Genome-Wide Identification of Aberrantly Methylated Promoter

Genome-wide identiﬁcation of aberrantly methylated promoter associated CpG islands in acute lymphocytic leukemia

S-Q Kuang, W-G Tong, H Yang, W Lin, MK Lee, ZH Fang, Y Wei, J Jelinek, J-P Issa and G Garcia-Manero

Department of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, USA

We performed a genome-wide analysis of promoter associated information indicates that epigenetic silencing of tumor CpG island methylation using methylated CpG island amplifica- suppressor genes is important in ALL. tion (MCA) coupled to representational differential analysis (RDA) or a DNA promoter microarray in acute lymphoblastic Until recently, one of the limitations of studying DNA leukemia (ALL). We identified 65 potential targets of methyla- methylation has been the process to select gene(s) to study. It tion with the MCA/RDA approach, and 404 with the MCA/array. is possible that several hundred genes could be aberrantly Thirty-six (77%) of the genes identified by MCA/RDA were methylated and silenced in ALL.9 Some of these genes may be shared by the MCA/array approach. Chromosomal location of bona fide tumor suppressor genes, whose silencing could be these genes was evenly distributed in all autosomes. Function- essential to the pathogenesis of ALL, whereas others may be ally, 303 of these genes clustered in 18 molecular pathways. Of silenced as a consequence of global methylation dysregulation the 36 shared genes, 31 were validated and 26 were confirmed 10 as being hypermethylated in leukemia cell lines. Expression and not directly involved in oncogenesis. Therefore, an analysis of eight of these genes was epigenetically modulated unbiased whole genome approach to identify methylation by hypomethylating agents and/or HDAC inhibitors in leukemia silenced genes in ALL may reveal novel tumor suppressor genes cell lines. Subsequently, DNA methylation of 15 of these genes and enhance our understanding of the role of aberrant gene (GIPC2, RSPO1, MAGI1, CAST1, ADCY5, HSPA4L, OCLN, methylation in this disease. To approach this, we have EFNA5, MSX2, GFPT2, GNA14, SALL1, MYO5B, ZNF382 and MN1) was validated in primary ALL samples. Patients with performed two large-scale genome-wide studies of aberrantly methylation of multiple CpG islands had a worse overall methylated CpG islands in ALL. survival. This is the largest published list of potential methylation target genes in human leukemia offering the possibility of performing rational unbiased methylation studies in ALL. Materials and methods Leukemia (2008) 22, 1529–1538; doi:10.1038/leu.2008.130; published online 5 June 2008 Keywords: acute lymphoblastic leukemia; DNA methylation; Cell lines and ALL patient samples microarray; epigenetics; MCA; RDA The following human leukemia cell lines were studied: of lymphoid origin; MOLT4, Jurkat, Peer, T-ALL1, CEM, J-TAG, B- JAB, RS4, ALL1, Raji, REH and Ramos. Of myeloid origin: K562 and BV173, HL60, NB4, THP1, U937, ML1, OCI, HEL, MOLM13 and KBM5R. All were obtained from the American Type Culture Collection and were cultured in RPMI 1640 Introduction (Invitrogen, Carlsbad, CA, USA) supplemented with 10% fetal calf serum (Gemini Bio-Products, West Sacramento, CA, USA) Acute lymphoblastic leukemia (ALL) is characterized by a block and penicillin–streptomycin (Invitrogen). Cell suspensions from in differentiation of early lymphoid progenitors, which leads to 1 bone marrow aspiration specimens from patients with ALL were an accumulation of immature lymphoblasts. Together with obtained prior to any therapy and were stored at established genetic changes, epigenetic alterations such as aberrant DNA tissue banks at MD Anderson Cancer Center (MDACC) methylation of promoter associated CpG islands (CGIs) have a following institutional guidelines. All samples were collected role in downregulation of tumor suppressor genes and in the 2 using Ficoll-Paque density centrifugation. DNA was extracted molecular pathogenesis of ALL. Prior data from several from leukemia cell lines and ALL samples using standard laboratories have indicated that aberrant DNA methylation of phenol-chloroform methods. multiple CGIs is common in ALL3,4 and is associated with poor prognosis.5–7 Furthermore, we have demonstrated that methylation patterns at relapse are stable in a majority of patients with 8 Methylated CpG island amplification/representational ALL, and that the identification of residual methylation levels is difference analysis associated with a higher risk of relapse in patients in The principle underlying methylated CpG island amplification morphological remission (H Yang, submitted). We have also (MCA) involves amplification of closely spaced methylated SmaI demonstrated that the reactivation of an epigenetically silenced 11 sites (CCCGGG) to enrich for methylated CGIs. About 70% of gene, such as p57KIP2, results in selective induction of apoptosis CGIs contain two SmaI sites spaced closely enough (200–1000 only in cells in which the gene is methylated.2 This body of bases) to be amplified efficiently by PCR. Representational difference analysis (RDA) is a subtraction hybridization step Correspondence: Dr G Garcia-Manero, Department of Leukemia, used to identify CGIs methylated specifically in tester DNA. This University of Texas M.D. Anderson Cancer Center, Box 428, 1515 procedure was performed as described previously.11 MCA Holcombe Blvd, Houston, TX 77030, USA. E-mail: [email protected] amplicons were generated from a pool of DNA from three Received 21 November 2007; revised 23 April 2008; accepted 24 patients with ALL (testers). Of importance, the three ALL tester April 2008; published online 5 June 2008 patients were selected because they were refractory to induction DNA Methylation in ALL S-Q Kuang et al 1530 therapy with hyperCVAD chemotherapy.12 These three patients DNA bisulfite treatment and bisulfite pyrosequencing were diploid (Mixed Lineage Leukemia (MLL) and Philadelphia Bisulfite treatment of genomic DNA was performed as (Ph) chromosome negative). One was a male and two