HS6ST1 regulates acute myeloid leukemia chemotherapy resistance via TGF-β1 signaling

HS6ST1 regulates acute myeloid leukemia chemotherapy resistance via TGF-β1 signaling | 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 Article HS6ST1 regulates acute myeloid leukemia chemotherapy resistance via TGF-β1 signaling Christina Termini, Kelsey Woodruff, Diya Patel, Jack Peplinski, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8725671/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Despite therapeutic advances, relapse remains the leading cause of death in patients with acute myeloid leukemia (AML). Growth factor signaling controls AML survival, proliferation, relapse, and chemotherapy resistance. Here, we studied heparan sulfate proteoglycans, a class of molecules that bind growth factors via their heparan sulfate chains to change their signaling ability. Heparan sulfate-growth factor interactions are controlled by the addition of sulfate groups catalyzed by heparan sulfotransferases, such as those encoded by HS2ST1 and HS6ST1 . Using AML patient cohort analyses, we demonstrate that increased HS6ST1 expression is associated with worse survival and increased relapse risk for AML patients harboring KMT2A -rearrangements. Using cell line derived xenografts, we show that AML cells depleted of HS2ST1 , but not HS6ST1 , have increased bone marrow leukemic burden. Further, AML cells depleted of HS6ST1 are more sensitive to cytarabine than Control cells, suggesting that HS6ST1 regulates AML chemotherapy resistance. Heparan sulfate antagonism with surfen synergized with cytarabine to further support AML cell death compared to cytarabine alone. Mechanistically, we demonstrate that HS6ST1 depletion in AML cells reduces TGF-β1-mediated signaling, which diminishes cell survival upon cytarabine treatment. Together, our data show that HS6ST1 promotes AML cell chemotherapy resistance by supporting TGF-β1 signaling. Biological sciences/Cancer/Haematological cancer/Leukaemia/Acute myeloid leukaemia Biological sciences/Cancer/Cancer therapy/Cancer therapeutic resistance Biological sciences/Cell biology/Cell signalling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Acute myeloid leukemia (AML) is the deadliest blood cancer, with a 5-year overall survival rate stagnating around 30% ( 1 , 2 ). One aggressive form of AML occurs when patients harbor fusions of the lysine methyltransferase 2A ( KMT2A ) gene with various partners ( 3 , 4 ). Daunorubicin or idarubicin and cytarabine (Ara-C) are frontline chemotherapeutic agents for AML ( 5 , 6 ). While many patients achieve remission following initial chemotherapy treatments, disease relapse remains high because of the persistence of drug resistant cells that can expand following treatment ( 1 , 7 ). The AML growth factor milieu influences disease progression and AML chemotherapy resistance ( 8 – 10 ). Heparan sulfate proteoglycans are transmembrane proteins that facilitate growth factor signaling in normal and malignant cells ( 11 – 14 ). Heparan sulfate proteoglycans bear glycan chains that are composed of repeating disaccharide units ( 15 ). Heparan sulfates can be modified by the addition of negatively charged sulfate moieties at the N- , 2- O , 6- O , or 3 -O positions. Sulfation modifications are catalyzed by enzymes encoded by the genes NDST1-4, HS2ST1, HS6ST1-3 , or HS3ST1-7 , respectively ( 12 , 15 ). The type and amount of sulfate modifications present on the glycan chain influences heparan sulfate-growth factor interactions and signaling ( 16 , 17 ). Heparan sulfate proteoglycans and heparan sulfate modifications have important roles in cancer cell adhesion, proliferation, migration, drug resistance, and vascularization ( 13 , 18 – 21 ). Recent work identified syndecan-2 as an important regulator of hematopoietic stem cell quiescence via TGF-β1 signaling, and other work has demonstrated that heparan sulfate structure is important for B-cell maturation ( 22 , 23 ). Several cytokines important for normal and malignant hematopoiesis, including TGF-β1, CXCL12, FGF1 and 2, and PDGF, bind heparan sulfate ( 24 ). However, the impact of precise heparan sulfation patterns in AML is largely undefined. In this study, we show that heparan sulfation is dysregulated at the transcript and glycan levels in AML cells compared to normal hematopoietic cells. We identify an association between high HS6ST1 expression and poor survival outcomes for KMT2A- rearranged AML patients. Using CRISPR-edited MOLM-13 cells, we demonstrate that HS6ST1 is crucial for AML cell survival in response to Ara-C, and this occurs via TGF-β1 signaling. Our data highlights the critical function of heparan sulfation in AML, enabling us to expand current models of chemotherapy resistance by incorporating this crucial glycan modification. MATERIALS AND METHODS Study resources Detailed information for resources used throughout this study is included in Supplementary Table 1. Patients and Samples Samples were obtained from 2072 children and young adults (age 0–29 years) enrolled in clinical trials CCG-2961 (NCT00002798, n = 81), AAML03P1 (NCT00070174, n = 121), AAML0531 (NCT00372593, n = 795), and AAML1031 (NCT00372593, n = 1075) with written, informed consent collected from patients and their legal guardians in accordance with the Declaration of Helsinki. Each protocol was approved by the National Cancer Institute's central institutional review board (IRB) and the local IRB for each participating institution. Clinical data were available for all 2072 patients, with 1874 of those patients also having accompanying survival and transcriptomic data, and analyses were performed for that cohort with complete data. 68 normal bone marrow (NBM) samples were used as controls for expression analysis. Expression Analysis Batch-corrected mRNA data aligned to GRCh38 with STAR was used. Resulting normalized gene counts were converted into transcripts per million. Violin plots were generated with log10(TPM) using ggplot2 (v3.4.2). Wilcoxon test with Benjamini Hochberg adjustment was used to determine significance between AML subtypes and NBM expression levels for Heparan sulfation genes HS2ST1 (ENSG00000153936), HS6ST1 (ENSG00000136720), HS3ST1 (ENSG00000002587), and NDST1 (ENSG00000070614). Survival Analysis Kaplan-Meier survival curves were generated using the survival (v3.5-5) and survminer (v0.4.9) packages in R (v. 4.3.2). Survival times were calculated from time of diagnosis. Competing events such as death or induction failure were removed from cumulative incidence calculations to determine relapse risk. Survival curves were generated by binning samples into above (high) and below (low) median expression of the gene of interest. Glycosaminoglycan profiling Cryopreserved de-identified peripheral blood specimens from AML patients were obtained from the Fred Hutchinson Cancer Center/University of Washington Hematopoietic Diseases Repository. All participants provided written informed consent in accordance with the Declaration of Helsinki under the oversight of the Fred Hutch Institutional Review Office. Preparation and analysis was performed by the University of California San Diego GlycoAnalytics Core as previously reported ( 25 ). Mouse models Animal procedures were performed in accordance with the Fred Hutchinson Cancer Center Institutional Animal Care & Use Committee (PROTO2100049). Mice were housed and maintained in the Fred Hutch Comparative Medicine facility; mixed-sex adult mice 8-12-weeks of age were used for all studies. Mice were bred in house or purchased from the Fred Hutch Translational Research Model Services core. Cell culture MOLM-13, THP-1, and Kasumi-1 cells were obtained from the American Type Culture Collection and maintained per the manufacturer’s instructions. Cell lines were authenticated using the CLA IdentiFiler Plus PCR Amplification Kit. Cell line xenografts Mice were irradiated using a Mark 1 cesium irradiator (225 cGy). The following day, mice were intravenously injected with 1x10 6 MOLM-13 cells via tail vein. For leukemic burden studies, 14 days post-injection, mice were euthanized and bone marrow was isolated from one femur, lysed with ACK buffer, and processed in complete IMDM (IMDM + 10% FBS + 1% penicillin-streptomycin). For homing assays, 16 hours post-injection, bone marrow was isolated from two femurs and two tibias, lysed with ACK buffer, and processed in complete IMDM. Spleens were harvested, tissue was dissociated, lysed with ACK buffer, and processed in complete IMDM. Peripheral blood was collected into EDTA immediately prior to euthanasia, lysed using ACK buffer, and processed in 10% FBS/PBS. Lysed cells were stained using antibodies or isotype controls ( Supplemental Table 1 ) and analyzed by flow cytometry (LSRFortessa X-50 or BD FACSymphony A5). Data were analyzed using FlowJo software (v10.10.0). Frequencies are displayed as percent of live cells. Investigators were not blinded. No randomization was used. Sample sizes were estimated using power analyses to quantify the number of replicates needed to achieve at least 80% power and detect a ratio of 1.2 vs. null hypothesis of 1.0 with significance level of α = 0.013. Histology Organs were formalin fixed for 72 hours and washed with PBS. Femurs were decalcified for 14 days (0.5M EDTA, 4°C). Samples were paraffin-embedded and sectioned at 4 µm. Sections were stained with anti-human CD33 with nuclear counterstain. Broad regions of interest encompassing the full section (spleen and liver) or BM compartment, excluding the epiphysis (bone), were defined by a single observer. Primary classifiers were trained to exclude glass, fold and tear artifacts and (bone only) cortical bone. In the remaining tissue, the CD33 + area fraction was determined using the areaquant analysis plugin within HALO software (v3.6.4134). Lentiviral transductions A 24-well plate was coated with retronectin (50 µg, 2 hours) and blocked for 30 minutes (2% BSA). 