HIST1H2BB and MAGI2 Methylation and Somatic Mutations As Precision Medicine Biomarkers for Diagnosis and Prognosis of High-Grade Serous Ovarian Cancer Blanca L

Published OnlineFirst June 24, 2020; DOI: 10.1158/1940-6207.CAPR-19-0412

CANCER PREVENTION RESEARCH | RESEARCH ARTICLE

HIST1H2BB and MAGI2 Methylation and Somatic Mutations as Precision Medicine Biomarkers for Diagnosis and Prognosis of High-grade Serous Ovarian Cancer Blanca L. Valle1, Sebastian Rodriguez-Torres2,3, Elisabetta Kuhn4,5, Teresa Díaz-Montes6, Edgardo Parrilla-Castellar7, Fahcina P. Lawson1, Oluwasina Folawiyo1, Carmen Ili-Gangas8, Priscilla Brebi-Mieville8, James R. Eshleman9, James Herman3, Ie-Ming Shih4, David Sidransky1, and Rafael Guerrero-Preston1,10,11

ABSTRACT ◥ Molecular alterations that contribute to long-term (LT) (n ¼ 35). Immunoblot and clonogenic assays after pharma- and short-term (ST) survival in ovarian high-grade serous cologic unmasking show that HIST1H2BB and MAGI2 carcinoma (HGSC) may be used as precision medicine promoter methylation downregulates mRNA expression biomarkers. DNA promoter methylation is an early event levels in ovarian cancer cells. We then used qMSP in paired in tumorigenesis, which can be detected in blood and urine, tissue, ascites, plasma/serum, vaginal swabs, and urine from making it a feasible companion biomarker to somatic muta- a third cohort of patients with HGSC cancer (n ¼ 85) to test tions for early detection and targeted treatment workflows. the clinical potential of HIST1H2BB and MAGI2 in precision We compared the methylation profile in 12 HGSC tissue medicine workflows. We also performed next-generation samples to 30 fallopian tube epithelium samples, using the exome sequencing of 50 frequently mutated in human cancer Infinium Human Methylation 450K Array. We also used genes, using the Ion AmpliSeqCancer Hotspot Panel, to 450K methylation arrays to compare methylation among show that the somatic mutation profile found in tissue and HGSCs long-term survivors (more than 5 years) and short- plasma can be quantified in paired urine samples from term survivors (less than 3 years). We verified the array patients with HGSC. Our results suggest that HIST1H2BB results using bisulfite sequencing and methylation-specific and MAGI2 have growth-suppressing roles and can be used PCR (qMSP). in another cohort of HGSC patient samples as HGSC precision medicine biomarkers.

Introduction 1Otolaryngology Department, Head and Neck Cancer Research Division, The Johns Hopkins University, School of Medicine, Baltimore, Maryland. 2Depart- Ovarian cancer is the fifth cause of cancer-related deaths ment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, among women, and the most lethal gynecologic malignancy (1). Massachusetts. 3Department of Medicine, University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania. 4Division of Pathology, Fondazione IRCCS Clinical and molecular factors that contribute to long-term Ca’ Granda, Ospedale Maggiore Policlinico; Department of Biomedical, Surgical, (LT) and short-term (ST) survival in ovarian high-grade serous and Dental Sciences, University of Milan, Italy. 5Departments of Pathology, cancer (HGSC) are lacking and only a few molecular alterations Gynecology and Obstetrics, The Johns Hopkins University, School of Medicine, of response to therapy have been identified. Somatic mutations Baltimore, Maryland. 6The Lya Segall Ovarian Cancer Institute, Mercy Medical Center, Baltimore, Maryland. 7Department of Pathology, University of Washing- are rare in HGSC (2), BRCA1/2 germline mutations, and ton, Seattle, Washington. 8Laboratory Integrative Biology (LIBi), Center for homologous repair deficiency in HGSC are among the few Excellence in Translational Medicine-Scientific and Technological Bioresources validated molecular predictors of response to platinum therapy Nucleus (CEMT-BIOREN), Universidad de La Frontera, Temuco, Chile. 9Depart- – ment of Pathology, Johns Hopkins University, School of Medicine, Baltimore, and PARP inhibitors (3 6). Maryland. 10University of Puerto Rico School of Medicine, Department of DNA promoter methylation is an early event in tumorigen- Obstetrics and Gynecology, San Juan, Puerto Rico. 11LifeGene Biomarks Inc., esis, and can be detected in blood and other body fluids, making San Juan, Puerto Rico. it a feasible biomarker for early detection of tumors.(7–9) In Note: Supplementary data for this article are available at Cancer Prevention addition, DNA methylation has potential as a prognostic Research Online (http://cancerprevres.aacrjournals.org/). biomarker. For instance, the FDA has recently approved Corresponding Author: Rafael Guerrero-Preston, University of Puerto Rico EpiColon, a blood-based test for diagnosis of colorectal cancer School of Medicine, San Juan, PR 00927. E-mail: [email protected] based on methylation of septin 9 (10). Detection of promoter Cancer Prev Res 2020;13:1–12 methylation in tissues and biofluids represents a potential doi: 10.1158/1940-6207.CAPR-19-0412 biomarker strategy for ovarian cancer diagnosis and therapeu- 2020 American Association for Cancer Research. tic management within precision medicine workflows.

