Role of KMT2B and KMT2D histone 3, lysine 4 methyltransferases and DNA oxidation status in circulating breast cancer cells provide insights into cell-autonomous regulation of cancer stem cells

Role of KMT2B and KMT2D histone 3, lysine 4 methyltransferases and DNA oxidation status in circulating breast cancer cells provide insights into cell-autonomous regulation of cancer stem cells | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Role of KMT2B and KMT2D histone 3, lysine 4 methyltransferases and DNA oxidation status in circulating breast cancer cells provide insights into cell-autonomous regulation of cancer stem cells Alejandra I. Ferrer-Diaz, Garima Sinha, Andrew Petryna, Ruth Gonzalez-Bermejo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3822758/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Feb, 2024 Read the published version in Cell Communication and Signaling → Version 1 posted 7 You are reading this latest preprint version Abstract Background Breast cancer cells (BCCs) can remain undetected for decades in dormancy. These quiescent cells are similar to cancer stem cells (CSCs); hence their ability to initiate tertiary metastasis. Dormancy can be regulated by components of the tissue microenvironment such as bone marrow mesenchymal stem cells (MSCs) releasing exosomes to dedifferentiate BCCs into CSCs. The exosomes cargo includes histone 3, lysine 4 (H3K4) methyltransferases, KMT2B and KMT2D. A less studied mechanism of CSC maintenance is the process of cell-autonomous regulation, leading us to examine the roles for KMT2B and KMT2D in sustaining CSCs, and their potential as drug targets. Methods Use of pharmacological inhibitor of H3K4 (WDR5-0103), knockdown (KD) of KMT2B or KMT2D in BCCs, real time PCR, western blot, response to chemotherapy. RNA-seq and flow cytometry of blood from BC patient for markers of CSCs and DNA hydroxylases. In vivo studies with a dormancy model for response to chemotherapy. Results H3K4 methyltransferases can sustain CSCs, impart chemoresistance, maintain cycling quiescence, and reduce migration and proliferation of BCCs. In vivo studies validated KMT2’s role in dormancy and identified these genes as potential drug targets. DNA methylase (DNMT), predicted within a network with KMT2 to regulate CSCs, was determined to sustain circulating CSC-like in the blood of patients. Conclusion CSCs are sustained by H3K4 methyltransferases and DNA methylation. Overall, the findings provide crucial insights into the epigenetic regulatory mechanisms underlying BC dormancy with KMT2B and KMT2D as potential therapeutic targets. We do not propose to change the standard of care, but to monitor circulating BCCs as a functional indicator for targeted treatment to prolong BC remission, which will partly address health disparity. breast cancer dormancy epigenome resistance breast cancer cancer stem cell Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Breast cancer (BC), the most common cancer in women, remains a clinical problem ( 1 ). A major issue is based on challenges to eliminate dormant cancer cells ( 2 ). The preference of BC for bone marrow (BM) results in poor prognosis ( 3 ). Entry of BC cells (BCCs) in the BM occurs at any time during the disease as well as the period before clinical diagnosis. The latter could be years to decades when the cancer cells remain dormant ( 4 , 5 ). In BM, BCCs can survive as dormant cells for long- periods, even decades ( 6 ). Dormant BCCs remain in cycling quiescence, resist treatment, and adapt properties of stem cells ( 7 – 10 ). These properties have led to dormant BCCs referred as cancer stem cells (CSCs) ( 9 , 11 ). The stem cells properties of dormant BCCs are in line with the ability of these cells to reactivate into tertiary metastasis ( 9 , 12 ). Although drug resistance involves a complex mechanism, one must consider that treatment could be influenced by the shared properties between dormant BCCs and healthy endogenous stem cells. As an example, in BM, dormant BCCs are located with hematopoietic stem cells (HSCs), making it difficult to target the dormant cells without untoward effects of the HSCs ( 13 , 14 ). In this regard, imperative survival of HSCs would limit the therapeutic dose of a drug that targets dormant BCCs. Thus, it is important to understand how BCCs achieve dormancy in BM since this would allow for strategic development of methods to safely eradicate BCCs without affecting the hematopoietic system. Cells within tissue environment could influence dormancy. This occurs partly by support of BCC dedifferentiation to CSCs with concomitant alteration of BCC epigenome ( 13 , 15 , 16 ). Epigenetic changes impart functional plasticity of BCCs, including properties consistent with dormancy ( 17 , 18 ). BM endogenous cells - macrophages, fibroblasts and mesenchymal stem cells (MSCs) - are key support of BC dormancy ( 5 , 19 , 20 ). BCCs can instruct MSCs BM to release exosomes to initiate the process of BCC de-differentiation into CSCs ( 15 , 19 ). The released exosomes contained transcripts for epigenes, which include DNA and histone epigenetic modifiers, such as DNA methyltransferase-1 (DNMT1), and histone 3, lysine 4 (H3K4) methyltransferases - KMT2B and KMT2D. A focus on epigenetic modifiers in cancer, including the present study – BC dormancy - is based on the diminished paradigm of genomic instability as the sole contributor to cancer development ( 21 , 22 ). The literature showed low levels of gene mutations in some cancers, which strongly support roles for epigenomic modification in BCC function ( 23 – 25 ). We validated DNMT1, KMT2B and KMT2D, in exosomes released from BCCs that were exposed to MSCs ( 15 ). There are six members with the lysine methyltransferase 2 (KMT2) family of proteins, formerly referred to as mixed-lineage leukemia (MLL). KMT2 incorporates methyl group(s) at lysine 4 residues of histone 3 tail ends ( 26 ). KMT2s can add up to three methyl groups to lysine residues in which these epigenetic marks influence transcriptional activation ( 27 ). KMT2B and KMT2D are H3K4 methyltransferases involved human development ( 28 ). Abnormal placement of H3K4 methylation marks across the genome has been associated with cancer and patient survival ( 29 ). Reduced H3K4me2 is linked to poor survival whereas decreased H3K4me3 significantly improves patient prognosis ( 29 ). Although H3K4 methylation is associated with transcriptional activation, the overall cellular response depends on the deposited marks, and the affected downstream activated genes ( 29 ). Mutation of KMT2B in cancers, including BC, frequently leads to upregulated levels of KMT2B ( 30 ). In BC, KMT2B is recruited by the estrogen receptor-α to the IL-20 promoter to enhance H3K4 methylation and BCC proliferation ( 31 ). Targeting of both KMT2B and IL-20 in BC disrupts estrogen signaling ( 31 ). In a pan-cancer study evaluating the -intra and -inter tumor heterogeneity of the KMT2 gene, it was noted that KMT2B expression was significantly upregulated in 18 tumor types, including BC subtypes, and this correlated with an aggressive phenotype ( 32 ). KMT2D is frequently mutated in BC, resulting in treatment resistance ( 33 , 34 ). Inhibition of KMT2D in combination with current treatments such as PI3K inhibitors reduces tumor volume in ER-positive BC ( 35 , 36 ). Although KMT2D can act as an oncogene, the KMT2D subunit, UTX, can function as a tumor suppressor by reducing epithelial-to-mesenchymal transition ( 37 ). DNA methylation is a heritable epigenetic mark that dictates cellular identity by regulating gene expression ( 38 ). Deposition of a methyl group on the fifth carbon of cytosines (5-methylcytosine, 5mC) is a conserved epigenetic modification implicated in cellular memory and differentiation ( 39 ). 5mC modifications are specific to palindromic sequences of cytosines linked to guanines by phosphodiester bonds (CpG islands) ( 38 , 40 ). DNA methylation patterns are catalyzed by the DNA methyltransferase (DNMT) family - DNMT1, DNMT3A, and DNMT3B. DNMT1 is involved in the maintenance of methylated cytosines and acts preferably on hemimethylated DNA ( 41 ). Conversely, de novo methylation is accomplished by DNMT3A/B in a symmetric manner ( 42 ). Aberrant DNA methylation patterns are involved during the development and progression of cancer. DNMT1 expression is upregulated in BC tumors, and its deletion impedes CSC self-renewal and survival ( 43 ). The degree of DNMT1 expression depends on the BC subtype. For instance, triple negative BC (TNBC) and inflammatory BC have higher expression of DNMT1 as compared to luminal A ( 44 ). DNMT1 reduces expression of estrogen receptor, promotes epithelial mesenchymal transition and allows for expansion of the CSCs in TNBC ( 45 – 47 ). BCCs can enter and exit dormancy depending on cues from the microenvironment. After the BCCs dedifferentiate into CSCs, they can adapt long-term dormancy by GJIC between BCCs and BM resident hematopoietic and non-hematopoietic cells, changes in cytokine production by BCCs and endogenous stroma ( 15 , 19 , 20 , 48 , 49 ). In addition to MSC-derived exosomes facilitating BCC dedifferentiation to CSCs use of 3D bioprinting to recapitulate the hematopoietic system suggested cell-autonomous mechanisms of dedifferentiation ( 50 ). CSCs in the BM have survival advantages by forming GJIC with the endogenous stromal cells, and protection from the immune elimination by its ability to preferentially interact with MSCs ( 51 , 52 ). To this end, it is crucial to understand how CSCs survive. We studied the epigenetic role of two KMT2 genes in cell-autonomous control of CSCs using in vitro and in vivo studies. This study also reports on parallel studies in which we screen circulating BC in the blood of patients. We report on an indirect relationship between DNMT and its associated hydroxylase suggesting DNA methylation by DNMT and H3K4 in sustained dormancy. MATERIALS Ethics statement Human Subjects : Rutgers Institutional Review Board (IRB) approved the use of blood from BCC patients (Table 1 a). Table 1 a. Patient demographics Patients Age (Yrs) Stage (Diagnosis) Hormone Status Treatment 1 40 3 Her2 + ER- Pertuzumab, Trastuzumad Docetaxel, carboplatin 2 75 3a Her2- ER+ no treatment 3 50 4/Relapse Her2- ER- Abraxane, Gemcytabine 4 65 2a Her2 + ER+ Pertuzumab, Transtuzumab* 5 64 4/relapse Her2- ER- Sacituzumab, Govitecan 6 89 2a Her2- ER+ Exemestane 7 67 4/relapse Her2- ER+ (minimal) Sacituzumab, Govitecan 8 55 3a Her-ER+ Anastrozole ** 9*** Unknown 3 Her2 + ER+ Pertuzumab, trastuzumab, carboplatin, docetaxel 10*** Unknown 3 Unknown No treatment * Post-treatment (ended 6/23), followed by endocrine therapy (Letrozol). At relapse, treated with Docetaxel and Carboplatin. Due to low tolerance, switched to listed treatment (Patient 4). ** Patient was treated between July 2020-November 2020. This followed surgery. At blood draw, patient was on endocrine therapy with Anastrozole. Currently no active disease. *** Blood from these two patients were used for in vitro studies (Table 2 ) Table 1 b. Phenotype of circulating cytokeratin + cells Patients Cytokeratin % EpCam % Oct4 % Tet2 % 5hmC % CSC Support Treatment 1 .85 .085 .09 Not Done Not Done Yes Yes 2 5.92 .03 .02 .2 4.81 No No 3 .24 .04 .04 0 .23 Possible Yes 4 .79 .62 .76 0 0 Yes Yes 5 9 .51 .67 .39 0 Yes Yes 6 15.1 .39 6.3 .1 0 Yes Yes 7 .46 .01 .26 0 0 Yes Yes 8 .89 .79 .27 0 .85 No Yes 9 10 Shown are the percentages of gated cytokeratin + cells within the nucleated cells in peripheral blood. CSC support is indicated when the phenotype, evaluated as composite values, indicate that CSCs can be sustained as multipotent cells. Mice : The use of nude mice was approved by Rutgers Institutional Animal Care and Use Committee (IACUC), Newark Campus. Rutgers IACUC is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC). Mice were housed in the Comparative Medicine Resource center at Rutgers New Jersey Medical School. Reagents DMEM, RPMI-1640, L-glutamine, penicillin, streptomycin, dimethyl sulfoxide, geneticin, Glycerol, optiMEM, polybrene, trizol, trypan blue stain, platinum SYBR Green qPCR Supermix-UDG Kit, Supersignal West Femto Maximum Sensitivity Substrate, High-Capacity cDNA Reverse Transcription kit, and Total exosome isolation reagent were purchased from Thermo Fisher Scientific (Waltham, MA); ammonium persulfate, bovine serum albumin, magnesium chloride, N,N,N′,N′-Tetramethylethylenediamine (TEMED), NP-40, EDTA-free protease inhibitor, sodium chloride, fetal bovine sera (FBS), Ficoll Hypaque and Triton-X100 from Millipore-Sigma (St. Louis, MO); protein loading dye, Bradford protein reagent, and sodium dodecyl sulfate from BioRad (Hercules, CA). WDR5-0103 and doxorubicin were purchased from Tocris Bioscience (Minneapolis, MN). Acryl/Bis Solution (30%) 37.5:1 was purchased from VWR (Radnor, PA); puromycin from InvivoGen (San Diego, CA); TransIT-Lenti transfection reagent from Mirus; MTT-Assay and 5-Aza-2′-deoxycytidine from Abcam (Waltham, MA); p24 Rapid Titer Kit from Takara Bio (Mountainview, CA); and plasmid miniprep kit and RNeasy Mini Kit from Qiagen (Germantown, MD). Carboplatin and doxorubicin were obtained from University Hospital Pharmacy (Newark, NJ). Antibodies All primary human antibodies were against human antigens. Rabbit anti-cyclin D1 (1:1000 dilution), rabbit anti-Ki67 (1:250 dilution), and rabbit anti-vinculin (1:1000 dilution) were purchased from Abcam (Waltham, MA); rabbit anti-human p38 (1:1000 dilution), rabbit anti-MDR1 (1:1000 dilution), rabbit anti-CDK4 (1:1000 dilution), mouse anti-EpCam-AlexaFluor488 (1:100 dilution), goat anti-Tet2 (1:200 dilution), rabbit anti-5-hmC (1:200 dilution), and rabbit anti-human CDK6 (1:1000 dilution) from Cell Signaling (Danvers, MA); goat anti-rabbit IgG (1:2000 dilution) from ThermoFisher Scientific; goat anti-rabbit Alexa Fluor 594 (1:500 dilution) from Invitrogen (ThermoFisher); mouse anti-pan cytokeratin-PE (1:200 dilution), mouse anti-Oct 3/4-PerCPCy5.5 (1:5 dilution) from Becton Dickinson (San Jose, CA); donkey anti-rabbit IgG-AlexaFluor647 and donkey anti-goat IgG-AlexaFluor488 from Life Technologies; BD Lysing solution (Becton Dickinson). Vectors pOct4a-GFP vectors was donated by Dr. Wei Cui (Imperial College, London, UK) and was previously described from studies by our group ( 8 , 9 , 15 , 48 ). The description of pOct4a-dsRed was previously described ( 8 ). Human shRNA clone set against KMT2B, KMT2D, and scramble sequence shRNA control for psi-LCRU6GP were purchased from GeneCopoiea (Rockville, MD). Cell lines MDA-MB-231 and T47D were obtained from American Type Culture Collection and cultured as per their instruction. The MDA-MB-231 cell line is negative for estrogen, progesterone, and epidermal growth factor receptor, HER2. T47D is positive for these three receptors. Cells were grown with DMEM (MDA-MB-231) and RPMI supplemented with insulin (T47D) containing with 10% FBS, 2 mM L-glutamine, 100 IU/ml penicillin, 100 µg/ml streptomycin and 1% non-essential amino acid. The HEK293T cells were cultured in a similar manner to the MDA-MB-231 BCCs. MDA-MB-231 cells were stably transfected with the pOct4a-GFP (green fluorescence protein) or pOct4a-dsRED (red fluorescence protein), as described ( 9 ). The relative fluorescence intensities correlated with the expression of the stem cell gene, Oct4a ( 9 ). We selected and maintained cells expressing the reporter genes with Geneticin (500 µg/ml). BCCs expressing high levels of Oct4a (top 5%) were classified as CSCs, as described ( 9 ). Treatment of BCCs with H3K4 inhibitor WDR5-0103 BCCs were seeded in 6-well plates at 3.5x10 5 cells/well. After overnight incubation, the cells were treated with WDR5-0103 and vehicle for two days. The media were replaced every 24 h. At 48 h, BCCs were de-adhered with 0.25% trypsin-EDTA and then subjected to the following readouts: cell viability, flow cytometry, qPCR, and resistance to treatment. Preparation of lentiviral particles Lentiviral particles were prepared as described ( 48 ). The shRNA viral plasmids were inserted into One Shot Mach1T1 Phage-Resistant (Thermo Fisher Scientific) chemically competent E. coli . The DNA from the transformed bacteria was isolated with the Plasmid Miniprep Kit. The DNA was digested with BamH1 and EcoRI followed by gel electrophoresis validation. The transformed bacteria were cultured and amplified in LB broth supplemented with ampicillin (50 µg/ml). The plasmid DNA was collected after amplification of the bacteria. Lentiviral particles containing the isolated plasmids were propagated in HEK-293T cells (90% confluence). The packaging plasmids (5 µg/ml) were gently combined with the lentiviral plasmid of interest (5 µg/ml). The mixture was transferred to a tube containing 1 ml of Opti-MEM for gentle mixing, followed by adding 30 µl of TransIT-Lenti (Mirus Bio) reagent. The mixture was homogenized and then incubated at room temperature for 10 min to allow the formation of transfection complexes. The mix was added dropwise to HEK-293T cells, which were incubated at 37°C for 48 h. The media were collected and centrifuged at 300 g for 10 mins to remove the cellular debris. The supernatant containing the virus was filtered through 0.45 µm PVDF membrane and concentrated using the Lenti-X Concentrator (Takara Bio). The concentrated viral clones of KMT2B, KMT2D, DNMT1, and scramble shRNA were quantified with the Lenti-X p24 Rapid Titer Kit (Takara Bio). The viral nix was aliquoted into 0.5 ml low-protein binding tubes and stored in -80°C. Knockdown (KD) of KMT2B and KMT2D MDA-MB-231 and T47D cells with stable pOct4a-dsRed were seeded at a density of 5x10 4 /well in 24-well plates. We selected dsRed as a marker because the lentivirus for KMT2B and KMT2D express GFP (Figures S1 and S2). After 24 h, the cells were transduced at a multiplicity of infection of 1:1 and 4 µg/ml of polybrene with the shRNA lentivirus containing sequences for KMT2B, KMT2D, and scramble shRNA (GeneCopoeia). After 48 h, the media were changed and replenished with fresh media supplemented with 1.5 µg/ml of puromycin every 2 days for two weeks. The efficiency of transduction was evaluated by fluorescence microscopy for GFP and western blot (Figures S1 and S2). Carboplatin treatment and cell viability Trypan blue staining was used to assess cell viability in assays in which BCCs were treated with WDR5-0103 or vehicle. Similar assessments were conducted for BCCs, knockdown for KMT2B or KMT2D, or scramble sequence. Cells were manually counted on a hemocytometer. MTT assay (Abcam) was performed with BCCs seeded at 2x 10 4 cells/well in a 96-well plate. After overnight incubation, the cells were treated with vehicle or carboplatin (220 µg/ml) every 24 h or 2 days. After this, the cells were subjected to the MTT assay. Media were removed from the wells and replaced with the MTT reagent in serum-free media. The cells were incubated at 37° for 3 h. This was followed by adding MTT solvent to neutralize the reaction. The cells were incubated on a shaker for 15 mins at room temperature and then analyzed by measuring the absorbance at 590nm on the Synergy HTX (Biotek) microplate reader. Flow cytometry GFP and dsRED : Flow cytometry analysis was conducted to determine Oct4a GFP/dsRed intensity in BCCs after treatment with the epigenetic inhibitors and silencing of the epigenetic mediators. Cells were collected, resuspended in 500 µl of 1X PBS, and placed in 12x75mm polysterene tubes (MTC Bio). The cells were analyzed on a FACS Calibur (BD Biosciences) flow cytometer to measure GFP/dsREd intensity. The data were analyzed with FlowJo software (BD Biosciences). We designated the relative maturity of BCC based on our previous reports ( 9 ). Cells within the top 5% of GFP/dsRed fluorescence were designated as long-term repopulating CSCs (Oct4a-GFP/dsRed hi ). This was followed by Oct4aGFP/dsRed med , Oct4aGFP/dsRed low (early progenitors), and Oct4aGFP/dsRed neg (late progenitors). Phenotype of circulating BCCs in patients : Red blood cells were lysed with BD FACS lysing solution following manufacturer’s instructions. The lysed cells were tested with two panels of antibodies: Panel 1 contained antibodies against pan cytokeratin, Oct3/4 and EpCam; Panel 2 contained pan cytokeratin, Oct3/4, Tet2 and 5hmC. The concentrations of the antibodies are listed above. The concentrations of isotype added to the cells were similar to the amount of test antibodies. Panel 1 used intracellular labeling – cells were permeabilized at 4 0 C with 0.1% Triton X-100 for 10 mins followed by washing with 1x PBS. After this, the cells were labeled with the test antibodies and the appropriate isotype. The tubes were incubated in the dark for 30 mins at 4 0 C followed by washing with 1x PBS. The cells were immediately analyzed on the FAC Calibur (BD Biosciences). Panel 2 labeling used intracellular and extracellular labeling. The latter was first done by fixing with 3.7% formaldehyde at room temperature for 15 mins. Cells were washed with 1x PBS and then labeled with anti-EpCam and isotype. The incubation and wash was performed as for intracellular labeling. After this, the cells were labeled for pan-cytokerin, Oct3/4, Tet2 and 5hmC as described for Panel 1. Treatment of peripheral blood mononuclear cells (PBMCs) from BC patients Due to limited blood supplies, we studied the last two patients for in vitro response to chemotherapy and a DNA methylation inhibitor, Azacitidine. PBMCs were isolated from the blood of Patients 9 and 10 by Ficoll Hypaque gradient centrifugation. Cells (5x10 6 ) in 2 mL RPMI 1640 with 10% FBS were incubated. After 24 h, the cultures were incubated with vehicle (PBS), carboplatin (200 µM), or carboplatin (200 µM) + Azacitidine (2 µM). At day 7, the cells were analyzed by flow cytometry using Panels 1 and 2 antibodies, as described above for the other patients. Real-time PCR Total RNA was isolated using TRIzol reagent according to the manufacturer’s instruction (Thermo Fisher Scientific). The RNA was reverse transcribed into cDNA with the High-Capacity cDNA Reverse Transcription kit (Thermo Fisher Scientific) and amplified using the GeneAmp PCR System 9700 (Applied Biosystems). The cDNA was diluted with nuclease-free water to 200 ng/µl and then mixed with SYBR Green PCR Master Mix, primers