females. described.13 Please refer to Supplementary Information for Their median age was 33 years (range 27–66). Therefore, this further details. represents a group of patients with very poor prognosis not currently identifiable with current molecular techniques. As control, we used MCA amplicons generated from a pool of DNA Bisulfite sequencing from two healthy donors (drivers). These controls were one male Bisulfite sequencing was performed to confirm the pyrosequen- and one female, and their ages were 30 and 55 years (median cing results in selected samples. Please refer to Supplementary 42.5), respectively (P ¼ 0.48). Briefly, 5 mg of genomic DNA was Information for further details. digested with 100 units of SmaI (New England Biolabs, Ipswich, MA, USA) twice and then further digested with 20 units of XmaI (New England Biolabs). RMCA adaptor was prepared by RNA preparation and real-time PCR annealing of oligonucleotides RMCA 24 and RMCA 12, and Methods are described in Supplementary information. ligated to the digested DNA fragments using T4 DNA ligase (New England Biolabs). To amplify hypermethylated DNA 5-aza-20-deoxycytidine and/or trichostatin A treatment fragments, PCR was performed using 3 ml of each of the ligation To study the effect of epigenetic modulation, leukemia cell lines mixture as a template in a 100 ml volume containing 100 pmol were cultured in media supplemented with 5 mmol/l of 5-aza-20- RMCA 24 primer, 5 units of Taq DNA polymerase (Life deoxycytidine (Sigma, St Louis, MO, USA) for 4 days, 5 mmol/l of Technologies, Inc., Carlsbad, CA, USA), 4 mM MgCl ,16mM 2 5-aza-20-deoxycitidine for 4 days and then 500 nmol/l trichos- of NH4 (SO4) , 10 mg/ml of bovive serum albumin, and 5% v/v 2 tatin A (TSA) (ICN Biomedicals, Costa Mesa, CA, USA) for the dimethylsulphoxide. The reaction mixture was incubated at last 24 h or 500 nmol/l TSA for 24 h alone. Cells were split 12– 72 1C for 5 min and at 95 1C for 3 min, and then was subjected to 24 h before treatment. Mock-treated cells were cultured 25 cycles of 1 min at 95 1C and 3 min at 77 1C followed by a similarly. final extension of 10 min at 77 1C. Representational difference analysis was performed on the 11 test and driver MCA amplicons as described previously. Methylation loci mapping and molecular pathway Primers used for the first and second rounds of RDA were analysis JMCA24/JMCA12, and NMCA24/NMCA12. After the second The MapChart program (http://www.biometris.wur.nl/UK/Soft- round of hybridization and selective amplification, the MCA/ ware/MapChart/download) was used to generate a chromoso- RDA products were cloned into a pCR 4.0-TOPO vector mal map of methylation loci. The ingenuity pathway program (Invitrogen). To screen for inserts, a total of 800 clones were (http://analysis.ingenuity.com) was used to cluster gene function sequenced at a core facility (MDACC). Sequence homologies and signaling pathway networks. and chromosomal localization were identified using the Human Blat program (http://www.genome.ucsc.edu). A list of primers used in this study is shown in Supplementary Table 1. Statistical analysis Statistical analyses were performed using Prism 4 (GraphPad Software, Inc.) or Statistica 6 software (Statistica for Windows MCA/human proximal promoter DNA microarray version 6.0, StatSoft, Tulsa, OK, USA). The Fisher’s exact test The MCA amplicons generated above were labeled with Cy5- and t-tests were used to compare gene methylation frequencies dUTP (tester) or Cy3-dUTP (driver) according to the manufac- or expression levels in leukemia cell lines or ALL patients and turer’s protocol (Invitrogen). Five micrograms of Cy5- and Cy3- normal control groups. The Spearman non-parametric test was labeled MCA amplicons were mixed and hybridized to the used to determine correlations. Effect on survival was analyzed Agilent human proximal promoter microarray slides. These as previously described.5 All reported P values were two-sided, contain approximately 17 000 of the best defined human and Po0.05 was considered statistically significant. transcript associated promoters covering À1.0 kb upstream and þ 0.3 kb downstream from the transcription start site (Agilent Technologies, Santa Clara, CA, USA). The hybridization Results reaction was carried out in rotisserie hybridization oven at 65 1C, 10 r.p.m. for 40 h and subsequently washed following the Global identification of hypermethylated genes in Ph Agilent aCGH array hybridization protocol. Hybridized slides negative ALL using MCA/RDA and MCA/microarray were scanned using an Agilent G2565AA scanner (Agilent techniques Technologies). The data from scanned microarray images was First, we used the MCA/RDA approach to identify aberrantly extracted using Agilent feature extraction software. Image methylated CGIs in ALL. We recovered and sequenced 800 analysis was done using the Agilent G4477AA Chip Analytics clones. Chromosomal locations, gene homology and CGI 1.2 software package. Genes corresponding to each promoter information of the sequenced clones were analyzed using the DNA spotted on the array were identified and then selected by Human Blat program (http://www.genome.ucsc.edu). Of the the following criteria: a tester’s Cy5 signal intensity of more than 800 clones, 217 (27%) contained CGIs, 74 (9.3%) were 2000 and a Cy5 signal intensity ratio of tester versus driver of localized within or close to (o200 bp) the promoter region of more than 2. Promoter specific CGI methylation was further identifiable genes and 62 of these 74 clones were matched to determined using an in silico gene-filtering search method. known human gene sequences (Supplementary Table 2). Of Genes containing CGI in their promoter regions based on results note, five of these clones contained bidirectional promoters.11 from USCS Human Blat analysis (http://www.genome.ucsc.edu) These corresponded to either two known genes, or to one and having two putative SmaI cutting sites in their CGIs (putative known gene, one mRNA/or spliced EST. In total, therefore, 65 MCA targets) were selected for further validation. genes were identified.