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Until recently, epithelial ovarian cancers were thought to Library kit 2.0 according to the manufacturer’s instructions arise from the ovarian surface epithelial cells (11). However, (Life Technologies). Included in this panel were primers for 207 recent studies suggest that many of HGSCs arise from lesions in amplicons covering 2,800 Catalog of Somatic Mutations in the fallopian tubes (12–17). In this study, we sought to identify Cancer of 50 genes with known cancer associations: ABL1, genes differentially methylated between fallopian tube tissue AKT1, ALK, APC, ATM, BRAF, CDH1, CDKN2A, CSF1R, and HGSC and test whether ovarian cancer associated DNA CTNNB1, EGFR, ERBB2, ERBB4, EZH2, FBXW7, FGFR1, methylation and somatic mutations measured in tissue can be FGFR2, FGFR3, FLT3, GNA11, GNAS, GNAQ, HNF1A, HRAS, reproducibly measured in urine samples. IDH1, JAK2, JAK3, IDH2, KDR, KIT, KRAS, MET, MLH1, MPL, NOTCH1, NPM1, NRAS, PDGFRA, PIK3CA, PTEN, Materials and Methods PTPN11, RB1, RET, SMAD4, SMARCB1, SMO, SRC, STK11, TP53 and VHL. (COSMIC, http://cancer.sanger.ac.uk/cancer Patient samples genome/projects/cosmic). Ten nanograms DNA from the tumor The study population consists of samples from three patient samples was used as the template to prepare the library. cohorts (n ¼ 77) and publicly available data from 1,742 Amplified libraries were quantified using the Qubit 2.0 Fluo- patients: a retrospective cohort of HGSC formalin fixed and rometer and the High Sensitivity Qubit Assay Kit (Life Tech- paraffin embedded (FFPE) samples selected from Johns Hop- nologies). Amplified libraries were assessed for quality (size and kins Pathology Department tumor bank (n ¼ 12); a cohort of concentration) using the Agilent 2100 Bioanalyzer Instrument women who were seen in the Ohio State University School of (Agilent Technologies) following the Bioanalyzer standard Medicine Obstetrics and Gynecology Department (n ¼ 30); a protocol. The AmpliSeq libraries were clonally amplified on cohort of patients with HGSC who were seen in Mercy Medical to Ion Sphere Particles (ISP) using emulsion PCR following Center in Baltimore, MD (n ¼ 35); and data from the Cancer standard Ion Torrent protocols. ISP preparation was per- Genome Atlas Project (TCGA). The inclusion criterion was to formed using the automated Ion Torrent OneTouch2 system have a clinical diagnosis of HGCS (ICD9-CM code 183), all following the manufacturer’s protocol (MAN0007220 Revision determined by pathologists at two different institutions Hop- 4.0). The Qubit Fluorometer was used to assess ISP quality after kins and Mercy Medical Center. The Institutional Review ISP preparation but before ISP enrichment. Up to eight speci- Boards of Ohio State School of Medicine, Mercy Medical mens were barcoded with Ion Xpress Barcode Adapters (Life Center and Johns Hopkins School of Medicine (NA_00020633) Technologies), pooled, and run on a single Ion 318 chip. This approved the research protocols. Informed written consent was includes multiple patient samples and one control, which we obtained from all patients included in the study. rotate among water, normal, and a mix of positive control cell lines. Cancer genome atlas project data TCGA data was downloaded and analyzed for DNA meth- Methylation 450K arrays ylation alterations using the minfi package. Somatic mutation We sought to determine genes differentially methylated in and expression data were downloaded from the cBioPortal ovarian cancer as compared with fallopian tube epithelium, (http://www.cbioportal.org/). therefore we compared the methylation status in 12 HGSC FFPE tissue samples to 30 fallopian tube epithelium samples DNA extraction using the genome-wide Infinium HumanMethylation 450K DNA was extracted from frozen normal fallopian tube Array. The HumanMethylation450K DNA BeadChip assay epithelium, FFPE HGSC tissue samples, as well as from normal was used to perform unbiased genome-wide DNA methylation and ovarian cancer cell lines. Biofluids DNA was extracted as analysis. Bisulfite modification of genomic DNA (2 mg) was described previously (11, 18). The protocol for trans-renal performed with EpiTect Bisulfite Kit (Qiagen) according to the DNA extraction reduces the possibility of contamination from manufacturer’s protocol. We hybridized bisulfite-converted urinary tract DNA, by selecting for small and extracellular DNA to the HumanMethylation450K array to identify differ- DNA. Samples were digested using 1% SDS and 20 ug/mL entially methylated regions (DMR) in HGSC FFPE samples proteinase K for 48–72 hours at 48C, followed by phenol/ (n ¼ 12) and normal fallopian tube epithelium frozen tissue chloroform extraction and ethanol precipitation. samples (n ¼ 30). To validate these results, we performed quantitative methylation-specific PCR (qMSP) in another Discovery with next-generation sequencing cohort of HGSC patient samples (n ¼ 35). We examined the Ion AmpliSeqCancer Hotspot Panel v2 to We read the data into R using the illuminaio package (19). profile 50 frequently mutated in human cancer genes in FPPE For data normalization, we used the minfi package to apply the tissue DNA from four patients with short-term survival and 4 Noob background subtraction and dye-bias correction (20) patients with long-term survival, the eight of which looked followed by normalization and identification of DMRs between identical under the microscope in a pathology laboratory. The cases and controls (21). The minfi package provides tools for cancer Hotspot Panel was also examined paired tissue, plasma, analyzing Illumina’s methylation arrays and includes methods and urine samples from two patients with HGSC. Libraries for for preprocessing, quality assessment, and detection of differ- the discovery cohort were generated using the Ion AmpliSeq entially methylated regions from the kilobase to the megabase