of interest, and nuclease-free water. Real-time PCR was conducted on a 7300 Real-time PCR system (Thermo Fisher Scientific) at 50°C for 2 mins, 95°C for 10 mins followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute. The following primers were used on the PCR mix: Oct4a : Forward 5´ctg aag cag aag agg atc ac 3´; Reverse 5´gct ttg cat atc tcc tga ag 3´; KLF4 : Forward 5´aac ctt acc act gtg act gg 3´; Reverse 5´cat atc cac tgt ctg gga tt 3´; Nanog : Forward 5' caa tgg tgt gac gca ggg at 3´; Reverse 5' gac tgg atg ttc tgg gtc tgg 3'; Notch1 : Forward 5´cca agt ata gcc tat ggc aga a 3´; Reverse 5´ aag tct gac gtc cct cac 3´; Sox2 : Forward 5´taa ctg tcc atg cgc tgg tt 3´; Reverse 5´ agg atat agt aca cgc tgc cc 3´; β-actin : Forward 5´gcc cta taa aac cca gcg gc 3´, Reverse 5´aga ggc gta cag gga tag ca 3´; GAPDH : Forward 5' cag aag act gtg gat ggc c 3´, Reverse: 5´ cca cct tct tga tgt cat c 3´. Western blot Cells were resuspended in 50–100 µl of lysis buffer composed of 50 mM Tris-HCL (pH 7.4), 100 mM NaCl, 2 mM MgCl 2 , 10% glycerol, 1% NP-40, and two tablets of EDTA-protease inhibitor cocktail (Millipore-Sigma). Cell lysates were exposed to freeze/thaw cycles which consisted of 2 mins in liquid nitrogen followed by 2 mins in the water bath (37°C). Cell lysates were centrifuged at 2,000 g for 10 mins and the supernatant containing the proteins was collected for downstream applications. The concentration of the proteins was determined with the Bradford Protein Assay Reagent (BioRad) and by using BSA (BioRad) as a control. The extracts (15 µg) were electrophoresed on a 12% SDS-PAGE gel and then transferred onto Immobilon-P PVDF membranes (ThermoFisher Scientific). The membranes were washed with 1X PBS tween for 10 mins and blocked with 3% non-fat milk diluted in 1x PBS for 20 mins. The membranes were incubated overnight at 4°C on a shaker with the primary antibodies of interest such as anti-p38, anti-CDK4, anti-CDK6, anti-cyclinD1, anti-MDR1, anti-KMT2B, anti-β-actin, or anti-vinculin at a 1:1000 in 3% non-fat milk. The primary antibodies were removed, and the membranes were washed and blocked with 3% non-fat milk. This was following by incubation with secondary HRP tagged antibody 1:2000 in 3% non-fat milk for 2 h at 4°C. The membranes were washed for 20 mins and then developed with the Super Signal West Femto Maximum Sensitivity Substrate for 5 mins. The protein bands were imaged using the using the ChemiDoc XRA (BioRad) system. Scratch Assay BCCs were seeded at 3 x 10 5 cells/well in 6-well plates. Once the cells achieved 100% confluency, a scratch was performed in the middle of the well from top to bottom using a 200 µl pipette tip and assessed gap closing by microscopy at 0, 24, 48, 72, and 96 h. RNA sequencing (RNA-seq) Total RNA was extracted with RNAeasy mini kit (Qiagen) from KMT2B, KMT2D KD MDA-MB-231, or with scramble shRNA. The samples were submitted to the Genomics Center at Rutgers New Jersey Medical School for RNA-seq. Depletion of ribosomal RNA was conducted with the Ribo-Zero Gold kit (Illumina) followed by preparation of Next-Generation sequencing cDNA libraries using the NEB Ultra II Library Preparation Kit and NEBNext Multiplex Oligos for Illumina (Dual Index Primers Set 1). Quality control of the libraries was assessed with the Qubit high sensitivity kit and fluorometer (Thermo Fisher Scientific), Tapestation 2200 instrument and D1000 ScreenTapes (Agilent). Next, the cDNA libraries were diluted to 2 nM, denatured, and sequenced on the NextSeq instrument using the 1X75 cycle high throughput kit. The BCL output files from the sequencing machine were converted into FASTq files with the BCL2FASTQ software (Illumina). Data analyses Normalization of RNA-seq data and identification of differentially expressed genes was assessed with the EdgeR package from R by using the Galaxy software. Genes with a fold change of -1.5 to + 1.5 and a p-value < 0.05 were considered as differentially expressed. The data were visualized through generation of principal component analyses (PCA) plot, heatmap, and volcano plot with an adjusted P-value of 0.05 as cutoff. Gene set enrichment analysis was conducted by using the Hallmark Gene set database. Identification and analysis of significant cellular pathways from the dataset was performed with the Ingenuity Pathway Analysis (IPA) software (Qiagen). A pathway was regarded as significant by the following cutoffs: genes exhibited a p-value < 0.05, expression log ratio of 1, and activated z-score of 2. In vivo BC dormancy Intravenous route : The establishment of BC dormancy was previously described ( 19 ). KMT2B, KMT2D KD BCCs, and BCCs with scramble shRNA (5x10 5 cells in 300 µl) were injected intravenously into 6-wk old nude athymic female mice. On days 3 and 5, mice were injected intraperitoneally with a low dose of carboplatin (2 mg/kg) or vehicle to establish BC dormancy in the BM. The mice were euthanized on day 7 and the organs such as liver, brain, lungs, and femur were harvested and placed in 3.7% formaldehyde for 48 h. Furthermore, the endosteal region of the BM was scraped and imaged with the EVOS FL Auto 2 Imaging System to identify the presence of GFP-positive BCCs. The harvested organs were embedded in paraffin and sectioned at the Histology Core Facility at Rutgers New Jersey Medical School. The presence of BCCs on the tissue sections was evaluated by immunohistochemistry. Orthotopic Route : MDA-MB-231 BCCs with stable pOct4a-GFP (5x10 5 ) were injected into the mammary fat pad of female (6 weeks) nude BALB/c. After 1 week, the mice were euthanized, and the femurs were harvested and scraped to evaluate the presence of BCCs. Immunohistochemistry Paraffin-embedded tissue sections were incubated overnight at 56ºC. The sections were dewaxed with xylene and ethanol and rehydrated with deionized water. Antigen retrieval from the tissue sections was performed with citrate buffer for 30 mins in a pressured water bath. The slides were washed twice with 1X PBS for 5 mins, and the cells were permeabilized with 0.1% Triton X-100. Next, the sections were washed with 1X PBS, followed by the addition of the primary antibody, Ki-67, at a final dilution of 1:250. The slides were then placed in a humidified chamber and incubated overnight at 37ºC. The following day, the tissue sections were washed thrice with 1X PBS, the secondary antibody was added at a final dilution of 1:500, and the slides were incubated for 2 h at room temperature. The slides were washed and analyzed by microscopy on the EVOS FL Auto 2. Statistical Analyses The data were analyzed on Sigma Plot 15 (Systat Software Inc) using the two-tail student’s t-test and two-way ANOVA to compare between groups. A p-value less than 0.05 was considered significant. RESULTS Prediction of cell-autonomous mediated CSC maintenance We previously reported on BCCs instructing MSCs to release exosomes with distinct RNA cargo ( 15 ). The exosomal cargo was responsible for the stepwise dedifferentiation of BCCs into CSCs ( 15 ). The changes within the exosomal cargo included transcripts for H3K4 modifiers, KMT2B and KMT2D. This led us to ask if endogenous KMT2B and KMT2D in CSCs can sustain multipotency. We first subjected the single cell RNA-seq data from the published studies to IPA ( 15 ). In this study, the BCCs were treated with exosomes from MSCs that were previously exposed to BCCs (primed MSCs), or from MSCs that were never exposed to BCCs (naïve MSCs). We overlaid the epigenetic modifiers from these datasets with the following pathways within IPA: neoplasia of cells; proliferation of stem cells; proliferation of cancer cells; and de-differentiation of beta islet cells. The output network identified KMT2B, KMT2D, and DNMT1 as regulators of stem cell genes (Fig. 1 A. Since dormant BCCs are functionally similar to CSCs, we proposed that endogenous KMT2B, KMT2D and DNMT1 could be involved in maintaining CSCs ( 9 ). We and others have addressed the role of the cancer niche on dormancy. Based on the information, combined with our other studies showing evidence of cell-autonomous method of dedifferentiation to CSCs, we focused this study to decipher how H3K4 regulate BCCs by cell-autonomous method ( 50 ). H3K4 methylation in BCC survival The number of methylation sites on H3K4 could influence cellular functions ( 53 ). We therefore sought the role of H3K4 methylation on BCC quiescence and multipotency with a pan pharmacological inhibitor, WDR5-0103. This inhibitor blunts H3K4 methylation by targeting the core subunit of the KMT2s, WDR5 ( 54 ). Dose-response and time-course studies with BCC viability as readouts identified the optimal conditions as 10 µg/ml of WDR5-0101 and 48 h treatment (Fig. S3). Since WDR5-0103 induced significant ( p = .03) BCC death, as compared to vehicle (Fig. 1 B), we deduced that H3K4 methylation is relevant to BCC survival. H3K4 methylation in CSC maintenance Due to some BCCs resisting WDR5-0103 treatment, we conducted studies to gain insights into the how the inhibitor could be affecting the different subsets ( 8 , 9 , 55 ). We previously reported that BCCs with stable pOct4a-GFP could delineate subsets since GFP intensity is directly proportional to Oct4a levels ( 9 ). We treated BCCs-pOct4a-GFP with WDR5-0103 for 48 h and then analyzed the surviving BCCs for GFP intensity by flow cytometry ( 9 ). WDR5-0103 significantly ( p < 0.05) reduced CSCs (Oct4a hi ), as compared to vehicle (Fig. 1 C). This correlated with an increase of late BC progenitors (Oct4a neg ) (Fig. 1 C), suggesting that the inhibitor induced CSCs to differentiate. Based on this finding, we deduced that loss of cell viability likely occurred in the non-CSC subset, indicating that blunted H3K4 leads to differentiation and cell death. We next asked if WDR5-0103-mediated decrease of CSCs correlated with reduced levels of multipotent-linked genes. Real time PCR indicated that WDR5-0103 significantly ( p < 0.05) decreased Oct4, Sox2 and Nanog mRNA, as compared to vehicle (Fig. 1 D). Altogether, pharmacological inhibition of H3K4 methylation indicated its role in preserving the CSC population. Synergistic effects of H3K4 inhibitor and carboplatin Inhibition of H3K4 methylation led to increase percentages of BC progenitors (Fig. 1 C), which are mostly cycling cells ( 9 , 15 ). This led us to ask if WDR5-0103-mediated differentiation of CSCs would sensitize BC progenitors to carboplatin. We treated BCCs with WDR5-0103 and/or 200 µg/mL carboplatin for two days. Carboplatin treatment resulted in ~ 55% cell death as compared to ~ 35% for WDR5-0103 treatment (Fig. 1 E). Together, carboplatin and WDR5-0103 showed significant ( p < 0.05) increase in cell death as compared to individual treatment (Fig. 1 E). Since CSCs have been shown to resist carboplatin treatment, we deduced that increased cell death by carboplatin and WDR5-0103 was partly due to the differentiation effects of WDR5-0103 (Fig. 1 F). Transcriptomic changes in KMT2B and KMT2D KD BCCs The data thus far supported a role for H3K4 methylation in CSC maintenance (Fig. 1 ). Further, interrogating H3K4 methylation led to CSC differentiation and chemosensitivity (Fig. 1 ). Since the experimental design contained only BCCs, the findings strongly suggested a cell-autonomous method for H3K4 methylation in CSCs. We therefore knocked down KMT2B and KMT2D in BCCs to study the individual role of two H3K4 methylases in CSCs. We performed RNA-seq analyses with the KD BCCs and control expressing scramble shRNA (Fig. 2 A). PCA of the RNA-seq data showed distinct clustering of the groups with negligible variability of the biological replicates within each group (Fig. 2 B). Heatmaps of genes showed distinct gene expressions between scramble and KMT2B or KMT2D KD BCCs (Figs. 2 C and 2 D). Gene ontology (GO) analyses of the RNA-seq data revealed that the upregulated pathways in KMT2B and KMT2D KD BCCs were associated with tumor progression, e.g., inflammatory cues and cell migration (Fig. 2 E). We selected the genes associated with tumor progression and then overlaid them with the dataset linked to tumor growth pathway in IPA. The output revealed that genes associated with tumor growth were enhanced in the KD BCCs, relative to scramble shRNA (Fig. 2 F). Collectively, the results showed that KMT2B and KMT2D KD changed the transcriptional landscape of BCCs; particularly, upregulating genes associated with tumor growth. Together, the predicted analyzes suggested a loss of a dormant phenotype in the KMT2B and KMT2D KD BCCs. Reduced CSCs in KMT2B and KMT2D KD BCCs Analyses of the RNA-seq data showed increases in genes linked to tumor growth in the KMT2B and KMT2D KD BCCs (Fig. 2 F). Furthermore, there were increases of BCC progenitors and decreased CSCs after treatment with H3K4 inhibitor (Fig. 1 ). We therefore asked if this change was specific to KMT2B or KMT2D. We analyzed the KD BCCs for subsets by flow cytometry, similar to Fig. 1 C ( 9 ). CSCs/Oct4a hi were significantly ( p < 0.05) decreased when KMT2B or KMT2D was KD (Figs. 3 A and 3 B). The data when presented as the absolute number of Oct4a hi BCCs showed significant ( p < 0.05) decreases, relative to scramble shRNA (Fig. 3 C). We also noted a similar decrease for Oct4a med BCCs (Figs. 3 A and 3 B). Since Oct4a hi and Oct4a med BCCs were primitive within BCC hierarchy, we deduced that their decrease was due to differentiation ( 9 ). This was corroborated by increases of BCC progenitors (Oct4a lo and Oct4a neg ) (Figs. 3 A and 3 B). Overall, we noted similar results with the pharmacological inhibitor of H3K4 methylase (Fig. 1 C). Next, we asked if decreased CSCs (Fig. 3 C) correlated with reduced transcript for stem cell-associated genes. Real time PCR showed significant ( p < 0.05) decreases in stem cell transcription factors, Oct4a, Sox2, Klf4, Nanog and Notch 1 in KMT2B and KMT2D KD BCCs, relative to scramble sequence (Fig. 3 D). In summary, the findings indicated that KMT2B and KMT2D KD reduced CSCs, and this seemed to be due to differentiation of CSCs to BC progenitors. Loss of cycling quiescence in BCCs KD for KMT2B or KMT2D KMT2B and KMT2D KD BCCs have increased progenitors and decreased CSCs (Fig. 3 ). Induced number of cycling BC progenitors by H3K4 inhibitor is in line with enhanced sensitivity to carboplatin (Figs. 1 E and 1 F). We asked if KMT2B or KMT2D KD could promote cell cycle progression by overlaying the differentially expressed genes from the RNA-seq data with cell cycle progression pathway in IPA. Indeed, the analyses indicated activation of cell cycle progression pathways in KMT2B and KMT2D KD BCCs (Fig. 4 A). Gene set enrichment analyses (GSEA) of the RNA-seq data showed a significant enhancement of E2F and G2M cell cycle progression pathways (Fig. 4 B). The predicted findings were confirmed in western blot for CDK4, CDK6, and cyclin D1 (Fig. 4 C). These increased proteins supported cell cycle transition from G1 to S phase ( 56 ). Reduced CSCs within KMT2B and KMT2D KD BCCs is expected to decrease the ability of BCCs to adapt dormancy ( 9 ). To address this, we examined the KD cells for p38 since its increase has been linked to cellular dormancy ( 57 , 58 ). Western blot analyses showed decreases in p38 bands when the extracts were taken from KMT2B and KMT2D KD BCCs, relative to scramble shRNA (Fig. 4 D). Altogether, these results indicated that KMT2B or KMT2D KD BCCs led to loss of cycling quiescence. Enhanced proliferation and migration by KMT2B and KMT2D KD BCCs Increased BC progenitors after KMT2B and KMT2D were KD suggested that these two genes could restrict BCC proliferation and favor a dormant state (Figs. 1 and 3 ). IPA analyses of KS versus scramble shRNA indicated activated pathways linked to proliferation and differentiation (Fig. 5 A). We verified significant ( p < 0.05) increases in the proliferation of the KD BCCs, relative to scramble shRNA (Figs. 5 B and 5 C). IPA predicted increase of cell migration pathway for KMT2B and KMT2D KD BCCs (Figs S4 and S5) was validated with scratch assays. There were increases in KMT2B and KMT2D KD BCCs migration as compared to scramble shRNA (Figs. 5 D and 5 E). While the gap for KMT2B closed at 48 h, similar closure took 72 h for KMT2D BCCs. BCCs with scramble shRNA failed to close the gaps. In summary, the findings demonstrated that KMT2B or KMT2D KD promoted BCC proliferation and migration. In vitro and in vivo response of KMT2B and KMT2D KD BCCs to chemotherapy We showed mostly progenitors in KMT2B and KMT2D KD BCCs were mostly progenitors (Figs. 1 –15). Since BC progenitors are mostly cycling cells, they are expected to be sensitive to chemotherapy ( 15 ). IPA of the RNA-seq data predicted chemosensitivity of KMT2B and KMT2D KD BCCs versus scramble shRNA (Figs S6 and S7). We treated BCCs, KD for KMT2B or KMT2D, or scramble shRNA with carboplatin (200 µg/ml), doxorubicin (1 µM) or vehicle. After 48 h, trypan blue exclusion indicated significant ( p < 0.05) cell death in the KMT2B or KMT2D KD BCCs, relative to scramble shRNA (Figs. 6 A- 6 D). Enhanced chemosensitivity of KMT2B and KMT2D KD BCCs correlated with decreased of the multidrug-resistant protein Pgp (Fig. 6 E). The in vivo studies used an established model of dormancy to test the response of KMT2 KD BCC to carboplatin (Fig. 6 F). BCCs, KD for KMT2B or KMT2D, or scramble shRNA (5x10 5 cells in 300 µl PBS) were injected into the tail vein of female nude mice (6 weeks). Our previous studies reported 48–72 h for BCCs to acquire dormancy in BM ( 9 , 19 ). We used this time as guide to treat the mice. Mice were inject via intraperitoneal route with vehicle or carboplatin (2 mg/kg) at days 3 and 5. At day 7, mice were euthanized, and the femurs harvested (Fig. 6 F). The endosteal region of one femur was scraped to identify BCCs, and the other decalcified for embedding in paraffin. In both analyses, GFP within the shRNA vector served as an indicator of BCCs. The sectioned tissues were labeled for Ki67. Fluorescence microscopy of the scraped tissue indicated less BCCs in mice that received the KMT2 KD BCCs and carboplatin treatment (Fig. 6 G). Similar treatment of mice with scramble shRNA identified an increase of BCCs (Fig. 6 G). Examination of sections from paraffin-embedded femurs indicated higher levels of Ki-67 in KMT2B and KMT2D KD BCCs, relative to scramble shRNA (Figs. 6 H and 6 I). Carboplatin treatment reduced Ki67 + BCCs in femurs (Figs G- 6 I). The results also showed continued proliferation, based on Ki67 even after carboplatin treatment. Overall, the in vivo and in vitro studies corroborated chemosensitivity of KMT2 KD BCCs. Chemosensitivity of KMT2B and KMT2D KD BCCs in brain We previously reported on brain metastasis when dormant MDA-MB- 231 BCCs were induced to reverse dormancy ( 48 ). We also showed that BCCs that exited dormancy were chemosensitive, resulting in reduced brain metastasis ( 48 ). Since the experimental evidence indicates that KMT2B and KMT2D maintain CSCs, we asked if their KD could recapitulate reverse dormancy (transition out of cellular quiescence), and if treated, this will reduce brain metastasis. We evaluated the brain sections of the mice that were treated with vehicle or carboplatin (Fig. 6 F). We counted 10 fields per section in three mice and then calculated the total number of BCCs in the brain. This resulted in significantly ( p < 0.05) more KMT2B and KMT2D KD BCCs in the vehicle treated mice, relative scramble shRNA (Figs. 7 B and 7 C). We counted the total number of BCCs in the carboplatin treated mice and used the values obtained in the brain of vehicle treated as 100% to calculate cell death in brain. The results indicated significant ( p < 0.05) cell death with the knockdown cells, relative to scramble (Figs. 7 D and 7 E). In summary, the results showed that targeting H3K4 could reduce brain metastasis. Epigenomic maintenance of CSC in BC patients Along with KMT2B and KMT2D, DNMT is predicted to be involved in multipotency (Fig. 1 ). To test the involvement of DNMT in CSC maintenance, we focused on the associated regulatory genes (Fig. 6 I). Specifically, on TET2 since it can oxidize 5mC to facilitate demethylation ( 59 ) (Fig. 6 I). Additionally, we previously reported on increased TET2 during the first phase when microenvironmental exosomes mediate dedifferentiation of BCCs towards CSCs ( 15 ). TET 2 is reduced during the second phase as differentiation complete, suggesting that retained methylation is relevant to sustain stemness in BCCs. The question is how DNA methylation, along with what is reported here for methylation of H3K4, sustain stemness. We tested pan-cytokeratin + cells in the blood of BC patients for stem cell associated genes and DNMT-linked genes (Tables 1 a and 1 b). As noted in the patient demographics, most subjects were treated anti-cancer regimen that caused low blood counts; hence limited amount of blood (Table 1 a). We could analyzed the samples with two panels of antibodies in which both gated pan cytokeratin + cells. Panel 1 studied the cytokeratin + cells for EpCAM and Oct3/4, and Panel 2, for Oct3/4, TET2 and 5hmC (hydroxylation) (Fig. 6 I). Patient 2 blood, which was taken before treatment, showed high levels of 5hmC, detectable TET2, and low levels of stem cell-associated Oct3/4 and EpCAM (Table 1 b) ( 8 ). Together, this profile suggested that the patient could be in the initial phase of dedifferentiating to CSCs but does not show support of CSC maintenance. Since this blood was tested before treatment, it is expected that there will be heterogeneous BCCs in the blood. Specifically, it is expected that before treatment there will be excessive BC progenitors as compared to CSCs. We propose that the BC in this patient could be at the initial phase of dedifferentiation because TET2 has been shown to be needed for step 1 dedifferentiation ( 15 ). Due to insufficient blood sample for Patient 1, the analysis was done only for panel 1 antibodies. The comparable percentages of Oct4 and EpCAM suggested the presence of circulating CSCs. Patient 6 who was 89 years at analyses, was on an estrogen modulator and showed the highest percentage of cytokeratin + cells that were positive for EpCAM and Oct3/4. This patient showed undetectable 5hmC, indicating methylation. Thus, Patient 6 mostly likely show high level of