Leukemia Table 1 Methylation proﬁle of leukemia cell lines and normal controls - Kuang S-Q ALL in Methylation DNA tal et

Bisulfite pyrosequencing was carried out to determine the methylation status of selected genes in 23 leukemia cell lines and 10 normal peripheral lymphocytic cells (control). Each column represents a cell line number, and each row indicates a gene identified by both methylated CpG island amplification/representational difference analysis (MCA/RDA) and MCA/DNA promoter microarray. Green box, methylation density o15%; yellow box, methylation density between 15–29.99%; pink grey box, methylation density 30–59.99%; red box, methylation density 460%. Methylation density 415% was used as the cutoff for hypermethylation. Methylation frequency was counted as the percentage of positive methylation numbers versus total numbers studied for each gene. P-values were calculated using a t-test comparing cell line methylatyion versus normal controls. Leukemia 1531 DNA Methylation in ALL S-Q Kuang et al 1532 Due to limited yield of this approach, we performed a second Validation of methylation target genes in leukemia cell genome-wide DNA methylation search using the MCA/DNA lines promoter microarray method. This analysis revealed that 923 Several of the genes identified by MCA/RDA and MCA/array in (5.4%) of the 17000 promoter specific transcripts featured on the this study have already been reported in the literature to undergo Agilent chip were expected to be hypermethylated in Ph promoter hypermethylation and gene silencing in various negative ALL samples. We further reduced the number of human cancers. These include, among several, p73,3 RUNX3,14 candidate genes by excluding unknown genes (spliced EST, RASSF1,15 MGMT,16 EGR3,17 SLC26A418 and TERT.19 This data mRNA and hypothetical proteins), sex chromosomal genes and validated the techniques used here. To further demonstrate that genes whose putative function were not related to oncogenesis, other genes identified by these techniques were also aberrantly and focused on 404 autosomal genes (Supplementary Table 3). methylated in leukemia, we selected 31 genes that had been We then compared the genes identified by the two techniques: identified both by the MCA/RDA and MCA/array. These MCA/RDA and MCA/microarray. Of the 65 known genes included: WNT2B, GIPC2, RSPO1, LHX4, TRIMP58, ECRG4, identified by MCA/RDA, only 47 were represented in the array, MAGI1, CAST1, ADCY5, HSPA4L, ZFP42, OCLN, EFNA5, 36 of them (77%) were found to be hypermethylated by the MSX2, GFPT2, PNMA2, GNA14, FOXE1, CRTAC1, DPYSL4, MCA/microarray approach. Information on these genes is shown FANK1, DCAMKL1, AGC1, SALL1, SLC13A5, PTPRM, MYO5B, in Supplementary Tables 2 and 3. FBX027, ZNF382, MN1, PCDH11.

