Downloaded by guest on October 1, 2021 www.pnas.org/cgi/doi/10.1073/pnas.1813666116 a DiMartino Jorge Leung K. Kevin the proteome remodel cell-surface that targets convergent and reveals variable cells AML azacitidine-treated of Multiomics D n M 8 ) thg oe,AAidcsrapid induces degradation induces AZA and AZA doses, trapping doses, lower covalent at high by cytotoxic; At treat hypomethylation is to DNA 9). and decade (8, a damage over DNA AML for and used efficaciously MDS azacitidine been DMNTi, such has methyltrans- One (AZA), 7). DNA (6, as (DMNTi) such inhibitors more ferase agents as are diseases hypomethylating such these to that suppressors believed sensitive is tumor it num- such, well-known a As (5). of including p16INK4a promoters genes, the of near methylation ber aberrant a of across repeats, gain and methylation fact, and elements of transposable of In primarily loss redistribution regions, (4). with the intergenic is islands dinucleotides AML CpG CpG in DNA methylated changes in the epigenetic is bases of change hallmark cytosine best-characterized of the occur sur- AML, that methylation sparse changes and epigenetic using MDS many cytometry Among in hematopoietic by 3). to (2, or As markers according morphology face (1). cell classified phenotypes differen- by routinely cancerous any lineages is into at AML cells occur such, driving can stage, changes tiation epigenetic aberrant lin- stem/progenitor cells, myeloid hematopoietic their As from nature. differentiate cells in eage diverse epigenetically and genetically M discuss AML we lines, AZA. Given cell with combination level. four in AML pathway for the but strategies therapeutic in the levels potential responses protein at heterogeneous and responses gene the common individual in to the increase converged at led an effects treatment and AZA diverse such, pathways to As pathways. metabolic response defense in immune decrease proteins a surface and showed being RNA enrich- regulated set differentially TRPM4 proteome of Gene analysis surface proteins. lines, ment regulated and cell commonly protein, no surface four found a analysis all encoding in gene Tran- only treatment the lines. surface AZA cell coding and upon up-regulated the transcriptome commonly genes among five the identified differed in analysis and scriptome changes subtle lines, levels; methyla- were cell omics DNA proteome all three reduced in globally all treatment tion at AZA overlap while substantial however, showed AML methylome, lines Untreated DNA proteome. cell the these cell-surface the levels: on and three treatment transcriptome, at differentthe AZA characterized representing of was lines effect lines cell cell The effects AML differentiation. the four of investigated on stages we treatment patients, AZA com- AML in of in use been AZA for not with targets has bination therapeutic proteome potential cell-surface the identify AML, To on and defined. AZA MDS of treat impact DNA the to a yet used is differentiation widely (AZA) inhibitor hematopoietic Azacitidine methyltransferase alterations. abnormal Wollscheid) epigenetic Bernd and of aberrant leukemia Kelleher, L. with diseases myeloid Neil Gundry, Rebekah acute are by and reviewed (AML) 2018; (MDS) 23, August syndromes review for Myelodysplastic (sent 2018 19, November Wells, A. James by Contributed 02140 MA Cambridge, Corporation, Celgene Sciences, Predictive and Informatics 94158; CA Francisco, San Corporation, eateto hraetclCeity nvriyo aiona a rnic,C 94143; CA Francisco, San California, of University Chemistry, Pharmaceutical of Department | azacitidine eoypatcsnrms(D)adauemyeloid acute are that and malignancies hematopoietic are (MDS) (AML) leukemia syndromes yelodysplastic | a agtdiscovery target ao Nguyen Aaron , b n ae .Wells A. James and , | multiomics c eateto nomtc n rdcieSine,CleeCroain a ig,C 22;and 92121; CA Diego, San Corporation, Celgene Sciences, Predictive and Informatics of Department b a Shi Tao , a,1 | ufc proteomics surface c i Tang Lin , c iohnNi Xiaochun , aadpsto:Poemc aahv endpstdt rtoecag Consortium ProteomeXchange to deposited ( been have data Proteomics deposition: Data the under Published 1073/pnas.1813666116/-/DCSupplemental. y at online information supporting contains article This 1 h ainlCne o itcnlg nomto eeEpeso miu database, Omnibus Expression Gene Information https://www.ncbi.nlm.nih.gov/geo Biotechnology for Center National the identifier B.W., and University; Northwestern Z N.L.K., ETH Wisconsin; of A.N., College K.K.L., Medical and R.G., Reviewers: data; paper. analyzed y the X.N. wrote and J.A.W. K.K.L. L.T., and T.S., research; L.T., T.S., K.K.L., designed J.A.W. research; and performed J.D., A.N. K.J.M., and L.E., A.N., K.K.L., contributions: Author changes AZA if clear a not about is it targeting but patients, proteins, AML cell-surface for dozen ther- development antibody-based of in numerous treatment are apeutics the there for Currently, AZA (15). with AML combination interest thera- in antibody-derived significant strategies for is peutic targets protein there potential surface and identifying toward cancers, in antibodies RAS-driven generated in recently targets 9). (8, has needed group are Our options therapeutic additional patients, AML engaging and activating is by system. mimicry, effects immune the antitumor viral induce termed cells to phenomenon, cancer thought This (14) endoge- retroviruses. of colorectal reactivation nous through and responses 13) interferon induce in (12, can AZA methylation cervical that shown of of recently was treatment loss It 11). to (10, DNA synthesized leading newly methyltransferases, DNA of ...rcie eerhfnigfo egn oprto u oproa financial and personal K.K.L. no Corporation. but Celgene Corporation of Celgene equity. employees or from gain are funding A.N., J.D. research Corporation. and Celgene received the K.J.M., J.A.W. by L.E., funded X.N., was L.T., study T.S., This statement: interest of Conflict and proteomecentral.proteomexchange.org owo orsodnesol eadesd mi:[email protected] Email: addressed. be should correspondence whom To n ersnsavlal eoret teswouethese use who models. others AML to as lines resource cell valuable date to a lines cell represents these and for proteins surface of In potential the number identified AZA. greatest serval has with experiment discuss proteomics combinations surface we the in addition, AML lines, for cell strategies four therapeutic heterogeneous the the in Given promi- responses defense. most individ- immune and The metabolism of the of up-regulation level. down-regulation at to the pathway were converged effects responses the changes nent diverse these at and responses had treatment level, common protein AZA lines and that cell gene found ual AML We four needed. in disease is ther- heterogeneous strategy additional but a apeutic (AZA), azacitidine is with (AML) treated commonly leukemia myeloid Acute Significance lhuhAAtetethsdmntae lnclbnfi in benefit clinical demonstrated has treatment AZA Although GSE123207 d urich. ¨ ar Escoubet Laure , PXD011298 y b y ).y pgntc hmtcCne fEclec,Celgene Excellence, of Center Thematic Epigenetics NSlicense.y PNAS .Mtyoeadtasrpoedtst aebe eoie to deposited been have datasets transcriptome and Methylome ). c yeJ MacBeth J. Kyle , (SuperSeries i h asV ate eoioy(dataset repository partner MassIVE the via ) ceso nos. accession GSE123211, www.pnas.org/lookup/suppl/doi:10. NSLts Articles Latest PNAS b , d eatetof Department GSE123140 | f6 of 1