CSCs. We deduced that Patient 8 showed limited support for CSCs, despite undetectable TET2. However, Patient 8 showed an increase of 5hmC, which could occur by another TET protein (Fig. 6 I). In summary, this section indicated that in addition to H3K4, sustained 5mC could be important to maintain CSCs in BC patients during treatment. Finally, we used the last two blood samples (Patients 9 and 10) to determine if azacitidine could differentiate CSCs (Table 2 ). If so, this could indicate a potential treatment after standard treatment. We treated the cells with carboplatin and/or azacitidine for 7 days. An analyses of the mononuclear cells with Panels 1 and 2 antibodies indicated that Patient 9, who was treated, showed evidence of CSC-like in the blood. However, azacitidine led to detectable DNA hydroxylase to methylate the DNA, which is consistent with BC progenitors. Patient 10 was collected before treatment and this showed mixed population regardless of treatment. Table 2 Timeline changes in cytokeratin + cells in patients’ PBMCs In vitro Treatment Cytokeratin % EpCam % Oct4 % Tet2 % 5hmC % Remarks Patient 9 CSCs at collection; Reduced CSC and enhanced DNA methylase with treatment Untreated .06 .02 .02 0 0 Carboplatin .34 .29 .01 .06 .06 Carboplatin + Azacitidine .33 .13 .04 .07 .19 Patient 10 Blood from untreated patient showed BCCs with DNA hydroxylase Untreated .48 .04 .03 .03 .39 Carboplatin .2 .04 .06 .02 .06 Carboplatin + Azacitidine .45 .15 .04 .05 .06 Table showed two patients whose blood was used for in vitro studies with mononuclear cells. The cells were treated with carboplatin and/or azacitidine. At day 7, the cells were analyzed by phenotype using the two panel of antibodies described in Table 1 . Discussion This study reports on a cell-autonomous method elicited by H3K4 methylation to sustain multipotency in CSCs (Figs. 1 – 6 ). This role of H3K4 methylation appears to be aided by DNMT (Table 1 , Fig. 1 A). In a previous study in which we examined the role of MSC-derived exosomes on BC dedifferentiation, we identified DNMT as a regulator of CSC maintenance (Fig. 1 A) ( 15 ). More importantly, we noted an indirect relationship between markers of stemness and DNMT associated proteins in cytokeratin + cells from treated BC patients (Table 1 ). The relevance of this study indicated that DNA methylation rather than its hydroxylation is important in CSCs. This was an intriguing observation since evidence of DNA hydroxylation has been shown to be relevant during the initial phase of BCCs dedifferentiating to CSCs ( 15 ). This is line with a need for gene transcription during the early phase as BCCs begin to dedifferentiate and once the cells attain multipotency/CSCs, transcription is diminished to attain quiescence. Since dormant BCCs are similar to CSCs ( 9 ), the present findings provide insights into a different method by which BCCs attain dormancy. In this case, we showed a mechanism of cell autonomy and this incorporate H3K4 methylases. Similar BCC quiescence can be supported in the BM by microenvironmental cells such as MSCs, macrophages and stromal fibroblasts ( 19 , 26 ). Indeed, H3K4 methylation marks have been proposed as drug targets ( 60 ). However, the question is how epigenetic targeted drugs should be used to treat BC. Of course, this study showed a role for H3K4 and perhaps DNA methylation in CSC maintenance. We do not propose to change the standard of care, but to monitor circulating BCCs as a functional indicator for targeted treatment to prolong BC remission. Studies with hematological cancer showed that induced differentiation of leukemia stem cells with bortezomib could lead to chemosensitivity ( 61 ). Despite the limited number of analyzed BCCs in blood (Table 1 ), the evidence showed intriguing information that similar analyses could be used in expanded studies to guide how BC patients are treated. We were intrigued by the results of the small studies with patient blood (Table 1 ). The data showed that aggressive BC with Her2+, there were resistant CSC-like cytokeratin + cells. These cells appear to be maintained for CSCs due to decreased TET2, which can prevent DNA hydroxylation, hence maintaining methylation. This small study was conducted with patients from the University Hospital with economically disadvantage population. It is not unusual for these patients to be first diagnosed with late-stage BC. Due to limited samples and number of patients who consented, we took the opportunity with Patients 9 and 10 to examine the differences between cytokeratin + BCCs from a patient who was treated and the other who was not treated. As expected, based on the outcome shown in Table 1 , Patient 9 had CSC-like in the blood but this changed when the mononuclear cells were challenged with azacidtidine (Table 2 ). The untreated patient 10 started with heterogeneous BCCs and this continues to be the same with azacitidine. The data discussed in the previous paragraph for leukemia, combined with this study indicated that this should require a large study with global impact. This will help address healthcare disparities in BC worldwide. Our findings shed therapeutic insight across global markets marked by healthcare inequities. In the meantime, drugs are available that could be repurposed to target the CSCs after standard treatment. This is important for long-term remission rather than short recurrences into metastatic BC. Targeting KMT2s shared WDR5 core subunit with WDR5-0103, led to cell death but concomitant differentiation into chemosensitive cells (Fig. 1 ) ( 62 , 63 ). Studies with the pharmacological agent as well as the molecular KD provided insights into how CSCs are maintained in dormancy. The experimental studies indicated that H3K4 methylation is important to maintain CSCs while preventing differentiation to chemosensitive BCCs (Fig. 1 ). The increased BC progenitors after pharmacological targeting of H3K4 was due to both KMT2B and KMT2D, based on the KD studies. The in vitro findings, were tested in vivo in nude mice with an established model of BC dormancy in the BM (Fig. 6 ) ( 9 ). We noted sensitivity to carboplatin when KMT2B and KMT2D KD BCCs were injected in mice (Fig. 6 ). This was interesting because these BCCs were in an environment of MSCs that can release exosomes with H3K4 methylase. Thus, the findings indicated that KMT2B and KMT2D could be relevant drug targets. Future studies are needed to determine how the treatment could be done safely since the BM is home to hematopoietic stem cells that are likely to share similar H3K4 marks. Similarly, if the findings in Table 1 could be a guide to treatment, this would require safety studies. Most chemotherapies target rapidly proliferating cells more efficiently than quiescent dormant BCCs ( 64 ). We noted synergism between carboplatin and WDR5-0103 with respect to cell death (Fig. 1 E). This was partly explained by WDR5-0103 being able to differentiate CSCs into proliferating BC progenitors (Fig. 1 ). WDR5-0103 inhibits global H3K4 methylation by targeting all members of the KMT2 family ( 54 ). However, among the KMT2 family, data using previous exosomal cargo with ability to dedifferentiate BCCs to CSCs, predicted roles for KMT2B and KMT2D (Fig. 1 A). Using knockdown studies, the data supported roles for KMT2B and KMT2D in sustained stemness in CSCs. Insights into a strong role for cell-autonomous regulation of CSCs were derived from bioprinting of BCCs in methylcellulose ink ( 50 ). The printing occurred without endogenous BM cells, which led us to propose that the BCCs could survive with epigenomic changes. This bioprinting study led us to reanalyze the exosomal cargo from MSCs (Fig. 1 A) ( 15 ). RNA-seq data from KMT2B and KMT2D KD BCCs further supported cell-autonomous regulation. We noted upregulation of canonical pathways linked to cancer growth and proliferation when these two H3K4 methylases were knocked down (Figs. 2 and 4 ). These findings, together with the functional studies, indicated that cell-autonomous support of CSC by KMT2B and KMT2D sustain dormancy and drug resistance ( 9 ). KMT2B KD upregulated inflammatory pathways such as interleukin (IL)-1 signaling, Toll-like receptor signaling, and Pathogen-induced cytokine storm in BCCs (Fig. 2 ). KMT2D KD in BCCs resulted in activation of IL-signaling pathway and T-helper 1 signaling pathway (Fig. 2 ). There is a strong association between the upregulation of pro-inflammatory pathways and dormancy reversal ( 65 , 66 ). Disruption of direct cellular communication between BCCs and BM MSCs enhanced IL-1 secretion from the latter cells to mediate BCC proliferation ( 67 ). Furthermore, the IL-1 signaling pathway promotes cancer cell proliferation and angiogenesis through the activation of the NFκB ( 68 ). Importantly, toll-like receptor signaling and pathogen-induced cytokine storm are two factors that can induce dormancy reversal ( 69 – 72 ). Toll-like receptors recognize pathogen-associated and endogenous damage-associated molecular patterns to trigger a pro-inflammatory immune response that result in pathogen clearance ( 73 ). Collectively, the RNA-seq findings indicated that pathways associated with inflammation are enhanced in KMT2B and KMT2D KD BCCs. GSEA of the RNA-seq dataset confirmed the functional studies by showing that E2F targets and G2M checkpoint pathways are enriched in KMT2B and KMT2D KD BCCs, as compared to scramble BCCs (Fig. 4 ). The E2F transcription factors promote cell cycle progression by inducing cell entry into S-phase ( 74 – 76 ). The G2/M checkpoint prevents mitosis initiation if the cells present DNA damage ( 77 ). The transition of cells from the G2-phase to the M-phase of the cell cycle is consistent with cell proliferation ( 78 ). Activation of cell cycle progression in KMT2 KD BCCs was accompanied by increased proliferation and migration, which is in line with reverse dormancy (Fig. 4 ). There was a 24 h difference between KMT2D and KMT2B KD BCCs with respect to gap closure in the scratch assay (Figs. 5 D and 5 E). This could be attributed to the fact that these two proteins, although members of the KMT2 family, perform different H3K4 methylation modifications and this might contribute to gene expression variability ( 26 ). KMT2B and KMT2D KD enhanced BCC metastasis to the brain, which is consistent with dormancy reversal ( 48 ). However, carboplatin treatment significantly reduced KMT2B and KMT2D KD BCCs in the brain (Fig. 7 ). Previous studies have indicated that carboplatin can be used to treat tumors that have compromised the integrity of the blood-brain barrier ( 79 , 80 ). Thus, it is possible that carboplatin was able to eliminate the KMT2 KD BCCs in the brain since these cells might have compromised the blood-brain barrier. Although the main in vivo studies conducted established BC dormancy to the BM through intravenous injection, the model validated BC metastasis to the BM. This was deduced in studies in which we injected BCCs into the mammary fat pad of mice and then examined the femur before the tumor grew to recapitulate early dormancy (Fig. S8). The early detection of high GFP + BCCs indicated early migration to femurs. In summary, this study reported on a cell-autonomous method to maintain CSCs but showed that reversed methylation could chemosensitize the otherwise resistant CSCs. The study also showed a role for DNMT associated proteins as potential markers to guide treatment of BC patients following standard care. We show that circulating BCCs can be a functional indicator for targeted treatment to improve the durability of remission in BC. Declarations Authors’ contributions A. I. F-D. performed and designed the experiments, interpret the data and write a draft of the paper. G. S. performed the experiments, analyzed and interpret the data, and wrote the paper. A. P. performed the data pertaining to the human study, analyze the data and wrote the paper. R. G.-B. performed the experiments, analyzed the data and wrote the paper. Y. K. performed the experiments, analyzed the data, and edited the paper. O. A. performed the experiments, analyzed the data and edited the paper. S. A. P. contributed to the concepts, edited the paper and analyze the data. A.-H. N. coded the patient information, collected the blood, analyzed the data and edited the paper. P. R. edited the paper for final submission, conceived and designed the study, interpret the data and approved the final figures. Funding The work was supported by an award from METAvivor Foundation and a fellowship to AIFD from the New Jersey Commission on Cancer Research. Availability of data and materials The data presented in this study are available from the corresponding author. The RNA-seq data has been deposited in the Gene Expression Omnibus database (GEO). Declarations The use of mice and human blood have been approved as outlined in the Method section. Competing interests The authors declare no competing interest References DeSantis CE, Ma J, Gaudet MM, Newman LA, Miller KD, Goding Sauer A, et al. Breast cancer statistics, 2019. CA: Cancer J for Clinicians. 2019;69:438–51. Meltzer A. Dormancy and breast cancer. J Surg Oncol. 1990;43:181–8. Demir L, Akyol M, Bener S, Payzin KB, Erten C, Somali I, et al. Prognostic Evaluation of Breast Cancer Patients with Evident Bone Marrow Metastasis. The Breast J. 2014;20:279–87. Aguirre-Ghiso JA, Sosa MS. Emerging topics on disseminated cancer cell dormancy and the paradigm of metastasis. Ann Rev Cancer Biol. 2018;2:377–93. Walker ND, Patel J, Munoz JL, Hu M, Guiro K, Sinha G, et al. The bone marrow niche in support of breast cancer dormancy. Cancer Lett. 2016;380:263–71. Price TT, Burness ML, Sivan A, Warner MJ, Cheng R, Lee CH, et al. Dormant breast cancer micrometastases reside in specific bone marrow niches that regulate their transit to and from bone. Sci Transl Med. 2016;8:340ra73–ra73. Talmadge JE. Clonal selection of metastasis within the life history of a tumor. Cancer Res. 2007;67:11471–5. Bliss SA, Paul S, Pobiarzyn PW, Ayer S, Sinha G, Pant S, et al. Evaluation of a developmental hierarchy for breast cancer cells to assess risk-based patient selection for targeted treatment. Sci Rep. 2018;8:367. Patel SA, Ramkissoon SH, Bryan M, Pliner LF, Dontu G, Patel PS, et al. Delineation of breast cancer cell hierarchy identifies the subset responsible for dormancy. Sci Rep. 2012;2:906. Ayob AZ, Ramasamy TS. Cancer stem cells as key drivers of tumour progression. J Biomed Sci. 2018;25:1–18. Allan AL, Vantyghem SA, Tuck AB, Chambers AF. Tumor Dormancy and Cancer Stem Cells: Implications for the Biology and Treatment of Breast Cancer Metastasis. Breast Dis. 2007;26:87–98. Carcereri de Prati A, Butturini E, Rigo A, Oppici E, Rossin M, Boriero D, et al. Metastatic breast cancer cells enter into dormant state and express cancer stem cells phenotype under chronic hypoxia. J Cell Biochem. 2017;118:3237–48. Giordano A, Gao H, Cohen E, Anfossi S, Khoury J, Hess K, et al. Clinical relevance of cancer stem cells in bone marrow of early breast cancer patients. Ann Oncol. 2013;24:2515–21. Domen J, Wagers A, Weissman IL. Bone marrow (hematopoietic) stem cells. Regen Med. 2006;2:14–28. Sandiford OA, Donnelly RJ, El-Far MH, Burgmeyer LM, Sinha G, Pamarthi SH, et al. Mesenchymal Stem Cell-Secreted Extracellular Vesicles Instruct Stepwise Dedifferentiation of Breast Cancer Cells into Dormancy at the Bone Marrow Perivascular Region. Cancer Res. 2021;81:1567–82. Debeb BG, Lacerda L, Xu W, Larson R, Solley T, Atkinson R, et al. Histone Deacetylase Inhibitors Stimulate Dedifferentiation of Human Breast Cancer Cells Through WNT/β-Catenin Signaling. Stem Cells. 2012;30:2366–77. Allis CD, Jenuwein T. The molecular hallmarks of epigenetic control. Nat Rev Genet. 2016;17:487–500. Crea F, Saidy NRN, Collins CC, Wang Y. The epigenetic/noncoding origin of tumor dormancy. Trends Mol Med. 2015;21:206–11. Bliss SA, Sinha G, Sandiford OA, Williams LM, Engelberth DJ, Guiro K, et al. Mesenchymal Stem Cell-Derived Exosomes Stimulate Cycling Quiescence and Early Breast Cancer Dormancy in Bone Marrow. Cancer Res. 2016;76:5832–44. Anabella LM, Taborga M, Corcoran KE, Bryan M, Patel PS, Rameshwar P. SDF-1α regulation in breast cancer cells contacting bone marrow stroma is critical for. Biol Chem. 2003;278:21631–38. Ferrer AI, Trinidad JR, Sandiford O, Etchegaray JP, Rameshwar P. Epigenetic dynamics in cancer stem cell dormancy. Cancer Metastasis Rev. 2020;39:721–38. Zhu K, Xie V, Huang S. Epigenetic regulation of cancer stem cell and tumorigenesis. Adv Cancer Res. 2020;148:1–26. Plass C, Oakes C, Blum W, Marcucci G. Epigenetics in acute myeloid leukemia. Semin Oncol. 2008;35:378–87. Chatterjee A, Rodger EJ, Eccles MR. Epigenetic drivers of tumourigenesis and cancer metastasis. Semin Cancer Biol. 2018;51:149–59. Lotem J, Sachs L. Epigenetics and the plasticity of differentiation in normal and cancer stem cells. Oncogene. 2006;25:7663–72. Rao RC, Dou Y. Hijacked in cancer: the KMT2 (MLL) family of methyltransferases. Nat Rev Cancer. 2015;15:334–46. Hyun K, Jeon J, Park K, Kim J. Writing, erasing and reading histone lysine methylations. Exp Mol Med. 2017;49:e324. Fullgrabe J, Kavanagh E, Joseph B. Histone onco-modifications. Oncogene. 2011;30:3391–403. Li S, Shen L, Chen KN. Association between H3K4 methylation and cancer prognosis: A meta-analysis. Thorac Cancer. 2018;9:794–9. Natarajan TG, Kallakury BV, Sheehan CE, Bartlett MB, Ganesan N, Preet A, et al. Epigenetic regulator MLL2 shows altered expression in cancer cell lines and tumors from human breast and colon. Cancer Cell Int. 2010;10:13. Su C-H, Lin IH, Tzeng T-Y, Hsieh W-T, Hsu M-T. Regulation of IL-20 Expression by Estradiol through KMT2B-Mediated Epigenetic Modification. PLoS ONE. 2016;11:e0166090. Zhu J, Liu Z, Liang X, Wang L, Wu D, Mao W, et al. A Pan-Cancer Study of KMT2 Family as Therapeutic Targets in Cancer. J Oncol. 2022;2022:1–10. Angus L, Smid M, Wilting SM, van Riet J, Van Hoeck A, Nguyen L, et al. The genomic landscape of metastatic breast cancer highlights changes in mutation and signature frequencies. Nat Genet. 2019;51:1450–8. Nik-Zainal S, Davies H, Staaf J, Ramakrishna M, Glodzik D, Zou X, et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature. 2016;534:47–54. Zhang Z, Richmond A. The Role of PI3K Inhibition in the Treatment of Breast Cancer, Alone or Combined With Immune Checkpoint Inhibitors. Front Mol Biosci. 2021;8:648663. Toska E, Osmanbeyoglu HU, Castel P, Chan C, Hendrickson RC, Elkabets M, et al. PI3K pathway regulates ER-dependent transcription in breast cancer through the epigenetic regulator KMT2D. Science. 2017;355:1324–30. Choi HJ, Park JH, Park M, Won HY, Joo HS, Lee CH, et al. UTX inhibits EMT-induced breast CSC properties by epigenetic repression of EMT genes in cooperation with LSD1 and HDAC1. EMBO Rep. 2015;16:1288–98. Smith ZD, Meissner A. DNA methylation: roles in mammalian development. Nat Rev Genet. 2013;14:204–20. Breiling A, Lyko F. Epigenetic regulatory functions of DNA modifications: 5-methylcytosine and beyond. Epigenetics Chromatin. 2015;8:24. Wu H, Zhang Y, Reversing. DNA methylation: mechanisms, genomics, and biological functions. Cell. 2014;156:45–68. Sharif J, Muto M, Takebayashi S, Suetake I, Iwamatsu A, Endo TA, et al. The SRA protein Np95 mediates epigenetic inheritance by recruiting Dnmt1 to methylated DNA. Nature. 2007;450:908–12. Okano M, Bell DW, Haber DA, Li E. DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell. 1999;99:247–57. Pathania R, Ramachandran S, Elangovan S, Padia R, Yang P, Cinghu S, et al. DNMT1 is essential for mammary and cancer stem cell maintenance and tumorigenesis. Nat Commun. 2015;6:6910. Shin E, Lee Y, Koo JS. Differential expression of the epigenetic methylation-related protein DNMT1 by breast cancer molecular subtype and stromal histology. J Transl Med. 2016;14:87. Wong KK. DNMT1: A key drug target in triple-negative breast cancer. Semin Cancer Biol. 2021;72:198–213. Zhu X, Lv L, Wang M, Fan C, Lu X, Jin M, et al. DNMT1 facilitates growth of breast cancer by inducing MEG3 hyper-methylation. Cancer Cell Int. 2022;22:56. Shen B, Li Y, Ye Q, Qin Y. YY1-mediated long non-coding RNA Kcnq1ot1 promotes the tumor progression by regulating PTEN via DNMT1 in triple negative breast cancer. Cancer Gene Ther. 2021;28:1099–112. Sinha G, Ferrer AI, Ayer S, El-Far MH, Pamarthi SH, Naaldijk Y, et al. Specific N-cadherin–dependent pathways drive human breast cancer dormancy in bone marrow. Life Sci Alliance. 2021;4:7. Tivari S, Lu H, Dasgupta T, De Lorenzo MS, Wieder R. Reawakening of dormant estrogen-dependent human breast cancer cells by bone marrow stroma secretory senescence. Cell Commun Signaling. 2018;16:1–18. Moore CA, Siddiqui Z, Carney GJ, Naaldijk Y, Guiro K, Ferrer AI, et al. A 3D Bioprinted Material That Recapitulates the Perivascular Bone Marrow Structure for Sustained Hematopoietic and Cancer Models. Polymers. 2021;13:480. Corcoran KE, Trzaska KA, Fernandes H, Bryan M, Taborga M, Srinivas V, et al. Mesenchymal stem cells in early entry of breast cancer into bone marrow. PLoS ONE. 2008;3:e2563. Patel SA, Dave MA, Bliss SA, Giec-Ujda AB, Bryan M, Pliner LF, et al. T(reg)/Th17 polarization by distinct subsets of breast cancer cells is dictated by the interaction with mesenchymal stem cells. J Cancer Stem Cell Res. 2014;2:2014. Shilatifard A. Molecular implementation and physiological roles for histone H3 lysine 4 (H3K4) methylation. Curr Opin Cell Biol. 2008;20:341–8. Lu K, Tao H, Si X, Chen Q. The Histone H3 Lysine 4 Presenter WDR5 as an Oncogenic Protein and Novel Epigenetic Target in Cancer. Front Oncol. 2018;8:502. Oakes SR, Gallego-Ortega D, Ormandy CJ. The mammary cellular hierarchy and breast cancer. Cell Mol Life Sci. 2014;71:4301–24. Bertoli C, Skotheim JM, de Bruin RA. Control of cell cycle transcription during G1 and S phases. Nat Rev Mol Cell Biol. 2013;14:518–28. Sosa MS, Avivar-Valderas A, Bragado P, Wen HC, Aguirre-Ghiso JA. ERK1/2 and p38alpha/beta signaling in tumor cell quiescence: opportunities to control dormant residual disease. Clin Cancer Res. 2011;17:5850–7. Kudaravalli S, den Hollander P, Mani SA. Role of p38 MAP kinase in cancer stem cells and metastasis. Oncogene. 2022;41:3177–85. Moore LD, Le T, Fan G. DNA Methylation and Its Basic Function. Neuropsychopharmacol. 