection. This region encompassed the first exon of an alternative isoform of IGFALS and overlapped with a hypomethylated region in three breast tumors with publicly available whole-genome bisulfite sequencing (WGBS) data (50).

Sequencing reads indicated allelic methylation in the four patients in our cohort (Figure 6B), whereas the hypomethylation observed in tumor WGBS was more extensive and complete in some areas. All four of these patients had a heterozygous SNP within the DMR

(chr16:1,844,801; T>C; rs9924224) where unmethylated reads were associated with the polymorphic cytosine allele and methylated reads were associated with the reference thymine allele (Figures 6C-F). In Patient 2981, this effect was consistent over 37 reads (24 unmethylated reads overlapping the cytosine and 13 methylated reads overlapping the thymine). This effect was also consistent in Patient 8192 over 25 reads (9 unmethylated reads with cytosine and 16 methylated reads with thymine). Patient 16251 had 27 total reads overlapping this region with 20 unmethylated reads overlapping the cytosine and 7 methylated reads overlapping the thymine. We observed more mixed methylation in Patient 6305 where 4 methylated reads were associated with the polymorphic cytosine (25% of reads) and one

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unmethylated read associated with the thymine (5.3% of reads). Average IGFALS expression is also significantly increased in TCGA breast tumors compared to normal tissue (RSEM+1 5.60 vs. 3.78, p<2.2x10-16, Figure 6G).

Discussion

DNA methylation can have profound effects on transcription and splicing (51,52), and deregulation of both of these processes are hallmarks in the initiation of cancer (53,54). Despite clear changes in breast tumor methylomes (10,55), the role of methylation in cancer predisposition has remained less clear, with some proposing that epigenetic changes in normal body cells are indicative of global mechanistic disruption (56). Using high depth-of-coverage bisulfite sequencing across the genome, we provide evidence for epimutations in the form of allelic methylation at TEX14, PTEN, CDH1, IGFALS, miR-596, and TATDN1/NDUFB9, comprising 9% of our entire cohort (6.5% and 17% of affected non-carriers and affected carriers, respectively). Our methylation study is the first of its kind to definitively identify both hypermethylated and hypomethylated alleles at known tumor suppressor and proto-oncogene loci in peripheral blood, respectively. Bisulfite sequencing at base-pair resolution should remain the gold standard to identify allelic methylation changes.

Our findings did not detect altered DNA methylation at the BRCA1 promoter, whereas previous studies targeted at this locus have identified slightly elevated promoter hypermethylation in germline DNA (9,10,25). Although we identified statistically significant changes in methylation ratios across the TSS windows, the fact that they that do not hold up to further scrutiny at base- pair resolution suggests that probe-specific assays could be subject to significant noise or false positives. Our deep sequencing approach is also not well-suited to detect methylated

Chen J, et al. “DNA Methylation and High-Risk Breast Cancer” 18

mosaicism at very low frequencies (i.e. < 2%), which would have unknown biological impact and could be attributed to numerous sources. Although the recent discovery of heritable BRCA1 methylated alleles in breast cancer was found in 2/49 families (17), the size of our cohort suggests that it could exist at much lower rates in the general population.

Since all cancer patients in this cohort, regardless of carrier mutation status, were selected for early onset breast cancer, the co-occurrence of mutations in both the genome and epigenome of some patients could raise the possibility of epialleles as modifiers of breast cancer risk in the setting of known pathogenic high-risk mutations. Due to our sample size, our data remains insufficient for any predictive cancer-risk assessment of these epialleles in such confounded settings. It remains possible that the altered methylated regions in these patients represent secondary events, or acquired changes in systemic DNA methylation. This could be particularly true for the additional seven patients who we identify to have increased PTEN promoter hypermethylation at levels that are inconsistent with allelic methylation. A potential mechanism behind this possibility remains unclear, as each of the three carrier cases have mutations in different genes (BRCA1, BRCA2, and PALB2). Also, since we identify candidate epialleles in patients with various chemotherapy regimens, including those who never had chemotherapy by the time of collection, we rule out the potential for therapy-related changes in immune cell composition or systemic therapy-related methylation changes.