SYSTEMS BIOLOGY the expression levels of these proteins (16). Furthermore, over A 3 KG1a B ) 15 ongoing clinical trials are investigating the combination of HL60 5 HNT34 AZA and checkpoint inhibitors in various leukemias and solid AML193 2 tumors, since AZA induces checkpoint inhibitory molecules on 1 0 methylation (10 Density of hyper- and hypo- Intersection set size both tumor and immune cells (7, 17). To identify cell-surface 1 markers, cell-surface capture proteomics has recently emerged HL60 as a highly sensitive target discovery technology and has been KG1a 0123456 HNT34 used to define a large number of common and distinct mark- 0.0 0.2 0.4 0.6 0.8 1.0 Beta value AML193 C 21012345 ers in AML (18–20). Taken together, a broader understanding Methylation set size KG1a in each cell line (105) HL60 of how AZA treatment remodels the cell-surface proteome in HNT34 AML193 E 0 )

AML cells could aid in identifying surface protein targets for 5 antibody-based therapy, leading to unique immunotherapies for 1 use in combination with AZA. Density

Using a multiomics approach, we characterized four AML 2 de-methylation (10 Intersection set size of

cell lines, representing different stages of differentiation, and 01234 0.0 0.2 0.4 0.6 0.8 1.0 KG1a studied the changes in DNA methylation, RNA expression, and Beta value surface proteome induced by AZA treatment. Across the four D AML193 KG1a HNT34 cell lines, AZA reduced DNA methylation in nearly all of the HL60 HNT34 HL60 AML193 6 4 2 0 hypermethylated CpG sites probed, but surprisingly the changes De-methylation set size -0.5 0.0 0.5 in each cell line(105) in gene expression and surface protein expression were few and Change in beta value after AZA treatment diverse. Transcriptome analysis identified only one gene encod- Fig. 1. AZA treatment drives global DNA demethylation among all four ing a surface protein that is commonly up-regulated in all four AML cell lines. (A) Vehicle-treated cell lines have a bimodal distribution of cell lines, and surface proteomics analysis did not identify any genome-wide beta values (kernel density estimation). (B) Vehicle-treated commonly regulated proteins. Despite little overlap, functional cells share a high proportion of hypermethylated and hypomethylated sites. analysis revealed some common responses among the four cell Overlapping hypermethylated sites (red, beta values >0.8) and hypomethy- lines—down-regulation of genes and proteins in metabolism and lated sites (blue, beta values <0.2) are indicated by upward and downward up-regulation of genes in immune response. Collectively, our bars, respectively, in the vertical bar graph. The specific overlapping groups study detailed the distinct impact of AZA treatment in four AML are indicated by the black solid points below the bar graph. Total hyperme- cell line at the individual gene level and illustrated that functional thylated and hypomethylated sites found in each cell line are indicated in networks are commonly regulated. the horizontal bar graph. (C) AZA-treated cells have decreased hypermethy- lated beta values indicating DNA demethylation. (D) DNA demethylation Results in AZA-treated cells is shown by median change in beta value for KG1a (−0.097), HL60 (−0.28), HNT34 (−0.132), and AML193 (−0.099). (E) A high Methylome in AML Cells and its Regulation by AZA. Four well-char- proportion of demethylated sites are common among the four cell lines, acterized AML cell lines, KG1a, HL60, HNT34, and AML193, indicated by downward bars in the vertical bar graph (decrease in beta value were chosen to reflect a gradient of differentiation stages along >0.1, false discovery rate adjusted P < 0.05). the myeloid lineage, according to the French–American–British (FAB) classification system (SI Appendix, Rationale for Cell Line). The four cell lines exhibited varied mutation profiles; treatment reduced methylation in nearly all of the hyperme- PHF6 (PHD Finger Protein 6) was the only gene mutated thylated sites probed (Fig. 1 C and D and SI Appendix, Fig. among all four cell lines, while genes like TET1 (Tet Methylcy- S2). The median change in methylation across all CpG sites tosine Dioxygenase 1), DNMT3B (DNA Methyltransferase 3B), ranged from −0.097 for KG1a cells to −0.28 for HL60 cells and NRAS (NRAS Proto-Oncogene) showed distinct mutation (Fig. 1D). The greater reduction of DNA methylation seen in patterns in the four cell lines (Dataset S1). HL60 compared with the other three cell lines could be due to a We determined the DNA methylome of each cell line using lower basal expression of the de novo DNA methyltransferases the Illumina Infinium EPIC array. The baseline DNA methyla- (DNMT3A and DNMT3B) in the HL60 cells, as detected in the tion profile for each cell line exhibited a bimodal distribution, transcriptome data (SI Appendix, Fig. S3). Comparing the effects representing hypermethylated and hypomethylated CpG sites of AZA across all four cell lines, a large proportion of CpG (Fig. 1A). The number of hypermethylated sites (beta value sites with reduced DNA methylation were shared by all four cell >0.8) was highest for AML193 and lowest for HL60, following lines (37% or 247,715 shared out of 657,868 total sites probed) a general trend of increasing hypermethylation from the least (Fig. 1E). The loci with reduced methylation induced by AZA to most differentiated cell line (Fig. 1B). KG1a does not follow were uniformly distributed across the genome (SI Appendix, Fig. this trend of increasing baseline hypermethylation, potentially S4). Further gene set enrichment analysis (GSEA) of the AZA- due to its differentiation from the KG1 parental cell line and regulated DNA methylation loci did not identify any functionally a deviation from the annotated early progenitor lineage. Com- enriched gene sets. paring the methylation status across the four cell lines, 55% (342,320/628,240) of loci were commonly hypermethylated (beta Transcriptome in AML Cell Lines and Its Regulation by AZA. As value >0.8) and 39% (108,968/278,579) of loci were commonly DNA methylation at promoter CpG islands can be associated hypomethylated (beta value <0.2) (Fig. 1B). This indicates a with transcriptional silencing, we next assessed RNA expres- high degree of similarity in the methylomes among the four sion profiles of the four cell lines. Using an approach similar to cell lines. comparing baseline DNA methylation status, highly and lowly To study the effects of AZA treatment on the regulation of expressed genes were defined by the expression levels from the DNA methylation, each of the four cell lines was treated with highest and lowest tertiles in each cell line. At baseline, 53% AZA (0.5 µM) for 3 d, followed by a 4-d drug holiday to max- (10,971 out of 20,517 total) of all high- and low-expressing genes imally reduce DNA methylation (SI Appendix, Fig. S1A). With were common to all four cell lines (Fig. 2A). Despite a shared this treatment regimen, AZA inhibited cell growth to varying gene expression profile, functional analysis using GSVA with degrees in the AML cell lines, with ∼85% growth inhibition in a hallmark set of key pathways indicated that each AML cell AML193 cells (most sensitive) and ∼30% inhibition in HL60 line had a distinct biological state. Specifically, cell-cycle-related cells (least sensitive) (SI Appendix, Table S1). Remarkably, AZA genes were highly expressed in AML193 and KG1a cells, MYC