2013;38:23–38. Yang L, Jin M, Jeong KW. Histone H3K4 Methyltransferases as Targets for Drug-Resistant Cancers. Biol. 2021;10:581. Sherman LS, Patel SA, Castillo MD, Unkovic R, Taborga M, Gergues M, et al. NFĸB Targeting in Bone Marrow Mesenchymal Stem Cell-Mediated Support of Age-Linked Hematological Malignancies. Stem Cell Rev Rep. 2021;17:2178–92. Wysocka J, Swigut T, Milne TA, Dou Y, Zhang X, Burlingame AL, et al. WDR5 associates with histone H3 methylated at K4 and is essential for H3 K4 methylation and vertebrate development. Cell. 2005;121:859–72. Lu K, Tao H, Si X, Chen Q. The Histone H3 Lysine 4 Presenter WDR5 as an Oncogenic Protein and Novel Epigenetic Target in Cancer. Front Oncol. 2018;8:502. Ghajar CM. Metastasis prevention by targeting the dormant niche. Nat Rev Cancer. 2015;15:238–47. Park SY, Nam JS. The force awakens: metastatic dormant cancer cells. Exp Mol Med. 2020;52:569–81. Risson E, Nobre AR, Maguer-Satta V, Aguirre-Ghiso JA. The current paradigm and challenges ahead for the dormancy of disseminated tumor cells. Nat Cancer. 2020;1:672–80. Greco SJ, Patel SA, Bryan M, Pliner LF, Banerjee D, Rameshwar P. AMD3100-mediated production of interleukin-1 from mesenchymal stem cells is key to chemosensitivity of breast cancer cells. Am J Cancer Res. 2011;1:701–15. Diep S, Maddukuri M, Yamauchi S, Geshow G, Delk NA. Interleukin-1 and Nuclear Factor Kappa B Signaling Promote Breast Cancer Progression and Treatment Resistance. Cells. 2022;11:1673. Baram T, Rubinstein-Achiasaf L, Ben-Yaakov H, Ben-Baruch A. Inflammation-Driven Breast Tumor Cell Plasticity: Stemness/EMT, Therapy Resistance and Dormancy. Front Oncol. 2020;10:614468. Chernosky NM, Tamagno I. The Role of the Innate Immune System in Cancer Dormancy and Relapse. Cancers. 2021;13:5621. Tivari S, Lu H, Dasgupta T, De Lorenzo MS, Wieder R. Reawakening of dormant estrogen-dependent human breast cancer cells by bone marrow stroma secretory senescence. Cell Commun Signal. 2018;16:48. Walker ND, Elias M, Guiro K, Bhatia R, Greco SJ, Bryan M, et al. Exosomes from differentially activated macrophages influence dormancy or resurgence of breast cancer cells within bone marrow stroma. Cell Death Dis. 2019;10:59. Ahmed A, Redmond HP, Wang JH. Links between Toll-like receptor 4 and breast cancer. Oncoimmunol. 2013;2:e22945. Muller H, Moroni MC, Vigo E, Petersen BO, Bartek J, Helin K. Induction of S-phase entry by E2F transcription factors depends on their nuclear localization. Mol Cell Biol. 1997;17:5508–20. Helin K. Regulation of cell proliferation by the E2F transcription factors. Curr Opin Genet Dev. 1998;8:28–35. Xie D, Pei Q, Li J, Wan X, Ye T. Emerging Role of E2F Family in Cancer Stem Cells. Front Oncol. 2021;11:723137. Stark GR, Taylor WR. Analyzing the G2/M checkpoint. Methods Mol Biol. 2004;280:51–82. Barnaba N, LaRocque JR. Targeting cell cycle regulation via the G2-M checkpoint for synthetic lethality in melanoma. Cell Cycle. 2021;20:1041–51. Warren KE. Beyond the Blood:Brain Barrier: The Importance of Central Nervous System (CNS) Pharmacokinetics for the Treatment of CNS Tumors, Including Diffuse Intrinsic Pontine Glioma. Front Oncol. 2018;8:239. Bailleux C, Eberst L, Bachelot T. Treatment strategies for breast cancer brain metastases. Br J Cancer. 2021;124:142–55. Additional Declarations No competing interests reported. Supplementary Files SupplementalInformation.docx Cite Share Download PDF Status: Published Journal Publication published 12 Feb, 2024 Read the published version in Cell Communication and Signaling → Version 1 posted Editorial decision: Revision requested 21 Jan, 2024 Reviews received at journal 15 Jan, 2024 Reviewers agreed at journal 06 Jan, 2024 Reviewers invited by journal 06 Jan, 2024 Submission checks completed at journal 01 Jan, 2024 Editor assigned by journal 01 Jan, 2024 First submitted to journal 29 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3822758","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264678005,"identity":"66c55013-e436-4a9d-b989-16113a08c3cd","order_by":0,"name":"Alejandra I. Ferrer-Diaz","email":"","orcid":"","institution":"Rutgers New Jersey Medical School","correspondingAuthor":false,"prefix":"","firstName":"Alejandra","middleName":"I.","lastName":"Ferrer-Diaz","suffix":""},{"id":264678006,"identity":"99f0e80a-68a4-476b-ad61-3e23ec54a402","order_by":1,"name":"Garima Sinha","email":"","orcid":"","institution":"Rutgers New Jersey Medical School","correspondingAuthor":false,"prefix":"","firstName":"Garima","middleName":"","lastName":"Sinha","suffix":""},{"id":264678007,"identity":"04fd9b74-8b78-4476-bca0-1ccafba04cc3","order_by":2,"name":"Andrew Petryna","email":"","orcid":"","institution":"Rutgers New Jersey Medical School","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Petryna","suffix":""},{"id":264678008,"identity":"532a579a-e58d-4301-a46e-e745db98454c","order_by":3,"name":"Ruth Gonzalez-Bermejo","email":"","orcid":"","institution":"University of Puerto Rico","correspondingAuthor":false,"prefix":"","firstName":"Ruth","middleName":"","lastName":"Gonzalez-Bermejo","suffix":""},{"id":264678009,"identity":"90e2aec8-f691-44ac-a74e-222c55e74792","order_by":4,"name":"Yannick Kenfack","email":"","orcid":"","institution":"Rutgers New Jersey Medical School","correspondingAuthor":false,"prefix":"","firstName":"Yannick","middleName":"","lastName":"Kenfack","suffix":""},{"id":264678010,"identity":"ae3f7200-517f-4905-9e91-02e95173420c","order_by":5,"name":"Oluwadamilola Adetayo","email":"","orcid":"","institution":"Rutgers New Jersey Medical School","correspondingAuthor":false,"prefix":"","firstName":"Oluwadamilola","middleName":"","lastName":"Adetayo","suffix":""},{"id":264678011,"identity":"804924b4-7fb4-4293-a322-4bc701d3d5ac","order_by":6,"name":"Shyam A. Patel","email":"","orcid":"","institution":"UMass Memorial Medical Center, UMass Chan Medical School","correspondingAuthor":false,"prefix":"","firstName":"Shyam","middleName":"A.","lastName":"Patel","suffix":""},{"id":264678012,"identity":"7e6e7f7a-025c-45ae-b73b-442b4331bfd1","order_by":7,"name":"Anupama-Hood Nehra","email":"","orcid":"","institution":"Rutgers New Jersey Medical School","correspondingAuthor":false,"prefix":"","firstName":"Anupama-Hood","middleName":"","lastName":"Nehra","suffix":""},{"id":264678013,"identity":"b7a07d10-1326-4e9a-97f8-38c51ca41880","order_by":8,"name":"Pranela Rameshwar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYLACxgYGBn4GhgQMQfxaJBugWniI1mJwAMohqMW8/XTiw587Dssb3254uvEHw73E/fyLj338wWAju+EAdi0yZ3I3G/OeOWy47c6BtNs8DMWJPRLPkmfzMKQZ49IiwZC7TZqx7TbjthsJabeBQQDUcsaYmYHhcCJOLfxvt//82XbbfvOMhLSbP8Bazn9m/MHwH7cWidxtDLxttxM3SCSk3eABaeHvYQaGwQE8Wt5uluZt+588A+QwHoME454bbMbMPAbJxjNxOix348efbWm2/TNygA6rSJBt7z/8mPFHhZ1sHw4tSIAnARg7IFOANJhBGLBDTeUnbPooGAWjYBSMLAAAWvRlsbzJ7XoAAAAASUVORK5CYII=","orcid":"","institution":"Rutgers New Jersey Medical School","correspondingAuthor":true,"prefix":"","firstName":"Pranela","middleName":"","lastName":"Rameshwar","suffix":""}],"badges":[],"createdAt":"2023-12-30 00:44:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3822758/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3822758/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12964-024-01512-1","type":"published","date":"2024-02-12T15:01:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49147030,"identity":"080ab942-bbf3-4e62-a0df-fa546e18c376","added_by":"auto","created_at":"2024-01-03 20:36:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":510876,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eH3K4 as a potential target to differentiate CSCs. A) \u003c/strong\u003eShown are the overlay of the epigenetic modifiers KMT2B, KMT2D, and DNMT1, with pathways associated with cancer development in IPA - differentiation of beta islet cells, proliferation of stem cells, proliferation of cancer cells, and neoplasia.\u003cstrong\u003e \u003c/strong\u003eThe RNA-Seq data for the exosomes were previously reported (15).\u003cstrong\u003e B) \u003c/strong\u003eViable MDA-MB-231 BCCs were counted after exposure to WDR5-0103 (10 mg/ml) using trypan blue exclusion. The data are presented for three biological replicates. \u003cstrong\u003eC) \u003c/strong\u003eBCCs with WDR5-0103 (10 mg/ml) or vehicle. The viable cells were analyzed for BCC subsets by flow cytometry. Subsets were demarcated based on relative fluorescence intensities (Oct4a expression). The data are presented for four biological replicates. \u003cstrong\u003eD) \u003c/strong\u003eReal time PCR assessed levels of the stem cell-associated genes - Oct4a, Sox2, Klf4, and Nanog in BCCs treated with WDR5-0103 or vehicle. The values for vehicle were assigned as 1 and the experimental values presented as fold change. The results are presented for three biological replicates. \u003cstrong\u003eE) \u003c/strong\u003eMDA-MB-231 BCCs were treated with 10 µg/ml WDR5-0103 and/or 200 µg/mL carboplatin each day for 48 h. Control cells were treated with vehicle (DMSO). Cell death was assessed by trypan blue exclusion. The results are shown for three biological studies. *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05. \u003cstrong\u003eF) \u003c/strong\u003eThe diagram summarizes the findings in this figure – WDR5-0103 differentiates CSCs to Oct4(lo) BCC progenitors, known to cycle (15). Addition of carboplatin target the Oct4(lo) and the non-CSCs.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3822758/v1/2f460d06783a72e83125e70d.jpg"},{"id":49147031,"identity":"33e567b8-0d0c-498b-a78e-662f47f0779d","added_by":"auto","created_at":"2024-01-03 20:36:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":710845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRNA-Seq analyses of KMT2B and KMT2D KD BCCs. A) \u003c/strong\u003eShown is the experimental design in which MDA-MB-231 and T47D expressing Oct4a-dsRed were transduced with KMT2D-GFP tagged shRNA at MOI of 1:50,000. \u003cstrong\u003eB) \u003c/strong\u003ePrincipal component analysis (PCA) of the different groups of cells that were subjected to RNA-Seq. The plot was established with normalized data \u003cstrong\u003eC \u0026amp; D) \u003c/strong\u003eHeatmaps of the normalized genes are shown for scramble shRNA and depicting expression differences between scramble and KMT2B (C) or KMT2D KD BCCs. \u003cstrong\u003eE) \u0026nbsp;\u003c/strong\u003eGene ontology showing canonical pathways upregulated in KMT2B and KMT2D KD BCCs. \u003cstrong\u003eF) \u003c/strong\u003eGenes selected by IPA with functional link to the tumor growth pathway are shown in KMT2B and KNT2D knockdown BCCs. Network show activation of the growth of tumor pathway in botb KD BCCs.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3822758/v1/6e42093dad5089d22d006da2.jpg"},{"id":49147036,"identity":"390faba0-e133-4875-883a-c100d55c9e30","added_by":"auto","created_at":"2024-01-03 20:36:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":474794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecreased CSCs in KMT2B and KMT2D KD reduced CSCs but increased BC late-progenitor. (A) \u003c/strong\u003eMDA-MB-231 BCCs with the Oct4a-dsRED reporter vector and transduced with the KMT2D-GFP shRNA were subjected to flow cytometry to measure the relative dsRED intensity. \u003cstrong\u003e(B\u003c/strong\u003e)\u0026nbsp; Percent distribution of BCC subsets after transduction with scramble-shRNA or KMT2D shRNA.\u003cstrong\u003e (C) \u003c/strong\u003eTotal number of CSCs (Oct4a\u003csup\u003ehigh\u003c/sup\u003e) in scramble BCCs and KMT2D knockdown BCCs. Each experiment was repeated thrice, where a *p\u0026lt;0.05 was considered significant. \u003cstrong\u003eD) \u003c/strong\u003eReat time PCR for stem cell-associated genes in KMT2B and KMT2D KD BCCs. Control PCR used cDNA from BCCs with scramble shRNA. The value for scramble shRNA is assigned 1 and the experimental values are presented as the fold change. The results show three biological replicates, mean ± SD, *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 vs. scramble shRNA.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3822758/v1/864948d6bf4f2d10f5278550.jpg"},{"id":49147536,"identity":"e1a5c0e3-5e49-4f80-8002-63377997b1ad","added_by":"auto","created_at":"2024-01-03 20:52:40","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":524286,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKMT2B and KMT2D in cycling quiescence of BCCs\u003c/strong\u003e. \u003cstrong\u003eA)\u003c/strong\u003eOverlay of upregulated genes associated in KMT2B and KMT2D knockdown BCCs with cell cycle progression patway in IPA. \u003cstrong\u003eB) \u003c/strong\u003eGene set enrichment analysis (GSEA) shows upregulation of the E2F targets and G2M checkpoint pathways in KMT2B knockdown and KMT2D knockdown BCCs relative to scramble BCCs. \u003cstrong\u003eC) \u003c/strong\u003eRepresentative western blot for Cyclin D1, CDK6, and CDK4 in KMT2B or KMT2D KD, and scramble triple positive (T47D) BCCs. Right graphs show the normalized densities for three biological experiments. \u003cstrong\u003eD) \u003c/strong\u003eWestern blot for p38 with extracts from KMT2B and KMT2D KD BCCs or BCCs with scramble shRNA.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3822758/v1/b37ab910c9a1573aa91ede4e.jpg"},{"id":49147307,"identity":"49269d04-cd94-498e-b505-57fac339a25b","added_by":"auto","created_at":"2024-01-03 20:44:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":645807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnhanced proliferation and migration in KMT2 KD BCCs. A) \u003c/strong\u003eIPA analyses identified enhanced differentiation and proliferation in KMT2B and KMT2D KD BCCs. \u003cstrong\u003eB \u0026amp; C) \u003c/strong\u003eProliferation rate of KMT2B (B) and KMT2D (C) KD BCCs compared to scramble BCCs. Results are presented as the mean of total viable cells ± SD, n=3, *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05. \u003cstrong\u003eD \u0026amp;E) \u003c/strong\u003eShown are representative images of scratch assays with KMT2B and KMT2D KD BCCs (D). The scratch areas were quantified and presented as the mean diameter and closure of the scratched area ± SD, n=3, *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 vs the KD BCCs.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3822758/v1/fe86e561dec7eacabab470b1.jpg"},{"id":49147035,"identity":"cbb579d1-db81-4899-be73-0d4406842470","added_by":"auto","created_at":"2024-01-03 20:36:40","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":667330,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn vitro\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vivo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e chemosensitivity of KMT2B and KMT2D KD BCCs. A \u0026amp; B)\u003c/strong\u003e KMT2B (A) or KMT2D (B) KD BCCs were treated with 200 µg/mL carboplatin for 48 h. Control BCCs were transfected with scramble shRNA and similarly treated. The cells were analyzed with the MTT assay and the results presented as mean±SD cell death for three biological replicates. Each replicate contained three technical studies. \u003cstrong\u003eC \u0026amp; D)\u003c/strong\u003e The studies in `A’ were repeated except for treatment with doxorubicin (1 µM) for 48 h. \u003cstrong\u003eE) \u003c/strong\u003eWestern blot for P-gp with extracts from KMT2B and KMT2D KD BCCs, and BCCs with scramble shRNA. The lower graph showed the mean fold change±SD of normalized bands, n=3. * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05. \u003cstrong\u003eF) \u003c/strong\u003eDiagram showing the \u003cem\u003ein vivo\u003c/em\u003e model in which\u003cstrong\u003e \u003c/strong\u003eKMT2B or KMT2D KD MDA-MB-231 BCCs were injected in the tail veins of 6-wk female nude mice. Control mice were injected with BCCs containing scramble shRNA. Shown are the timeline (days 3 and 5) treatment with carboplatin (5 mg/kg) or vehicle (1X PBS) on days 3 and 5. The studies were terminated at day 7. \u003cstrong\u003eG)\u003c/strong\u003e Femurs from the euthanized mice in `F’ were scaped at the endosteal region and then immediately examined on an Evos \u003cem\u003eFl2\u003c/em\u003e for GPF-positive BCCs. Shown are representative images\u003cstrong\u003e \u003c/strong\u003efor five femurs, each from a different mouse. \u003cstrong\u003eH) \u003c/strong\u003eFemurs\u003cstrong\u003e \u003c/strong\u003efrom the mice described in F and G were decalcified and embedded in paraffin. Sections were analyzed on the Evos \u003cem\u003eFl2\u003c/em\u003efor GFP (green) or labeled for Ki67 (blue) (teal cells = green (BCCs) + blue (Ki67). Shown are representative image at 10X magnification. \u003cstrong\u003eI)\u003c/strong\u003e Figures show the total number of GFP+ cells in 10 fields of sections from mouse femurs and KI67+ cells within the GFP+ cells. * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 vs. scramble Ki67\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3822758/v1/8268c7b81e8aa8242efce174.jpg"},{"id":49147034,"identity":"d9eb4c7e-240e-434b-89fe-c48e23eef7d7","added_by":"auto","created_at":"2024-01-03 20:36:40","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":398121,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKMT2B and KMT2D KD promote tertiary metastasis to the brain. A) \u003c/strong\u003eMDA-MB-231 BCCs (5x105) with scramble sequence-GFP, KMT2B KD-GFP, and KMT2D KD-GFP were injected intravenously into nude female mice as for Fig. 6. On day 7, the brain was harvested, and the paraffin-embedded slides were imaged to assess for BCCs (green) and for Ki67 cells (teal cells: green/BCCs + blue/Ki67). Image magnification: 100x.\u003cstrong\u003e B and C) \u003c/strong\u003eThe total number of BCCs for three mice treated with vehicle \u0026nbsp;were counted in 10 fields per section. The data are presented as the mean±SD total number of viable cells in brains of mice injected with KMT2B (B) or KMT2D (C) KD, or scramble shRNA. \u003cstrong\u003eD and E) \u003c/strong\u003ePercent cell death after carboplatin treatment was calculated with the total number of BCCs for vehicle set as 100% viablity. The data for KMT2B (D) and KMT2D (E) are calculated as the mean±SD. \u003cstrong\u003eF) \u003c/strong\u003eDNA methylation and oxidation cycle: Genomic DNA is methylated (5mC) by DNA methyltransferase enzymes (DNMTs). TET enzymes (TET1, TET2, TET3) can successively oxidize 5mC into 5hmC, 5fC and 5caC. Subsequently, 5fC and 5caC can be excised by TDG and further repair by the Base Excision Repair (BER) system leading to DNA de-methylation.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3822758/v1/52c22986840f12756d2f1220.jpg"},{"id":51323164,"identity":"4d5f6fd3-8c11-45d9-acd9-4540554e2fa4","added_by":"auto","created_at":"2024-02-19 15:15:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1652190,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3822758/v1/bccceede-a960-46d6-9f55-dee1b33ce433.pdf"},{"id":49147038,"identity":"27226859-df48-48dd-a862-e9e66a55fed8","added_by":"auto","created_at":"2024-01-03 20:36:40","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":4144683,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3822758/v1/5b45c7da53be485dfe48ba09.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Role of KMT2B and KMT2D histone 3, lysine 4 methyltransferases and DNA oxidation status in circulating breast cancer cells provide insights into cell-autonomous regulation of cancer stem cells","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBreast cancer (BC), the most common cancer in women, remains a clinical problem (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). A major issue is based on challenges to eliminate dormant cancer cells (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The preference of BC for bone marrow (BM) results in poor prognosis (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Entry of BC cells (BCCs) in the BM occurs at any time during the disease as well as the period before clinical diagnosis. The latter could be years to decades when the cancer cells remain dormant (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In BM, BCCs can survive as dormant cells for long- periods, even decades (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Dormant BCCs remain in cycling quiescence, resist treatment, and adapt properties of stem cells (\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). These properties have led to dormant BCCs referred as cancer stem cells (CSCs) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The stem cells properties of dormant BCCs are in line with the ability of these cells to reactivate into tertiary metastasis (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough drug resistance involves a complex mechanism, one must consider that treatment could be influenced by the shared properties between dormant BCCs and healthy endogenous stem cells. As an example, in BM, dormant BCCs are located with hematopoietic stem cells (HSCs), making it difficult to target the dormant cells without untoward effects of the HSCs (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In this regard, imperative survival of HSCs would limit the therapeutic dose of a drug that targets dormant BCCs. Thus, it is important to understand how BCCs achieve dormancy in BM since this would allow for strategic development of methods to safely eradicate BCCs without affecting the hematopoietic system.\u003c/p\u003e \u003cp\u003eCells within tissue environment could influence dormancy. This occurs partly by support of BCC dedifferentiation to CSCs with concomitant alteration of BCC epigenome (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Epigenetic changes impart functional plasticity of BCCs, including properties consistent with dormancy (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). BM endogenous cells - macrophages, fibroblasts and mesenchymal stem cells (MSCs) - are key support of BC dormancy (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). BCCs can instruct MSCs BM to release exosomes to initiate the process of BCC de-differentiation into CSCs (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The released exosomes contained transcripts for epigenes, which include DNA and histone epigenetic modifiers, such as DNA methyltransferase-1 (DNMT1), and histone 3, lysine 4 (H3K4) methyltransferases - KMT2B and KMT2D.