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scale. We performed preprocessing with the minfi package MAGI2, and the reference gene ACTB provide the relative applying a version of subset quantile normalization to the methylation level (100 target gene/reference gene). Meth and Unmeth intensities separately. The distribution of type I and type II probes was forced to be the same by first Reverse-transcription RT-PCR quantile normalizing the type II probes across samples and Total RNA was assessed for HIST1H2BB, MAGI2, and then interpolating a reference distribution to which the type I GAPDH expression levels using quantitative real-time reverse probes are normalized. The stratified quantile normalization transcription (RT)-PCR (TaqMan). Reverse transcription was method is implemented by the preprocessQuantile function performed with random hexamer primers and Superscript II (the function does no background correction and removes Reverse Transcriptase (Invitrogen Corp.) according to manu- zeros using the fix2 MethOutlier function). This algorithm facturer’s instructions. Quantitative RT-PCR was then per- relies on the assumptions necessary for quantile normaliza- formed on the Applied Biosystems 7900 Sequence Detection tion and involves both within- and between-sample normal- Instrument (Applied Biosystems) using TaqMan expression ization. We then intersected the statistically significant assays (Life Technologies). DMRs (FWER P < 0.05) that discriminate ovarian cancer 0 from fallopian tube epithelium and DMRs that discriminate 5-Aza-2 -deoxycytidine and Trichostatin A treatments HGSC lesions with short- (<2 years) and long-term survival Ovarian cancer cells were incubated in the presence or (>5years). absence of 2.5 mmol/L 5-Aza-2-deoxycytidine for 4 days. Tri- chostatin A (TSA) was added on the last day and total RNA was Cell culture extracted, and RT-PCR performed to measure MAGI2 mRNA Normal ovarian cell line Ose2a was grown in DMEM-F12. expression. The ovarian cancer cell lines OVCAR5 and SkOV3 were cultured in McCoy media. Ovarian cancer cell line IGROV siRNA transfections and clonogenic assays was cultured in RPMI1640 and CaOV3 cells were grown in CaOV3 and OVCAR5 cells were transfected with 25 nmol/L DMEM. All cells were grown in the presence of penicillin/ MAGI2 siRNA (Santa Cruz Biotechnology; sc) or control streptomycin (100 U/mL penicillin and 100 mg/mL strepto- siRNA (Dharmacon). Total RNA was extracted 48 hours mycin) and supplemented with 10% FBS. posttransfection and RT-PCR analysis of MAGI2 mRNA expression was performed. For clonogenic assays, 48 hours Bisulfite sequencing after transfection, cells were plated in 6-well plates and allowed Bisulfite conversion of 1–2 mg of genomic DNA (from to grow for approximately 10 days. Colonies were then visu- ovarian cancer cell lines, normal fallopian tube tissue, and alized by crystal violet (0.5% crystal violet in 50% methanol) HGSC FFPE tissue) was performed using the EpiTect Bisulfite staining. kit (Qiagen) and used for amplification by qMSP. Bisulfite- converted DNA from the ovarian cancer cell lines was ampli- Immunoblotting analysis fied by touchdown PCR, using TaqMan primers and probes Cells were washed and cell lysates were prepared in RIPA designed to a region in the promoter of HIST1H2BB or MAGI2. lysis buffer (150 mmol/L Tris-HCl, pH 6.8, 25% glycerol, This amplified region is contained within the area detected in and 5% SDS). Cell lysates were separated by SDS-PAGE on the methylation arrays and comprises 13 CpG sites in 4%–12% or 10%–20% Tris-Glycine gels and transferred to HIST1H2BB, and 23 CpG sites in MAGI2. These primers polyvinylidene difluoride (PVDF) membranes. The mem- amplify the methylated and unmethylated sequences. The branes were blocked with TBS-T þ 5% nonfat dry milk and products of touchdown PCR were run on a 1.5% agarose gel incubated overnight at 4 C with antibodies specific for the and the corresponding DNA bands were excised from the gel indicated proteins (H2B, MAGI2, or GAPDH). After washing, and extracted using the Gel extraction kit (Qiagen). DNA was the membranes were incubated with horseradish peroxidase– sent for bisulfite-sequencing to Genewiz. Sequences were man- conjugated secondary antibodies. Protein detection was per- ually analyzed to determine whether these regions of the formed by enhanced chemiluminescence (Amersham). HIST1H2BB and MAGI2 promoters were methylated; methylated cytosines remain as cytosines while unmethylated cyto- Identification of DNA methylation and somatic mutation sines convert to uracils after bisulfite conversion and uracils alterations in FFPE samples, cervical swabs, plasma, and then convert to thymines during amplification (22). urine Spearman rank correlation coefficient calculation was con- Quantitative methylation-specific PCR ducted on all pairwise comparisons of normalized DNA meth- Specific TaqMan primers and probes were designed to ylation ratios measurements in the biological specimen types amplify the bisulfite-converted promoter region of (tumor, cervical swabs, serum/plasma, ascites, and urine) to HIST1H2BB and MAGI2, primers amplify the methylated determine a nonparametric measure of correlation. For these DNA. Titration of normal human methylated DNA (Zymo same measurement comparisons, we conducted pairwise Research) was used to generate a standard curve for absolute Wilcoxon signed-rank tests. We also plotted box plots quantification. The ratio between the values of HIST1H2BB or with square root transformed y-axis of normalized DNA