100 GIPC2 100 RSPO1 100 MAGI 75 75 75

50 50 50

25 25 25 Methylation (%) Methylation(%) Methylation (%) 0 0 0 Controls Patients Cell lines Controls Patients Cell lines Controls Patients Cell lines N=10 N=31 N=23 N=8 N=46 N=23 N=8 N=45 N=23

100 100 CAST1 ADCY5 100 HSPA4L 80 75 75 60 50 50 40 20 25 25 Methylation (%) Methylation (%) Methylation(%) 0 0 0 Controls Patients Cell lines Controls Patients Cell lines Controls Patients Cell lines N=11 N=57 N=23 N=8 N=56 N=23 N=13 N=35 N=23

100 OCLN 100 EFNA5 100 MSX2 80 75 75 60 50 50 40 25 25 20 Methylation (%) Methylation Methylation (%) Methylation(%) 0 0 0 Controls Patients Cell lines Controls Patients Cell lines Controls Patients Cell lines N=7 N=41 N=23 N=11 N=58 N=22 N=11 N=55 N=23

100 100 SALL1 100 GFPT GNA14 75 75 75

50 50 50

25 25 25 Methylation (%) Methylation(%) Methylation (%) 0 0 0 Controls Patients Cell lines Controls Patients Cell lines Controls Patients Cell lines N=9 N=35 N=23 N=9 N=44 N=23 N=10 N=41 N=23

100 MYO5B 100 ZNF38 100 MN1 75 80 2 75 60 50 50 40 25 20 25 Methylation (%) Methylation Methylation (%) Methylation(%) 0 0 0 Controls Patients Cell lines Controls Patients Cell lines Controls Patients Cell lines N=7 N=36 N=23 N=10 N=46 N=23 N=11 N=53 N=23

Figure 1 Methylation levels of 15 selected genes identiﬁed by both MCA-RDA and MCA/DNA promoter microarray in normal controls, primary acute lymphoblastic leukemia (ALL) patients and leukemia cell lines. DNA methylation was analyzed using bisulﬁte pyrosequencing. The name of each gene is shown in the upper left corner of each graph. N represents the number of cases studied for each gene. Each graph represents the results of an individual gene indicated in the left upper corner of each graph.

Leukemia DNA Methylation in ALL S-Q Kuang et al 1533 Table 2 Methylation proﬁle of primary ALL samples and normal controls

Bisulfite pyrosequencing was carried out to determine the methylation status. Each row indicates a gene identified by both methylated CpG island amplification/representational difference analysis (MCA/RDA) and MCA/DNA promoter microarray. White box, not done; green box, methylation density o15%; yellow box, methylation density between 15–29.99%; pink grey box, methylation density 30–59.99%; red box, methylation density 460%. Methylation density 415% was used as the cutoff for hypermethylation. Methylation frequency was counted as the percentage of positive methylation numbers versus total numbers studied for each gene. MCA tester and drivers are the results on the patient samples used for the MCA experiement. P-values were calculated using a t-test comparing cell line methylatyion versus normal controls.

Methylation validation was performed using bisulﬁte pyrose- revealed that methylation of these genes was generally quencing in 23 leukemia cell lines, 10 normal controls and the 3 correlated with each other (Supplementary Table 4). Ph negative ALL samples originally used for MCA. Methylation results are shown in Table 1, Figure 1 and Table 2. Five genes (TRIMP58, ECRG4, ZFP42, FANK1 and FBXO27) were found Analysis of gene methylation in primary ALL methylated in both leukemia cells lines and normal controls We subsequently evaluated the methylation status of 15 samples (Table 1), and were considered as non-informative. The randomly selected genes of the 31 genes analyzed in cell lines other 26 genes were methylated in at least 2 of the 23 leukemia in 61 pretreatment samples from patients with ALL. Patient cell lines but not or rarely in normal control samples (Table 1). characteristics are shown in Supplementary Table 4. Results are Spearman coefﬁcient analysis of the 26 genes in leukemia cells shown in Figure 1 and Table 2. Using a methylation cutoff of