\u003c/p\u003e \u003cp\u003eA focus on epigenetic modifiers in cancer, including the present study \u0026ndash; BC dormancy - is based on the diminished paradigm of genomic instability as the sole contributor to cancer development (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The literature showed low levels of gene mutations in some cancers, which strongly support roles for epigenomic modification in BCC function (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). We validated DNMT1, KMT2B and KMT2D, in exosomes released from BCCs that were exposed to MSCs (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). There are six members with the lysine methyltransferase 2 (KMT2) family of proteins, formerly referred to as mixed-lineage leukemia (MLL). KMT2 incorporates methyl group(s) at lysine 4 residues of histone 3 tail ends (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). KMT2s can add up to three methyl groups to lysine residues in which these epigenetic marks influence transcriptional activation (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKMT2B and KMT2D are H3K4 methyltransferases involved human development (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Abnormal placement of H3K4 methylation marks across the genome has been associated with cancer and patient survival (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Reduced H3K4me2 is linked to poor survival whereas decreased H3K4me3 significantly improves patient prognosis (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Although H3K4 methylation is associated with transcriptional activation, the overall cellular response depends on the deposited marks, and the affected downstream activated genes (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Mutation of KMT2B in cancers, including BC, frequently leads to upregulated levels of KMT2B (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In BC, KMT2B is recruited by the estrogen receptor-α to the IL-20 promoter to enhance H3K4 methylation and BCC proliferation (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Targeting of both KMT2B and IL-20 in BC disrupts estrogen signaling (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In a pan-cancer study evaluating the -intra and -inter tumor heterogeneity of the KMT2 gene, it was noted that KMT2B expression was significantly upregulated in 18 tumor types, including BC subtypes, and this correlated with an aggressive phenotype (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). KMT2D is frequently mutated in BC, resulting in treatment resistance (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Inhibition of KMT2D in combination with current treatments such as PI3K inhibitors reduces tumor volume in ER-positive BC (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Although KMT2D can act as an oncogene, the KMT2D subunit, UTX, can function as a tumor suppressor by reducing epithelial-to-mesenchymal transition (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDNA methylation is a heritable epigenetic mark that dictates cellular identity by regulating gene expression (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Deposition of a methyl group on the fifth carbon of cytosines (5-methylcytosine, 5mC) is a conserved epigenetic modification implicated in cellular memory and differentiation (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). 5mC modifications are specific to palindromic sequences of cytosines linked to guanines by phosphodiester bonds (CpG islands) (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). DNA methylation patterns are catalyzed by the DNA methyltransferase (DNMT) family - DNMT1, DNMT3A, and DNMT3B. DNMT1 is involved in the maintenance of methylated cytosines and acts preferably on hemimethylated DNA (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Conversely, \u003cem\u003ede novo\u003c/em\u003e methylation is accomplished by DNMT3A/B in a symmetric manner (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Aberrant DNA methylation patterns are involved during the development and progression of cancer. DNMT1 expression is upregulated in BC tumors, and its deletion impedes CSC self-renewal and survival (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The degree of DNMT1 expression depends on the BC subtype. For instance, triple negative BC (TNBC) and inflammatory BC have higher expression of DNMT1 as compared to luminal A (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). DNMT1 reduces expression of estrogen receptor, promotes epithelial mesenchymal transition and allows for expansion of the CSCs in TNBC (\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBCCs can enter and exit dormancy depending on cues from the microenvironment. After the BCCs dedifferentiate into CSCs, they can adapt long-term dormancy by GJIC between BCCs and BM resident hematopoietic and non-hematopoietic cells, changes in cytokine production by BCCs and endogenous stroma (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). In addition to MSC-derived exosomes facilitating BCC dedifferentiation to CSCs use of 3D bioprinting to recapitulate the hematopoietic system suggested cell-autonomous mechanisms of dedifferentiation (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). CSCs in the BM have survival advantages by forming GJIC with the endogenous stromal cells, and protection from the immune elimination by its ability to preferentially interact with MSCs (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). To this end, it is crucial to understand how CSCs survive. We studied the epigenetic role of two \u003cem\u003eKMT2\u003c/em\u003e genes in cell-autonomous control of CSCs using \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies. This study also reports on parallel studies in which we screen circulating BC in the blood of patients. We report on an indirect relationship between DNMT and its associated hydroxylase suggesting DNA methylation by DNMT and H3K4 in sustained dormancy.\u003c/p\u003e"},{"header":"MATERIALS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHuman Subjects\u003c/span\u003e: Rutgers Institutional Review Board (IRB) approved the use of blood from BCC patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ea.\u003c/b\u003e Patient demographics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge (Yrs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003cp\u003e(Diagnosis)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHormone Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHer2\u0026thinsp;+\u0026thinsp;ER-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePertuzumab, Trastuzumad Docetaxel, carboplatin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHer2- ER+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eno treatment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/Relapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHer2- ER-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAbraxane, Gemcytabine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHer2\u0026thinsp;+\u0026thinsp;ER+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePertuzumab, Transtuzumab*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/relapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHer2- ER-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSacituzumab, Govitecan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHer2- ER+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExemestane\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/relapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHer2- ER+ (minimal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSacituzumab, Govitecan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHer-ER+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAnastrozole **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHer2\u0026thinsp;+\u0026thinsp;ER+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePertuzumab, trastuzumab, carboplatin, docetaxel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo treatment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* Post-treatment (ended 6/23), followed by endocrine therapy (Letrozol). At relapse, treated with Docetaxel and Carboplatin. Due to low tolerance, switched to listed treatment (Patient 4).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e** Patient was treated between July 2020-November 2020. This followed surgery. At blood draw, patient was on endocrine therapy with Anastrozole. Currently no active disease.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*** Blood from these two patients were used for in vitro studies (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eb.\u003c/b\u003e Phenotype of circulating cytokeratin\u0026thinsp;+\u0026thinsp;cells\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCytokeratin\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eEpCam\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOct4\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTet2\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5hmC\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCSC Support\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Done\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot Done\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePossible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eShown are the percentages of gated cytokeratin\u0026thinsp;+\u0026thinsp;cells within the nucleated cells in peripheral blood. CSC support is indicated when the phenotype, evaluated as composite values, indicate that CSCs can be sustained as multipotent cells.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMice\u003c/span\u003e: The use of nude mice was approved by Rutgers Institutional Animal Care and Use Committee (IACUC), Newark Campus. Rutgers IACUC is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC). Mice were housed in the Comparative Medicine Resource center at Rutgers New Jersey Medical School.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eReagents\u003c/h2\u003e \u003cp\u003eDMEM, RPMI-1640, L-glutamine, penicillin, streptomycin, dimethyl sulfoxide, geneticin, Glycerol, optiMEM, polybrene, trizol, trypan blue stain, platinum SYBR Green qPCR Supermix-UDG Kit, Supersignal West Femto Maximum Sensitivity Substrate, High-Capacity cDNA Reverse Transcription kit, and Total exosome isolation reagent were purchased from Thermo Fisher Scientific (Waltham, MA); ammonium persulfate, bovine serum albumin, magnesium chloride, N,N,N\u0026prime;,N\u0026prime;-Tetramethylethylenediamine (TEMED), NP-40, EDTA-free protease inhibitor, sodium chloride, fetal bovine sera (FBS), Ficoll Hypaque and Triton-X100 from Millipore-Sigma (St. Louis, MO); protein loading dye, Bradford protein reagent, and sodium dodecyl sulfate from BioRad (Hercules, CA). WDR5-0103 and doxorubicin were purchased from Tocris Bioscience (Minneapolis, MN). Acryl/Bis Solution (30%) 37.5:1 was purchased from VWR (Radnor, PA); puromycin from InvivoGen (San Diego, CA); TransIT-Lenti transfection reagent from Mirus; MTT-Assay and 5-Aza-2\u0026prime;-deoxycytidine from Abcam (Waltham, MA); p24 Rapid Titer Kit from Takara Bio (Mountainview, CA); and plasmid miniprep kit and RNeasy Mini Kit from Qiagen (Germantown, MD). Carboplatin and doxorubicin were obtained from University Hospital Pharmacy (Newark, NJ).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAntibodies\u003c/h2\u003e \u003cp\u003eAll primary human antibodies were against human antigens. Rabbit anti-cyclin D1 (1:1000 dilution), rabbit anti-Ki67 (1:250 dilution), and rabbit anti-vinculin (1:1000 dilution) were purchased from Abcam (Waltham, MA); rabbit anti-human p38 (1:1000 dilution), rabbit anti-MDR1 (1:1000 dilution), rabbit anti-CDK4 (1:1000 dilution), mouse anti-EpCam-AlexaFluor488 (1:100 dilution), goat anti-Tet2 (1:200 dilution), rabbit anti-5-hmC (1:200 dilution), and rabbit anti-human CDK6 (1:1000 dilution) from Cell Signaling (Danvers, MA); goat anti-rabbit IgG (1:2000 dilution) from ThermoFisher Scientific; goat anti-rabbit Alexa Fluor 594 (1:500 dilution) from Invitrogen (ThermoFisher); mouse anti-pan cytokeratin-PE (1:200 dilution), mouse anti-Oct 3/4-PerCPCy5.5 (1:5 dilution) from Becton Dickinson (San Jose, CA); donkey anti-rabbit IgG-AlexaFluor647 and donkey anti-goat IgG-AlexaFluor488 from Life Technologies; BD Lysing solution (Becton Dickinson).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eVectors\u003c/h2\u003e \u003cp\u003epOct4a-GFP vectors was donated by Dr. Wei Cui (Imperial College, London, UK) and was previously described from studies by our group (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). The description of pOct4a-dsRed was previously described (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Human shRNA clone set against KMT2B, KMT2D, and scramble sequence shRNA control for psi-LCRU6GP were purchased from GeneCopoiea (Rockville, MD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCell lines\u003c/h2\u003e \u003cp\u003eMDA-MB-231 and T47D were obtained from American Type Culture Collection and cultured as per their instruction. The MDA-MB-231 cell line is negative for estrogen, progesterone, and epidermal growth factor receptor, HER2. T47D is positive for these three receptors. Cells were grown with DMEM (MDA-MB-231) and RPMI supplemented with insulin (T47D) containing with 10% FBS, 2 mM L-glutamine, 100 IU/ml penicillin, 100 \u0026micro;g/ml streptomycin and 1% non-essential amino acid. The HEK293T cells were cultured in a similar manner to the MDA-MB-231 BCCs.\u003c/p\u003e \u003cp\u003eMDA-MB-231 cells were stably transfected with the pOct4a-GFP (green fluorescence protein) or pOct4a-dsRED (red fluorescence protein), as described (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The relative fluorescence intensities correlated with the expression of the stem cell gene, Oct4a (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). We selected and maintained cells expressing the reporter genes with Geneticin (500 \u0026micro;g/ml). BCCs expressing high levels of Oct4a (top 5%) were classified as CSCs, as described (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTreatment of BCCs with H3K4 inhibitor WDR5-0103\u003c/h2\u003e \u003cp\u003eBCCs were seeded in 6-well plates at 3.5x10\u003csup\u003e5\u003c/sup\u003e cells/well. After overnight incubation, the cells were treated with WDR5-0103 and vehicle for two days. The media were replaced every 24 h. At 48 h, BCCs were de-adhered with 0.25% trypsin-EDTA and then subjected to the following readouts: cell viability, flow cytometry, qPCR, and resistance to treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePreparation of lentiviral particles\u003c/h2\u003e \u003cp\u003eLentiviral particles were prepared as described (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). The shRNA viral plasmids were inserted into One Shot Mach1T1 Phage-Resistant (Thermo Fisher Scientific) chemically competent \u003cem\u003eE. coli\u003c/em\u003e. The DNA from the transformed bacteria was isolated with the Plasmid Miniprep Kit. The DNA was digested with \u003cem\u003eBamH1\u003c/em\u003e and \u003cem\u003eEcoRI\u003c/em\u003e followed by gel electrophoresis validation. The transformed bacteria were cultured and amplified in LB broth supplemented with ampicillin (50 \u0026micro;g/ml). The plasmid DNA was collected after amplification of the bacteria. Lentiviral particles containing the isolated plasmids were propagated in HEK-293T cells (90% confluence). The packaging plasmids (5 \u0026micro;g/ml) were gently combined with the lentiviral plasmid of interest (5 \u0026micro;g/ml). The mixture was transferred to a tube containing 1 ml of Opti-MEM for gentle mixing, followed by adding 30 \u0026micro;l of TransIT-Lenti (Mirus Bio) reagent. The mixture was homogenized and then incubated at room temperature for 10 min to allow the formation of transfection complexes. The mix was added dropwise to HEK-293T cells, which were incubated at 37\u0026deg;C for 48 h. The media were collected and centrifuged at 300 \u003cem\u003eg\u003c/em\u003e for 10 mins to remove the cellular debris. The supernatant containing the virus was filtered through 0.45 \u0026micro;m PVDF membrane and concentrated using the Lenti-X Concentrator (Takara Bio). The concentrated viral clones of KMT2B, KMT2D, DNMT1, and scramble shRNA were quantified with the Lenti-X p24 Rapid Titer Kit (Takara Bio). The viral nix was aliquoted into 0.5 ml low-protein binding tubes and stored in -80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eKnockdown (KD) of KMT2B and KMT2D\u003c/h2\u003e \u003cp\u003eMDA-MB-231 and T47D cells with stable pOct4a-dsRed were seeded at a density of 5x10\u003csup\u003e4\u003c/sup\u003e/well in 24-well plates. We selected dsRed as a marker because the lentivirus for KMT2B and KMT2D express GFP (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). After 24 h, the cells were transduced at a multiplicity of infection of 1:1 and 4 \u0026micro;g/ml of polybrene with the shRNA lentivirus containing sequences for KMT2B, KMT2D, and scramble shRNA (GeneCopoeia). After 48 h, the media were changed and replenished with fresh media supplemented with 1.5 \u0026micro;g/ml of puromycin every 2 days for two weeks. The efficiency of transduction was evaluated by fluorescence microscopy for GFP and western blot (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCarboplatin treatment and cell viability\u003c/h2\u003e \u003cp\u003eTrypan blue staining was used to assess cell viability in assays in which BCCs were treated with WDR5-0103 or vehicle. Similar assessments were conducted for BCCs, knockdown for KMT2B or KMT2D, or scramble sequence. Cells were manually counted on a hemocytometer.\u003c/p\u003e \u003cp\u003eMTT assay (Abcam) was performed with BCCs seeded at 2x 10\u003csup\u003e4\u003c/sup\u003e cells/well in a 96-well plate. After overnight incubation, the cells were treated with vehicle or carboplatin (220 \u0026micro;g/ml) every 24 h or 2 days. After this, the cells were subjected to the MTT assay. Media were removed from the wells and replaced with the MTT reagent in serum-free media. The cells were incubated at 37\u0026deg; for 3 h. This was followed by adding MTT solvent to neutralize the reaction. The cells were incubated on a shaker for 15 mins at room temperature and then analyzed by measuring the absorbance at 590nm on the Synergy HTX (Biotek) microplate reader.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFlow cytometry\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGFP and dsRED\u003c/span\u003e: Flow cytometry analysis was conducted to determine Oct4a GFP/dsRed intensity in BCCs after treatment with the epigenetic inhibitors and silencing of the epigenetic mediators. Cells were collected, resuspended in 500 \u0026micro;l of 1X PBS, and placed in 12x75mm polysterene tubes (MTC Bio). The cells were analyzed on a FACS Calibur (BD Biosciences) flow cytometer to measure GFP/dsREd intensity. The data were analyzed with FlowJo software (BD Biosciences). We designated the relative maturity of BCC based on our previous reports (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Cells within the top 5% of GFP/dsRed fluorescence were designated as long-term repopulating CSCs (Oct4a-GFP/dsRed\u003csup\u003ehi\u003c/sup\u003e). This was followed by Oct4aGFP/dsRed\u003csup\u003emed\u003c/sup\u003e, Oct4aGFP/dsRed\u003csup\u003elow\u003c/sup\u003e (early progenitors), and Oct4aGFP/dsRed\u003csup\u003eneg\u003c/sup\u003e (late progenitors).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePhenotype of circulating BCCs in patients\u003c/span\u003e: Red blood cells were lysed with BD FACS lysing solution following manufacturer\u0026rsquo;s instructions. The lysed cells were tested with two panels of antibodies: Panel 1 contained antibodies against pan cytokeratin, Oct3/4 and EpCam; Panel 2 contained pan cytokeratin, Oct3/4, Tet2 and 5hmC. The concentrations of the antibodies are listed above. The concentrations of isotype added to the cells were similar to the amount of test antibodies.\u003c/p\u003e \u003cp\u003ePanel 1 used intracellular labeling \u0026ndash; cells were permeabilized at 4\u003csup\u003e0\u003c/sup\u003eC with 0.1% Triton X-100 for 10 mins followed by washing with 1x PBS. After this, the cells were labeled with the test antibodies and the appropriate isotype. The tubes were incubated in the dark for 30 mins at 4\u003csup\u003e0\u003c/sup\u003eC followed by washing with 1x PBS. The cells were immediately analyzed on the FAC Calibur (BD Biosciences).