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methylation ratio measurements to visualize differences in 6,000 bp from the promotor and with L-values ≥4 when biological specimen type means among matched pairs from comparing short term and long term HGSC survivors, obtained the same patients. Each gene’s pairwise tests (9 each, for which in a previous analysis. Wilcoxon signed-rank tests could be conducted) were consid- For the paired samples, we compared the inter-rater agree- ered independent families of tests, therefore did not require ment of Ion AmpliSeq Cancer Hotspot Panel (Life Technol- adjusting alpha for the total pairwise tests. Because the results ogies Corporation) somatic genotype prediction in cancer would be interpreted as exploratory rather than definite and related genes using Cohen kappa coefficient. Two versions of each comparison could be considered to address a question this analysis were conducted: One in which all missing pre- with considerably different implications (e.g., discovering more dictions for SNVs were kept in the analysis; and another one methylated gene in urine than in serum/plasma has a consid- which only kept SNVs with predictions for both compared erably different practical implication than discovering more measures. methylated gene in ascites than in tumor), we considered each pairwise comparison to belong to a family of a secondary Results hypothesis and therefore kept each pairwise alpha at 0.05. HIST1H2BB and MAGI2 are differentially methylated in Supporting this choice, the false discovery rate of rejecting the HGSC null hypothesis of no difference when it is true is expected to be Our genome-wide analysis identified 161 gene-associated no more than 5% due to random chance for each gene at the DMRs that differ between HGSC and fallopian tube epithelium unadjusted alpha of 0.05; however, 22% of comparisons for fi samples. These genes are involved in regulation of transcription each gene had signi cant differences at the 0.05 level. (GO:0045449 and GO:0006355); regulation of metabolic pro- We analyzed the concordance of somatic genomic variants cess (GO:0051252); regulation of apoptosis (GO:004298); reg- across four biological specimen types: paired FFPE samples, ulation of programmed cell death (GO:0043067); and cervical epithelium, plasma, and urine in two patients with (GO:0010941). Specifically, we found HIST1H2BB and MAGI2 HGSC. promoters were the most differentially methylated regions, by P For the FFPE samples, we tested differences of proportions value measurement, when comparing HGSC with normal between survival groups for all loci mutated at least once in each fi fallopian tube samples (Fig. 1A) and, in subset analysis, both survival group, for every speci c locum at the single nucleotide promoters gained methylation in patients with HGSC with variants (SNV) level, and by gene for all SNV loci per gene in long-term survival (n ¼ 6) compared with patients with short- the Hot Spot panel. We also constructed logistic regression term survival (n ¼ 6; Fig. 1B). Consistent with our 450K array models for each binary DNA methylation-mutation combina- results, our quantitative MSP (qMSP) results confirmed higher tion coded, as dependent variable, mean methylation beta value methylation levels of HIST1H2BB and MAGI2 in patients with across all CpGs in a gene (where ≤0.3 was coded as nonmethy- > long-term survival compared with short-term survivors lated and 0.3 was coded as methylated and ordinal coded, as (Fig. 1C). Together, our results suggest that HIST1H2BB and independent variable, each SNV loci per gene. Because of the MAGI2 are differentially methylated in HGSC. small sample size (n ¼ 8), we calculated the P value from the likelihood ratio comparing the model with the predictor SNV HIST1H2BB and MAGI2 are methylated in ovarian cancer loci per gene to the null model of a simple average of the cell lines and methylation is inversely correlated with dependent variable, because this is the most accurate method expression for calculating P values with such low sample sizes. Histograms To determine whether HIST1H2BB and MAGI2 were also were analyzed and summary statistics were calculated for the methylated in established ovarian cancer cell lines, we per- gene count data of significant associations, before adjustment. formed bisulfite-sequencing and qMSP analysis. We designed We performed FDR adjustments for each analysis with more primers to a region of the HIST1H2BB promoter spanning than one test, and either reported based only on the adjusted P 300–500 bp upstream of the TSS. This region comprises 13 values or noted where adjusted P values became nonsignificant. CpG sites also contained within the differentially methylated We set our alpha at 0.05 for these analyses. We prepared a region identified by the methylation array. As shown in Sup- clustered heatmap of all significant associations between each plementary Fig. S1A, all CpG sites in this HIST1H2BB pro- SNV loci per gene and DNA methylation status per gene. In this moter region are methylated in two of the ovarian cancer cell figure, every SNV loci per gene were denoted in the rows while lines examined, CaOV3 and IGROV as compared with normal DNA methylation status per gene were denoted in the columns. ovarian cells Ose2a, which is completely unmethylated. For this Intersections between specific rows and columns represent the gene, one of the cell lines, SkOV3 was partially methylated and association for that mutation–methylation pair, where a green OVCAR5 was completely unmethylated. To confirm our spot signifies that the pair has a significant association and a results, we also performed qMSP analyses using methylation blank spot signifies that the pair does not have a significant specific TaqMan primers and probes to the methylated region association. Because of the large amount of DNA methylation of HIST1H2BB. CaOV3 and IGROV had higher methylation measurements per gene, we only identified in the x-axis genes levels, whereas OVCAR5 and SkOV3 had lower methylation, with significantly different methylated regions (DMR) less than consistent with our bisulfite-sequencing results.

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Figure 1. HIST1H2BB and MAGI-2 are differentially methylated in HGSC as compared with normal fallopian tube epithelium; and in HGSCs of patients with long-term survival as compared with short-term survival. DNA was extracted, bisulﬁte-converted and analyzed by 450K Methylation arrays and qMSP. A, Dot plots of methylation levels (Beta values) on promoter CpG sites of HIST1H2BB and MAGI-2 in 30 normal fallopian tube samples (red) as compared with 12 HGSC samples (black). B, Dot plots of methylation levels (Beta values) on promoter CpG sites of HIST1H2BB and MAGI-2 in 6 HGSC samples with long-term survival (red) as compared with 6 HGSC samples with short-term survival (black). C, qMSP analysis of DNA from 6 HGSC tissue samples of long-term survivors (blue) as compared with 6 HGSC tissue samples of short- term survivors (red), using primers to promoter CpG sites of HIST1H2BB or MAGI-2. Overall survival time in months is shown. D, qMSP analysis of DNA from ovarian cancer cells and a normal ovarian cell line (Ose2a), using primers to promoter CpG sites of HIST1H2BB or MAGI-2. E, RT-PCR analysis in ovarian cancer cells and a normal ovarian cell line, using primers to measure mRNA expression of HIST1H2BB or MAGI-2.

We also analyzed methylation of the MAGI2 promoter by expression levels (Fig. 1E). For MAGI2, most cells have low bisulﬁte-sequencing and qMSP. MAGI2 was completely meth- mRNA expression levels consistent with increased methylation ylated in 23 CpG sites in the promoter region in one of the (Fig. 1E). cancer cell lines examined, OVCAR5, and partially methylated We performed immunoblots to determine whether the in IGROV cancer cells. In contrast, normal ovarian cells, were expression of HIST1H2BB and MAGI2 was also decreased at completely unmethylated (Supplementary Fig. S1B). Results of the protein level. H2B.F is the protein product of HIST1H2BB, qMSP analysis show MAGI2 methylation in all ovarian cancer and its levels were lower in the 4 ovarian cancer cell lines cell lines, in OVCAR5 and IGROV, as well as in 2 additional cell examined, as compared with normal ovarian cells. Similarly, lines examined. Therefore, our results suggest that HIST1H2BB MAGI2 protein levels were lower in the ovarian cancer cells, as and MAGI2 are methylated in ovarian cancer cells (Fig. 1D). compared with normal ovarian cells. Taken together, these We performed real-time RT-PCR to measure mRNA expres- results suggest that methylation of HIST1H2BB and MAGI2 sion of HIST1H2BB and MAGI2 in ovarian cancer cells to could lead to their decreased mRNA and protein expression in determine whether HIST1H2BB and MAGI2 promoter meth- ovarian cancer cells (Supplementary Fig. S2A). ylation was associated with silencing of expression. Methyla- tion of HIST1H2BB and MAGI2 is inversely correlated with MAGI2 is reexpressed upon cell treatment with 0 mRNA expression in most cell lines. CaOV3 and IGROV demethylating agent 5-aza-2 -deoxycytidine which have higher HIST1H2BB methylation levels have lower Inhibiting methylation should cause reexpression of MAGI2 HIST1H2BB mRNA expression, and SkOV3 and OVCAR5 or HIST1H2BB if methylation is contributing to their silencing have lower HIST1H2BB methylation levels and higher mRNA of expression. Therefore, we treated ovarian cancer cells