5x10 4 MOLM-13 cells were seeded on the retronectin-coated wells and infected with lentiviral vectors containing guide RNAs targeting Control, HS2ST1 , or HS6ST1 (MOI = 25) (Supplementary Table 1). Cells were spin occulated (30 minutes, 1000 rpm, 32°C) and incubated for two days before the media was changed. 1–2 weeks after transduction, GFP + cells were sorted using a BD FACSymphony S6 and expanded. In vitro chemotherapy treatment 2.5x10 5 MOLM-13 cells were seeded in complete RPMI supplemented with DMSO vehicle or Ara-C to a final concentration of 0.5 µM and incubated at 37°C, 5% CO 2 for 24 or 72 hours. Cells were then stained in 1X Annexin binding buffer with fluorochrome conjugated Annexin V and 7-AAD and analyzed via flow cytometry. For surfen experiments, cells were treated with surfen to a final concentration of 40 µM. For TGFβ-1 co-treatment experiments, cells were treated with recombinant human TGFβ-1 to a final concentration of 5 ng/mL. RT-qPCR and RNA Sequencing : RNA was isolated using the Qiagen RNeasy Micro Kit. For RT-qPCR analyses, RNA was reverse transcribed using the Applied Biosystems High-Capacity cDNA Reverse Transcription Kit. Gene expression was analyzed using an Applied Biosystems QuantStudio 5 PCR machine. For RNA sequencing, library preparation and sequencing was performed using a NextSeq 2000 P2-100. Gene set enrichment analysis was performed against the C5: Ontology Gene Sets. Data are deposited in GEO (GSE314673). Intracellular flow cytometry Cells were stained with BD Fixable Viability Stain, washed, and fixed with BD Fixation/Permeabilization solution. Cells were stained with primary and secondary antibodies, washed, and analyzed via flow cytometry. Mean fluorescence intensity (MFI) was calculated using FlowJo. CellTrace Violet 2.5x10 5 MOLM-13 cells were stained with CellTrace Violet according to manufacturer instructions, seeded in complete RPMI, and treated with either vehicle or human TGF-β1 to a final concentration of 5 ng/mL. Cells were incubated at 37°C, 5% CO 2 for 72 hours, stained with 7-AAD and analyzed via flow cytometry. Western Blotting 1x10 7 MOLM-13 cells were harvested and lysed in RIPA buffer supplemented with protease and phosphatase inhibitors. 10–25 µg of each sample was separated using gel electrophoresis and transferred onto a PVDF membrane. Membranes imaged using a Li-Cor Odyssey system after antibody staining. Quantification was performed using the Image Studio Lite software (v5.2.5). RESULTS Heparan sulfate is dysregulated at the transcriptional and glycan scales in AML We first assessed the expression of heparan sulfotransferase genes HS2ST1 , HS3ST1 , HS6ST1 , and NDST1 in healthy individuals and AML patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database (Fig. 1 A). Bulk RNA sequencing revealed significantly lower HS2ST1 and HS3ST1 expression in AML patient bone marrow mononuclear cells than normal bone marrow (NBM) mononuclear cells, while HS6ST1 and NDST1 expression were similar among these groups (Fig. 1 B). AML patients with KMT2A- rearrangements and FLT3- ITD mutations expressed significantly less HS2ST1 and HS3ST1 than NBM cells. In contrast, patients harboring FLT3- ITD mutations expressed significantly more HS6ST1 compared to NBM, while NDST1 expression was similar between these groups (Fig. 1 B). These data suggest that the transcriptional profile of heparan sulfotransferase genes differs in AML and NBM cells. We next used liquid chromatography mass spectrometry to analyze heparan sulfate modifications of peripheral blood mononuclear cells (PBMCs) from normal patients and AML patients. AML patient characteristics are detailed in Supplemental Table 2 . Total heparan sulfate amounts were similar in AML and normal cells (Fig. 1 C). Each heparan sulfate disaccharide can bear zero, one, two, or three sulfate groups. Heparan sulfate disaccharides containing three sulfate groups were less frequent in AML PBMCs compared to normal PBMCs (Fig. 1 D). AML PBMCs had lower fractions of N- and 2 -O heparan sulfate compared to normal PBMCs (Fig. 1 E). AML PBMCs had significantly less D0S0 N- monosulfated and D2S6 trisulfated disaccharides and significantly more D0A0 unsulfated and D0A6 6- O monosulfated disaccharides compared to normal PBMCs (Fig. 1 F-G). Taken together, these data suggest that AML cells express distinct heparan sulfate landscapes with fewer sulfate modifications compared to normal cells. Increased HS6ST1 expression is associated with worse survival outcomes in KMT2A- rearranged AML patients We next assessed whether heparan sulfotransferase gene expression is associated with differential AML patient outcomes. We classified TARGET AML patients according to their bone marrow expression of HS2ST1 , HS6ST1 , HS3ST1 , and NDST1 relative to the cohort median. Among all AML patients, individuals with lower HS2ST1 expression had significantly worse event-free survival than those with high HS2ST1 expression, but overall survival outcomes were similar (Fig. 2 A; Supplemental Fig. 1A ). Overall survival ( Supplemental Fig. 1B-D ) and event-free survival (Fig. 2 B-D) were similar in AML patients regardless of HS6ST1, HS3ST1 , or NDST1 expression levels. However, among AML patients with KMT2A -rearrangements, increased HS6ST1 expression correlated with significantly worse event-free and overall survival compared to patients expressing less HS6ST1 (Fig. 2 F; Supplemental Fig. 1F ). HS2ST1, HS3ST1 , and NDST1 expression did not stratify patients harboring KMT2A -rearrangements according to differential survival outcomes (Fig. 2 E, G-H; Supplemental Fig. 1E, G-H ). These data indicate that increased HS6ST1 expression correlates with worse survival outcomes in AML patients with KMT2A- rearrangements. Depletion of heparan sulfotransferases remodels the AML transcriptome Previous studies highlight that heparan sulfation controls cancer cell functions ( 13 , 14 , 19 , 26 ), leading us to hypothesize that heparan sulfation may regulate AML cells to support disease progression. To test this, we used CRISPR-Cas9 to knockdown HS2ST1 and HS6ST1 in the MOLM-13 cell line, which harbors KMT2A-MLLT3 and FLT3 -ITD mutations. Compared to sgControl cells, sg HS2ST1 and sg HS6ST1 cells had decreased HS2ST1 and HS6ST1 protein expression, respectively, and high indel contribution in target genes. ( Supplemental Fig. 2A-D ). Bulk RNA sequencing and principal component analysis revealed sgControl, sg HS2ST1 , and sg HS6ST1 cells cluster distinctly, suggesting unique transcriptional profiles (Fig. 3 A). Compared to sgControl cells, 1096 genes were downregulated, and 825 genes were upregulated in sg HS2ST1 cells (Fig. 3 B). In sg HS6ST1 cells, 1257 downregulated genes and 575 upregulated genes were detected compared to sgControl cells (Fig. 3 C). Gene Set Enrichment Analysis revealed differentially enriched heparan sulfate proteoglycan-dependent processes in sg HS2ST1 and sg HS6ST1 cells compared to sgControl cells (Fig. 3 D-E). More specifically, sg HS2ST1 cells were negatively enriched for gene sets associated with transmembrane signaling receptor activity and cell adhesion molecule binding and positively enriched for phosphatidylinositol binding and cytokine mediated signaling pathways (Fig. 3 D, F). sg HS2ST1 cells were also negatively enriched for TNF-α and TGF-β signaling and positively enriched for interferon alpha and gamma response hallmark gene sets (Fig. 3 J). sg HS6ST1 cells were negatively enriched for gene sets associated with growth factor binding, cytokine receptor activity, cytokine binding, glycosaminoglycan binding, signaling receptor regulator activity, plasma membrane signaling receptor complex, and carbohydrate binding compared to sgControl cells (Fig. 3 E, F-I). sg HS6ST1 cells were also negatively enriched for the interferon gamma response, IL2-STAT5, TNF-α, IL6-JAK-STAT3, TGF-β, and Notch signaling hallmark gene sets and positively enriched for PI3K/AKT/MTOR genes (Fig. 3 K). These data indicate that heparan sulfate modifications catalyzed by HS2ST1 and HS6ST1 have distinct and far-reaching effects on proteoglycan-mediated signaling processes in AML cells. Depletion of HS2ST1 increases AML bone marrow burden in vivo Signaling-related pathways identified from our RNA sequencing analyses are known to impact cancer cell proliferation, leading us to test the potential function of heparan sulfation in AML cell growth ( 27 – 29 ). We tested the impact of HS2ST1 and HS6ST1 expression on AML growth in vivo using xenograft studies. sgControl, sg HS2ST1 , or sg HS6ST1 cells were intravenously transplanted into irradiated NSG mice, and AML burden was quantified using quantitative histology and flow cytometry at 2 weeks-post injection (Fig. 4 A). Bone marrow immunohistochemistry showed similar AML burden between all groups, however there was a trend towards increased burden in sg HS2ST1- transplanted animals (Fig. 4 B-C). Spleen, liver, and peripheral blood AML burden was similar in all groups (Fig. 4 B, D-G). Flow cytometry showed a significant increase in AML bone marrow burden upon transplant with sg HS2ST1 cells compared to sgControl cells (Fig. 4 H-I). These data reveal that depletion of HS2ST1 , but not HS6ST1 , enhances AML bone marrow burden in vivo. We also assessed cell homing by transplanting sgControl, sg HS2ST1 , and sg HS6ST1 into NSG mice (225 cGy) and analyzing AML burden 16 hours post-transplant ( Supplemental Fig. 3A ). sgControl, sg HS2ST1 , or sg HS6ST1 cell homing to the bone marrow or spleen were similar ( Supplemental Fig. 3B-C ), suggesting that HS2ST1 -mediated changes in AML bone marrow burden occur independently of homing. Higher HS6ST1 expression correlates with increased relapse risk in KMT2A -r AML patients Disease relapse driven by chemotherapy-resistant AML cells is a major barrier to long term patient survival ( 30 ). We therefore interrogated whether heparan sulfotransferase expression correlated with relapse risk in AML. Low HS2ST1 expression predicted higher relapse risk across AML subtypes ( Supplemental Fig. 4A ). There were no significant differences in relapse risk based on HS3ST1, HS6ST1 , and NDST1 expression across AML subtypes ( Supplemental Fig. 4B-D ). HS2ST1 , HS3ST1 and NDST1 expression were not correlated with KMT2A- rearranged patient relapse risk (Figs. 5 A, C-D). In contrast, high bone marrow HS6ST1 expression was associated with significantly increased relapse risk in KMT2A- rearranged AML patients (Fig. 5 B). These data demonstrate that high HS6ST1 correlates with increased relapse in patients with KMT2A -rearranged leukemias. Cytarabine treatment alters heparan sulfotransferase gene expression in AML cell lines Previous studies show that the heparan sulfate landscape differs in therapy-refractory residual breast cancer tumor cells compared to the primary tumor ( 20 ). These findings led us to test whether therapy-refractory AML cells express distinct heparan sulfate landscapes that enable them to resist chemotherapy and promote relapse. MOLM-13 cells expressed increased HS2ST1, HS6ST1 , and NDST1 and decreased HS3ST1 at 24- and 72-hours post-Ara-C treatment compared to vehicle-treated controls (Fig. 5 E). Kasumi-1 cells had significantly increased HS2ST1 and HS6ST1 expression at 24-hours post-Ara-C treatment compared to vehicle, but HS3ST1 and NDST1 expression were unchanged (Fig. 5 F). HS2ST1, HS6ST1 , and NDST1 expression were significantly elevated in THP-1 cells at 72 hours post-Ara-C treatment compared to vehicle controls (Fig. 5 G). These data suggest that chemotherapy resistant AML cells express different heparan sulfate transcriptomic profiles compared to their chemosensitive counterparts. Liquid chromatography mass spectrometry revealed significantly increased D2H6 and decreased D2A0 disaccharides in MOLM-13 cells treated with Ara-C compared to vehicle-treated cells (Fig. 5 H). In Kasumi-1 cells, there was significantly decreased D0A0 and significantly increased D2S0 and D2S6 disaccharides upon Ara-C treatment (Fig. 5 I). There were no significant changes in the heparan sulfate disaccharide composition in THP-1 cells after treatment with Ara-C (Fig. 5 J). These data show that Ara-C remodels the AML heparan sulfate landscape at the glycan level, suggesting that the precise structure of heparan sulfate may influence AML cells chemotherapy responses. HS6ST1 depletion promotes sensitivity to cytarabine Because Ara-C treatment increases heparan sulfotransferase gene expression, we investigated whether HS2ST1 or HS6ST1 regulate AML sensitivity to Ara-C. After 72 hours, Vehicle-treated sgControl, sg HS2ST1 , and sg HS6ST1 cells exhibited similar levels of live and necrotic cells, while sg HS6ST1 cells had slightly increased apoptotic cells (Fig. 6 A-B). However, upon Ara-C treatment, sg HS2ST1 cells had significantly more live cells than sgControl cells, suggesting they are more resistant to chemotherapy (Fig. 6 C). sg HS6ST1 cells had significantly fewer live cells compared to sgControl cells, accompanied by increased apoptotic cells, indicating they are more chemosensitive (Fig. 6 C). Consistent with these data, there was a four-fold reduction in the Ara-C IC50 for sg HS6ST1 cells compared to sgControl cells (Fig. 6 D-E). Together, these data show that HS6ST1 and HS2ST1 distinctly regulate AML chemotherapy sensitivity. Surfen is a small molecule antagonist of heparan sulfate ( 31 ). To evaluate whether heparan sulfate blockade is therapeutically effective for AML, we treated MOLM-13 and THP-1 cells with surfen alone and in combination with Ara-C. Compared to vehicle treatment, surfen treatment decreased cell viability in MOLM-13 and THP-1 cells, but not to the same level as Ara-C. However, combinatorial treatment of MOLM-13 or THP-1 cells with Ara-C and surfen significantly decreased cell viability compared to Ara-C treatment alone (Fig. 6 F-I). These data show that heparan sulfate antagonism synergizes with Ara-C to promote AML cytotoxicity. HS6ST1 is promotes TGF-β1 signaling to regulate AML survival upon chemotherapy treatment Our RNA sequencing data indicates that compared to sgControl cells, sg HS6ST1 cells were negatively enriched for pathways related to growth factor signaling including TGF-β1 (Fig. 3 C). Bone marrow TGF-β1 levels are significantly increased in relapsed/refractory AML patients, and high TGFB1 expression predicts adverse prognoses ( 32 , 33 ). TGF-β1 supplementation promotes AML chemoresistance in vitro by inducing a quiescent-like G 0 shift ( 34 , 35 ). We therefore hypothesized that depleting HS6ST1 may impact TGF-β1 signaling in AML, rendering cells more susceptible to Ara-C. Using intracellular flow cytometry, we measured phospho-SMAD2/3, a response element downstream of TGF-β1. TGF-β1 stimulation increased phospho-SMAD2/3 levels in sgControl and sg HS2ST1 compared to vehicle treatment (Fig. 7 A-B). However, phospho-SMAD2/3 expression upon TGF-β1 stimulation of sg HS6ST1 cells was muted compared to sgControl cells (Fig. 7 A-B). Similarly, Ara-C treatment induced SMAD2/3 phosphorylation in all cell lines relative to vehicle treatment (Fig. 7 C-D). However, phospho-SMAD2/3 expression was significantly lower in Ara-C-treated sg HS2ST1 and sg HS6ST1 than Ara-C-treated sgControl cells (Fig. 7 C-D). We next measured cell divisions at baseline and upon TGF-β1 stimulation using CellTrace Violet staining. sg HS2ST1 cells had a lower proliferation index than sgControl cells at baseline and after stimulation with TGF-β1. sg HS6ST1 proliferation index was not significantly different from sgControl cells at baseline, but it was significantly higher than sgControl cells after TGF-β1 treatment (Fig. 7 E-F). These data indicate that depletion of HS6ST1 decreases TGF-β1-mediated SMAD2/3 activation and supports cell proliferation. As TGF-β1 is known to promote AML chemotherapy resistance ( 32 ), we reasoned that sg HS6ST1 cells may exhibit reduced cell survival abilities compared to sgControl cells due to an impaired ability to respond to TGF-β1. To test this, cells were treated with Ara-C and TGF-β1 and cell death was quantified with Annexin-V/7-AAD staining. TGF-β1 treatment alone had no impact on cell survival (Fig. 7 G-H). However, TGF-β1 treatment in combination with Ara-C promoted the viability of sg HS2ST1 cells and sg HS6ST1 cells compared to Ara-C treatment alone. However, even with this increase in viability, sg HS6ST1 cells do not achieve the level of cell survival detected in sgControl cells. These data suggest that HS6ST1 cells have an impaired ability to respond to TGF-β1 signaling, which sensitizes cells to Ara-C. Taken together, our findings indicate that HS6ST1 regulates AML chemotherapy resistance, and depletion of HS6ST1 can sensitize AML cells to Ara-C, while impairing their ability to respond to TGF-β1 stimuli. DISCUSSION We reported previously that the heparan sulfate proteoglycan syndecan-2 regulates normal hematopoietic stem cell functions ( 36 ); however, the effect of heparan sulfates in malignant hematopoiesis had not been fully analyzed. Our study identified a crucial link between the heparan sulfate biosynthesis machinery and AML patient survival outcomes and chemotherapy resistance. This work adds to a growing body of literature defining the roles of heparan sulfation in cancer physiology, such as work from others showing that 6- O sulfation is necessary for survival-promoting signaling in treatment refractory dormant breast cancer residual tumor cells ( 20 ). Further, the presence and structures of syndecan-1 heparan sulfate chains regulate multiple myeloma proliferation and signaling ( 37 , 38 ). Our findings provide a foundation to better understand the function of heparan sulfates in other hematological malignancies and hematopoietic disorders. Mechanistically, we