Leukemia 1534 Leukemia N ehlto nALL in Methylation DNA - Kuang S-Q tal et

Figure 2 Gene expression in leukemia cell lines and primary leukemia specimens, and effect of epigenetic modulation on gene expression. Gene expression was analyzed with real-time PCR in normal bone marrow (BM), peripheral blood specimens (PB) from normal controls, as well as primary acute lymphoblastic leukemia (ALL) samples (ALL). The figure below each sample or cell line indicates the percent of methylation. Leukemia cells were treated with 5-aza-20-deoxycytidine (DAC) only, trichostatin A (TSA) only or both (D þ T) as described. Expression of GIPC2 (a), MAGI1 (b), ADCY5 (c), HSPA4L (d), OCLN (e), EFNA5 (f), GNA14 (g) and MYO5B (h) genes was analyzed by real-time PCR. C, control untreated cells. M % methylation density. DNA Methylation in ALL S-Q Kuang et al 1535 15% (as described previously),3 methylation frequencies (per- HSPA4L, OCLN, EFNA5, GNA14 and MYO5B was restored in cent of patients in which a gene was methylated) ranged from 23 methylated leukemia cell lines by either 5-aza-20-dexocytidine to 100%. Two patients had methylation of 1 gene, 2 of 2 genes, with or without TSA (Figure 2), a phenomenon associated with 3 of 3 genes, 14 of 4 genes, 8 of 5 genes, 8 of 6 genes, 11 of 7 gene demethylation (data not shown). These data indicates that genes, 7 of 8 genes, 5 of 9 genes and 3 of 10 genes. No patient the DNA methylation and histone deacetylation of the genes had methylation of more than 11 genes. analyzed here are associated with suppressed gene expression in To further confirm the results of the pyrosequencing assays, leukemia cell lines. Furthermore, a relationship was also DNA methylation of RSPO1, ADCY5, HSPA4L and MYO5B was observed between methylation and gene expression in primary confirmed in one ALL sample, one normal control and the Raji ALL samples (Figure 2). cell line using bisulfite sequencing. Sequencing results were in concordance with the results obtained by pyrosequencing (Supplementary Figure 1). Spearman coefficient analysis of all Physical mapping of methylated loci and pathway 15 genes revealed that methylation of these genes was analysis of microarray data significantly correlated with each other (Supplementary Table The 404 methylated genes identified by MCA/microarray were 4). We also observed an excellent correlation between cell line analyzed according to their chromosomal location and found to methylation results and patients (data not shown). be distributed evenly in the 22 autosomes. The physical distribution of these loci was further mapped using MapChart program (Figure 3). We also performed pathway and functional Gene expression and effect of epigenetic modulation analysis of these genes using the Ingenuity Pathway Analysis To examine the role of the DNA methylation in the control of program (http://analysis.ingenuity.com). The initial analysis gene expression, eight genes were studied.20–27 Leukemia cell divided 303 out of the 404 genes into 29 functional networks, lines were treated with 5-aza-20-deoxycytidine with or without with the majority of genes clustered into 18 networks (Table 3). TSA. In general, expression of GIPC2, MAGI1, ADCY5, Genes clustered in these networks are involved in a wide variety

1 2 3 4 5 6 7 8 9 10 11 29(7%) 31(8%) 26(6%) 26(6%) 25(6%) 15(4%) 25(6%) 24(6%) 28(7%) 21(5%) 19(5%) TP73 SOX11 DGKQ TERT DUSP22 MAFK PER3 RRM2 OGG1 TACC3 IRF4 MAD1L1 MSRA PSIP1 PFKP SPEN ROCK2 IRAK2 CRMP1 BASP1 FOXQ1 RBAK GATA4 MTAP IL15RA NBL1 ALK TIMP4 HTRA3 FOXC1 ACTB EGR3 CDKN2A NMT2 USH1C EPHB2 CYP1B1 THRB LGI2 TXNDC5 DFNA5 TNFRSF10A FANCG VIM E2F2 MTA3 CTDSPL RBPSUH AMACR BAT2 HOXA5 LOXL2 TPM2 EPC1 PTPRJ RUNX3 SIX2 TSP50 SKP2 COL11A2 PDE1C EXTL3 MELK ITGB1 UBE2L6 SESN2 MSH6 MAP4 PACSIN1 CAMK2B NRG1 GNA14 NRP1 FADS1 BMP8A VRK2 CDC25A KDR PTK7 ZNF703 TLE4 SLC18A3 STIP1 IGFBP7 OCLN BMP8B REL SEMA3F GTPBP2 BCL7B SFRP1 UBQLN1 NCOA4 RPS6KA4 TRIT1 MXD1 HYAL2 EPHA5 MSH3 SDCBP GAS1 UBE2D1 MEN1 RASA1 CACNA2D1 PPT1 TGFA RASSF1 CXCL6 AKAP9 CA8 CTSL RHOBTB1 EHD1 CXCL2 EFNA5 HTR1B RNF11 CACNA2D2 CDK6 NCOA2 FANCC HK1 SYVN1 DHCR24 MAL ACY1 FGF5 TNFAIP8 ME1 E2F5 ABCA1 DDIT4 RELA HSD17B4 NPTX2 GIPC2 STARD7 ALAS1 GNB2 SDC2 KLF4 PAPSS2 CFL1 MAP4K4 BAP1 TSPAN5 LOX TSPYL5 TXN CRTAC1 PACS1 GSTM1 FBN2 CUTL1 ECRG4 IL17RB PITX2 RPL30 DBC1 BTRC MTL5 WNT2B IRF1 SVH CAST TIFA SLC26A4 RSPO2 GSN NEURL FGF3 SYCP1 PTPRG SPOCK EXT1 LHX6 SLC18A2 SERPINH1 BIN1 FGF2 NRCAM MAGI1 NUDT6 MATR3 NOV HSPA5 FANK1 PGR DND1 PTPRK CAV1 IGSF11 HSPA4L ADCY8 AK1 MGMT PPP2R1B ARNT HDAC3 SMO GATA2 GAB1 BAI1 PKN3 DPYSL4 ZBTB16 EFNA4 PCDH1 MEST TRH EDNRA PODXL GML BARHL1 ST14 MAPBPIP SOX14 KIBRA SCRIB TSC1 ARHGEF11 NR3C2 NKX2-5 NOM1 HOXD13 FAM62C SLC22A3 HLXB9 PLEC1 WDR5 IGSF4B NAB1 GMPS STC2 RXRA SAP30 PTPRN2 IGSF8 GLS IL12A MSX2 PDCD2 LHX3 HAND2 CREG1 TMEFF2 NSD1 TRAF2 VEGFC LHX4 SUMO1 GRK6 COBRA1 FAT FEV GFPT2 ZFP42 INHA IRS1 HLX1 HRB WNT3A NCL ARF1 GBX2 HES6 PPP1R7 BOK