Genetic control of methylation, or methylation QTL (meQTL), may exist for 15% of methylated regions (57,58). Previous studies have also identified meQTL in blood that coincide with breast cancer GWAS SNPs in the NHGRI catalog (59), inviting speculation that these SNPs may reflect heritable epivariants such as the ones we identify here. Since cis-meQTL have been identified at distances from 2-10 kb and even up to 1 Mb from the methylated region itself, our target enrichment approach does not allow us to corroborate these previous findings due to the

Chen J, et al. “DNA Methylation and High-Risk Breast Cancer” 19

lack of sequencing coverage surrounding DMRs. Indeed, although we find heterozygous SNPs in patients that confirm the allelic nature of the epivariants observed at IGFALS and TATDN1, we did not observe heterozygous SNPs within other methylated alleles. It should be noted that other patients, including controls, were also heterozygous at the IGFALS and TATDN1 DMR

SNPs without a change in methylation, suggesting that these are not putative meQTL.

Despite the inability to make predictive cancer-risk claims or to identify novel candidate predisposition genes, recurrent IGFALS methylation identifies an intriguing candidate that might not have been identified in a standard comparison between tumor and normal. Notably, the epialleles we observe overlap with the TSS of an alternative IGFALS isoform, and any potential functional roles for IGFALS in breast cancer will need to be studied in more detail with a more appropriate cohort and system.

Importantly, this study is based on a single-institution cohort, and thus faces several limitations that can only be addressed with multiple validation studies and larger cohorts. We did note a statistically significant difference of 6.8 mean years between the ages of collection between the cancer patients and the healthy controls, and methylation has been previously reported to change with age (60). Although we are unable to correct for these differences in our study, we note that age-related global DNA hypomethylation can be separate from changes at discrete regions, of which we identify instances of both hypo- and hypermethylation. Also, as our cohort consists of patients spread throughout the country who had their blood collected over the span of 9 years, additional biospecimens from these individuals remain difficult or impossible to obtain. Thus, we cannot conclusively show the functional effects of these methylation alleles.

This is evident in case of the intragenic hypomethylation we identify within CDH1, which could result in alternative or aberrant expression of the variant containing only cytoplasmic domains that could then affect Wnt-signaling (61). Alternatively, since gene-body methylation is

Chen J, et al. “DNA Methylation and High-Risk Breast Cancer” 20

associated with highly expressed genes and can function to maintain high fidelity transcription and proper splicing (52,62), loss of methylation at this locus may result in lowered gene expression or hypomorphic transcripts.

Additionally, without access to these individuals’ first-degree relatives, we cannot construct pedigrees to determine if what we have identified are sporadic epimutations, or truly heritable epivariants. As we examined methylation in peripheral blood, these DMRs largely exist in white blood cells and could be attributable to acquired or environmental factors, such as immune responses to the cancer or other infection. Future studies involving familial cohorts and multiple tissue types, including tumors, will be needed to address whether these alleles are constitutional and heritable. Despite these limitations, our study suggests that more research is warranted to identify the impact of the epigenome in the predisposition and development of breast cancer.

Acknowledgements

We extend our thanks to the many women who generously participated in the study. We also thank all members of the Snyder and Ford labs for helpful discussions. This work used the

Genome Sequencing Service Center by Stanford Center for Genomics and Personalized

Medicine Sequencing Center, supported by the grant award NIH S10OD020141. MKH is supported by grants from Sigrid Juselius Foundation, Orion Research Foundation and Päivikki ja Sakari Sohlberg Foundation. JJG was supported by fellowships from the Jane Coffin Childs

Memorial Fund for Medical Research, Stanford Cancer Institute and Susan G. Komen

Foundation, as well as funding from ASCO, the Conquer Cancer Foundation and the Breast

Cancer Research Foundation. NJ was supported by an EMBO Long-Term Fellowship (ALTF

325-2014). JMF is supported by the BRCA Foundation and the Breast Cancer Research

Foundation. MPS is supported by grants from the NIH including a Centers of Excellence in

Genomic Science award (5P50HG00773504).