2 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1813666116 Leung et al. Downloaded by guest on October 1, 2021 Downloaded by guest on October 1, 2021 aigadageain n erae xrsino tRNA of gene these expression general, In decreased synthesis. acid and amino and aggregation, aminoacylation sig- activation and platelet naling hemostasis, complement, response, epithelial coagulation, gamma of interferon apoptosis, expression increased transition, included mesenchymal sets gene 2D four all enriched (Fig. in AZA lines by GSEA regulated cell gene with pathways unique analysis common functional-level several the line, showed miR-24-1-5p–mediated Despite in recently cell each (21). levels targeting was in expression regulation reduced by lines PAK4 in acts cell of found regulation and four suppressor tumors all tumor ovarian in a up-regulated as (Germ implicated be GCNA to and LINC01088 gene found noncoding 5), The Peptidase). Protein Acidic (cAMP Nuclear Cell CREB5 Binding B2), Element member Responsive family Pro- (MAGE Cation Basic MAGEB2 (Pro-Platelet Potential PPBP tein), 4), Receptor member cod- (Transient M up-regulated subfamily TRPM4 commonly Channel The were 2C). genes (Fig. lines ing cell more up-regulated four uniformly much all gene noncoding in also one with and lines, were coding cell demethy- five the expression significantly among only divergent were be gene to sites appeared on and CpG stochastic effects all The of 70% lated). 2.4%) to methy- to DNA (45 (∼0.8 for line observed lation those cell with each compared lower in much expression were gene in changes The to from cells varied KG1a genes regulated differentially of ber i.S6 Fig. Appendix, dominant (SI a transcriptome have the not did on AZA by effect that in not and suggesting observed line treatment, cell changes by AZA variable clustering large top showed the profiles the expression of regulated gene clustering despite Hierarchical differentially line, methylome. was DNA cell the transcriptome AML the each of in genes) all of term S5). (GO) Fig. Ontology Appendix, (SI Gene death pathways cell of and activation differentiation list in differences set revealed also gene analysis expanded GSVA Similarly, an 2B). (Fig. using TGF-β cells HNT34 and in cells, genes HL60 signaling Notch and KG1a in genes AZA pathway upon size enriched effect are normalized processes (GSEA pathways biological (K) Up-regulated Common KEGG lines. (D) and cell graph. (R), four bar Reactome the (H), horizontal Hallmark among the using change common in performed (fold are indicated was respectively score genes (GSEA) are or bars, six (variation analysis line (blue) enrichment only sets downward cell set and and gene each Gene line, (red) hallmark treatment. for upward cell genes MSigDB the each regulated the by to (blue) differentially indicated using downward unique Total are levels and are genes (red) baseline AZA down-regulated upward by at and by induced genes lines indicated genes cell are regulated four genes differentially expressed the Most (n tertile) of graph (bottom (GSVA) lowly bar analysis and vertical variation tertile) the (top in highly respectively, Overlapping bars, baseline. at lines cell four 2. Fig. en tal. et Leung <−1). . o2.4% to (∼0.8 percentage small a only treatment, AZA Upon eeepeso rfie r iia mn the among similar are profiles expression Gene (A) lines. cell AML four the in changes transcriptome subtle and unique induces treatment AZA 0 ee o L0adHT4cls(i.2C). (Fig. cells HNT34 and HL60 for genes ∼500 and C A ifrnilyerce genes enriched Differentially 0 0 0 0 300 200 100 0 100 200 i.S7 Fig. Appendix , SI e iei ahcl line cell each in size set