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with demethylating agent 5-aza-20-deoxycytidine to determine these genes are known tumor suppressors. However, neither of whether methylation might be involved in silencing of MAGI2 these two differences at the SNV level remained statistically or HIST1H2BB. Silencing of expression by methylation is often significant after FDR adjustment, possibly due to the lower accompanied by deacetylation, for that reason we also treated sample size of measured loci at the SNV level for each test cells with a deacetylase inhibitor, Trichostatin A. In all ovarian (Supplementary Table S2). cancer cells, treatment with 5-aza-20-deoxycytidine with or A considerable fraction (15%) of combinations of differen- without TSA increased expression of MAGI2 (Supplementary tially methylated regions (DMR) associations with gene muta- Fig. S2B). This suggests that MAGI2 expression is regulated by tions were found to have unadjusted P values under 0.05, which methylation in ovarian cancer cells. is three times what is expected by chance; however, none of these 62,272 significant associations out of 426,104 tests MAGI2 downregulation increases survival of ovarian remained significant after FDR adjustment that took into cancer cells account all conducted tests probably due to small sample sizes To determine whether the silencing of MAGI2 might have a available for these calculations. These significant, yet unad- role in the growth of ovarian cancer cells, we set to down- justed associations displayed some interesting patterns, name- regulate MAGI2 in CaOV3 and OVCAR 5 cells, which have ly, in these association with P values below 0.05, only 6,154 out protein expression of MAGI2 (Supplementary Fig. S3A and of 8696 DMRs measured (71% of DMRs, or 47% taking false S3B). We downregulated MAGI2 with siRNA and performed positives into account) were represented in this group, and the clonogenic assays (Supplementary Fig. S3C). Downregulation 6,191 DMRs were associated with ≤5 SNVs or ≥35 SNVs out of of MAGI2 expression increased survival of ovarian cancer cells the 49 gene mutations in the Hot Spot panel, while the 49 in CaOV3 and OVCAR5 cells, evidenced by an increased mutated genes displayed a normal distribution, of each being number of colonies in the MAGI2 siRNA–treated cells. These associated with an average of 1,271 DMRs (SD of 402 DMRs) results suggest that the presence of MAGI2 has a growth- out of 8,696 DMRs (15% of DMRs; Supplementary Table S3). A inhibitory role and inhibiting its expression by methylation heatmap was created to visualize and confirm these patterns we might confer a growth advantage to the ovarian cancer cells. found in the significant associations (Supplementary Fig. S4).

Identification of genomic and epigenomic alterations in Identification of genomic and epigenomic alterations in FFPE samples from short-term and long-term survival cervical swabs, plasma, and urine patients We found somatic mutations and single nucleotide variants Statistically significant differentially mutated genes were (SNVs) in paired tissue from cervical swabs, serum/plasma and usually not mutated in the short survival samples. In statisti- urine, showing that liquid biopsies, which are measured in cally significant differentially mutated genes, the proportion of blood/plasma, can also be measured in urine with high con- mutations by gene was always higher in the long survival group, cordance (Supplementary Tables S4 and S5). To quantify how with the exception of the CSF1R gene, which was more mutated faithfully these genotypes were identified in urine, we calcu- in the short survival group. The CSF1R (Colony Stimulating lated a concordance statistic using Cohen kappa coefficient, Factor 1 Receptor) gene plays an important role in innate which revealed that for SNVs present in each comparison pair, immunity and in inflammatory processes (Supplementary the correlation was 0.84, 0.77, and 0.92 for Tissue-Plasma, Table S1). Tissue-Urine, and Plasma-Urine in patient TG01, and 0.6, 0.6, The proportion of mutated alleles at the SNV level in the long and 1, respectively, for patient TG04 (Table 1). These data survival group was statistically significantly larger than the represent concordance of genomic variant detection in paired proportion in the short survival group. The only significantly tissue, plasma, and urine samples, with very high concordance different mutation proportions at the SNV level were the chr17 between plasma and urine. 7578395 G!A allele in the rs587780070 SNV of the TP53 gene We also found HIST1H2BB and MAGI2 promoter methyl- and the chr9 21971120 G!A allele in the rs121913388 SNV of ation in cervical swabs, plasma/serum, and urine in a subset of the CDKN2A gene (GRCh37), with long survival samples the samples tested, suggesting that these could be used as having more of the mutated TP53 allele and short survival noninvasive early detection biomarkers. Our data suggest samples having more of the mutated CDKN2A allele. Both of vaginal swab DNA has lower DNA methylation levels when

Table 1. Cohen Kappa (95% conﬁdence interval and percent agreement) for somatic mutations measured in tumor, plasma, and urine.

Specimen Patient 1 (without Patient 2 (without comparisons Patient 1 missing predictions) Patient 2 missing predictions)

Tissue-plasma 0.68 (0.42–0.93); 71% 0.84 (0.62–1.06); 86% 0.18 (0.06–0.42); 27% 0.6 (0.19–1.01); 67% Tissue-urine 0.61 (0.34–0.89); 88% 0.77 (0.51–1.02);79% 0.23 (0.02–0.47); 32% 0.6 (0.19–1.01); 67% Plasma-urine 0.87 (0.69–1.05); 88% 0.92 (0.76–1.08); 93% 0.82 (0.63–1.01); 86% 1 (1,1); 100%

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ABHIST1H2BB Tumor TrDNA Serum/plasma Vaginal swab Ascites MAGI2 Tumor TrDNA Serum/plasma Vaginal swab Ascites