show that HS6ST1 regulates TGF-β1 signaling to support AML cell survival upon Ara-C. Several studies highlight critical functions for heparan sulfate proteoglycans in regulating TGF-β1 signaling ( 39 – 42 ). Work on hepatocellular carcinoma demonstrated a connection between the 6- O heparan sulfate endosulfatase SULF1 and TGF-β1 signaling ( 41 ). However, HS6ST1 decorates heparan sulfates during the biosynthesis process in the endoplasmic reticulum, while SULF1 acts extracellularly after heparan sulfate proteoglycan trafficking. Therefore, the push and pull of heparan sulfate biosynthesis and post-synthesis processing should be carefully analyzed in the future to resolve the distinct contributions of these processes in AML and how they change during disease. Correlations between TGF-β1 and AML survival outcomes are well-characterized, but inhibiting TGF-β1 receptors in AML can induce expression of drug efflux pumps, weakening treatment efficacy ( 32 , 33 , 35 , 43 ). Targeting heparan sulfate to extrinsically reprogram TGF-β1 signaling in AML cells could represent a therapeutic avenue to improve patient outcomes. In our study, we show that the heparan sulfate antagonist surfen promotes AML cell killing alone and in combination with Ara-C. Other groups showed that surfen decreases tumorigenicity of Ewing sarcoma cells by reprogramming growth factor signaling ( 44 ). Surfen can also inhibit glioblastoma invasion by blocking chondroitin sulfate, another type of glycosaminoglycan ( 45 ). Further, others have shown heparan sulfate mimetics can therapeutically target colorectal cancer stem cells ( 46 ). Our results build on these studies by supporting the notion that targeting extracellular glycans represents a viable way to inhibit blood cancer progression, starting with therapeutic relevance in AML. We showed that AML patient peripheral blood mononuclear cells express heparan sulfate disaccharide profiles that are distinct from normal donor cells. However, open questions regarding heparan sulfate structural changes in AML remain. A landmark study using single-chain variable fragment antibodies targeting differentially modified heparan sulfate showed that distinct heparan sulfate patterns can identify hematopoietic cells primed for different lineages ( 47 ). However, heparan sulfate chains are between 40 and 300 sugar residues long, and sulfate modifications and patterning are both important to coordinate growth factor signaling ( 12 , 17 ). While disaccharide analysis can provide insight into which modifications are present, tools have yet to be created to depict heparan sulfate patterning and chain length with sufficient resolution to accurately discern complete structural motifs. The field will benefit from more robust mass spectrometry methods to characterize the structures of entire heparan sulfate chains, another layer of information that likely informs AML cell responses to chemotherapy. Declarations COMPETING INTERESTS : The authors declare no competing interests. AUTHORSHIP CONTRIBUTIONS Project conception and supervision (CMT), performed experiments (KAW, DP, NJS, MWH), analyzed data (KAW, DP, JHP, SM, CMT), wrote the manuscript with input from all authors (KAW and CMT). ACKNOWLEDGEMENTS: We are grateful to Cyd McKay, Christina Root, and Alex Hastie for their assistance with animal studies, Taylor Billings for performing cell line authentication, and Logan Wallace and Rhonda Ries for help procuring data for initial TARGET analyses. We would like to acknowledge the excellent assistance provided by the Fred Hutchinson Cancer Center Comparative Medicine, Experimental Histopathology, Flow Cytometry, and Genomics & Bioinformatics Shared Resources (Pritha Chanana). We are grateful for the technical assistance provided by Biswa Choudhury from the UCSD GlycoAnalytics Core. This investigation was supported by 5K01DK126989 from the National Institutes of Health (CMT), a pilot award from Safeway, Inc. via the Fred Hutch Cancer Consortium and a Scholar Award from the V Foundation for Cancer Research (V2024-028). KAW was supported by a Fellowship from the Fred Hutchinson Cancer Center Office of Faculty Affairs and the Graduate Research Fellowship Program from the National Science Foundation (DGE-2140004). This research was supported by NIH P30 CA015704 to the Fred Hutch/University of Washington/Seattle Children's Cancer Consortium, which includes the Comparative Medicine, Experimental Histopathology, Flow Cytometry, and Genomics & Bioinformatics Shared Resources. The results published here are in part based upon data generated by the Therapeutically Applicable Research to Generate Effective Treatments ( https://www.cancer.gov/ccg/research/genome-sequencing/target ) initiative, phs000218. The data used for this analysis are available at the Genomic Data Commons ( https://portal.gdc.cancer.gov ). This manuscript is the result of funding in whole or in part by the National Institutes of Health (NIH). It is subject to the NIH Public Access Policy. 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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-8725671","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":588568599,"identity":"ef3b700e-efb2-4f21-ba04-c9f7526a4e1e","order_by":0,"name":"Christina Termini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3PsQrCMBCA4ZTAuaRkTdGHSCl0cfBVFEGXDo5OEilkEueCiK/hWAlk0t1BUBHcBNFF1EGt4mS1biL5t8B95A4hk+ln44hQHCNki+uDfZqGB3FkOSEsI7kpnZXQQah308as4Gl7udgMVYv2hLVqHtMJ01DtBXxNfJ3z3P5YMTaLsTvpphMOxMMBV8SfS8jbUjHOyuC0O+8I3SfEk5A7ZSQEJ4QDAH4ScXh3S+2+2PUo7PRl3YmmldBti3RCQ7XCwVmVqARru5FFSqPqaCnO6eRVlkCW/I7c+vIXk8lk+usuxLFJ1nK6FyUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1831-1347","institution":"Fred Hutch Cancer Centre","correspondingAuthor":true,"prefix":"","firstName":"Christina","middleName":"","lastName":"Termini","suffix":""},{"id":588568600,"identity":"acb33a3b-e4b0-4b9f-aeb7-5b83f50942c2","order_by":1,"name":"Kelsey Woodruff","email":"","orcid":"https://orcid.org/0000-0003-1923-1813","institution":"Fred Hutch Cancer Centre","correspondingAuthor":false,"prefix":"","firstName":"Kelsey","middleName":"","lastName":"Woodruff","suffix":""},{"id":588568601,"identity":"f3fb6385-f529-48f7-9161-e5f7052c35fa","order_by":2,"name":"Diya Patel","email":"","orcid":"","institution":"Fred Hutchinson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Diya","middleName":"","lastName":"Patel","suffix":""},{"id":588568602,"identity":"a18680e4-743a-46e6-a62d-03d17723bf93","order_by":3,"name":"Jack Peplinski","email":"","orcid":"https://orcid.org/0000-0001-5097-0652","institution":"Fred Hutchinson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Jack","middleName":"","lastName":"Peplinski","suffix":""},{"id":588568603,"identity":"5070a270-364f-4428-9eb4-baf03ae6d0dd","order_by":4,"name":"Nicollette Setiawan","email":"","orcid":"","institution":"Fred Hutchinson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Nicollette","middleName":"","lastName":"Setiawan","suffix":""},{"id":588568604,"identity":"fb8a8b84-1b69-48ac-b5bf-eca1a6b4b0df","order_by":5,"name":"Matthew Hagen","email":"","orcid":"https://orcid.org/0000-0002-9812-2262","institution":"Fred Hutchinson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Hagen","suffix":""},{"id":588568605,"identity":"8d687d3a-0976-4acb-8940-82aa3fe16fa2","order_by":6,"name":"Soheil Meshinchi","email":"","orcid":"https://orcid.org/0000-0002-6276-4423","institution":"Fred Hutchinson Cancer Research Center","correspondingAuthor":false,"prefix":"","firstName":"Soheil","middleName":"","lastName":"Meshinchi","suffix":""}],"badges":[],"createdAt":"2026-01-29 00:25:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8725671/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8725671/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102597713,"identity":"fb015419-9820-4302-9c7b-6255f171dee7","added_by":"auto","created_at":"2026-02-13 12:26:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":572951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeparan sulfate is dysregulated at the transcriptional and glycan level in AML patients. (A) \u003c/strong\u003eDiagram depicting heparan sulfate chain modifications performed by \u003cem\u003eHS2ST1, HS3ST1, HS6ST1, \u003c/em\u003eand \u003cem\u003eNDST1\u003c/em\u003e.