Intersection Size of high and low AML193

HNT34 gene expression 5000 2500 2500 5000 Intersection Size KG1a of differentially HL60 AML193 0 HNT34 enriched gene KG1a HL60 100 100 200 0 057uiu ee apdfrom mapped genes unique 20,517 = A 6 ee for genes ∼160 and .Tenum- The ). N,and WNT, , RB,GN,LINC01088 GCNA, CREB5, AE2 PPBP, MAGEB2, .The B). omngenes Common TRPM4 , oedt okonimnpeoyigC akr fthe Appendix, (SI of agreement markers remarkable S2 showed Table CD also immunophenotyping lines transcrip- cell known four and to proteomics data pro- our associated tome between (integrin Comparison CD47 (16). and tein) (SIGLEC-3) AML, CD33 of targets as therapeutic such several common were the pro- identified Among of markers 3B). estimate (Fig. CD an markers reported these markers for and abundance CD lines tein of cell number four extensive the markers on proteins an clas- CD expressed detected Historically, of 232 we staining 3A). Here, of on (Fig. cells. dependent set on lines is lineage common cell cell a four of sification all and in lines, identified detected were cell were proteins four membrane the a surface among baseline, unique At 875 from of S2). data total (Dataset proteomics cells of subset the vehicle-treated a among heavy-labeled extrapolated profiles we protein lines, surface S8 cell repli- baseline four Fig. pairwise the biological Appendix , compare and the (SI To mode, B). reproducibility for good SILAC ratios showed reverse enrichment performed cates the and was of experiment forward comparisons Each the (22, S1B). both (SILAC) Fig. culture in cell Appendix, in (SI acids amino 23) modified sta- by using labeling a treatment isotope AZA using protocol by ble induced proteins lines changes glycosylated the cell quantified N-linked and four of probed the capture we of cell-surface AZA, proteomes by surface induced the targets therapeutic AZA. novel by identify Regulation Proteome Cell-Surface Unique of repression and response immune metabolism. of activation represent sets nqet ahcl ie n ny1 rtiswr signifi- were proteins 3D). 13 1), (Fig. Receptor C3b/C4b lines only (Complement cell CR1 as were two and such least proteins, changes at Some line, the the in regulated cell of across differentially majority AZA cantly 10% each the upon to to fact, regulated 5 In commonly unique regu- or 3C). was 47, differentially (Fig. protein no treatment to of and 22 number lines, from cell The ranged S1A). quantification proteins SILAC lated Fig. using Appendix , proteins (SI membrane of expression B D enx xlrdhwAAtetetafce h surface the affected treatment AZA how explored next We - K H AML193 R itntbooia ttssonb eeset gene by shown states biological Distinct (B) probesets). >54,000 -- HL60 HNT34 HNT3 ). - KG1a4 HL60 -- AMINOACYL_TRNA_BIOSYNTHESIS TRNA_AMINOACYLATION AMINO_ACID_SYNTHESIS_AND_INTERCONVERSION_TRANSAMINATION CYTOSOLIC_TRNA_AMINOACYLATION PLATELET_ACTIVATION_SIGNALING_AND_AGGREGATION HEMOSTASIS INTERFERON_GAMMA_RESPONSE COMPLEMENT COAGULATION APOPTOSIS EPITHELIAL_MESENCHYMAL_TRANSITION AML193 - KG1a - UNFOLDED_PROTEIN_RESPONSE NOTCH_SIGNALING PANCREAS_BETA_CELLS P53_PATHWAY COMPLEMENT APICAL_JUNCTION TNFA_SIGNALING_VIA_NFKB PROTEIN_SECRETION APOPTOSIS TGF_BETA_SIGNALING WNT_BETA_CATENIN_SIGNALING COAGULATION KRAS_SIGNALING_DN ANGIOGENESIS OXIDATIVE_PHOSPHORYLATION FATTY_ACID_METABOLISM MYC_TARGETS_V2 MYC_TARGETS_V1 BILE_ACID_METABOLISM REACTIVE_OXIGEN_SPECIES_PATHWAY MTORC1_SIGNALING DNA_REPAIR ANDROGEN_RESPONSE CHOLESTEROL_HOMEOSTASIS UV_RESPONSE_DN MITOTIC_SPINDLE PI3K_AKT_MTOR_SIGNALING E2F_TARGETS G2M_CHECKPOINT INTERFERON_GAMMA_RESPONSE INTERFERON_ALPHA_RESPONSE elline Cell Treatment