2.0

0.6 0.03 0.05

0.03 0.02 1.5

0.4

1.0

0.2 0.5 Square root of methylation (sqrt(100*MAGI2/ACTB)) of methylation Square root Square root of methylation (sqrt(100*HIST1H2BB/ACTB)) of methylation Square root

0.0 0.0

Tumor TrDNA Serum/plasma Vaginal swab Ascites Tumor TrDNA Serum/plasma Vaginal swab Ascites

Figure 2. Magnitude of methylation differs in DNA from paired tumor, urine (trans-renal DNA -TrDNA), plasma/serum, vaginal swabs, and ascites specimens. HIST1H2BB and MAGI2 methylation was measured by quantitative Methylation Specific PCR (qMSP) and compared in DNA from paired tumor, urine (TrDNA), plasma/serum, vaginal swab, and ascites specimens. A, Boxplots show that qMSP levels of HIST1H2BB were significantly higher in TrDNA compared with vaginal swabs (P ¼ 0.03), and higher in serum/plasma compared with vaginal swabs (P ¼ 0.03). B, Boxplots show that qMSP levels of MAGI2 methylation were higher in TrDNA compared with serum/ plasma (P ¼ 0.02), and higher in TrDNA compared with vaginal swabs (P ¼ 0.05). compared with trans-renal (tr-DNA) DNA from urine Promoter DNA methylation, which occurs on cytosine (HIST1H2BB P ¼ 0.03; MAGI2 P ¼ 0.05) and serum/plasma nucleotides across CpG islands, results in gene silencing and DNA (HIST1H2BB P ¼ 0.03; MAGI2 P ¼ 0.02) for the genes represents a major epigenetic alteration in human cancer. measured. The data also indicate TrDNA is a better indicator of Methylationspecific PCR can amplify these modifications as DNA methylation status than serum/plasma in the genes candidate biomarkers in cancer cells. Such candidate bio- measured (Fig. 2). The data also suggest the different biological markers are widely scattered across the entire genome, specimen measures are correlated. The correlation between amounting to a total of 200300 promoter-methylated DNA methylation measures between tumor and TrDNA were genes (25). After rigorous validation, superior biomarker 1 and 0.5 for HIST1H2BB and MAGI2, respectively. The candidates representing cancerspecific methylation are correlation between DNA methylation measures between now considered ready for use in clinical decisionmaking tumor and ascites were 1 for both HIST1H2BB and MAGI2. in the development of therapeutic strategies, with possible The correlation between tumor and vaginal swab DNA meth- use extending even to cases where cancer surgery is ylation was 0.5 in both HIST1H2BB and MAGI2. indicated (26–29). In this study, we have used epigenome wide and molecular biology tools to show that HIST1H2BB and MAGI2 are newly Discussion described tumor suppressor genes (TSG) differentially meth- DNA methylation in HGSC ylated in HGSC tissue samples when compared with normal Epigenetic alterations are found in primary human cancers, fallopian tube epithelium. We show that the expression of and such aberrations are composed of DNA methylation and HIST1H2BB and MAGI2 is reduced in ovarian cancer cells with its linked histone modification. DNA methylation occurs in promoter methylation of these genes, a hallmark of TSGs. We cytosine residues among the CpG islands of the promoter also show that HIST1H2BB and MAGI2 promoter methylation regions of individual genes (23). Methylated cytosines can be discriminates between long-term and short-term HGSC sur- bound by methylCpGbinding protein 2 (MeCP2), and the vivors. Finally, we use massively parallel exome sequencing and resulting proteinnucleotide can be incorporated into protein molecular biology tools to show that HIST1H2BB and MAGI2 complexes that include histone modification enzymes leading promoter methylation and HGSC-associated somatic muta- to dynamic changes in chromatin structure (24). As a result, tions, can be measured in tissue, plasma, and urine samples. DNA methylation can result in gene silencing due to impaired Together, we identify molecular markers that can be added access of transcription factors through condensed and closed to precision medicine workflows for HGSC diagnosis and chromatin. treatment.