\u003cstrong\u003e (B) \u003c/strong\u003eExpression of \u003cem\u003eHS2ST1, HS6ST1, HS3ST1, \u003c/em\u003eand \u003cem\u003eNDST1 \u003c/em\u003ein normal vs AML bone marrow analyzed from the TARGET AML database. (\u003cem\u003en=68 NBM, n=2072 AML, n=466 KMT2A-r, n=350 FLT3-ITD; statistics denote Wilcoxon tests followed by Benjamin Hochberg adjustments; *p\u0026lt;0.05, **p\u0026lt;0.01, ****p\u0026lt;0.0001\u003c/em\u003e). \u003cstrong\u003e(C) \u003c/strong\u003eTotal heparan sulfate, \u003cstrong\u003e(D) \u003c/strong\u003ethe fraction of HS bearing 0, 1, 2, or 3 sulfate modifications, and \u003cstrong\u003e(E) \u003c/strong\u003ethe\u003cstrong\u003e \u003c/strong\u003efraction of unsulfated, \u003cem\u003eN\u003c/em\u003e-, 2-\u003cem\u003eO\u003c/em\u003e, and 6-\u003cem\u003eO \u003c/em\u003esulfated HS was quantified using liquid chromatography mass-spectrometry in normal and AML PBMCs. (\u003cem\u003en=6 normal PBMC, n=5 AML PBMC; Statistics denote an unpaired t-test or multiple unpaired t-tests followed by a Holm-Šídák correction; *p\u0026lt;0.05\u003c/em\u003e). \u003cstrong\u003e(F) \u003c/strong\u003eSchematic depicting possible sulfation combinations on heparan sulfate disaccharides. \u003cstrong\u003e(G) \u003c/strong\u003eSpecific heparan sulfate disaccharide composition in PBMCs isolated from normal or AML individuals, and, at right averaged disaccharide composition among analyzed specimens. (\u003cem\u003en=6 normal PBMC, n=5 AML PBMC; error bars show SEM; statistics denote a MANOVA; *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"F1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8725671/v1/cfd9353b4ed0175e51ab9cbb.jpg"},{"id":102597720,"identity":"534deffc-a80c-419d-9bd2-20b1f75dffce","added_by":"auto","created_at":"2026-02-13 12:26:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":737967,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eHS6ST1 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eexpression predicts poor event-free survival in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eKMT2A-\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003erearranged AML. \u003c/strong\u003eTARGET patient event free survival data across leukemia subtypes was analyzed based on above-median (high) or below-median (low) expression of \u003cstrong\u003e(A)\u003c/strong\u003e \u003cem\u003eHS2ST1\u003c/em\u003e, \u003cstrong\u003e(B)\u003c/strong\u003e\u003cem\u003e HS6ST1\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e\u003cstrong\u003e(C)\u003c/strong\u003e\u003cem\u003eHS3ST1\u003c/em\u003e,\u003cem\u003e \u003c/em\u003eor \u003cstrong\u003e(D)\u003c/strong\u003e \u003cem\u003eNDST1 \u003c/em\u003ein bone marrow mononuclear cells from \u003cem\u003ede novo\u003c/em\u003e patients (\u003cem\u003en=1,874 AML patients, statistics denote the log-rank test\u003c/em\u003e)\u003cem\u003e \u003c/em\u003eTARGET patient event free survival data among patients with \u003cem\u003eKMT2A\u003c/em\u003e-rearranged AML was analyzed based on above-median (high) or below-median (low) expression of \u003cstrong\u003e(E)\u003c/strong\u003e \u003cem\u003eHS2ST1\u003c/em\u003e, \u003cstrong\u003e(F)\u003c/strong\u003e\u003cem\u003eHS6ST1\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e\u003cstrong\u003e(G)\u003c/strong\u003e\u003cem\u003e HS3ST1\u003c/em\u003e,\u003cem\u003e \u003c/em\u003eor \u003cstrong\u003e(H)\u003c/strong\u003e \u003cem\u003eNDST1\u003c/em\u003e.\u003cem\u003e(n=412 KMT2A-rearranged AML patients, statistics denote log-rank tests\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"F2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8725671/v1/47393c113fec35583ddcf96d.jpg"},{"id":102597797,"identity":"c6691e5c-1d41-4673-8140-cd77d700f47b","added_by":"auto","created_at":"2026-02-13 12:26:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":938432,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAblating \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eHS2ST1 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eHS6ST1 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003etranscriptionally reprograms AML cells. (A)\u003c/strong\u003e Principal component analysis generated from bulk RNA sequencing data from sgControl, sg\u003cem\u003eHS2ST1, \u003c/em\u003eand sg\u003cem\u003eHS6ST1 \u003c/em\u003eMOLM-13 cells; each point represents one technical replicate. Volcano plot showing differentially expressed genes comparing sgControl to \u003cstrong\u003e(B)\u003c/strong\u003esg\u003cem\u003eHS2ST1 \u003c/em\u003eand \u003cstrong\u003e(C)\u003c/strong\u003e sg\u003cem\u003eHS6ST1\u003c/em\u003e MOLM-13 cells. Pathways in the molecular function gene set significantly differently expressed in \u003cstrong\u003e(D)\u003c/strong\u003esg\u003cem\u003eHS2ST1 \u003c/em\u003eand \u003cstrong\u003e(E)\u003c/strong\u003e sg\u003cem\u003eHS6ST1 \u003c/em\u003ecells compared to sgControl cells using Gene Set Enrichment Analysis. Bolded terms indicate pathways related to known heparan sulfate functions. Leading edge analysis of \u003cstrong\u003e(F)\u003c/strong\u003ecytokine mediated signaling pathways, \u003cstrong\u003e(G)\u003c/strong\u003e glycosaminoglycan binding, \u003cstrong\u003e(H)\u003c/strong\u003egrowth factor binding, and \u003cstrong\u003e(I)\u003c/strong\u003e plasma membrane signaling receptor complex gene sets. Pathways in the hallmark gene set significantly differently expressed in \u003cstrong\u003e(J)\u003c/strong\u003e sg\u003cem\u003eHS2ST1 \u003c/em\u003eand \u003cstrong\u003e(K)\u003c/strong\u003e sg\u003cem\u003eHS6ST1 \u003c/em\u003eMOLM-13 cells compared to sgControl MOLM-13 cells. (\u003cem\u003en=3 biological replicates; statistics show adjusted p-values\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"F3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8725671/v1/1972510e226bfbe79ca8dcda.jpg"},{"id":102597722,"identity":"88a65ab5-45d0-4706-baf6-6888863f6c9b","added_by":"auto","created_at":"2026-02-13 12:26:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":608181,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDepletion of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eHS2ST1 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003epromotes AML burden \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vivo. \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Experimental design for \u003cem\u003ein vivo \u003c/em\u003eAML burden study. \u003cstrong\u003e(B)\u003c/strong\u003eMicrographs of femurs, livers, and spleens of mice at day +14 after transplant with sgControl, sg\u003cem\u003eHS2ST1\u003c/em\u003e, or sg\u003cem\u003eHS6ST1\u003c/em\u003e MOLM-13 cells stained with an anti-human CD33 antibody. Scale bar = 100 µm. Area fraction quantification of human CD33+ leukemic burden in \u003cstrong\u003e(C)\u003c/strong\u003e femurs, \u003cstrong\u003e(D)\u003c/strong\u003e livers, and \u003cstrong\u003e(E) \u003c/strong\u003espleens (n\u003cem\u003e=4-5 mice per organ, statistics denote Brown-Forsythe and Welch ANOVA with Dunnet correction; error bars denote SEM\u003c/em\u003e). \u003cstrong\u003e(F)\u003c/strong\u003eRepresentative flow cytometry scatter plots and \u003cstrong\u003e(G) \u003c/strong\u003equantification of peripheral blood human CD33 burden from xenografts at day +14. (n=13 mice) \u003cstrong\u003e(H)\u003c/strong\u003eRepresentative flow cytometry scatter plots and \u003cstrong\u003e(I)\u003c/strong\u003e quantification of bone marrow human CD33 burden from xenograft at day +14. (n\u003cem\u003e=13 mice; statistics denote one-way ANOVAs with Holm-Sidak corrected t-tests; **p\u0026lt;0.01; error bars denote SEM\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"F4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8725671/v1/0424c020673cd3e2d1971d27.jpg"},{"id":102597787,"identity":"82b74064-4d40-4a4e-ad30-275c94597fdb","added_by":"auto","created_at":"2026-02-13 12:26:34","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":626613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCytarabine treatment remodels heparan sulfotransferases gene expression in AML. \u003c/strong\u003eTARGET AML patient relapse risk for patients with \u003cem\u003eKMT2A-\u003c/em\u003erearranged AML based on above-median (high) or below-median (low) expression of \u003cstrong\u003e(A)\u003c/strong\u003e \u003cem\u003eHS2ST1,\u003c/em\u003e \u003cstrong\u003e(B)\u003c/strong\u003e\u003cem\u003eHS6ST1, \u003c/em\u003e\u003cstrong\u003e(C)\u003c/strong\u003e\u003cem\u003e HS3ST1, \u003c/em\u003eor \u003cstrong\u003e(D)\u003c/strong\u003e \u003cem\u003eNDST1 (n=286 KMT2A-rearranged AML patients). \u003c/em\u003eExpression of \u003cem\u003eHS2ST1, HS6ST1, HS3ST1, \u003c/em\u003eand \u003cem\u003eNDST1 \u003c/em\u003eafter a 24-hour treatment with 0.5 µM Ara-C in \u003cstrong\u003e(E)\u003c/strong\u003e MOLM-13, \u003cstrong\u003e(F)\u003c/strong\u003e Kasumi-1, or \u003cstrong\u003e(G)\u003c/strong\u003e THP-1 cells. Gene expression was normalized to the vehicle-treated sample (dotted line). (\u003cem\u003en=3-9 biological replicates, Statistics denote an unpaired t-test comparing Ara-C treated samples to vehicle treatment; *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, ****p\u0026lt;0.0001; error bars denote SEM\u003c/em\u003e). Liquid chromatography mass spectrometry analysis for specific heparan sulfate disaccharides expressed by \u003cstrong\u003e(H)\u003c/strong\u003e MOLM-13, \u003cstrong\u003e(I)\u003c/strong\u003eKasumi-1, and \u003cstrong\u003e(J)\u003c/strong\u003e THP-1 cells after a 72-hour treatment with DMSO or 0.5 µM Ara-C. (\u003cem\u003en=2-3 biological replicates per condition per cell line; statistics denote nested ANOVAs; *p\u0026lt;0.05; error bars denote SEM\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"F5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8725671/v1/c6d2eda6bc1b58adde7889c5.jpg"},{"id":102746799,"identity":"9c900a7d-d022-4c03-ad13-c1f6102ce70e","added_by":"auto","created_at":"2026-02-16 09:01:23","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":809142,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eHS6ST1 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003epromotes