GSVA enrichment score . na es n elln) (C line). cell one least at in >0.2

GSEA normalized line Cell Treatment -

n adjusted and >2 effect size vehicle KG1a HNT34 HL60 AML193 -0.4 -0.2 0 0.2 0.4 -2 -1 0 1 2 NSLts Articles Latest PNAS na fotto effort an In P value | <0.05). A f6 of 3 and >1 )

SYSTEMS BIOLOGY AB200 cell lines one cell line one cell line CD280 CD184 CD10 CD105 CD257 CD276 CD305 CD182 100 CD95 CD83 CD262 CD112 CD157 CD144 CD84 CD120a CD150 CD92 CD151 CD332 CD300f CD119 CD318 Intersection Size of 0 CD100 CD46 CD68 KG1a CD82 CD361 CD31 CD205 CD91 HL60 CD300a CD220 CD183 CD49f CD32 CD73 HNT34 CD99 CD329 CD156b CD230 AML193 CD316 CD164 CD282 CD131 CD14 CD69 CD85d 0 200 400 600 CD37 CD162 CD25 Number of proteins CD54 CD70 CD66c CD102 CD270 CD11b CD304 CD44 CD35 CD120b CD55 CD85h CD141 20 CD58 CD39 CD49a C CD224 CD96 CD206 CD312 CD132 CD66e 10 CD155 CD62L CD244 CD135 CD127 CD317 CD294 CD1d 0 CD48 CD331 CD22 CD63 CD222 CD200 CD107a CD172b CD337 CD107b CD124 10 CD275 CD56 CD90 CD101 CD315 CD265 protein expression Intersection Size of 20 CD232 CD30 CD185

up- and down-regulated CD116 CD36 CD325 AML193 CD339 CD80 CD19 CD148 CD210 CD229 KG1a CD172a CD328 CD221 CD284 CD85j HL60 CD49b CD142 CD53 CD217 CD358 CD86 HNT34 CDw210b CD64 CD286 CD11c CD89 CD218a CD62P -20 -10 001010 20 30 CD4 CD243 Differentially enriched proteins CD261 CD49c set size in each cell line CD126 CD130 CD74 CD59 CD153 CD226 CD327 CD66a TREML2 5 CD38 CD41 DE ADGRE3 CD47 CD61 SLC1A5 4 CD49d CD6 HL60 SLC38A1 CD298 CD143 KG1a

CD11a HNT34 3 CD7

PVRL1 CD38 CD97 AML193 CANX CR1 CD49e CD34 SEMA4A LRP1 CD108

) 2 ITGAX CD14 CD111 CD6 CD166 CD352 CD33 CD117 PRNP 1 BST1 ITGAM CD51 et al. CD87 CD156c CD170 0510 15 2025 30 CD101 CD13 0 CD321 Log (sum of peptide intensity) NTRK1 CD43 CD239 10 CD18 CD228 MPO -1 PVRL1 CD302

ATRA enrichment CD71

GGT5 2 INSR CD29 CD274 (Hoffmann -2 CD50 CD40 ITGAM JAG1 ADAM15

Log CD45 CD123 CR1 SEMA4A CD98 CD109 -3 CD147 CD93 -4 HL60

KG1a SLC38A1 HNT34 AML193 -5 HL60 HL60 -2-1012345 KG1a KG1a -3 -2 -1 0 1 2 3 Log AZA enrichment HNT34 HNT34 log2(fold enrichment) 2 AML193 AML193

Fig. 3. Surface proteome changes induced by AZA treatment in the four AML cell lines. (A) Surface proteins identified in the four vehicle-treated AML cell lines. Overlapping proteins identified are indicated in the vertical bar graph and the specific overlapping groups are indicated by the black solid points below the bar graph. Total surface proteins identified in each cell line are indicated in the horizontal bar graph. (B) CD markers identified by surface proteomics in vehicle-treated sample. The heat map is shaded from yellow to red to reflect estimated abundance (logarithmic sum of peptide intensities for each protein). (C) AZA induced unique changes on the cell-surface proteome. Overlapping up-regulated and down-regulated proteins are indicated by upward and downward bars, respectively, in the vertical bar graph [median stable isotope labeling by amino acids in cell culture (SILAC) ratio >2 or <2, P value <0.05]. The specific overlapping groups are indicated by the black solid points below the bar graph. Total differentially regulated surface proteins for each cell line are indicated in the horizontal bar graph showing variable surface proteome regulation by AZA. No commonly regulated protein was identified among the AZA-treated cell lines. (D) Proteins with significant changes in at least two cell lines are shown to illustrate distinct regulation of surface proteins by AZA (n = 13). (E) Comparison of surface proteomics data between AZA treatment and all-trans retinoic acid (ATRA) treatment in HL60 cells. Pearson correlation between the two datasets is 0.44. Data for ATRA treatment in HL60 was obtained from Hofmann et al. (18).