Chen J, et al. “DNA Methylation and High-Risk Breast Cancer” 21

Author Contributions

Research design: MPS, JMF, JC, MKH. Investigation: JC, MKH, JJG, NJ. Analysis: JC, MKH,

JJG, NJ. Manuscript: JC, MKH, JMF, MPS. Supervision: MPS, JMF. JC and MKH contributed equally to this project.

Disclosure of Interests

MPS is cofounder and scientific advisory board member of Personalis, SensOmics, Qbio,

January, Akna, Filtricine, and Tailai. MPS is on the scientific advisory board of Genapsys and

Jupiter. MPS owns stock in Abcam and Epinomics.

Chen J, et al. “DNA Methylation and High-Risk Breast Cancer” 22

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Table 1. Clinical details for the 99 cancer patients.

n Cancer type Breast (only) 87 Breast + ovarian 4 Ovarian (only) 6 Other Cancer 2 Breast cancer details Histology DCIS 7 IDC 73 LCIS 1 ILC 5 Other or not specified 5 Receptor Status ER (+ | - | NA) 61 | 26 | 4 PR (+ | - | NA) 50 | 35 | 6 HER2 (+ | - | NA) 13 | 58 | 20 Number of cancers 1 66 2 27 3+ 6 Mutations identified from clinical panel testing No mutation (Affected Non-carrier) 76 Known deleterious mutation(s)* (Affected Carrier) 23 BRCA1 or BRCA2 13 Other** 11 Chemotherapy at collection Active chemotherapy 20 Previous chemotherapy 38 Never chemotherapy 41 Family history of breast/ovarian cancer Yes 59 No 40 * One individual possessed deleterious mutations in both BRCA1 and MUTYH ** Other genes include: ATM, BRCA1, BRCA2, CHEK2, MLH1, MSH2, MUTYH, PALB2, PMS2, and RAD51C.

Chen J, et al. “DNA Methylation and High-Risk Breast Cancer” 30

Table 2. Demographic details for the cancer and healthy cohorts.

Cancer Cohort Healthy Controls p Number enrolled 99 49 Age of cancer onset (years) 41.8 ± 9.7 NA Under 20 1 (1%) 20-29 8 (8%) 30-39 35 (35%) 40-49 37 (37%) 50-59 14 (14%) 60+ 4 (4%) Age at collection (years) 46.9 ± 11.1 40.1 ± 8.6 7.7x10-5 Under 29 1 (1%) 0 (Student’s t)* 30-39 30 (30%) 24 (49%) 40-49 29 (29%) 16 (33%) 50-59 24 (24%) 8 (16%) 60+ 15 (15%) 1 (2%) Race > 0.05 (χ2) Caucasian 67 (67%) 42 (86%) Non-Hispanic 61 (61%) 34 (69%) Hispanic 5 (5%) 7 (14%) Middle Eastern 1 (1%) 1 (2%) Asian 20 (20%) 3 (6%) Indian 4 (4%) 4 (8%) Black 2 (2%) 0 Multiracial 4 (4%) 0 Other or NA 2 (2%) 0 Age of Menarche (mean, years) 12.3 (±1.4) ND Menopausal status > 0.05 (χ2)** Pre-menopausal 39 (39%) 42 (86%) Treatment-induced 33 (33%) NA Peri-menopausal 4 (4%) ND Post-menopausal 23 (23%) 7 (14%) Parity No children 30 (30%) ND 1 19 (19%) 2 34 (34%) 3+ 16 (16%) Age at 1st pregnancy (mean, years) 27.6 (±5.7) ND BMI (mean) 25.8 (±5.5) ND ND = Not determined, NA = Not applicable * p=7.7x10-5 comparing age at collection; p>0.05 when comparing with age of cancer onset. ** χ2 p>0.05 when combining pre-menopause and treatment-induced menopause; χ2 p=0.023 when excluding treatment-induced menopause

Chen J, et al. “DNA Methylation and High-Risk Breast Cancer” 31

Figure 1. Methylation at the BRCA1 and BRCA2 promoters is similar across the cohort.