\u003c/p\u003e \u003cp\u003ePanel 2 labeling used intracellular and extracellular labeling. The latter was first done by fixing with 3.7% formaldehyde at room temperature for 15 mins. Cells were washed with 1x PBS and then labeled with anti-EpCam and isotype. The incubation and wash was performed as for intracellular labeling. After this, the cells were labeled for pan-cytokerin, Oct3/4, Tet2 and 5hmC as described for Panel 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTreatment of peripheral blood mononuclear cells (PBMCs) from BC patients\u003c/h2\u003e \u003cp\u003eDue to limited blood supplies, we studied the last two patients for \u003cem\u003ein vitro\u003c/em\u003e response to chemotherapy and a DNA methylation inhibitor, Azacitidine. PBMCs were isolated from the blood of Patients 9 and 10 by Ficoll Hypaque gradient centrifugation. Cells (5x10\u003csup\u003e6\u003c/sup\u003e) in 2 mL RPMI 1640 with 10% FBS were incubated. After 24 h, the cultures were incubated with vehicle (PBS), carboplatin (200 \u0026micro;M), or carboplatin (200 \u0026micro;M)\u0026thinsp;+\u0026thinsp;Azacitidine (2 \u0026micro;M). At day 7, the cells were analyzed by flow cytometry using Panels 1 and 2 antibodies, as described above for the other patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eReal-time PCR\u003c/h2\u003e \u003cp\u003eTotal RNA was isolated using TRIzol reagent according to the manufacturer\u0026rsquo;s instruction (Thermo Fisher Scientific). The RNA was reverse transcribed into cDNA with the High-Capacity cDNA Reverse Transcription kit (Thermo Fisher Scientific) and amplified using the GeneAmp PCR System 9700 (Applied Biosystems). The cDNA was diluted with nuclease-free water to 200 ng/\u0026micro;l and then mixed with SYBR Green PCR Master Mix, primers of interest, and nuclease-free water. Real-time PCR was conducted on a 7300 Real-time PCR system (Thermo Fisher Scientific) at 50\u0026deg;C for 2 mins, 95\u0026deg;C for 10 mins followed by 40 cycles of 95\u0026deg;C for 15 seconds and 60\u0026deg;C for 1 minute. The following primers were used on the PCR mix: \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOct4a\u003c/span\u003e: Forward 5\u0026acute;ctg aag cag aag agg atc ac 3\u0026acute;; Reverse 5\u0026acute;gct ttg cat atc tcc tga ag 3\u0026acute;; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eKLF4\u003c/span\u003e: Forward 5\u0026acute;aac ctt acc act gtg act gg 3\u0026acute;; Reverse 5\u0026acute;cat atc cac tgt ctg gga tt 3\u0026acute;; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNanog\u003c/span\u003e: Forward 5' caa tgg tgt gac gca ggg at 3\u0026acute;; Reverse 5' gac tgg atg ttc tgg gtc tgg 3'; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNotch1\u003c/span\u003e: Forward 5\u0026acute;cca agt ata gcc tat ggc aga a 3\u0026acute;; Reverse 5\u0026acute; aag tct gac gtc cct cac 3\u0026acute;; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSox2\u003c/span\u003e: Forward 5\u0026acute;taa ctg tcc atg cgc tgg tt 3\u0026acute;; Reverse 5\u0026acute; agg atat agt aca cgc tgc cc 3\u0026acute;; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eβ-actin\u003c/span\u003e: Forward 5\u0026acute;gcc cta taa aac cca gcg gc 3\u0026acute;, Reverse 5\u0026acute;aga ggc gta cag gga tag ca 3\u0026acute;; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGAPDH\u003c/span\u003e: Forward 5' cag aag act gtg gat ggc c 3\u0026acute;, Reverse: 5\u0026acute; cca cct tct tga tgt cat c 3\u0026acute;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot\u003c/h2\u003e \u003cp\u003eCells were resuspended in 50\u0026ndash;100 \u0026micro;l of lysis buffer composed of 50 mM Tris-HCL (pH 7.4), 100 mM NaCl, 2 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 10% glycerol, 1% NP-40, and two tablets of EDTA-protease inhibitor cocktail (Millipore-Sigma). Cell lysates were exposed to freeze/thaw cycles which consisted of 2 mins in liquid nitrogen followed by 2 mins in the water bath (37\u0026deg;C). Cell lysates were centrifuged at 2,000 \u003cem\u003eg\u003c/em\u003e for 10 mins and the supernatant containing the proteins was collected for downstream applications. The concentration of the proteins was determined with the Bradford Protein Assay Reagent (BioRad) and by using BSA (BioRad) as a control. The extracts (15 \u0026micro;g) were electrophoresed on a 12% SDS-PAGE gel and then transferred onto Immobilon-P PVDF membranes (ThermoFisher Scientific). The membranes were washed with 1X PBS tween for 10 mins and blocked with 3% non-fat milk diluted in 1x PBS for 20 mins. The membranes were incubated overnight at 4\u0026deg;C on a shaker with the primary antibodies of interest such as anti-p38, anti-CDK4, anti-CDK6, anti-cyclinD1, anti-MDR1, anti-KMT2B, anti-β-actin, or anti-vinculin at a 1:1000 in 3% non-fat milk. The primary antibodies were removed, and the membranes were washed and blocked with 3% non-fat milk. This was following by incubation with secondary HRP tagged antibody 1:2000 in 3% non-fat milk for 2 h at 4\u0026deg;C. The membranes were washed for 20 mins and then developed with the Super Signal West Femto Maximum Sensitivity Substrate for 5 mins. The protein bands were imaged using the using the ChemiDoc XRA (BioRad) system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eScratch Assay\u003c/h2\u003e \u003cp\u003eBCCs were seeded at 3 x 10\u003csup\u003e5\u003c/sup\u003e cells/well in 6-well plates. Once the cells achieved 100% confluency, a scratch was performed in the middle of the well from top to bottom using a 200 \u0026micro;l pipette tip and assessed gap closing by microscopy at 0, 24, 48, 72, and 96 h.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRNA sequencing (RNA-seq)\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted with RNAeasy mini kit (Qiagen) from KMT2B, KMT2D KD MDA-MB-231, or with scramble shRNA. The samples were submitted to the Genomics Center at Rutgers New Jersey Medical School for RNA-seq.\u0026nbsp;Depletion of ribosomal RNA was conducted with the Ribo-Zero Gold kit (Illumina) followed by preparation of Next-Generation sequencing cDNA libraries using the NEB Ultra II Library Preparation Kit and NEBNext Multiplex Oligos for Illumina (Dual Index Primers Set 1). Quality control of the libraries was assessed with the Qubit high sensitivity kit and fluorometer (Thermo Fisher Scientific), Tapestation 2200 instrument and D1000 ScreenTapes (Agilent). Next, the cDNA libraries were diluted to 2 nM, denatured, and sequenced on the NextSeq instrument using the 1X75 cycle high throughput kit. The BCL output files from the sequencing machine were converted into FASTq files with the BCL2FASTQ software (Illumina).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eData analyses\u003c/h2\u003e \u003cp\u003eNormalization of RNA-seq data and identification of differentially expressed genes was assessed with the EdgeR package from R by using the Galaxy software. Genes with a fold change of -1.5 to +\u0026thinsp;1.5 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered as differentially expressed. The data were visualized through generation of principal component analyses (PCA) plot, heatmap, and volcano plot with an adjusted P-value of 0.05 as cutoff. Gene set enrichment analysis was conducted by using the Hallmark Gene set database. Identification and analysis of significant cellular pathways from the dataset was performed with the Ingenuity Pathway Analysis (IPA) software (Qiagen). A pathway was regarded as significant by the following cutoffs: genes exhibited a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, expression log ratio of 1, and activated z-score of 2.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn vivo\u003c/b\u003e \u003cb\u003eBC dormancy\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIntravenous route\u003c/span\u003e: The establishment of BC dormancy was previously described (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). KMT2B, KMT2D KD BCCs, and BCCs with scramble shRNA (5x10\u003csup\u003e5\u003c/sup\u003ecells in 300 \u0026micro;l) were injected intravenously into 6-wk old nude athymic female mice. On days 3 and 5, mice were injected intraperitoneally with a low dose of carboplatin (2 mg/kg) or vehicle to establish BC dormancy in the BM. The mice were euthanized on day 7 and the organs such as liver, brain, lungs, and femur were harvested and placed in 3.7% formaldehyde for 48 h. Furthermore, the endosteal region of the BM was scraped and imaged with the EVOS FL Auto 2 Imaging System to identify the presence of GFP-positive BCCs. The harvested organs were embedded in paraffin and sectioned at the Histology Core Facility at Rutgers New Jersey Medical School. The presence of BCCs on the tissue sections was evaluated by immunohistochemistry.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOrthotopic Route\u003c/span\u003e: MDA-MB-231 BCCs with stable pOct4a-GFP (5x10\u003csup\u003e5\u003c/sup\u003e) were injected into the mammary fat pad of female (6 weeks) nude BALB/c. After 1 week, the mice were euthanized, and the femurs were harvested and scraped to evaluate the presence of BCCs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry\u003c/h2\u003e \u003cp\u003eParaffin-embedded tissue sections were incubated overnight at 56\u0026ordm;C. The sections were dewaxed with xylene and ethanol and rehydrated with deionized water. Antigen retrieval from the tissue sections was performed with citrate buffer for 30 mins in a pressured water bath. The slides were washed twice with 1X PBS for 5 mins, and the cells were permeabilized with 0.1% Triton X-100. Next, the sections were washed with 1X PBS, followed by the addition of the primary antibody, Ki-67, at a final dilution of 1:250. The slides were then placed in a humidified chamber and incubated overnight at 37\u0026ordm;C. The following day, the tissue sections were washed thrice with 1X PBS, the secondary antibody was added at a final dilution of 1:500, and the slides were incubated for 2 h at room temperature. The slides were washed and analyzed by microscopy on the EVOS FL Auto 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eThe data were analyzed on Sigma Plot 15 (Systat Software Inc) using the two-tail student\u0026rsquo;s t-test and two-way ANOVA to compare between groups. A p-value less than 0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of cell-autonomous mediated CSC maintenance\u003c/h2\u003e \u003cp\u003eWe previously reported on BCCs instructing MSCs to release exosomes with distinct RNA cargo (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The exosomal cargo was responsible for the stepwise dedifferentiation of BCCs into CSCs (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The changes within the exosomal cargo included transcripts for H3K4 modifiers, KMT2B and KMT2D. This led us to ask if endogenous KMT2B and KMT2D in CSCs can sustain multipotency. We first subjected the single cell RNA-seq data from the published studies to IPA (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In this study, the BCCs were treated with exosomes from MSCs that were previously exposed to BCCs (primed MSCs), or from MSCs that were never exposed to BCCs (na\u0026iuml;ve MSCs). We overlaid the epigenetic modifiers from these datasets with the following pathways within IPA: neoplasia of cells; proliferation of stem cells; proliferation of cancer cells; and de-differentiation of beta islet cells. The output network identified KMT2B, KMT2D, and DNMT1 as regulators of stem cell genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. Since dormant BCCs are functionally similar to CSCs, we proposed that endogenous KMT2B, KMT2D and DNMT1 could be involved in maintaining CSCs (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). We and others have addressed the role of the cancer niche on dormancy. Based on the information, combined with our other studies showing evidence of cell-autonomous method of dedifferentiation to CSCs, we focused this study to decipher how H3K4 regulate BCCs by cell-autonomous method (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eH3K4 methylation in BCC survival\u003c/h2\u003e \u003cp\u003eThe number of methylation sites on H3K4 could influence cellular functions (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). We therefore sought the role of H3K4 methylation on BCC quiescence and multipotency with a pan pharmacological inhibitor, WDR5-0103. This inhibitor blunts H3K4 methylation by targeting the core subunit of the KMT2s, WDR5 (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Dose-response and time-course studies with BCC viability as readouts identified the optimal conditions as 10 \u0026micro;g/ml of WDR5-0101 and 48 h treatment (Fig. S3). Since WDR5-0103 induced significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.03) BCC death, as compared to vehicle (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), we deduced that H3K4 methylation is relevant to BCC survival.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eH3K4 methylation in CSC maintenance\u003c/h2\u003e \u003cp\u003eDue to some BCCs resisting WDR5-0103 treatment, we conducted studies to gain insights into the how the inhibitor could be affecting the different subsets (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). We previously reported that BCCs with stable pOct4a-GFP could delineate subsets since GFP intensity is directly proportional to Oct4a levels (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). We treated BCCs-pOct4a-GFP with WDR5-0103 for 48 h and then analyzed the surviving BCCs for GFP intensity by flow cytometry (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). WDR5-0103 significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) reduced CSCs (Oct4a\u003csup\u003ehi\u003c/sup\u003e), as compared to vehicle (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). This correlated with an increase of late BC progenitors (Oct4a\u003csup\u003eneg\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), suggesting that the inhibitor induced CSCs to differentiate. Based on this finding, we deduced that loss of cell viability likely occurred in the non-CSC subset, indicating that blunted H3K4 leads to differentiation and cell death.\u003c/p\u003e \u003cp\u003eWe next asked if WDR5-0103-mediated decrease of CSCs correlated with reduced levels of multipotent-linked genes. Real time PCR indicated that WDR5-0103 significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) decreased Oct4, Sox2 and Nanog mRNA, as compared to vehicle (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Altogether, pharmacological inhibition of H3K4 methylation indicated its role in preserving the CSC population.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eSynergistic effects of H3K4 inhibitor and carboplatin\u003c/h2\u003e \u003cp\u003eInhibition of H3K4 methylation led to increase percentages of BC progenitors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), which are mostly cycling cells (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This led us to ask if WDR5-0103-mediated differentiation of CSCs would sensitize BC progenitors to carboplatin. We treated BCCs with WDR5-0103 and/or 200 \u0026micro;g/mL carboplatin for two days. Carboplatin treatment resulted in ~\u0026thinsp;55% cell death as compared to ~\u0026thinsp;35% for WDR5-0103 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Together, carboplatin and WDR5-0103 showed significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) increase in cell death as compared to individual treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Since CSCs have been shown to resist carboplatin treatment, we deduced that increased cell death by carboplatin and WDR5-0103 was partly due to the differentiation effects of WDR5-0103 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eTranscriptomic changes in KMT2B and KMT2D KD BCCs\u003c/h2\u003e \u003cp\u003eThe data thus far supported a role for H3K4 methylation in CSC maintenance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Further, interrogating H3K4 methylation led to CSC differentiation and chemosensitivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Since the experimental design contained only BCCs, the findings strongly suggested a cell-autonomous method for H3K4 methylation in CSCs. We therefore knocked down KMT2B and KMT2D in BCCs to study the individual role of two H3K4 methylases in CSCs. We performed RNA-seq analyses with the KD BCCs and control expressing scramble shRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePCA of the RNA-seq data showed distinct clustering of the groups with negligible variability of the biological replicates within each group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Heatmaps of genes showed distinct gene expressions between scramble and KMT2B or KMT2D KD BCCs (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Gene ontology (GO) analyses of the RNA-seq data revealed that the upregulated pathways in KMT2B and KMT2D KD BCCs were associated with tumor progression, e.g., inflammatory cues and cell migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eWe selected the genes associated with tumor progression and then overlaid them with the dataset linked to tumor growth pathway in IPA. The output revealed that genes associated with tumor growth were enhanced in the KD BCCs, relative to scramble shRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Collectively, the results showed that KMT2B and KMT2D KD changed the transcriptional landscape of BCCs; particularly, upregulating genes associated with tumor growth. Together, the predicted analyzes suggested a loss of a dormant phenotype in the KMT2B and KMT2D KD BCCs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eReduced CSCs in KMT2B and KMT2D KD BCCs\u003c/h2\u003e \u003cp\u003eAnalyses of the RNA-seq data showed increases in genes linked to tumor growth in the KMT2B and KMT2D KD BCCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Furthermore, there were increases of BCC progenitors and decreased CSCs after treatment with H3K4 inhibitor (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We therefore asked if this change was specific to KMT2B or KMT2D. We analyzed the KD BCCs for subsets by flow cytometry, similar to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). CSCs/Oct4a\u003csup\u003ehi\u003c/sup\u003e were significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) decreased when KMT2B or KMT2D was KD (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The data when presented as the absolute number of Oct4a\u003csup\u003ehi\u003c/sup\u003e BCCs showed significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) decreases, relative to scramble shRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). We also noted a similar decrease for Oct4a\u003csup\u003emed\u003c/sup\u003e BCCs (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Since Oct4a\u003csup\u003ehi\u003c/sup\u003e and Oct4a\u003csup\u003emed\u003c/sup\u003e BCCs were primitive within BCC hierarchy, we deduced that their decrease was due to differentiation (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This was corroborated by increases of BCC progenitors (Oct4a\u003csup\u003elo\u003c/sup\u003e and Oct4a\u003csup\u003eneg\u003c/sup\u003e) (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Overall, we noted similar results with the pharmacological inhibitor of H3K4 methylase (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we asked if decreased CSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) correlated with reduced transcript for stem cell-associated genes. Real time PCR showed significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) decreases in stem cell transcription factors, Oct4a, Sox2, Klf4, Nanog and Notch 1 in KMT2B and KMT2D KD BCCs, relative to scramble sequence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). In summary, the findings indicated that KMT2B and KMT2D KD reduced CSCs, and this seemed to be due to differentiation of CSCs to BC progenitors.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eLoss of cycling quiescence in BCCs KD for KMT2B or KMT2D\u003c/h2\u003e \u003cp\u003eKMT2B and KMT2D KD BCCs have increased progenitors and decreased CSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Induced number of cycling BC progenitors by H3K4 inhibitor is in line with enhanced sensitivity to carboplatin (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). We asked if KMT2B or KMT2D KD could promote cell cycle progression by overlaying the differentially expressed genes from the RNA-seq data with cell cycle progression pathway in IPA. Indeed, the analyses indicated activation of cell cycle progression pathways in KMT2B and KMT2D KD BCCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Gene set enrichment analyses (GSEA) of the RNA-seq data showed a significant enhancement of E2F and G2M cell cycle progression pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The predicted findings were confirmed in western blot for CDK4, CDK6, and cyclin D1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). These increased proteins supported cell cycle transition from G1 to S phase (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eReduced CSCs within KMT2B and KMT2D KD BCCs is expected to decrease the ability of BCCs to adapt dormancy (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). To address this, we examined the KD cells for p38 since its increase has been linked to cellular dormancy (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Western blot analyses showed decreases in p38 bands when the extracts were taken from KMT2B and KMT2D KD BCCs, relative to scramble shRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Altogether, these results indicated that KMT2B or KMT2D KD BCCs led to loss of cycling quiescence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eEnhanced proliferation and migration by KMT2B and KMT2D KD BCCs\u003c/h2\u003e \u003cp\u003eIncreased BC progenitors after KMT2B and KMT2D were KD suggested that these two genes could restrict BCC proliferation and favor a dormant state (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). IPA analyses of KS versus scramble shRNA indicated activated pathways linked to proliferation and differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). We verified significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) increases in the proliferation of the KD BCCs, relative to scramble shRNA (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIPA predicted increase of cell migration pathway for KMT2B and KMT2D KD BCCs (Figs S4 and S5) was validated with scratch assays. There were increases in KMT2B and KMT2D KD BCCs migration as compared to scramble shRNA (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). While the gap for KMT2B closed at 48 h, similar closure took 72 h for KMT2D BCCs. BCCs with scramble shRNA failed to close the gaps. In summary, the findings demonstrated that KMT2B or KMT2D KD promoted BCC proliferation and migration.