ITGAM (Integrin Subunit Alpha M), and MPO (Myeloperoxi- mechanism of action, both ATRA and AZA treatment of HL60 dase), appeared to be generally up-regulated by AZA treatment, cells are known to induce granulocytic and monocytic differenti- but this was statistical significance in only two or three cell lines. ation. To this end, we compared our HL60 dataset to the existing Western blot of the regulation of ITGAM (CD11b), a common dataset and observed a considerable overlap of changes in the marker of neutrophil/monocyte differentiation, was consistent surface proteome (Pearson correlation of 0.44; Fig. 3F). Among with SILAC quantification (SI Appendix, Fig. S9). Specifically, the up-regulated proteins identified in both datasets are sev- expression of ITGAM was up-regulated in HL60 and AML193 eral known monocytic differentiation markers such as ITGAM cells, did not change in KG1a, and was down-regulated in (CD11b), CD14, and CD38, as well as some previously undefined HNT34. Together, the surface proteomics analysis, similar to markers such as ADGRE3 and CR1. It is remarkable that even gene expression analysis, suggests that the effects of AZA are though the two molecules target different cellular functions a largely dependent on the inherent differences among the AML number of common targets emerged. As such, these proteins are cell lines. Hierarchical clustering of significantly enriched pro- potential therapeutic targets for subtypes of AML that undergo tein expression showed a distinctive protein regulation profile differentiation upon AZA or ATRA treatment. in each cell line (SI Appendix, Fig. S8C). Further functional analysis using GSEA with GO terms indicated an increase in Comparisons of Methylome, Transcriptome, and Surface Proteome immune response and a decrease of various transmembrane Profiles. Having all three omics datasets allowed for compar- transporters (SI Appendix, Fig. S10). The pathway analysis of isons of cellular states of the four cell lines with and without proteomics was consistent with the pathway analysis of transcrip- AZA at the DNA, RNA, and surface protein levels. Hierarchi- tomics, showing activation of immune response and repression of cal clustering of each omics dataset was dominated by variation metabolism. among the individual cell lines rather than variation due to AZA Previously, Hofmann et al. (18) used three cell-surface cap- treatment (Fig. 4A). At the DNA methylation level, KG1a (M1) ture techniques to identify a total of ∼500 surface proteins clustered with HL60 (M2), while AML193 (M5) clustered with between two AML cell lines (HL60 and NB4) that represent HNT34 (M4), consistent with their FAB classifications. At the the M2 and M3 stages of AML. Indeed, comparison of our transcriptome and surface proteome levels, however, KG1a (M1) HL60 datasets showed an overlap of 230 identified proteins and HNT34 (M4) clustered together, while AML193 (M5) and (SI Appendix, Fig. S11). To understand cellular differentiation, HL60 (M2) clustered together. KG1a and HNT34 are cell lines Hofmann et al. (18) further characterized the surface proteome known to be nonresponsive to differentiation agents such as GM- in response to all-trans retinoic acid (ATRA). Despite different CSF (24, 25), while AML193 and HL60 have been shown to

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ih emsigfo nlzn n yeo aa eaotda adopted we data, of type one analyzing from missing be might regions methyla- promoter of DNA gene body. subset by corresponding gene the or small for either a accounted at only directly changes tion why be explain can may changes RNA This and 32). positive shown (31, both have be negative can Studies transcription corre- level. on effects not protein methylation or were that transcript status either methylation with in lated changes tracked levels well, transcript of and reasonably genes/proteins protein while these that for found we datasets interest, a omics 4D three (Fig. in the lines cell Comparing expression the of in protein one regulated least significantly at were surface that proteins and cell-surface of subset expression, protein gene to due lation, likely are levels turnover. steady-state protein and regulation and cor- whole-cell differences mRNA The Such other (28–30). between 4C). lines in cell (Fig. reported using experiments 0.71 previously proteomics to been 0.41 has from respondence ranging (r) correlation Pearson coefficients pro- showed treatment and AZA RNA after in expression abundance. mass tein changes highest of the the between nature comparison with the Quantitative proteins reflects detecting likely 4B in This (Fig. spectrometry S12A). overall Fig. signal transcriptional Appendix, higher SI a datasets have proteomic to the in tend identified proteins surface that showed may cell-surface these and morphology of transcriptome to state cellular proteome. correlated functional closely by the more There- defined is but cells methylome, 27). lines the (26, cell with reagents correlate four various the changes. to methylation fore, for response plotted are in site start differentiate Log transcriptional the line. of cell bp one 1,500 least within sites at CpG in for changes values protein beta significant in changes with average genes scaled for and datasets expression, omics of Comparison (D) xrsinrnefo .4frH6 el o07 o N3 el r ero orlto) infiatyu-addw-euae ee n rtisare proteins and genes mass down-regulated by and identified up- genes Significantly log correlation). of with Pearson and points (r, (blue), outlier cells proteins one HNT34 surface and for (P two be 0.71 respectively removing to green, (C to after annotated levels. and cells expression red genes HL60 treatment. gene in all AZA for higher and highlighted of 0.44 have vehicle from (green), proteins between surface differences range genes than that expression all rather illustrate of lines (cyan) cell profiles experiment between spectrometry expression differences by RNA dominated Representative were (B) data proteome surface and transcriptome, 4. Fig. en tal. et Leung