A) Boxplots showing the distribution of average methylation ratios between cohort subgroups in windows spanning the transcriptional start sites (TSS) for BRCA1 and BRCA2. The BRCA1 window spanned 3,368 bp, whereas the BRCA2 windows was 2 kb, beginning 1.5 kb upstream of the distal TSS and extending 500 bp beyond any other proximal TSS. Statistically significant average methylation differences are indicated by asterisks (**p < 0.01, ****p < 0.0001).

Heatmaps at base-pair resolution, however, show the absence of abnormal methylation ratios across Healthy Controls, Unaffected Carriers, and all Cancer Patients at the TSS of BRCA1 (B) and BRCA2 (C). The gene structures of BRCA1 and BRCA2 are displayed below at their appropriate locations. Thin grey rectangles indicate the UTRs and thick blue rectangles indicate

CDS; introns are represented by dashed lines. Arrows indicate putative TSS starts and the direction of transcription.

Chen J, et al. “DNA Methylation and High-Risk Breast Cancer” 32

Figure 2. DNA methylation epi-alleles are identified at known cancer-related genes.

A) Heatmap displaying individual CpG methylation ratios at the TEX14 promoter in Patient

2417 (arrow) indicates hypomethylation compared to the rest of the cohort, with 56.9% methylation compared to 90.7% methylation in healthy controls. The gene structures of TEX14 and neighboring RAD51C are represented below at their appropriate locations. B) CpG methylation states across individual sequencing reads from Patient 2417 were bimodal at 0% and 100% methylation (red line), suggesting heterozygous allelic methylation when compared to healthy controls (blue line). C) Heatmap displaying individual CpG methylation ratios at the

PTEN promoter indicates hypermethylation in Patient 2282 (arrow, 30.9% methylation compared to 1.39% methylation in healthy controls). Arrowheads indicate seven additional patients with hypermethylation to a lesser extent (18.2% methylation). D) CpG methylation states across individual sequencing reads from Patient 2282 were bimodal (red line), suggesting heterozygous allelic methylation compared to healthy controls (blue line).

Chen J, et al. “DNA Methylation and High-Risk Breast Cancer” 33

Figure 3. An intragenic epi-allele at an alternative promoter for CDH1.

A) Heatmap of CpG methylation ratios at base-pair resolution identifies Patient 2980 (arrow) with 61.8% methylation in a region upstream of an alternative start site for a CDH1 isoform.

Healthy controls averaged 94.9% methylation across this region. The gene structure of CDH1 is shown below; the thin grey rectangle indicates an alternative 5’-UTR, and the thick blue rectangle indicates the new starting coding region; intronic regions of the canonical CDH1 isoform is represented by a dashed line. An arrow indicates a potential alternative start site and the direction of transcription. B) Kernel densities of sequencing reads at this region are bimodal in Patient 2980 (red line), with reads being completely methylated or completely unmethylated, suggesting allelic methylation. Sequencing reads in the control cohort (blue line) are predominantly 100% methylated, with some reads that have 50-80% methylation.

Chen J, et al. “DNA Methylation and High-Risk Breast Cancer” 34

Figure 4. Genome-wide epialterations identified in the cancer cohort

A) Venn diagram depicting the enrichment of epialterations identified at an FDR < 0.05 and cancer-predisposition genes in the cancer cohort (p=2.65 x 10-3). Four epialterations overlapped the cancer-predisposition gene list (37) whereas none were overlapping in the control cohort (not shown). B) Heatmap of CpG methylation ratios at base-pair resolution identifies Patient 2203 (arrow) with 40.1% methylation in a region that encompasses miR-596.

Healthy controls averaged 4.01% methylation at this region. The location of miR-596 is shown below with an arrow indicating the direction of transcription. C) Kernel densities of sequencing reads at this region are bimodal in Patient 2203 (red line), with reads being completely methylated or completely unmethylated, suggesting allelic methylation. Sequencing reads in the control cohort (blue line) remain unmethylated.

Chen J, et al. “DNA Methylation and High-Risk Breast Cancer” 35

Figure 5. Allelic methylation spanning the shared promoter between TATDN1 and

NDUFB1 in Patient 14246.