12 13 14 15 16 17 18 19* 20 21 22 14(3.5%) 10(2.5%) 9(2.2%) 14(3.5%) 15(4%) 13(3.2%) 8(2%) 22(5.4%) 11(2.7%) 6(15%) 6(15%) WNK1 BAIAP3 PALM TRAF7 PLD2 MRCL3 PRG2 RASSF2 TPTE EPS8 PKMYT1 SLC13A5 PTPRM MUM1 PAX1 CREBBP EFNB3 ROCK1 GNA11 TP53INP2 CXADR MN1 BCAT1 TNFRSF19 KIAA0323 THBS1 EEF2K PER1 MIB1 MATK RBL1 GABPA NF2 APRIN SNX6 MAPK6 MAPK3 FLCN RBBP8 ZBTB7A SRC OLIG2 TST COL2A1 DCAMKL1 SSTR1 ALDH1A2 CORO1A SREBF1 DSC3 ICAM1 ADA ETS2 PGEA1 ACVRL1 RCBTB2 USP3 VKORC1 IGFBP4 PSTPIP2 ICAM5 NCOA3 UBE2G2 L3MBTL2 CSAD MGAT2 RCBTB1 MEGF11 NKD1 ADAM11 KEAP1 CEBPB TOB2 HOXC10 BMP4 TLE3 SALL1 DLX4DLX4 FVT1 RAB3A PFDN4 ERBB3 PKM2 HERPUD1 COL1A1 GDF15 GNAS RASSF3 COX5A CDH5 SFRS1 CCNE1 TNFRSF6B DYRK2 IMP3 NFATC3 SUMO2 CEBPA ARNT2 MARVELD3 NPTX1 ZNF382 GPC6 PRIMA1 BNC1 NTN4 APRT SIX5 CLDN10 BCL11B NTRK3 APAF1 HIF3A DZIP1 CCNK IQGAP1 CRY1 SEPW1 LIG4 YY1 NR2F2 RASAL1 IRS2 ATF5 KLK10 PPP2R1A KLP1 TRIM28

Figure 3 Chromosomal localization of 404 genes identified by MCA/DNA promoter microarray. Each chromosome is numbered at the top. The figure below the chromosome number represents the number and percentage of methylated loci identified in each chromosome. Gene names are indicated beside each methylated locus.

Leukemia DNA Methylation in ALL S-Q Kuang et al 1536 Table 3 Functional class of genes identiﬁed by MCA/DNA promoter