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SLC7A5

PTPRM Treatment na fott dniyAAidcdfntoa oue that modules functional AZA-induced identify to effort an In methy- DNA in changes of magnitude the compared we Next, expression protein surface and gene between Comparison

SLC43A1 line Cell

EFNA1 Surface protei

log2(gene expression) Height 10 15 SLC1A4 600 200 400

5

0

CD69 All genes

mc oprsnbtenmtyoe rncitm,adsraepoem nALclstetdb Z.( AZA. by treated cells AML in proteome surface and transcriptome, methylome, between comparison Omics Surface protei SLC1A5

SLC3A2 +

KCNMA1 n genes

detected by MS HL60 +

TMEM206

SLC39A14 n genes -- TNFSF8

MME

--

TNFRSF8

SLC2A5 + CD34

C KG1a LAIR1 +

NRROS

SLC4A7 Methylome

SLC38A1 log (fold change of protein expression) CLEC12B 2 INSR -20246

-

MFAP3 AML193 SEMA4A r log -

0.61 =

PVR -20246

2 P2RX7 + fl hneo eeexpression) gene of change (fold

●

PRNP ●

● ● + ● ● TREML2 ● ● ● ●

● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● PTPRF ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● - ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ADAM15 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● HNT34 ● ● ● ● SDK2 ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● -

● ● ● ● CD48 ●

●

KG1a ● ● ● ●

ATP11A ● ● ● + ● ● ●

and JAG1 ● ●

● SLC12A4 + CD6

Treatment value VASN

i.S12B). Fig. Appendix, SI elline Cell

DPEP2 Height

SLCO4A1 0.61 = cor 200 400 600 HTR1F 0

● ● CD22

.5frbt eeadpoenepeso rfie.FrK1 n L0 orlto a calculated was correlation HL60, and KG1a For profile). expression protein and gene both for <0.05

2 FLRT2

++ ABCC4

fl hneo rti expression) protein of change (fold AML193

M6PR

++

PTGER2

SORL1 log (fold change of protein expression)

2 -

TGFBR1

SLC44A1 -20246 -

SLITRK6

CD84 log --

r SIGLEC6 0.41 =

20246 4 2 0 -2 2 H --

HLA−B expression) gene of change (fold

Transcriptome L ● ● SEMA6A ● 6

● ● ● ●

● 0 ● ● ● ● ● SUN2 ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● CD38 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● MMP14 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● and ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● DIAPH1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● + ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● F2R ● ● ● ● ● ● HL60 ● ●

●

KG1a ● ● ● +

ABCA1 ●

● ● TSPAN13 ● ●

- MCOLN1

ITGA6 ● ●

- ITGB3

coscl ie,fntoa ewrsapae ob commonly be level to gene Appendix, appeared (SI the networks regulated at functional synchronized lines, be cell transcription not across and might exerted methylation AZA although by be regulation that this might found Through (33). we that levels analysis, transcription consequence and methylation functional at further jointly the to Epigenome) for and probe Transcriptome the Integrating Mod- ules (Significance-based SMITE algorithm, developed recently eietfidsvrlpeiul nendmressc as such markers undefined HL60, previously in treatment several ATRA of novel identified data potential we published a Compar- previously represent AZA. with to may combination ing and in AML lines for cell target therapeutic four treat- cell AZA all protein, by apoptotic surface in up-regulated the commonly a ment be study encoding to to gene found surviv- was One interest TRPM4, future. the of the be of in also surfaceomics population could the it surface on cells, to and focused 5 ing mRNA from we ranging in Although subdued, changes more 10%. while much 70% were sites, to expression 45 CpG protein from probed ranging all methylation, of DNA to in led reduction treatment AZA global cells. vehicle-treated in pattern of that expression study showed lines multiomics expres- and cell transcripts, gene Our at AML into expression. lines four manifests protein cell the that surface AML how and the and sion compare level to surface epigenetic omics and us the three transcriptome, allowed at RNA This lines methylome, proteome. cell DNA AML the different asked levels: four we crucial affects study, be this AZA will In how question. sources biological omics any orthogonal understanding integrat- to from accessible, analysis widely data more ing becomes technology omics As Discussion

SLC6A8 + TRPM4

H

ADGRE3 + N ITGAV T

3

SEMA7A 4

CD276

FOLR1

LY6G6C Treatment

PGAP3 log (fold change of protein expression) line Cell

,rsetvl.Dse ie (y lines Dashed respectively. >0, BST1 2 Height

CANT1 -20246 200 400 600

0

SIGLEC7

CD40 log r GALR2 0.71 =

-

20246 4 2 0 -2 IL17RA 2 M13H6 HNT34 HL60 AML193

● ● ● ● fl hneo eeexpression) gene of change (fold 0 ufc rtisoelpi ehlto or methylation in overlap proteins surface ∼50%