cytarabine resistance in MOLM-13 cells. (A) \u003c/strong\u003eRepresentative flow cytometry scatter plots depicting Annexin-V/7-AAD staining of sgControl, sg\u003cem\u003eHS2ST1, \u003c/em\u003eand sg\u003cem\u003eHS6ST1 \u003c/em\u003eMOLM-13 cells treated with DMSO or 0.5 µM Ara-C for 72 hours and quantification of live, apoptotic, and necrotic cells in \u003cstrong\u003e(B)\u003c/strong\u003evehicle-treated MOLM-13 cells or \u003cstrong\u003e(C)\u003c/strong\u003e Ara-C-treated MOLM-13 cells (\u003cem\u003en=6 biological replicates across n=3 independent experiments\u003c/em\u003e). \u003cstrong\u003e(D)\u003c/strong\u003e Ara-C IC50 curves and \u003cstrong\u003e(E)\u003c/strong\u003e quantification from sgControl, sg\u003cem\u003eHS2ST1, \u003c/em\u003eand sg\u003cem\u003eHS6ST1 \u003c/em\u003eMOLM-13 cells (\u003cem\u003en=3 independent experiments, statistics denote a one-way ANOVA with Holm-Šidák correction; *p\u0026lt;0.05\u003c/em\u003e). \u003cstrong\u003e(F)\u003c/strong\u003eRepresentative flow cytometry scatter plots from MOLM-13 cells treated with DMSO, 0.5 µM Ara-C, 40 µM surfen, or both for 72-hours and analyzed using Annexin-V/7-AAD staining, and \u003cstrong\u003e(G)\u003c/strong\u003e quantification (n=6 biological replicates across 3 independent experiments). \u003cstrong\u003e(H)\u003c/strong\u003e Representative flow cytometry scatter plots from THP-1 cells treated with DMSO, 0.5 µM Ara-C, 40 µM surfen, or both for 72-hours and analyzed using Annexin-V/7-AAD staining, and \u003cstrong\u003e(I)\u003c/strong\u003equantification (\u003cem\u003en=6 biological replicates across 3 independent experiments; statistics denote a one-way ANOVA with Holm-Šidák correction; *p\u0026lt;0.05, ****p\u0026lt;0.0001; error bars denote SEM\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"F6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8725671/v1/3602cbde73dffcc1be68782e.jpg"},{"id":102597815,"identity":"2866c83a-31a8-446b-8241-1f1b25cc2fc4","added_by":"auto","created_at":"2026-02-13 12:26:35","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":911353,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eHS6ST1 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003epromotes TGF-β1 signaling and chemotherapy resistance in AML. (A)\u003c/strong\u003e Representative flow cytometry histograms depicting phospho-SMAD2/3 expression in sgControl, sg\u003cem\u003eHS2ST1\u003c/em\u003e, and sg\u003cem\u003eHS6ST1 \u003c/em\u003eMOLM-13 cells treated with 5 ng/mL TGF-β1 or vehicle for 2 hours and \u003cstrong\u003e(B)\u003c/strong\u003e quantification. \u003cstrong\u003e(C)\u003c/strong\u003e Representative flow cytometry histograms depicting phospho-SMAD2/3 expression in sgControl, sg\u003cem\u003eHS2ST1\u003c/em\u003e, and sg\u003cem\u003eHS6ST1 \u003c/em\u003eMOLM-13 cells treated with 0.5 µM Ara-C or vehicle for 24 hours and \u003cstrong\u003e(D)\u003c/strong\u003e quantification (\u003cem\u003en=4-6 biological replicates across 2-3 independent experiments, statistics denote a two-way ANOVA with Holm-Šidák corrected t-tests; *p\u0026lt;0.05, ***p\u0026lt;0.001\u003c/em\u003e). \u003cstrong\u003e(E)\u003c/strong\u003e Representative flow cytometry histograms depicting CellTrace Violet dye levels in sgControl, sg\u003cem\u003eHS2ST1, \u003c/em\u003eand sg\u003cem\u003eHS6ST1\u003c/em\u003e MOLM-13\u003cem\u003e \u003c/em\u003ecells stained with CellTrace Violet and grown in complete media or complete media + 5 ng/mL TGF-β1 for 72 hours. \u003cstrong\u003e(F)\u003c/strong\u003eProliferation index was calculated from CellTrace Violet data using FlowJo V10.10 (\u003cem\u003en=6 biological replicates across 3 independent experiments; Statistics denote a two-way ANOVA with Holm-Šidák t-tests; **p\u0026lt;0.01, ****p\u0026lt;0.0001\u003c/em\u003e). \u003cstrong\u003e(G)\u003c/strong\u003e Representative flow cytometry scatter plots for sgControl, sg\u003cem\u003eHS2ST1, \u003c/em\u003eand sg\u003cem\u003eHS6ST1 \u003c/em\u003eMOLM-13\u003cem\u003e \u003c/em\u003ecells treated with DMSO, 0.5 µM Ara-C, 5 ng/mL TGF-β1, or both Ara-C and TGF-β1 for 72-hours and stained using Annexin-V/7-AAD and (\u003cstrong\u003eH)\u003c/strong\u003e analysis of live, apoptotic, and necrotic cells (\u003cem\u003en=6 biological replicates across 3 independent experiments; statistics denote a two-way ANOVA with Holm-Šidák correction; *p\u0026lt;0.05, ****p\u0026lt;0.0001; error bars denote SEM\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"F7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8725671/v1/024d7d9655ae7bde3f452376.jpg"},{"id":106724079,"identity":"7ce551de-0a15-4fc3-96d2-78bb7b1a18c1","added_by":"auto","created_at":"2026-04-12 18:25:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6554863,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8725671/v1/6598f155-9443-4494-9865-a20e2ea37a59.pdf"},{"id":102597721,"identity":"a5435619-51c0-44a8-a9eb-f6eaab5c1cde","added_by":"auto","created_at":"2026-02-13 12:26:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1563957,"visible":true,"origin":"","legend":"Supplemental material","description":"","filename":"SupplementalMaterialCombined.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8725671/v1/0c825a4ed48352e63452e870.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"HS6ST1 regulates acute myeloid leukemia chemotherapy resistance via TGF-β1 signaling","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAcute myeloid leukemia (AML) is the deadliest blood cancer, with a 5-year overall survival rate stagnating around 30% (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). One aggressive form of AML occurs when patients harbor fusions of the lysine methyltransferase 2A (\u003cem\u003eKMT2A\u003c/em\u003e) gene with various partners (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Daunorubicin or idarubicin and cytarabine (Ara-C) are frontline chemotherapeutic agents for AML (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). While many patients achieve remission following initial chemotherapy treatments, disease relapse remains high because of the persistence of drug resistant cells that can expand following treatment (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe AML growth factor milieu influences disease progression and AML chemotherapy resistance (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Heparan sulfate proteoglycans are transmembrane proteins that facilitate growth factor signaling in normal and malignant cells (\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Heparan sulfate proteoglycans bear glycan chains that are composed of repeating disaccharide units (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Heparan sulfates can be modified by the addition of negatively charged sulfate moieties at the \u003cem\u003eN-\u003c/em\u003e, 2-\u003cem\u003eO\u003c/em\u003e, 6-\u003cem\u003eO\u003c/em\u003e, or 3\u003cem\u003e-O\u003c/em\u003e positions. Sulfation modifications are catalyzed by enzymes encoded by the genes \u003cem\u003eNDST1-4, HS2ST1, HS6ST1-3\u003c/em\u003e, or \u003cem\u003eHS3ST1-7\u003c/em\u003e, respectively (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The type and amount of sulfate modifications present on the glycan chain influences heparan sulfate-growth factor interactions and signaling (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Heparan sulfate proteoglycans and heparan sulfate modifications have important roles in cancer cell adhesion, proliferation, migration, drug resistance, and vascularization (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Recent work identified syndecan-2 as an important regulator of hematopoietic stem cell quiescence via TGF-β1 signaling, and other work has demonstrated that heparan sulfate structure is important for B-cell maturation (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Several cytokines important for normal and malignant hematopoiesis, including TGF-β1, CXCL12, FGF1 and 2, and PDGF, bind heparan sulfate (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, the impact of precise heparan sulfation patterns in AML is largely undefined.\u003c/p\u003e \u003cp\u003eIn this study, we show that heparan sulfation is dysregulated at the transcript and glycan levels in AML cells compared to normal hematopoietic cells. We identify an association between high \u003cem\u003eHS6ST1\u003c/em\u003e expression and poor survival outcomes for \u003cem\u003eKMT2A-\u003c/em\u003erearranged AML patients. Using CRISPR-edited MOLM-13 cells, we demonstrate that \u003cem\u003eHS6ST1\u003c/em\u003e is crucial for AML cell survival in response to Ara-C, and this occurs via TGF-β1 signaling. Our data highlights the critical function of heparan sulfation in AML, enabling us to expand current models of chemotherapy resistance by incorporating this crucial glycan modification.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e \u003cstrong\u003eStudy resources\u003c/strong\u003e \u003cp\u003eDetailed information for resources used throughout this study is included in \u003cb\u003eSupplementary Table\u0026nbsp;1.