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn vitro and in vivo\u003c/b\u003e \u003cb\u003eresponse of KMT2B and KMT2D KD BCCs to chemotherapy\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe showed mostly progenitors in KMT2B and KMT2D KD BCCs were mostly progenitors (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;15). Since BC progenitors are mostly cycling cells, they are expected to be sensitive to chemotherapy (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). IPA of the RNA-seq data predicted chemosensitivity of KMT2B and KMT2D KD BCCs versus scramble shRNA (Figs S6 and S7). We treated BCCs, KD for KMT2B or KMT2D, or scramble shRNA with carboplatin (200 \u0026micro;g/ml), doxorubicin (1 \u0026micro;M) or vehicle. After 48 h, trypan blue exclusion indicated significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) cell death in the KMT2B or KMT2D KD BCCs, relative to scramble shRNA (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Enhanced chemosensitivity of KMT2B and KMT2D KD BCCs correlated with decreased of the multidrug-resistant protein Pgp (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe \u003cem\u003ein vivo\u003c/em\u003e studies used an established model of dormancy to test the response of KMT2 KD BCC to carboplatin (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). BCCs, KD for KMT2B or KMT2D, or scramble shRNA (5x10\u003csup\u003e5\u003c/sup\u003ecells in 300 \u0026micro;l PBS) were injected into the tail vein of female nude mice (6 weeks). Our previous studies reported 48\u0026ndash;72 h for BCCs to acquire dormancy in BM (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). We used this time as guide to treat the mice. Mice were inject via intraperitoneal route with vehicle or carboplatin (2 mg/kg) at days 3 and 5. At day 7, mice were euthanized, and the femurs harvested (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). The endosteal region of one femur was scraped to identify BCCs, and the other decalcified for embedding in paraffin. In both analyses, GFP within the shRNA vector served as an indicator of BCCs. The sectioned tissues were labeled for Ki67.\u003c/p\u003e \u003cp\u003eFluorescence microscopy of the scraped tissue indicated less BCCs in mice that received the KMT2 KD BCCs and carboplatin treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). Similar treatment of mice with scramble shRNA identified an increase of BCCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). Examination of sections from paraffin-embedded femurs indicated higher levels of Ki-67 in KMT2B and KMT2D KD BCCs, relative to scramble shRNA (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). Carboplatin treatment reduced Ki67\u0026thinsp;+\u0026thinsp;BCCs in femurs (Figs G-\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). The results also showed continued proliferation, based on Ki67 even after carboplatin treatment. Overall, the \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e studies corroborated chemosensitivity of KMT2 KD BCCs.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eChemosensitivity of KMT2B and KMT2D KD BCCs in brain\u003c/h3\u003e\n\u003cp\u003eWe previously reported on brain metastasis when dormant MDA-MB- 231 BCCs were induced to reverse dormancy (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). We also showed that BCCs that exited dormancy were chemosensitive, resulting in reduced brain metastasis (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Since the experimental evidence indicates that KMT2B and KMT2D maintain CSCs, we asked if their KD could recapitulate reverse dormancy (transition out of cellular quiescence), and if treated, this will reduce brain metastasis. We evaluated the brain sections of the mice that were treated with vehicle or carboplatin (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). We counted 10 fields per section in three mice and then calculated the total number of BCCs in the brain. This resulted in significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) more KMT2B and KMT2D KD BCCs in the vehicle treated mice, relative scramble shRNA (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). We counted the total number of BCCs in the carboplatin treated mice and used the values obtained in the brain of vehicle treated as 100% to calculate cell death in brain. The results indicated significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) cell death with the knockdown cells, relative to scramble (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). In summary, the results showed that targeting H3K4 could reduce brain metastasis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eEpigenomic maintenance of CSC in BC patients\u003c/h2\u003e \u003cp\u003eAlong with KMT2B and KMT2D, DNMT is predicted to be involved in multipotency (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To test the involvement of DNMT in CSC maintenance, we focused on the associated regulatory genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). Specifically, on TET2 since it can oxidize 5mC to facilitate demethylation (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). Additionally, we previously reported on increased TET2 during the first phase when microenvironmental exosomes mediate dedifferentiation of BCCs towards CSCs (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). TET 2 is reduced during the second phase as differentiation complete, suggesting that retained methylation is relevant to sustain stemness in BCCs. The question is how DNA methylation, along with what is reported here for methylation of H3K4, sustain stemness. We tested pan-cytokeratin\u0026thinsp;+\u0026thinsp;cells in the blood of BC patients for stem cell associated genes and DNMT-linked genes (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eAs noted in the patient demographics, most subjects were treated anti-cancer regimen that caused low blood counts; hence limited amount of blood (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). We could analyzed the samples with two panels of antibodies in which both gated pan cytokeratin\u0026thinsp;+\u0026thinsp;cells. Panel 1 studied the cytokeratin\u0026thinsp;+\u0026thinsp;cells for EpCAM and Oct3/4, and Panel 2, for Oct3/4, TET2 and 5hmC (hydroxylation) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003ePatient 2 blood, which was taken before treatment, showed high levels of 5hmC, detectable TET2, and low levels of stem cell-associated Oct3/4 and EpCAM (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Together, this profile suggested that the patient could be in the initial phase of dedifferentiating to CSCs but does not show support of CSC maintenance. Since this blood was tested before treatment, it is expected that there will be heterogeneous BCCs in the blood. Specifically, it is expected that before treatment there will be excessive BC progenitors as compared to CSCs. We propose that the BC in this patient could be at the initial phase of dedifferentiation because TET2 has been shown to be needed for step 1 dedifferentiation (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDue to insufficient blood sample for Patient 1, the analysis was done only for panel 1 antibodies. The comparable percentages of Oct4 and EpCAM suggested the presence of circulating CSCs. Patient 6 who was 89 years at analyses, was on an estrogen modulator and showed the highest percentage of cytokeratin\u0026thinsp;+\u0026thinsp;cells that were positive for EpCAM and Oct3/4. This patient showed undetectable 5hmC, indicating methylation. Thus, Patient 6 mostly likely show high level of CSCs. We deduced that Patient 8 showed limited support for CSCs, despite undetectable TET2. However, Patient 8 showed an increase of 5hmC, which could occur by another TET protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). In summary, this section indicated that in addition to H3K4, sustained 5mC could be important to maintain CSCs in BC patients during treatment.\u003c/p\u003e \u003cp\u003eFinally, we used the last two blood samples (Patients 9 and 10) to determine if azacitidine could differentiate CSCs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). If so, this could indicate a potential treatment after standard treatment. We treated the cells with carboplatin and/or azacitidine for 7 days. An analyses of the mononuclear cells with Panels 1 and 2 antibodies indicated that Patient 9, who was treated, showed evidence of CSC-like in the blood. However, azacitidine led to detectable DNA hydroxylase to methylate the DNA, which is consistent with BC progenitors. Patient 10 was collected before treatment and this showed mixed population regardless of treatment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTimeline changes in cytokeratin\u0026thinsp;+\u0026thinsp;cells in patients\u0026rsquo; PBMCs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e Treatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCytokeratin\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEpCam\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOct4\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTet2\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5hmC\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRemarks\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePatient 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCSCs at collection; Reduced CSC and enhanced DNA methylase with treatment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUntreated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarboplatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarboplatin +\u003c/p\u003e \u003cp\u003eAzacitidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePatient 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBlood from untreated patient showed BCCs with DNA hydroxylase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUntreated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarboplatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarboplatin +\u003c/p\u003e \u003cp\u003eAzacitidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eTable showed two patients whose blood was used for \u003cem\u003ein vitro\u003c/em\u003e studies with mononuclear cells. The cells were treated with carboplatin and/or azacitidine. At day 7, the cells were analyzed by phenotype using the two panel of antibodies described in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study reports on a cell-autonomous method elicited by H3K4 methylation to sustain multipotency in CSCs (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This role of H3K4 methylation appears to be aided by DNMT (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In a previous study in which we examined the role of MSC-derived exosomes on BC dedifferentiation, we identified DNMT as a regulator of CSC maintenance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). More importantly, we noted an indirect relationship between markers of stemness and DNMT associated proteins in cytokeratin\u0026thinsp;+\u0026thinsp;cells from treated BC patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The relevance of this study indicated that DNA methylation rather than its hydroxylation is important in CSCs. This was an intriguing observation since evidence of DNA hydroxylation has been shown to be relevant during the initial phase of BCCs dedifferentiating to CSCs (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This is line with a need for gene transcription during the early phase as BCCs begin to dedifferentiate and once the cells attain multipotency/CSCs, transcription is diminished to attain quiescence.\u003c/p\u003e \u003cp\u003eSince dormant BCCs are similar to CSCs (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), the present findings provide insights into a different method by which BCCs attain dormancy. In this case, we showed a mechanism of cell autonomy and this incorporate H3K4 methylases. Similar BCC quiescence can be supported in the BM by microenvironmental cells such as MSCs, macrophages and stromal fibroblasts (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Indeed, H3K4 methylation marks have been proposed as drug targets (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). However, the question is how epigenetic targeted drugs should be used to treat BC. Of course, this study showed a role for H3K4 and perhaps DNA methylation in CSC maintenance. We do not propose to change the standard of care, but to monitor circulating BCCs as a functional indicator for targeted treatment to prolong BC remission. Studies with hematological cancer showed that induced differentiation of leukemia stem cells with bortezomib could lead to chemosensitivity (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Despite the limited number of analyzed BCCs in blood (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the evidence showed intriguing information that similar analyses could be used in expanded studies to guide how BC patients are treated.\u003c/p\u003e \u003cp\u003eWe were intrigued by the results of the small studies with patient blood (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The data showed that aggressive BC with Her2+, there were resistant CSC-like cytokeratin\u0026thinsp;+\u0026thinsp;cells. These cells appear to be maintained for CSCs due to decreased TET2, which can prevent DNA hydroxylation, hence maintaining methylation. This small study was conducted with patients from the University Hospital with economically disadvantage population. It is not unusual for these patients to be first diagnosed with late-stage BC. Due to limited samples and number of patients who consented, we took the opportunity with Patients 9 and 10 to examine the differences between cytokeratin\u0026thinsp;+\u0026thinsp;BCCs from a patient who was treated and the other who was not treated. As expected, based on the outcome shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Patient 9 had CSC-like in the blood but this changed when the mononuclear cells were challenged with azacidtidine (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The untreated patient 10 started with heterogeneous BCCs and this continues to be the same with azacitidine. The data discussed in the previous paragraph for leukemia, combined with this study indicated that this should require a large study with global impact. This will help address healthcare disparities in BC worldwide. Our findings shed therapeutic insight across global markets marked by healthcare inequities. In the meantime, drugs are available that could be repurposed to target the CSCs after standard treatment. This is important for long-term remission rather than short recurrences into metastatic BC.\u003c/p\u003e \u003cp\u003eTargeting KMT2s shared WDR5 core subunit with WDR5-0103, led to cell death but concomitant differentiation into chemosensitive cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). Studies with the pharmacological agent as well as the molecular KD provided insights into how CSCs are maintained in dormancy. The experimental studies indicated that H3K4 methylation is important to maintain CSCs while preventing differentiation to chemosensitive BCCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The increased BC progenitors after pharmacological targeting of H3K4 was due to both KMT2B and KMT2D, based on the KD studies.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003ein vitro\u003c/em\u003e findings, were tested \u003cem\u003ein vivo\u003c/em\u003e in nude mice with an established model of BC dormancy in the BM (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). We noted sensitivity to carboplatin when KMT2B and KMT2D KD BCCs were injected in mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This was interesting because these BCCs were in an environment of MSCs that can release exosomes with H3K4 methylase. Thus, the findings indicated that KMT2B and KMT2D could be relevant drug targets. Future studies are needed to determine how the treatment could be done safely since the BM is home to hematopoietic stem cells that are likely to share similar H3K4 marks. Similarly, if the findings in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e could be a guide to treatment, this would require safety studies.\u003c/p\u003e \u003cp\u003eMost chemotherapies target rapidly proliferating cells more efficiently than quiescent dormant BCCs (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). We noted synergism between carboplatin and WDR5-0103 with respect to cell death (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). This was partly explained by WDR5-0103 being able to differentiate CSCs into proliferating BC progenitors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). WDR5-0103 inhibits global H3K4 methylation by targeting all members of the KMT2 family (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). However, among the KMT2 family, data using previous exosomal cargo with ability to dedifferentiate BCCs to CSCs, predicted roles for KMT2B and KMT2D (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Using knockdown studies, the data supported roles for KMT2B and KMT2D in sustained stemness in CSCs.\u003c/p\u003e \u003cp\u003eInsights into a strong role for cell-autonomous regulation of CSCs were derived from bioprinting of BCCs in methylcellulose ink (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). The printing occurred without endogenous BM cells, which led us to propose that the BCCs could survive with epigenomic changes. This bioprinting study led us to reanalyze the exosomal cargo from MSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). RNA-seq data from KMT2B and KMT2D KD BCCs further supported cell-autonomous regulation. We noted upregulation of canonical pathways linked to cancer growth and proliferation when these two H3K4 methylases were knocked down (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings, together with the functional studies, indicated that cell-autonomous support of CSC by KMT2B and KMT2D sustain dormancy and drug resistance (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKMT2B KD upregulated inflammatory pathways such as interleukin (IL)-1 signaling, Toll-like receptor signaling, and Pathogen-induced cytokine storm in BCCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). KMT2D KD in BCCs resulted in activation of IL-signaling pathway and T-helper 1 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There is a strong association between the upregulation of pro-inflammatory pathways and dormancy reversal (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). Disruption of direct cellular communication between BCCs and BM MSCs enhanced IL-1 secretion from the latter cells to mediate BCC proliferation (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). Furthermore, the IL-1 signaling pathway promotes cancer cell proliferation and angiogenesis through the activation of the NFκB (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Importantly, toll-like receptor signaling and pathogen-induced cytokine storm are two factors that can induce dormancy reversal (\u003cspan additionalcitationids=\"CR70 CR71\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). Toll-like receptors recognize pathogen-associated and endogenous damage-associated molecular patterns to trigger a pro-inflammatory immune response that result in pathogen clearance (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). Collectively, the RNA-seq findings indicated that pathways associated with inflammation are enhanced in KMT2B and KMT2D KD BCCs.\u003c/p\u003e \u003cp\u003eGSEA of the RNA-seq dataset confirmed the functional studies by showing that E2F targets and G2M checkpoint pathways are enriched in KMT2B and KMT2D KD BCCs, as compared to scramble BCCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The E2F transcription factors promote cell cycle progression by inducing cell entry into S-phase (\u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). The G2/M checkpoint prevents mitosis initiation if the cells present DNA damage (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). The transition of cells from the G2-phase to the M-phase of the cell cycle is consistent with cell proliferation (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). Activation of cell cycle progression in KMT2 KD BCCs was accompanied by increased proliferation and migration, which is in line with reverse dormancy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There was a 24 h difference between KMT2D and KMT2B KD BCCs with respect to gap closure in the scratch assay (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). This could be attributed to the fact that these two proteins, although members of the KMT2 family, perform different H3K4 methylation modifications and this might contribute to gene expression variability (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKMT2B and KMT2D KD enhanced BCC metastasis to the brain, which is consistent with dormancy reversal (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). However, carboplatin treatment significantly reduced KMT2B and KMT2D KD BCCs in the brain (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Previous studies have indicated that carboplatin can be used to treat tumors that have compromised the integrity of the blood-brain barrier (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). Thus, it is possible that carboplatin was able to eliminate the KMT2 KD BCCs in the brain since these cells might have compromised the blood-brain barrier.\u003c/p\u003e \u003cp\u003eAlthough the main \u003cem\u003ein vivo\u003c/em\u003e studies conducted established BC dormancy to the BM through intravenous injection, the model validated BC metastasis to the BM. This was deduced in studies in which we injected BCCs into the mammary fat pad of mice and then examined the femur before the tumor grew to recapitulate early dormancy (Fig. S8). The early detection of high GFP\u0026thinsp;+\u0026thinsp;BCCs indicated early migration to femurs. In summary, this study reported on a cell-autonomous method to maintain CSCs but showed that reversed methylation could chemosensitize the otherwise resistant CSCs. The study also showed a role for DNMT associated proteins as potential markers to guide treatment of BC patients following standard care. We show that circulating BCCs can be a functional indicator for targeted treatment to improve the durability of remission in BC.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA. I. F-D. performed and designed the experiments, interpret the data and write a draft of the paper. \u0026nbsp;G. S. performed the experiments, analyzed and interpret the data, and wrote the paper. A. P. performed the data pertaining to the human study, analyze the data and wrote the paper. R. G.