ID Genes Focus Top functions genes

1 ADA, AMACR, CXCL2, EPHB2, ETS2, FEV, FGF5, FOXC1, GDF15, 35 Cellular Growth and proliferation, cell death, ICAM1, IL12A, IL15RA, IRF1, IRF4, L3MBTL2, MAFK, MAP4K4, cancer MEN1, MXD1, NCL, NF2, RASA1, REL, RELA, SDC2, SDCBP, TACC3, TERT, TNFAIP8, TNFRSF19, TNFRSF10A (includes EG:8797), TNFRSF6B, TRAF2, TXN, ZBTB7A 2 ACTB, ARF1, BIN1, CCNE1, CDH5, CFL1, CRY1, EGR3, EPS8, 35 Cellular growth and proliferation, cellular GAB1 (includes EG:2549), GABPA, GATA2, GSN, HLXB9, IGSF8 movement, cell morphology (includes EG:93185), ITGB1, KDR, KEAP1, MATK, NRG1, NRP1, PER1, PER3, PITX2, PLD2, PTPRG, PTPRJ, RAB3A, SEMA3F, SFRS1, SKP2, SRC, SUMO2 (includes EG:6613), TOB2, VEGFC 3 AK1, CDC25A, CDK6, CDKN2A, CDKN2D, COL11A2, COL1A1, 35 Cell cycle, cellular growth and proliferation, COL2A1, CREG1, E2F2, E2F5, FGF2, FGF3, IGFBP4, IGFBP7, cancer LOX, MELK, MGMT, MSX2, NBL1, NOV, OLIG2, PLEC1, PPP2R1A, PPP2R1B (includes EG:5519), RBL1, ROCK2, RPL30, RRM2, SFRP1, TGFA, THBS1, TP73, VIM, YY1 4 ALDH1A2, CEBPA, CEBPB, CREBBP, CSAD, CUTL1, GATA4, 35 Gene expression, viral function, developmental GNA11, HAND2, HOXA5, HOXD13, HSPA5, INHA, IRS2, MAPK3, disorder ME1, MN1, MTA3, NCOA2, NCOA3, NCOA4 (includes EG:8031), NFATC3, NR3C2, PCDH1, PGR, RPS6KA4, RUNX3, RXRA, SCRIB, SREBF1, SUMO1, SYCP1, THRB, TRAF7, TRH 5 ABCA1, ACVRL1, ADA, APAF1, CCNK, COX5A, CRMP1, CTSL, 17 Inflammatory disease, respiratory disease, DHCR24, DPYSL4, FADS1, GDF15, LOXL2, MAPK6, PFKP, organismal development PKM2, TPM2 6 CAV1, CXADR, CXCL6, FLCN, GNA14, GNB2, HDAC3, MAD1L1, 17 Cancer, cellular growth and proliferation, MSRA, MTAP, OGG1, PALM, PPP1R7, SAP30, TLE3, ZBTB16, hematological disease ZFP42 7 ARNT, ARNT2, BASP1, BMP4, CLDN10, EEF2K, GAS1, KLF4, 17 Gene expression, connective tissue development MRCL3, NEURL, NKX2-5, NMT2, SERPINH1, STIP1, UBE2G2, and function, skeletal and muscular system UBE2L6, WNT3A development and function 8 ACY1, CYP1B1, DLX4, EFNA4, EFNA5, EFNB3 (includes 16 Nervous system development and function, EG:1949), EPHA5, ERBB3, GBX2, NAB1, NCL, NTRK3, PKMYT1, tissue development, cellular development SLC18A3, SMO, SYVN1, 9 BAP1, BCL11B, DDIT4, GSTM1, MAP4, MIB1, NR2F2, PACSIN1, 15 Protein degradation, protein synthesis, post- PODXL, PSIP1, RNF11, ST14, TRIM28, UBE2D1, UBQLN1 translational modification 10 FANCC, FANCG, GDF15, GLS, HES6, HSD17B4, HSPA4L, 15 Genetic disorder, hematological disease, cell MATR3, NSD1, RBBP8, RBPSUH, SALL1, SPEN, SSTR1, TSC1 cycle 11 ABCA1, DSC3, EHD1, EXT1, GFPT2, HIF3A, HLX1, NRP1, 14 Cardiovascular system development and RCBTB2, SFRP1, SLC26A4, SPOCK1, TIMP4, TST function, embryonic development, tissue development 12 APRT, CAST (includes EG:26059), DCAMKL1, EDNRA, EPS8, 14 Amino-acid metabolism, Post-translational GRK6, HRB, MAL, MATK, NPTX2, PTPRK, SNX6, WNK1, ZNF703 modification, small molecule biochemistry 13 GIPC2, KDR, MAGI1, NCOA2, NPTX1, NRCAM (includes 13 Skeletal and muscular system development and EG:4897), PACS1, PDE1C, PGEA1, PTK7, SIX2, SIX5, SOX11 function, tissue development, cancer 14 ARHGEF11, BCAT1, BTRC, DGKQ, FBN2, OCLN, PDCD2, 13 Gene expression, cancer, reproductive system PFDN4, ROCK1, SESN2, SIAHBP1, TLE4, ZBTB7A disease 15 BAI1, BAIAP3 (includes EG:8938), BOK, CACNA2D2, HERPUD1, 13 Cell death, renal and Urological disease, HYAL2, ICAM5, IQGAP1 (includes EG:8826), LHX4, LHX6, embryonic development PTPRM, RASSF1, TNFRSF10A (includes EG:8797) 16 AKAP9, ALAS1, ATF5, BCL7B, DYRK2, GABPA, GNAS (includes 13 Nervous system development and function, EG:2778), HK1, MEST, MTL5, PRIMA1, PTPRN2, SLC18A2 skeletal and muscular system development and function, cell death 17 COBRA1, CTDSPL, EPC1, GMPS, LHX3, LIG4, MSH3, MSH6, 12 DNA replication, recombination and repair, PAX1, RBAK, RBBP8, WDR5 cancer, gene expression 18 ALK, BNC1, CACNA2D1, CAMK2B, CORO1A, DUSP22, GNAS 12 Cancer, cell signaling, post-translational (includes EG:2778), IL17RB, IRAK2, IRS1, RASSF2, WNT2B modification 19 IMP3 1 RNA post-transcriptional modification 20 BMP8A 1 Cell morphology, gene expression 21 FAT 1 Cellular compromise, Nervous system development 22 IGSF4B 1 Cancer, cell-to-cell signaling 23 PAPSS2 1 Developmental disorder, genetic disorder 24 RASAL1 1 Cellular development 25 MAPBPIP (includes EG:28956) 1 Cancer, cell death, reproductive system disease 26 HTR1B 1 Neurological disease, organismal injury 27 PKN3 1 Cellular assembly and organization 28 VKORC1 1 Genetic disorder, hematological disease, 29 USH1C 1 Genetic disorder, neurological disease