\u003c/b\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePatients and Samples\u003c/strong\u003e \u003cp\u003e Samples were obtained from 2072 children and young adults (age 0\u0026ndash;29 years) enrolled in clinical trials CCG-2961 (NCT00002798, n\u0026thinsp;=\u0026thinsp;81), AAML03P1 (NCT00070174, n\u0026thinsp;=\u0026thinsp;121), AAML0531 (NCT00372593, n\u0026thinsp;=\u0026thinsp;795), and AAML1031 (NCT00372593, n\u0026thinsp;=\u0026thinsp;1075) with written, informed consent collected from patients and their legal guardians in accordance with the Declaration of Helsinki. Each protocol was approved by the National Cancer Institute's central institutional review board (IRB) and the local IRB for each participating institution. Clinical data were available for all 2072 patients, with 1874 of those patients also having accompanying survival and transcriptomic data, and analyses were performed for that cohort with complete data. 68 normal bone marrow (NBM) samples were used as controls for expression analysis.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExpression Analysis\u003c/strong\u003e \u003cp\u003eBatch-corrected mRNA data aligned to GRCh38 with STAR was used. Resulting normalized gene counts were converted into transcripts per million. Violin plots were generated with log10(TPM) using ggplot2 (v3.4.2). Wilcoxon test with Benjamini Hochberg adjustment was used to determine significance between AML subtypes and NBM expression levels for Heparan sulfation genes \u003cem\u003eHS2ST1\u003c/em\u003e (ENSG00000153936), \u003cem\u003eHS6ST1\u003c/em\u003e (ENSG00000136720), \u003cem\u003eHS3ST1\u003c/em\u003e (ENSG00000002587), and \u003cem\u003eNDST1\u003c/em\u003e (ENSG00000070614).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSurvival Analysis\u003c/strong\u003e \u003cp\u003eKaplan-Meier survival curves were generated using the survival (v3.5-5) and survminer (v0.4.9) packages in R (v. 4.3.2). Survival times were calculated from time of diagnosis. Competing events such as death or induction failure were removed from cumulative incidence calculations to determine relapse risk. Survival curves were generated by binning samples into above (high) and below (low) median expression of the gene of interest.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGlycosaminoglycan profiling\u003c/strong\u003e \u003cp\u003eCryopreserved de-identified peripheral blood specimens from AML patients were obtained from the Fred Hutchinson Cancer Center/University of Washington Hematopoietic Diseases Repository. All participants provided written informed consent in accordance with the Declaration of Helsinki under the oversight of the Fred Hutch Institutional Review Office. Preparation and analysis was performed by the University of California San Diego GlycoAnalytics Core as previously reported (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMouse models\u003c/strong\u003e \u003cp\u003eAnimal procedures were performed in accordance with the Fred Hutchinson Cancer Center Institutional Animal Care \u0026amp; Use Committee (PROTO2100049). Mice were housed and maintained in the Fred Hutch Comparative Medicine facility; mixed-sex adult mice 8-12-weeks of age were used for all studies. Mice were bred in house or purchased from the Fred Hutch Translational Research Model Services core.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCell culture\u003c/strong\u003e \u003cp\u003eMOLM-13, THP-1, and Kasumi-1 cells were obtained from the American Type Culture Collection and maintained per the manufacturer\u0026rsquo;s instructions. Cell lines were authenticated using the CLA IdentiFiler Plus PCR Amplification Kit.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCell line xenografts\u003c/strong\u003e \u003cp\u003eMice were irradiated using a Mark 1 cesium irradiator (225 cGy). The following day, mice were intravenously injected with 1x10\u003csup\u003e6\u003c/sup\u003e MOLM-13 cells via tail vein. For leukemic burden studies, 14 days post-injection, mice were euthanized and bone marrow was isolated from one femur, lysed with ACK buffer, and processed in complete IMDM (IMDM\u0026thinsp;+\u0026thinsp;10% FBS\u0026thinsp;+\u0026thinsp;1% penicillin-streptomycin). For homing assays, 16 hours post-injection, bone marrow was isolated from two femurs and two tibias, lysed with ACK buffer, and processed in complete IMDM. Spleens were harvested, tissue was dissociated, lysed with ACK buffer, and processed in complete IMDM. Peripheral blood was collected into EDTA immediately prior to euthanasia, lysed using ACK buffer, and processed in 10% FBS/PBS. Lysed cells were stained using antibodies or isotype controls (\u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e) and analyzed by flow cytometry (LSRFortessa X-50 or BD FACSymphony A5). Data were analyzed using FlowJo software (v10.10.0). Frequencies are displayed as percent of live cells. Investigators were not blinded. No randomization was used. Sample sizes were estimated using power analyses to quantify the number of replicates needed to achieve at least 80% power and detect a ratio of 1.2 vs. null hypothesis of 1.0 with significance level of α\u0026thinsp;=\u0026thinsp;0.013.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHistology\u003c/strong\u003e \u003cp\u003eOrgans were formalin fixed for 72 hours and washed with PBS. Femurs were decalcified for 14 days (0.5M EDTA, 4\u0026deg;C). Samples were paraffin-embedded and sectioned at 4 \u0026micro;m. Sections were stained with anti-human CD33 with nuclear counterstain. Broad regions of interest encompassing the full section (spleen and liver) or BM compartment, excluding the epiphysis (bone), were defined by a single observer. Primary classifiers were trained to exclude glass, fold and tear artifacts and (bone only) cortical bone. In the remaining tissue, the CD33\u003csup\u003e+\u003c/sup\u003e area fraction was determined using the areaquant analysis plugin within HALO software (v3.6.4134).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLentiviral transductions\u003c/strong\u003e \u003cp\u003eA 24-well plate was coated with retronectin (50 \u0026micro;g, 2 hours) and blocked for 30 minutes (2% BSA). 5x10\u003csup\u003e4\u003c/sup\u003e MOLM-13 cells were seeded on the retronectin-coated wells and infected with lentiviral vectors containing guide RNAs targeting Control, \u003cem\u003eHS2ST1\u003c/em\u003e, or \u003cem\u003eHS6ST1\u003c/em\u003e (MOI\u0026thinsp;=\u0026thinsp;25) (Supplementary Table\u0026nbsp;1). Cells were spin occulated (30 minutes, 1000 rpm, 32\u0026deg;C) and incubated for two days before the media was changed. 1\u0026ndash;2 weeks after transduction, GFP\u003csup\u003e+\u003c/sup\u003e cells were sorted using a BD FACSymphony S6 and expanded.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e\u003cem\u003eIn vitro\u003c/em\u003e chemotherapy treatment\u003c/strong\u003e \u003cp\u003e2.5x10\u003csup\u003e5\u003c/sup\u003e MOLM-13 cells were seeded in complete RPMI supplemented with DMSO vehicle or Ara-C to a final concentration of 0.5 \u0026micro;M and incubated at 37\u0026deg;C, 5% CO\u003csub\u003e2\u003c/sub\u003e for 24 or 72 hours. Cells were then stained in 1X Annexin binding buffer with fluorochrome conjugated Annexin V and 7-AAD and analyzed via flow cytometry. For surfen experiments, cells were treated with surfen to a final concentration of 40 \u0026micro;M. For TGFβ-1 co-treatment experiments, cells were treated with recombinant human TGFβ-1 to a final concentration of 5 ng/mL.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRT-qPCR and RNA Sequencing\u003c/b\u003e: RNA was isolated using the Qiagen RNeasy Micro Kit. For RT-qPCR analyses, RNA was reverse transcribed using the Applied Biosystems High-Capacity cDNA Reverse Transcription Kit. Gene expression was analyzed using an Applied Biosystems QuantStudio 5 PCR machine. For RNA sequencing, library preparation and sequencing was performed using a NextSeq 2000 P2-100. Gene set enrichment analysis was performed against the C5: Ontology Gene Sets. Data are deposited in GEO (GSE314673).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIntracellular flow cytometry\u003c/strong\u003e \u003cp\u003eCells were stained with BD Fixable Viability Stain, washed, and fixed with BD Fixation/Permeabilization solution. Cells were stained with primary and secondary antibodies, washed, and analyzed via flow cytometry. Mean fluorescence intensity (MFI) was calculated using FlowJo.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCellTrace Violet\u003c/strong\u003e \u003cp\u003e2.5x10\u003csup\u003e5\u003c/sup\u003e MOLM-13 cells were stained with CellTrace Violet according to manufacturer instructions, seeded in complete RPMI, and treated with either vehicle or human TGF-β1 to a final concentration of 5 ng/mL. Cells were incubated at 37\u0026deg;C, 5% CO\u003csub\u003e2\u003c/sub\u003e for 72 hours, stained with 7-AAD and analyzed via flow cytometry.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eWestern Blotting\u003c/strong\u003e \u003cp\u003e1x10\u003csup\u003e7\u003c/sup\u003e MOLM-13 cells were harvested and lysed in RIPA buffer supplemented with protease and phosphatase inhibitors. 10\u0026ndash;25 \u0026micro;g of each sample was separated using gel electrophoresis and transferred onto a PVDF membrane. Membranes imaged using a Li-Cor Odyssey system after antibody staining. Quantification was performed using the Image Studio Lite software (v5.2.5).