-B. performed the experiments, analyzed the data and wrote the paper. Y. K. performed the experiments, analyzed the data, and edited the paper. O. A. performed the experiments, analyzed the data and edited the paper. S. A. P. contributed to the concepts, edited the paper and analyze the data. A.-H. N. coded the patient information, collected the blood, analyzed the data and edited the paper. P. R. edited the paper for final submission, conceived and designed the study, interpret the data and approved the final figures. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe work was supported by an award from METAvivor Foundation and a fellowship to AIFD from the New Jersey Commission on Cancer Research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data presented in this study are available from the corresponding author. The RNA-seq data has been deposited in the Gene Expression Omnibus database (GEO).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe use of mice and human blood have been approved as outlined in the Method section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDeSantis CE, Ma J, Gaudet MM, Newman LA, Miller KD, Goding Sauer A, et al. Breast cancer statistics, 2019. CA: Cancer J for Clinicians. 2019;69:438\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeltzer A. Dormancy and breast cancer. J Surg Oncol. 1990;43:181\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemir L, Akyol M, Bener S, Payzin KB, Erten C, Somali I, et al. Prognostic Evaluation of Breast Cancer Patients with Evident Bone Marrow Metastasis. The Breast J. 2014;20:279\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAguirre-Ghiso JA, Sosa MS. Emerging topics on disseminated cancer cell dormancy and the paradigm of metastasis. Ann Rev Cancer Biol. 2018;2:377\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalker ND, Patel J, Munoz JL, Hu M, Guiro K, Sinha G, et al. The bone marrow niche in support of breast cancer dormancy. Cancer Lett. 2016;380:263\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrice TT, Burness ML, Sivan A, Warner MJ, Cheng R, Lee CH, et al. Dormant breast cancer micrometastases reside in specific bone marrow niches that regulate their transit to and from bone. Sci Transl Med. 2016;8:340ra73\u0026ndash;ra73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalmadge JE. Clonal selection of metastasis within the life history of a tumor. Cancer Res. 2007;67:11471\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBliss SA, Paul S, Pobiarzyn PW, Ayer S, Sinha G, Pant S, et al. Evaluation of a developmental hierarchy for breast cancer cells to assess risk-based patient selection for targeted treatment. Sci Rep. 2018;8:367.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel SA, Ramkissoon SH, Bryan M, Pliner LF, Dontu G, Patel PS, et al. Delineation of breast cancer cell hierarchy identifies the subset responsible for dormancy. Sci Rep. 2012;2:906.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyob AZ, Ramasamy TS. Cancer stem cells as key drivers of tumour progression. J Biomed Sci. 2018;25:1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllan AL, Vantyghem SA, Tuck AB, Chambers AF. Tumor Dormancy and Cancer Stem Cells: Implications for the Biology and Treatment of Breast Cancer Metastasis. Breast Dis. 2007;26:87\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarcereri de Prati A, Butturini E, Rigo A, Oppici E, Rossin M, Boriero D, et al. Metastatic breast cancer cells enter into dormant state and express cancer stem cells phenotype under chronic hypoxia. J Cell Biochem. 2017;118:3237\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiordano A, Gao H, Cohen E, Anfossi S, Khoury J, Hess K, et al. Clinical relevance of cancer stem cells in bone marrow of early breast cancer patients. Ann Oncol. 2013;24:2515\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDomen J, Wagers A, Weissman IL. Bone marrow (hematopoietic) stem cells. Regen Med. 2006;2:14\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandiford OA, Donnelly RJ, El-Far MH, Burgmeyer LM, Sinha G, Pamarthi SH, et al. Mesenchymal Stem Cell-Secreted Extracellular Vesicles Instruct Stepwise Dedifferentiation of Breast Cancer Cells into Dormancy at the Bone Marrow Perivascular Region. Cancer Res. 2021;81:1567\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDebeb BG, Lacerda L, Xu W, Larson R, Solley T, Atkinson R, et al. Histone Deacetylase Inhibitors Stimulate Dedifferentiation of Human Breast Cancer Cells Through WNT/β-Catenin Signaling. Stem Cells. 2012;30:2366\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllis CD, Jenuwein T. The molecular hallmarks of epigenetic control. Nat Rev Genet. 2016;17:487\u0026ndash;500.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrea F, Saidy NRN, Collins CC, Wang Y. The epigenetic/noncoding origin of tumor dormancy. Trends Mol Med. 2015;21:206\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBliss SA, Sinha G, Sandiford OA, Williams LM, Engelberth DJ, Guiro K, et al. Mesenchymal Stem Cell-Derived Exosomes Stimulate Cycling Quiescence and Early Breast Cancer Dormancy in Bone Marrow. Cancer Res. 2016;76:5832\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnabella LM, Taborga M, Corcoran KE, Bryan M, Patel PS, Rameshwar P. SDF-1α regulation in breast cancer cells contacting bone marrow stroma is critical for. Biol Chem. 2003;278:21631\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerrer AI, Trinidad JR, Sandiford O, Etchegaray JP, Rameshwar P. Epigenetic dynamics in cancer stem cell dormancy. Cancer Metastasis Rev. 2020;39:721\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu K, Xie V, Huang S. Epigenetic regulation of cancer stem cell and tumorigenesis. Adv Cancer Res. 2020;148:1\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlass C, Oakes C, Blum W, Marcucci G. Epigenetics in acute myeloid leukemia. Semin Oncol. 2008;35:378\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatterjee A, Rodger EJ, Eccles MR. Epigenetic drivers of tumourigenesis and cancer metastasis. Semin Cancer Biol. 2018;51:149\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLotem J, Sachs L. Epigenetics and the plasticity of differentiation in normal and cancer stem cells. Oncogene. 2006;25:7663\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao RC, Dou Y. Hijacked in cancer: the KMT2 (MLL) family of methyltransferases. Nat Rev Cancer. 2015;15:334\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHyun K, Jeon J, Park K, Kim J. Writing, erasing and reading histone lysine methylations. Exp Mol Med. 2017;49:e324.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFullgrabe J, Kavanagh E, Joseph B. Histone onco-modifications. Oncogene. 2011;30:3391\u0026ndash;403.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Shen L, Chen KN. Association between H3K4 methylation and cancer prognosis: A meta-analysis. Thorac Cancer. 2018;9:794\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNatarajan TG, Kallakury BV, Sheehan CE, Bartlett MB, Ganesan N, Preet A, et al. Epigenetic regulator MLL2 shows altered expression in cancer cell lines and tumors from human breast and colon. Cancer Cell Int. 2010;10:13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu C-H, Lin IH, Tzeng T-Y, Hsieh W-T, Hsu M-T. Regulation of IL-20 Expression by Estradiol through KMT2B-Mediated Epigenetic Modification. PLoS ONE. 2016;11:e0166090.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu J, Liu Z, Liang X, Wang L, Wu D, Mao W, et al. A Pan-Cancer Study of KMT2 Family as Therapeutic Targets in Cancer. J Oncol. 2022;2022:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngus L, Smid M, Wilting SM, van Riet J, Van Hoeck A, Nguyen L, et al. The genomic landscape of metastatic breast cancer highlights changes in mutation and signature frequencies. Nat Genet. 2019;51:1450\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNik-Zainal S, Davies H, Staaf J, Ramakrishna M, Glodzik D, Zou X, et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature. 2016;534:47\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Richmond A. The Role of PI3K Inhibition in the Treatment of Breast Cancer, Alone or Combined With Immune Checkpoint Inhibitors. Front Mol Biosci. 2021;8:648663.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToska E, Osmanbeyoglu HU, Castel P, Chan C, Hendrickson RC, Elkabets M, et al. PI3K pathway regulates ER-dependent transcription in breast cancer through the epigenetic regulator KMT2D. Science. 2017;355:1324\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi HJ, Park JH, Park M, Won HY, Joo HS, Lee CH, et al. UTX inhibits EMT-induced breast CSC properties by epigenetic repression of EMT genes in cooperation with LSD1 and HDAC1. EMBO Rep. 2015;16:1288\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith ZD, Meissner A. DNA methylation: roles in mammalian development. Nat Rev Genet. 2013;14:204\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBreiling A, Lyko F. Epigenetic regulatory functions of DNA modifications: 5-methylcytosine and beyond. Epigenetics Chromatin. 2015;8:24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu H, Zhang Y, Reversing. DNA methylation: mechanisms, genomics, and biological functions. Cell. 2014;156:45\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharif J, Muto M, Takebayashi S, Suetake I, Iwamatsu A, Endo TA, et al. The SRA protein Np95 mediates epigenetic inheritance by recruiting Dnmt1 to methylated DNA. Nature. 2007;450:908\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkano M, Bell DW, Haber DA, Li E. DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell. 1999;99:247\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePathania R, Ramachandran S, Elangovan S, Padia R, Yang P, Cinghu S, et al. DNMT1 is essential for mammary and cancer stem cell maintenance and tumorigenesis. Nat Commun. 2015;6:6910.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin E, Lee Y, Koo JS. Differential expression of the epigenetic methylation-related protein DNMT1 by breast cancer molecular subtype and stromal histology. J Transl Med. 2016;14:87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong KK. DNMT1: A key drug target in triple-negative breast cancer. Semin Cancer Biol. 2021;72:198\u0026ndash;213.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu X, Lv L, Wang M, Fan C, Lu X, Jin M, et al. DNMT1 facilitates growth of breast cancer by inducing MEG3 hyper-methylation. Cancer Cell Int. 2022;22:56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen B, Li Y, Ye Q, Qin Y. YY1-mediated long non-coding RNA Kcnq1ot1 promotes the tumor progression by regulating PTEN via DNMT1 in triple negative breast cancer. Cancer Gene Ther. 2021;28:1099\u0026ndash;112.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinha G, Ferrer AI, Ayer S, El-Far MH, Pamarthi SH, Naaldijk Y, et al. Specific N-cadherin\u0026ndash;dependent pathways drive human breast cancer dormancy in bone marrow. Life Sci Alliance. 2021;4:7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTivari S, Lu H, Dasgupta T, De Lorenzo MS, Wieder R. Reawakening of dormant estrogen-dependent human breast cancer cells by bone marrow stroma secretory senescence. Cell Commun Signaling. 2018;16:1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore CA, Siddiqui Z, Carney GJ, Naaldijk Y, Guiro K, Ferrer AI, et al. A 3D Bioprinted Material That Recapitulates the Perivascular Bone Marrow Structure for Sustained Hematopoietic and Cancer Models. Polymers. 2021;13:480.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorcoran KE, Trzaska KA, Fernandes H, Bryan M, Taborga M, Srinivas V, et al. Mesenchymal stem cells in early entry of breast cancer into bone marrow. PLoS ONE. 2008;3:e2563.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel SA, Dave MA, Bliss SA, Giec-Ujda AB, Bryan M, Pliner LF, et al. T(reg)/Th17 polarization by distinct subsets of breast cancer cells is dictated by the interaction with mesenchymal stem cells. J Cancer Stem Cell Res. 2014;2:2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShilatifard A. Molecular implementation and physiological roles for histone H3 lysine 4 (H3K4) methylation. Curr Opin Cell Biol. 2008;20:341\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu K, Tao H, Si X, Chen Q. The Histone H3 Lysine 4 Presenter WDR5 as an Oncogenic Protein and Novel Epigenetic Target in Cancer. Front Oncol. 2018;8:502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOakes SR, Gallego-Ortega D, Ormandy CJ. The mammary cellular hierarchy and breast cancer. Cell Mol Life Sci. 2014;71:4301\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBertoli C, Skotheim JM, de Bruin RA. Control of cell cycle transcription during G1 and S phases. Nat Rev Mol Cell Biol. 2013;14:518\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSosa MS, Avivar-Valderas A, Bragado P, Wen HC, Aguirre-Ghiso JA. ERK1/2 and p38alpha/beta signaling in tumor cell quiescence: opportunities to control dormant residual disease. Clin Cancer Res. 2011;17:5850\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKudaravalli S, den Hollander P, Mani SA. Role of p38 MAP kinase in cancer stem cells and metastasis. Oncogene. 2022;41:3177\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore LD, Le T, Fan G. DNA Methylation and Its Basic Function. Neuropsychopharmacol. 2013;38:23\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Jin M, Jeong KW. Histone H3K4 Methyltransferases as Targets for Drug-Resistant Cancers. Biol. 2021;10:581.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSherman LS, Patel SA, Castillo MD, Unkovic R, Taborga M, Gergues M, et al. NFĸB Targeting in Bone Marrow Mesenchymal Stem Cell-Mediated Support of Age-Linked Hematological Malignancies. Stem Cell Rev Rep. 2021;17:2178\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWysocka J, Swigut T, Milne TA, Dou Y, Zhang X, Burlingame AL, et al. WDR5 associates with histone H3 methylated at K4 and is essential for H3 K4 methylation and vertebrate development. Cell. 2005;121:859\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu K, Tao H, Si X, Chen Q. The Histone H3 Lysine 4 Presenter WDR5 as an Oncogenic Protein and Novel Epigenetic Target in Cancer. Front Oncol. 2018;8:502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhajar CM. Metastasis prevention by targeting the dormant niche. Nat Rev Cancer. 2015;15:238\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark SY, Nam JS. The force awakens: metastatic dormant cancer cells. Exp Mol Med. 2020;52:569\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRisson E, Nobre AR, Maguer-Satta V, Aguirre-Ghiso JA. The current paradigm and challenges ahead for the dormancy of disseminated tumor cells. Nat Cancer. 2020;1:672\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreco SJ, Patel SA, Bryan M, Pliner LF, Banerjee D, Rameshwar P. AMD3100-mediated production of interleukin-1 from mesenchymal stem cells is key to chemosensitivity of breast cancer cells. Am J Cancer Res. 2011;1:701\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiep S, Maddukuri M, Yamauchi S, Geshow G, Delk NA. Interleukin-1 and Nuclear Factor Kappa B Signaling Promote Breast Cancer Progression and Treatment Resistance. Cells. 2022;11:1673.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaram T, Rubinstein-Achiasaf L, Ben-Yaakov H, Ben-Baruch A. Inflammation-Driven Breast Tumor Cell Plasticity: Stemness/EMT, Therapy Resistance and Dormancy. Front Oncol. 2020;10:614468.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChernosky NM, Tamagno I. The Role of the Innate Immune System in Cancer Dormancy and Relapse. Cancers. 2021;13:5621.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTivari S, Lu H, Dasgupta T, De Lorenzo MS, Wieder R. Reawakening of dormant estrogen-dependent human breast cancer cells by bone marrow stroma secretory senescence. Cell Commun Signal. 2018;16:48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalker ND, Elias M, Guiro K, Bhatia R, Greco SJ, Bryan M, et al. Exosomes from differentially activated macrophages influence dormancy or resurgence of breast cancer cells within bone marrow stroma. Cell Death Dis. 2019;10:59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed A, Redmond HP, Wang JH. Links between Toll-like receptor 4 and breast cancer. Oncoimmunol. 2013;2:e22945.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuller H, Moroni MC, Vigo E, Petersen BO, Bartek J, Helin K. Induction of S-phase entry by E2F transcription factors depends on their nuclear localization. Mol Cell Biol. 1997;17:5508\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelin K. Regulation of cell proliferation by the E2F transcription factors. Curr Opin Genet Dev. 1998;8:28\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie D, Pei Q, Li J, Wan X, Ye T. Emerging Role of E2F Family in Cancer Stem Cells. Front Oncol. 2021;11:723137.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStark GR, Taylor WR. Analyzing the G2/M checkpoint. Methods Mol Biol. 2004;280:51\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnaba N, LaRocque JR. Targeting cell cycle regulation via the G2-M checkpoint for synthetic lethality in melanoma. Cell Cycle. 2021;20:1041\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarren KE. Beyond the Blood:Brain Barrier: The Importance of Central Nervous System (CNS) Pharmacokinetics for the Treatment of CNS Tumors, Including Diffuse Intrinsic Pontine Glioma. Front Oncol. 2018;8:239.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBailleux C, Eberst L, Bachelot T. Treatment strategies for breast cancer brain metastases. Br J Cancer. 2021;124:142\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cell-communication-and-signaling","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccas","sideBox":"Learn more about [Cell Communication and Signaling](http://biosignaling.biomedcentral.com/)","snPcode":"12964","submissionUrl":"https://submission.nature.com/new-submission/12964/3","title":"Cell Communication and Signaling","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, dormancy, epigenome, resistance, breast cancer, cancer stem cell","lastPublishedDoi":"10.21203/rs.3.rs-3822758/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3822758/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBreast cancer cells (BCCs) can remain undetected for decades in dormancy. These quiescent cells are similar to cancer stem cells (CSCs); hence their ability to initiate tertiary metastasis. Dormancy can be regulated by components of the tissue microenvironment such as bone marrow mesenchymal stem cells (MSCs) releasing exosomes to dedifferentiate BCCs into CSCs. The exosomes cargo includes histone 3, lysine 4 (H3K4) methyltransferases, KMT2B and KMT2D. A less studied mechanism of CSC maintenance is the process of cell-autonomous regulation, leading us to examine the roles for KMT2B and KMT2D in sustaining CSCs, and their potential as drug targets.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUse of pharmacological inhibitor of H3K4 (WDR5-0103), knockdown (KD) of KMT2B or KMT2D in BCCs, real time PCR, western blot, response to chemotherapy. RNA-seq and flow cytometry of blood from BC patient for markers of CSCs and DNA hydroxylases. \u003cem\u003eIn vivo\u003c/em\u003e studies with a dormancy model for response to chemotherapy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eH3K4 methyltransferases can sustain CSCs, impart chemoresistance, maintain cycling quiescence, and reduce migration and proliferation of BCCs. \u003cem\u003eIn vivo\u003c/em\u003e studies validated KMT2\u0026rsquo;s role in dormancy and identified these genes as potential drug targets. DNA methylase (DNMT), predicted within a network with KMT2 to regulate CSCs, was determined to sustain circulating CSC-like in the blood of patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCSCs are sustained by H3K4 methyltransferases and DNA methylation. Overall, the findings provide crucial insights into the epigenetic regulatory mechanisms underlying BC dormancy with KMT2B and KMT2D as potential therapeutic targets. We do not propose to change the standard of care, but to monitor circulating BCCs as a functional indicator for targeted treatment to prolong BC remission, which will partly address health disparity.\u003c/p\u003e","manuscriptTitle":"Role of KMT2B and KMT2D histone 3, lysine 4 methyltransferases and DNA oxidation status in circulating breast cancer cells provide insights into cell-autonomous regulation of cancer stem cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-03 20:36:35","doi":"10.21203/rs.3.rs-3822758/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-01-21T15:44:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-01-15T06:21:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"591fb7aa-e8a5-4b2e-9d48-0557a9770b90","date":"2024-01-06T19:32:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-06T18:05:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-02T04:02:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-02T04:02:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cell Communication and Signaling","date":"2023-12-30T00:40:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cell-communication-and-signaling","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccas","sideBox":"Learn more about [Cell Communication and Signaling](http://biosignaling.biomedcentral.com/)","snPcode":"12964","submissionUrl":"https://submission.nature.com/new-submission/12964/3","title":"Cell Communication and Signaling","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b22720e-7704-470c-acc3-9b7c545ee85c","owner":[],"postedDate":"January 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-02-19T15:11:17+00:00","versionOfRecord":{"articleIdentity":"rs-3822758","link":"https://doi.org/10.1186/s12964-024-01512-1","journal":{"identity":"cell-communication-and-signaling","isVorOnly":false,"title":"Cell Communication and Signaling"},"publishedOn":"2024-02-12 15:01:30","publishedOnDateReadable":"February 12th, 2024"},"versionCreatedAt":"2024-01-03 20:36:35","video":"","vorDoi":"10.1186/s12964-024-01512-1","vorDoiUrl":"https://doi.org/10.1186/s12964-024-01512-1","workflowStages":[]},"version":"v1","identity":"rs-3822758","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3822758","identity":"rs-3822758","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}