A) Heatmap of individual CpG methylation ratios identifies Patient 14246 (arrow) with 43.4% methylation spanning the TSS and first exons of both TATDN1 and NDUFB1. Healthy controls average 6.6% methylation across this region. B) The methylation densities of the reads within

TATDN1 in Patient 16251 are sharply bimodal indicating allelic methylation consisting of completely methylated reads and unmethylated reads (red line). This is in contrast to the region overlapping NDUFB9 (C) where there is a broad distribution of partially methylated reads (red line). Healthy controls in both regions are unmethylated (blue lines). D) Allelic methylation within TATDN1 was confirmed by the presence of a heterozygous SNP within the DMR

(chr8:125,550,868; C>G; rs7009290). All 64 of the unmethylated reads (open circles) overlapped the reference cytosine allele whereas all 64 methylated reads (filled circles) overlapped the polymorphic guanine allele.

Chen J, et al. “DNA Methylation and High-Risk Breast Cancer” 36

Figure 6. Recurrent allelic methylation at the IGFALS promoter.

A) Heatmap displaying individual CpG methylation ratios identify a recurrent DMR in Patients

2981, 6305, 8192, and 16251 (arrows) with 59.3% methylation when compared to all other patients in the cohort, including healthy controls, who had 93.1% methylation. Publicly available

WGBS from 3 breast tumors also indicated hypomethylation at an overlapping region (bottom).

The gene structure of IGFALS is displayed below at its appropriate location. B) CpG methylation states across individual sequencing reads from these four patients were bimodal at

0% and 100% methylation (red lines), suggesting heterozygous allelic methylation when compared to healthy controls (blue line). Allelic methylation was confirmed by the presence of a heterozygous SNP within the DMR (chr16:1,844,801; T>C; rs9924224) in Patients 2981 (C),

6305 (D), 8192 (E), and 16251 (F). Reads with methylated CpGs (filled circles) were linked with the reference thymine allele, whereas reads with unmethylated CpGs (empty circles) overlapped the polymorphic cytosine allele. G) IGFALS expression is higher in breast carcinomas compared to normal breast tissues in the TCGA cohort (Average RSEM+1: 5.60 vs.3.78, p<2.2x10-16).

Chen J, et al. “DNA Methylation and High-Risk Breast Cancer” 37

Average BRCA1/2 Promoter Methylation

0.4

0.3 ** Affected Carrier Affected Noncarrier Healthy Control Unaffected Carrier 0.2 *** Methylation Ratio Methylation

0.1

BRCA1 BRCA2

B. C.

Healthy Healthy Controls Controls (n=49) (n=49)

Unaffected Unaffected Carriers Carriers (n=6) (n=6)

Cancer Cancer Patients Patients (n=99) (n=99)

Methylation Ratio 100%

75%

50%

25%

BRCA1 NBR2 0% BRCA2 Downloaded from clincancerres.aacrjournals.org on September 30, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on June 7, 2019; DOI: 10.1158/1078-0432.CCR-18-2423 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Figure 2 A. C.

Healthy Controls Healthy (n=49) Controls (n=49)

Unaffected Unaffected Carriers Carriers (n=6) (n=6)

Methylation Ratio

100% Cancer Patients 75% (n=99) Cancer Patients 50% (n=99) 25%

TEX14 RAD51C PTEN B. D.

Methylated Read Density (TEX14) Methylated Read Density (PTEN) chr17:56,769,048-56,769,761 chr10:89,623,973-89,624,168 35 15 1.2 1.0

30 Patient 2417 Density (n=99 reads) Patient 2282 Density (n=56 reads) 1.0 0.8 25

10 0.8 20 0.6

0.6 15 0.4

5 0.4 10

0.2 5 0.2 Control Cohort Density (n=1812 reads) Control Cohort Density (n=2251 reads)

0 0.0 0 0.0 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% CpG Methylation of Sequencing Reads CpG Methylation of Sequencing Reads