Leukemia DNA Methylation in ALL S-Q Kuang et al 1537 To demonstrate that the level of methylation identified here was associated with gene expression inactivation, we studied the effect of treating cell lines with a hypomethylating agent with or without a histone deacetylase inhibitor. As expected, treatment of cell lines with these epigenetic modulators resulted, in general, in gene reactivation, an effect that was mediated by induction of DNA hypomethylation. An inverse relationship between methylation and gene expression was also observed in primary ALL samples. Targets of aberrant DNA methylation were evenly distributed in all autosomes with no evidence of particular chromosomal hot spots for methylation. The function of these genes clustered in 29 different networks involved in multiple cellular functions. Finally, and as described in other previous studies,5,6 Figure 4 Survival analysis of patients with acute lymphoblastic methylation of multiple genes was associated with a worse leukemia (ALL) based on number of methylated genes. Patient were outcome. Those patients with methylation of more than four grouped in three different sets: methylation of 0–3 genes, 4–7 genes genes appeared to have a significant worse outcome when and 8–10 genes. These could include any the genes studied. treated with hyperCVAD based chemotherapy. The implications of this work are very significant. First, we of biological functions including cell growth and differentiation, have made public the largest list of potential targets of aberrant cell death, gene regulation, cell cycle, DNA replication and DNA methylation in ALL. This provides a significant source to repair, signal transduction, transport and metabolism. perform further studies of DNA methylation in ALL, and potentially other leukemias. We provide information that these genes cluster together in specific functional networks and Exploratory analysis of effect of DNA methylation in chromosomal locations. This provides the opportunity to survival investigate specific functions of genes located in specific Prior studies have indicated a relationship between hypermethy- chromosomal locations. It should be noted that this is not the lation of multiple genes and worse survival.5,6 To explore this first large-scale analysis of methylation in ALL. Recently, Taylor 28 effect, we analyzed the impact of methylation of gene(s) on et al. reported on 262 methylated genes using a different CGI overall survival. To do this analysis, patients were divided into microarray method. We compared the gene lists identified by three groups: those with methylation (X15%) of 0–3 genes, 4–7 our method and their CGI array and found that only nine genes genes and 8–10 genes. The median survival of patients in the (NASP, FEV, HRB, EFNA5, GTPBP2, RPIB9, CAV1, NOV and first group of patients had not been reached, whereas it was 173 SMC2L1) were concordant. This probably relates to the different weeks for the second group and 68 weeks for the third group methods used, in particular the regions analyzed, and the data is (P ¼ 0.012) (Figure 4). probably complementary and it argues that potentially other targets of methylation exist that have not been identified by these two studies combined. It also should be noted that we have identified a number of genes methylated in normal Discussion samples. The significance of this observation is not known at the present time. Using MCA/RDA and an MCA/DNA promoter microarray, we In summary, we have identified a group of aberrantly identified a large cohort of genes that are potential targets of methylated genes that can be used as potential epigenetic aberrant DNA methylation in ALL. Using MCA/RDA we biomarkers of ALL. We have demonstrated that ALL exhibits identified 65 targets of aberrant DNA methylation, and with widespread epigenetic alterations. Although probably not all the MCA/array 404. Thirty-one of the genes (77%) were methylation events identified here are relevant, clinically or identified by both techniques. Of these 31 genes, 26 genes functionally, in ALL directly, the study of this group of genes were found to be informative in cell lines (not methylated in may provide further insights into the pathogenesis of ALL. normal controls), and 15 genes were confirmed to be methylated in primary ALL samples. None of the genes that were validated as informative in cell lines was discarded by the Acknowledgements analysis of primary ALL samples. Furthermore, a number of these genes had been already shown to be hypermethylated in This work was supported by a Leukemia SPORE (P50CA100632) cancer. This evidence further demonstrates the validity of the Career Development Award to S-Q K, and NCI grants CA100067 assays used here. and CA105771, the Leukemia & Lymphoma Society of America In the current study, and as a proof of principle, we validated and a MD Anderson Cancer Center’s Physician-Scientist Award the methylation of 31 genes not previously reported to be from the Commonwealth Cancer Foundation for Research (all to hypermethylated in ALL. Methylation analysis excluded G G-M). TRIMP58, ECRG4, ZFP42, FANK, DHX32 and FBXO27 as they were found to be methylated in normal controls. The other 26 References genes were found to be specifically hypermethylated in leukemia cell lines, and 15 of them were further confirmed to be hypermethylated in a panel of 61 ALL patients. Methylation 1 Pui CH, Relling MV, Downing JR. Acute lymphoblastic leukemia. 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