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The MAGUK Inverted 2 (MAGI2) gene codes a PTEN- tion element. This gene is found in the large histone gene cluster interacting scaffold protein implicated in cancer on the basis on chromosome 6p22-p21.3 of rare, recurrent genomic translocations and deletions in Covalent modifications of histones play a crucial role in the various tumors. In the renal glomerulus, MAGI2 is exclu- regulation of gene expression. HIST1H2BB impacts transcrip- sively expressed in podocytes, specialized cells forming part tional regulation and elongation, mostly by ubiquitination. of the glomerular filter, where it interacts with the slit Histone H2B monoubiquitination (H2Bub1) has mainly been diaphragm protein nephrin (30). This encoded protein is described as a regulator of transcription elongation. Genome- characterized by two WW domains, a guanylate kinase-like wide profiles show that H2Bub1 levels are negatively correlated domain and multiple PDZ domains, the structural similarity with the accessibility of enhancers to transcriptional activators. of the membrane-associated guanylate kinase homolog The chromatin association of histone variant H2A.Z, which is (MAGUK) family. MAGI2 is a tight junction protein in evicted from enhancers for transcriptional activation, is stabi- epithelial tissues (31). lized by H2Bub1 by impairing access of the chromatin remo- In our study, MAGI2 exhibited a growth-inhibitory role in deler INO80. Thus, H2Bub1 acts as a gatekeeper of H2A.Z ovarian cancer cells because survival of ovarian cancer cells eviction and activation of inducible enhancers (39). H2B OVCAR5 and CaOV3 increased upon silencing of MAGI2 with ubiquitination (uH2B) also promotes histone eviction at dou- siRNA. MAGI2 promoter methylation has been observed in ble stranded breaks independent of resection or ATP- cervical scrapings from patients with endometrial and ovarian dependent chromatin remodelers. Cells lacking uH2B, or its cancer (32). MAGI2 is significantly methylated in Cervical E3 ubiquitin ligase Bre1, exhibit hyper-resection due to the loss Intraepithelial Neoplastic grade 3þ lesions. MAGI2 is poten- of H3K79 methylation (40). H3K79 methylation by the histone tially implicated in b-catenin signaling, suggesting the epige- methyltransferase Dot1 affects gene expression and the netic dysregulation of this signaling pathway during cervical response to DNA damage and is enhanced by monoubiquiti- cancer development (33). MAGI2 methylation, as well as nation of the C-terminus of histone H2B (H2Bub1). However, inactivating mutations of MAGI2, have also been reported in Dot1 and H2Bub1 are subject to bi-directional crosstalk and prostate cancer (34, 35), suggesting importance of MAGI2 Dot1 possesses chromatin regulatory functions that are inde- silencing across cancers. MAGI2 mRNA expression is pendent of its methyltransferase activity (41). Regulation of decreased in prostate cancer cells and patient samples, and Dot1-mediated H3K79 methylation and Set1-COMPASS– inclusion of MAGI2 in a biomarker gene panel improved the mediated H3K4 methylation by H2BK123 ubiquitination ability of the panel to discriminate between benign hyperplasia (H2Bub1) are evolutionarily conserved trans-histone cross- samples and prostate cancer (36). talk mechanisms. Ubiquitin acts as a “glue” to bind the MAGI2 seems to have tumor suppressor roles in cancer, nucleosome together for supporting Dot1/Set1-COMPASS among its roles it has been reported that MAGI2 interacts with functions (42, 43). Ubiquitylation of histone H2B at lysine tumor suppressor protein PTEN and inhibits AKT signal- residue 120 (H2BK120ub) is a prominent histone posttrans- ing (37), and increased PTEN protein stability and decreased lational modification (PTM) associated with the actively AKT activation induced by MAGI2 also inhibits proliferation transcribed genome. De novo ubiquitylation of H2BK120 is and migration of hepatocellular carcinoma cells (38). Alto- found to be highly sensitive to PTMs on the N-terminal tail gether, these reports suggest a growth-inhibitory role for of histone H2A, a crosstalk that extends to the common MAGI2 and that methylation and silencing could confer a histone variant H2A.Z (44). growth advantage to cancer cells. The impact of HIST1H2BB promoter methylation is not HIST1H2BB (Histone Cluster 1 H2B Family Member B) is a described in the literature. Silencing of HIST1H2BB by meth- protein coding gene. Among HIST1H2BB related pathways are ylation might be expected to lead to alterations in transcrip- Meiosis and Signaling by Rho GTPases. Gene Ontology (GO) tional regulation and elongation. Because of the role of H2B in annotations related to HIST1H2BB include sequence-specific the maintenance of nucleosome structure, silencing of DNA binding and protein heterodimerization activity. An HIST1H2BB could lead to chromatin remodeling and disrup- important paralog of HIST1H2BB is HIST1H2BN. tions in the general nucleosomal structure, impacting gene Histones are basic nuclear proteins that are responsible for expression. In this study, we found HIST1H2BB methylated in the nucleosome structure of the chromosomal fiber in eukar- ovarian cancer tissue and its expression reduced at the mRNA yotes. Nucleosomes consist of approximately 146 bp of DNA and protein levels in ovarian cancer cell lines. wrapped around a histone octamer composed of pairs of each In sum, we identified the promoter regions of HIST1H2BB of the four core histones (H2A, H2B, H3, and H4). The and MAGI2 to be differentially methylated in tumor when chromatin fiber is further compacted through the interaction compared to fallopian tube epithelium. We also found that of a linker histone, H1, with the DNA between the nucleosomes HIST1H2BB and MAGI2 promoter methylation discriminates to form higher order chromatin structures. This gene is intron- between HGSC patients with long-term survival compared less and encodes a replication-dependent histone that is a with short-term survivors. QMSP results and reverse transcrip- member of the histone H2B family. Transcripts from this gene tion RT-PCR analyses show that promoter methylation of lack polyA tails; instead, they contain a palindromic termina- HIST1H2BB and MAGI2 in ovarian cancer cell lines is inversely