Methylated Read Density (CDH1) chr16:68,856,954-68,857,037

1.2

10 Patient 2980 Density (n=33 reads) Healthy 1.0 Controls 8 (n=49) 0.8

6 0.6

4 0.4 Unaffected 2 0.2

Carriers Control Cohort Density (n=3052 reads) (n=6) 0 0.0

0% 20% 40% 60% 80% 100% CpG Methylation of Sequencing Reads

Cancer Patients (n=99)

Methylation Ratio 100%

75%

50%

25%

CDH1

4 Healthy Controls (n=49)

Gene-associated Epialterations (Cancer Cohort, Unaffected Carriers n=184) (n=6)

p=2.65x10-3 (hypergeometric)

C. Methylated Read Density (miR-596) chr8:1,765,429-1,765,789 Cancer Patients 1.5 15 (n=99) Patient 2203 Density (n=107 Reads)

1.0 10

5 0.5 Control Cohort Density (n=5840 Reads) 0 0.0

0% 20% 40% 60% 80% 100% CpG Methylation of Sequencing Reads Methylation Ratio 100%

75%

50%

25% MIR596 0%

C C C C A. D. C C C C C C C C C C C C Healthy C C C Controls C C (n=49) C C C C C C C C C C C C C C Unaffected C C C Carriers C C (n=6) C C C C C C C C C C C C C C C C C C C C C C C Cancer C G G Patients G G (n=99) G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G TATDN1 NDUFB9 G G G G G G Methylation G G G Ratio Reference C 0% 25% 50% 75%100% chr8:125550868

B. Methylation Read Density (TATDN1) C. Methylated Read Density (NDUFB9) chr8:125,550,406-125,551,070 chr8:125,551,461-125,551,881

1.5 Patient 14246 Density (n=873 reads) 10 Patient 14246 Density (n=544 reads) 2.0 30

8 1.5 1.0

6 20

1.0

4 0.5 10 0.5 2 Control Cohort Density (n=10924 reads) Control Cohort Density (n=15444 reads)

0 0.0 0 0.0

0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% CpG Methylation of Sequencing Reads CpG Methylation of Sequencing Reads Downloaded from clincancerres.aacrjournals.org on September 30, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on June 7, 2019; DOI: 10.1158/1078-0432.CCR-18-2423 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Figure 6 A. C. D. C C C C C C C C C C C C Healthy C C C C Controls C C C C (n=49) C C C C C C C C C C C C C C C C T C T C T C T C T Unaffected T T T T Carriers T T T T (n=6) T T T T T T T T T T T T T T T T T T T Reference T Reference T Methylation chr16:1844801 chr16:1844801 Patient 6305 Ratio Patient 2981 Cancer 100% Patients (n=99) 75% E. F. C C C C C C 50% C C C C C C C C C C C C 25% C C C T C T C T C T C 0% T C T C T C T C T T T T T T T T T T T T T T T Reference T Reference T chr16:1844801 chr16:1844801 Breast Cancer Patient 8192 Patient 16251 Tumors (n=3)

IGFALS

Methylated Read Density (IGFALS) B. chr16:1,844,768-1,845,114 G.

10 TCGA Breast Cancer Gene Expression (IGFALS)

1.2 ****

12 8 1.0 Patient Densities

6 0.8 8 RSEM + 1) 2 0.6 4

4 0.4 2981 (n=169) 2 6305 (n=173) 8192 (n=139) 0.2 Expression (Log 16251 (n=108) Control Cohort Density (n=4122 Reads) 0

0 0.0 0% 20% 40% 60% 80% 100% Normal (n=112) Breast Carcinomas (n=1093) CpG Methylation of Sequencing Reads

High-Resolution Bisulfite-Sequencing of Peripheral Blood DNA Methylation in Early-Onset and Familial Risk Breast Cancer Patients

Justin Chen, Maria K Haanpää, Joshua J. Gruber, et al.

Clin Cancer Res Published OnlineFirst June 7, 2019.

Updated version Access the most recent version of this article at: doi:10.1158/1078-0432.CCR-18-2423

Supplementary Access the most recent supplemental material at: Material http://clincancerres.aacrjournals.org/content/suppl/2019/06/07/1078-0432.CCR-18-2423.DC1

Author Author manuscripts have been peer reviewed and accepted for publication but have not yet been Manuscript edited.

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