● ● CD86 ● ● ● -

● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ESAM ● ● ● ● ●

● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● + ● ● ● CXCR4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● IL1RL1 ● ● ● ● ● ● orlto fcagsbtengn n protein and gene between changes of Correlation ) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

+ ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● FCER1A ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● NTRK1 ● ● ● ● ● ● ● HNT34

● ● ● ● ● ● - ●

● ● ● ● 2 ● ● ● LGALS1 ● ● ● ● ● ●

● ● ● ● ●

● MT Analysis SMITE COL14A1 + odcagsaepotdfrpoenadgene and protein for plotted are changes fold

ufc proteome Surface ● ●

ITGA2B ● ● ● ●

●

CD101 + ● ● ● ● LTBR ●

- NRP1 ●

MILR1

+ MPO

irrhlcutrn fmethylome, of clustering Hierarchal A)

MRC1 ● ●

-

KIT

PTPRJ --

CR1

ITGAM ++

PILRA -3

CD14 log2(fold change of protein expression) = LILRB2 0 p sites, CpG ∼80%

-2 0 2 4 6 KG1a 2-1 -2

ITGAX

NSLts Articles Latest PNAS and x LRP1

- r rw o reference. for drawn are ) Log log TFRC r

0.56 =

LILRA2 + 20246 4 2 0 -2 2

2 fl hneo eeexpression) gene of change (fold

PODXL change fold ●

aae S3 Dataset KLRG1 ● ● ●

0 ● ● ●

● ● ● elline Cell OGFOD3 ● ● ● ● ● ●

● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● TNFRSF10C ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 12 EPHA1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● KG1a HNT34 HL60 AML193 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● STEAP4 ● ● ● ● ● ● ● ● ● AML193 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● LY75 ● ● ● ● ● ● ● ● ● ●

● ●

● ● ● ● ● ●

ERAP1 ● ● ●

● ●

GLG1 ● ●

● ● ● ● GGT5 3

● Treatment SLC7A1 + - ● ● ● ● ). ●

elline Cell elline cell vehicle AZA 0.5µM | ∼53% KG1a HNT34 HL60 AML193 Transcript- proteome Surface methyl- f6 of 5 ome ome

SYSTEMS BIOLOGY ADGRE3 and CR1 that are potential therapeutic targets for sub- approach to detail the impact of AZA on AML at the individual- types of AML that undergo differentiation with AZA or ATRA gene level as well as the functional-pathway level. The heteroge- treatment. neous response of AZA treatment reflects the heterogeneity of Despite relatively few changes observed at the transcriptome cell types, implicating that a subtype-specific therapeutic strategy and surface proteome levels after AZA treatment, functional would be more suitable than a general antibody-based therapy analysis of RNA and protein regulation showed a general repres- against all AML. Three therapeutic candidate targets, TRPM4, sion of metabolism and activation of immune response across the ADGRE3, and CR1, were identified for treatment of AML in four cell lines. The repression of metabolism was consistent with combination with AZA, and we hope to validate these targets a general inhibition of cell growth, albeit to different degrees, in matched patient samples before and after AZA treatment suggesting a common response of the cells toward AZA treat- using more sensitive targeted proteomics methods such as par- ment (SI Appendix, Table S1). We also observed activation of allel reaction monitoring. Beyond specific validation of these immune-responsive genes, which is consistent with previous stud- targets, further experiments using a similar integrated omics ies showing that AZA treatment in cells of epithelial origin led approach to analyze clinical specimens will advance our under- to the transcription of endogenous retrovirus, and an induction standing of how DNMTi affect AML. Given the heterogenous of a number of immune response genes (AIM genes) related response we observed, developing techniques toward single-cell to antiviral response (13). Even though most of the defined parallel analysis of epigenome, transcriptome, and proteome will AZA-induced immune genes (AIM genes) were activated in the be paramount to deciphering the interaction between cell types current study, the magnitude of induction was variable among and different cellular states. all four cell lines (SI Appendix, Fig. S13). Recently, a number of clinical trials using combination therapy with AZA and check- Materials and Methods point inhibitors have shown some clinical efficacy, and it has been AML193, HL60, KG1a, and HNT34 cells were cultured in RPMI SILAC media postulated that this antiviral response induced by AZA can sensi- and treated with vehicle (DMSO) or 0.5 µM AZA daily for 3 d. Cells were tize various cancers (7, 17). Given the common functional impact cultured for another 4 d in drug-free RPMI SILAC media before they were among the different subtypes of AML cell lines as well as cervi- harvested for DNA methylation, gene expression, and surface proteomics cal (12, 13) and colorectal (14) cancer, combination therapy with analyses. Detailed materials and methods are included in SI Appendix. AZA and checkpoint inhibitor is a promising strategy for cancer types of hematopoietic origin. ACKNOWLEDGMENTS. This study was funded by the Celgene Corporation. K.K.L. was funded by a Canadian Institutes of Health Research Postdoc- AZA and other DNMTi have been approved for treatment toral Fellowship Award. J.A.W. was funded by NIH Grants R35GM122451 of MDS and AML for the past decade; however, unmet medi- and P41CA196276 and the Chan Zuckerberg Biohub Investigator award cal need remains for these patients. Here, we used a multiomics program.

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