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correlated with their mRNA and protein expression, when clusters of approximately 15% of promoter DNA methylation compared with normal ovarian cell lines. Treatment of ovarian events can each lead to large amount of mutations (≥71%) or a cancer cell lines with demethylating agent 5-aza-20-deoxycy- small (≤10%) amount of mutations. These DNA methylation tidine and deacetylase inhibitor TSA induce reexpression of clusters together amount to 47% of promoter DNA methyla- MAGI2. Downregulation of MAGI2 using siRNAs was shown tion events, which explain from 71% to 100% of mutated genes. to increase the growth of ovarian cancer cells, suggesting a We also quantified epigenomic alterations and somatic growth-inhibitory role for MAGI2 in ovarian cancer. The mutations in paired tissue, plasma/serum, and urine of ovarian survival results of our clonogenic assay suggest that MAGI2 cancer patients, to show their potential usefulness in ovarian expression could be a strong potential target for ovarian cancer cancer precision medicine workflows. We found that treatment interventions. This finding must be confirmed in HIST1H2BB and MAGI2 promoter DNA methylation and larger projects. QMSP also confirmed higher methylation levels somatic mutations in HGSC-related genes are reproducible in of HIST1H2BB and MAGI2 in patients with long-term survival plasma/serum and urine. compared with short-term survivors and in paired tissue, Detection of promoter DNA methylation and somatic muta- ascites, cervical swabs, plasma/serum, and urine samples. tions in various biofluids permits early detection of cancer cells Together, these exploratory data suggest that promoter meth- during perioperative courses of clinical treatment as is illus- ylation of HIST1H2BB and MAGI2 can detect HGSC and trated in Fig. 3. We performed preliminary analyses to identify identify patients with HGSC with poor survival. We surmise the biofluids in which genomic and promoter DNA methyl- they can be used as early detection, as well as diagnostic and ation measurements best correlate with paired tissue levels. prognostic biomarkers. Although tissue and biofluids data indicates TrDNA is a better Early detection and intervention are likely to be the most indicator of DNA methylation status than serum/plasma, this effective means for reducing morbidity and mortality of human finding might not be generalizable to other genes or cancer cancer. A noninvasive assay for detection of early-stage tumors, types, such as non-urinary tract–adjacent malignancies. We using massively parallel sequencing to evaluate sequence controlled for direct shedding of tumor cells into the urinary changes in circulating cell-free DNA, detected somatic muta- tract, a way to bypass renal filtration, by only isolating cell-free tions in 68% of patients with ovarian cancer with stage I or stage DNA in urine 150 bp long or smaller, a size that allows filtration II disease (45). Patients with HGSC with short-term survival through the kidneys. Therefore, these results are possibly (less than 2 years) are characterized by focal copy number gain generalizable to non-urinary tract–adjacent malignancies. of CCNE1, lack of BRCA mutation signature, low homologous Finally, to control for varying concentrations, we normalized recombination deficiency scores, and the presence of ESR1- to a housekeeping gene. We compared ratio measures of CCDC170 gene fusion (46). A BRCA genetic testing assay is methylation, which are independent of the varying concentra- now being recommended to identify the estimated 3%–9% of tion between biological specimen types, making our compar- patients with ovarian cancer with somatic BRCA1/2 mutations isons directly valid. Although our samples sizes for the com- who, in addition to germline carriers, could benefit from PARP parisons were small, the paired design leveraged sample size to inhibitor therapy (47). These data suggest that somatic muta- achieve statistical power high enough to detect significant tion and DNA methylation testing in biofluids can be a broadly difference in several comparisons. Our exploratory results applicable approach for noninvasive detection of tumors, suggest that trans-renal DNA from urine has the potential to useful for personalized screening and therapeutic management be a more sensitive noninvasive measure of circulating tumor of patients with HGSC (48). DNA methylation. We hypothesize this conclusion can be broadened to DNA in general, such as circulating tumor DNA. DNA methylation and somatic mutations associations in Circulating tumor DNA is a very promising powerful bio- short-term and long-term survival in HGSC marker of cancer for diagnosis and prognosis (48–50). We used a Cancer Hotspot sequencing panel to compare the The main limitation of this project is sample size. We are frequency of somatic mutations in patients with HGSC with showing associations, not cause and effect relationships in our short-term and long-term survival, whose FFPE tissue slides patient data. We did not set out to prove the DNA methylation were identical under the microscope. We found statistically drivers of HGSC. This would require another study design with significant differentially mutated genes and alleles when com- a much larger sample size to confirm the observed associations paring short-term and long-term survival samples. We also in this initial study, followed by work on patient-derived tumor found that 71% of DMRs had significant associations with grafts (51, 52). Ultimately, molecular analyses in precision mutations in HGSC. Most DMRs were usually associated either medicine may be helpful to better elucidate whether observed with ≤10% or with ≥71% of gene mutations in DMRs, and the genomic and epigenomic alterations represent a distinct entity average gene mutation was associated with 15% of DMRs. It is with clinical, immunophenotypic, and molecular characteris- also likely that many of the gene methylations were highly tics or an incidental phenomenon during malignant transfor- correlated due to varying factors (Supplementary Fig. S4). If we mation. Larger samples sizes will also provide the opportunity assume that promoter DNA methylation events occur before to systematically close the existing gap between diagnostic and mutations, we can therefore hypothesize that in ovarian HGSC prognostic data. Data science tools can be set in place to

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Figure 3. Diagram representing the use of precision methylation for personalized medicine workﬂows focused on ovarian cancer detection.

integrate molecular features, clinical information, and health acquisition, investigation, methodology, writing-original draft, writing- services organization data. review and editing. S. Rodriguez-Torres: Software, formal analysis, This challenge can be addressed with big data analytic investigation, writing-review and editing. E. Kuhn: Conceptualization, investigation, methodology, writing-review and editing. T. Díaz-Montes: strategies, which may include machine learning and computer Resources, supervision, investigation. E. Parrilla-Castellar: Investigation. algorithms to integrate patient demographic, psychosocial, F.P. Lawson: Investigation. O. Folawiyo: Investigation. C. Ili-Gangas: clinical, pathology, and molecular proﬁles with treatment Visualization. P. Brebi-Mieville: Visualization. J.R. Eshleman: Resources, recommendations, health insurance coverage, clinical, and data curation, software, formal analysis, investigation, writing-review and health information data. This integration can become the editing. J. Herman: Investigation, methodology, writing-review and editing. foundation for Precision Medicine platforms. In sum, we now I.-M. Shih: Resources, supervision, funding acquisition, investigation, have the understanding and capabilities of combining multiple writing-review and editing. D. Sidransky: Conceptualization, resources, supervision, funding acquisition, writing-review and editing. big data streams into next-generation precision medicine tools with machine learning and quantum entanglement capabilities, Acknowledgments which will allow us to obtain precise depictions of molecular, This research was supported by HERA Ovarian Cancer Foundation clinical, psychosocial, and contextual portraits to track health Outside the Box Grant (to B.L. Valle); Ovarian Cancer Research Alliance (to trajectories across the life-span. The markers described in this I.-M. Shih); National Cancer Institute U01CA84986 (to D. Sidransky) and manuscript may be useful to improve our understanding of K01CA164092 (to R. Guerrero-Preston); P50CA228991 (to I.-M. Shih) and HGSC survival, guide treatment options, and inform public National Institute on Minority Health and Health Disparities health strategies designed to improve HGSC survival rates. R44MD014911 (to R. Guerrero-Preston).

Disclosure of Potential Conflicts of Interest The costs of publication of this article were defrayed in part by the No potential conflicts of interest were disclosed. payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate Authors’ Contributions this fact. R. Guerrero-Preston: Conceptualization, resources, data curation, formal analysis, supervision, investigation, methodology, writing-original draft, Received August 29, 2019; revised April 15, 2020; accepted June 11, 2020; project administration, writing-review and editing. B.L. Valle: Funding published first June 24, 2020.

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HIST1H2BB and MAGI2 Methylation and Somatic Mutations as Precision Medicine Biomarkers for Diagnosis and Prognosis of High-grade Serous Ovarian Cancer

Blanca L. Valle, Sebastian Rodriguez-Torres, Elisabetta Kuhn, et al.

Cancer Prev Res Published OnlineFirst June 24, 2020.

Updated version Access the most recent version of this article at: doi:10.1158/1940-6207.CAPR-19-0412

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