\u003c/p\u003e \u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eHeparan sulfate is dysregulated at the transcriptional and glycan scales in AML\u003c/h2\u003e \u003cp\u003eWe first assessed the expression of heparan sulfotransferase genes \u003cem\u003eHS2ST1\u003c/em\u003e, \u003cem\u003eHS3ST1\u003c/em\u003e, \u003cem\u003eHS6ST1\u003c/em\u003e, and \u003cem\u003eNDST1\u003c/em\u003e in healthy individuals and AML patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Bulk RNA sequencing revealed significantly lower \u003cem\u003eHS2ST1\u003c/em\u003e and \u003cem\u003eHS3ST1\u003c/em\u003e expression in AML patient bone marrow mononuclear cells than normal bone marrow (NBM) mononuclear cells, while \u003cem\u003eHS6ST1\u003c/em\u003e and \u003cem\u003eNDST1\u003c/em\u003e expression were similar among these groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). AML patients with \u003cem\u003eKMT2A-\u003c/em\u003erearrangements and \u003cem\u003eFLT3-\u003c/em\u003eITD mutations expressed significantly less \u003cem\u003eHS2ST1\u003c/em\u003e and \u003cem\u003eHS3ST1\u003c/em\u003e than NBM cells. In contrast, patients harboring \u003cem\u003eFLT3-\u003c/em\u003eITD mutations expressed significantly more \u003cem\u003eHS6ST1\u003c/em\u003e compared to NBM, while \u003cem\u003eNDST1\u003c/em\u003e expression was similar between these groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These data suggest that the transcriptional profile of heparan sulfotransferase genes differs in AML and NBM cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next used liquid chromatography mass spectrometry to analyze heparan sulfate modifications of peripheral blood mononuclear cells (PBMCs) from normal patients and AML patients. AML patient characteristics are detailed in \u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e. Total heparan sulfate amounts were similar in AML and normal cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Each heparan sulfate disaccharide can bear zero, one, two, or three sulfate groups. Heparan sulfate disaccharides containing three sulfate groups were less frequent in AML PBMCs compared to normal PBMCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). AML PBMCs had lower fractions of \u003cem\u003eN-\u003c/em\u003e and 2\u003cem\u003e-O\u003c/em\u003e heparan sulfate compared to normal PBMCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). AML PBMCs had significantly less D0S0 \u003cem\u003eN-\u003c/em\u003emonosulfated and D2S6 trisulfated disaccharides and significantly more D0A0 unsulfated and D0A6 6-\u003cem\u003eO\u003c/em\u003e monosulfated disaccharides compared to normal PBMCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF-G). Taken together, these data suggest that AML cells express distinct heparan sulfate landscapes with fewer sulfate modifications compared to normal cells.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIncreased\u003c/b\u003e \u003cb\u003eHS6ST1\u003c/b\u003e \u003cb\u003eexpression is associated with worse survival outcomes in\u003c/b\u003e \u003cb\u003eKMT2A-\u003c/b\u003e\u003cb\u003erearranged AML patients\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe next assessed whether heparan sulfotransferase gene expression is associated with differential AML patient outcomes. We classified TARGET AML patients according to their bone marrow expression of \u003cem\u003eHS2ST1\u003c/em\u003e, \u003cem\u003eHS6ST1\u003c/em\u003e, \u003cem\u003eHS3ST1\u003c/em\u003e, and \u003cem\u003eNDST1\u003c/em\u003e relative to the cohort median. Among all AML patients, individuals with lower \u003cem\u003eHS2ST1\u003c/em\u003e expression had significantly worse event-free survival than those with high \u003cem\u003eHS2ST1\u003c/em\u003e expression, but overall survival outcomes were similar (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; \u003cb\u003eSupplemental Fig.\u0026nbsp;1A\u003c/b\u003e). Overall survival (\u003cb\u003eSupplemental Fig.\u0026nbsp;1B-D\u003c/b\u003e) and event-free survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-D) were similar in AML patients regardless of \u003cem\u003eHS6ST1, HS3ST1\u003c/em\u003e, or \u003cem\u003eNDST1\u003c/em\u003e expression levels. However, among AML patients with \u003cem\u003eKMT2A\u003c/em\u003e-rearrangements, increased \u003cem\u003eHS6ST1\u003c/em\u003e expression correlated with significantly worse event-free and overall survival compared to patients expressing less \u003cem\u003eHS6ST1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF; \u003cb\u003eSupplemental Fig.\u0026nbsp;1F\u003c/b\u003e). \u003cem\u003eHS2ST1, HS3ST1\u003c/em\u003e, and \u003cem\u003eNDST1\u003c/em\u003e expression did not stratify patients harboring \u003cem\u003eKMT2A\u003c/em\u003e-rearrangements according to differential survival outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, G-H; \u003cb\u003eSupplemental Fig.\u0026nbsp;1E, G-H\u003c/b\u003e). These data indicate that increased \u003cem\u003eHS6ST1\u003c/em\u003e expression correlates with worse survival outcomes in AML patients with \u003cem\u003eKMT2A-\u003c/em\u003erearrangements.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDepletion of heparan sulfotransferases remodels the AML transcriptome\u003c/h3\u003e\n\u003cp\u003ePrevious studies highlight that heparan sulfation controls cancer cell functions (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), leading us to hypothesize that heparan sulfation may regulate AML cells to support disease progression. To test this, we used CRISPR-Cas9 to knockdown \u003cem\u003eHS2ST1\u003c/em\u003e and \u003cem\u003eHS6ST1\u003c/em\u003e in the MOLM-13 cell line, which harbors \u003cem\u003eKMT2A-MLLT3\u003c/em\u003e and \u003cem\u003eFLT3\u003c/em\u003e-ITD mutations. Compared to sgControl cells, sg\u003cem\u003eHS2ST1\u003c/em\u003e and sg\u003cem\u003eHS6ST1\u003c/em\u003e cells had decreased HS2ST1 and HS6ST1 protein expression, respectively, and high indel contribution in target genes. (\u003cb\u003eSupplemental Fig.\u0026nbsp;2A-D\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eBulk RNA sequencing and principal component analysis revealed sgControl, sg\u003cem\u003eHS2ST1\u003c/em\u003e, and sg\u003cem\u003eHS6ST1\u003c/em\u003e cells cluster distinctly, suggesting unique transcriptional profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Compared to sgControl cells, 1096 genes were downregulated, and 825 genes were upregulated in sg\u003cem\u003eHS2ST1\u003c/em\u003e cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In sg\u003cem\u003eHS6ST1\u003c/em\u003e cells, 1257 downregulated genes and 575 upregulated genes were detected compared to sgControl cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Gene Set Enrichment Analysis revealed differentially enriched heparan sulfate proteoglycan-dependent processes in sg\u003cem\u003eHS2ST1\u003c/em\u003e and sg\u003cem\u003eHS6ST1\u003c/em\u003e cells compared to sgControl cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E). More specifically, sg\u003cem\u003eHS2ST1\u003c/em\u003e cells were negatively enriched for gene sets associated with transmembrane signaling receptor activity and cell adhesion molecule binding and positively enriched for phosphatidylinositol binding and cytokine mediated signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, F). sg\u003cem\u003eHS2ST1\u003c/em\u003e cells were also negatively enriched for TNF-α and TGF-β signaling and positively enriched for interferon alpha and gamma response hallmark gene sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ). sg\u003cem\u003eHS6ST1\u003c/em\u003e cells were negatively enriched for gene sets associated with growth factor binding, cytokine receptor activity, cytokine binding, glycosaminoglycan binding, signaling receptor regulator activity, plasma membrane signaling receptor complex, and carbohydrate binding compared to sgControl cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, F-I). sg\u003cem\u003eHS6ST1\u003c/em\u003e cells were also negatively enriched for the interferon gamma response, IL2-STAT5, TNF-α, IL6-JAK-STAT3, TGF-β, and Notch signaling hallmark gene sets and positively enriched for PI3K/AKT/MTOR genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eK). These data indicate that heparan sulfate modifications catalyzed by \u003cem\u003eHS2ST1\u003c/em\u003e and \u003cem\u003eHS6ST1\u003c/em\u003e have distinct and far-reaching effects on proteoglycan-mediated signaling processes in AML cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDepletion of\u003c/b\u003e \u003cb\u003eHS2ST1\u003c/b\u003e \u003cb\u003eincreases AML bone marrow burden\u003c/b\u003e \u003cb\u003ein vivo\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSignaling-related pathways identified from our RNA sequencing analyses are known to impact cancer cell proliferation, leading us to test the potential function of heparan sulfation in AML cell growth (\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). We tested the impact of \u003cem\u003eHS2ST1\u003c/em\u003e and \u003cem\u003eHS6ST1\u003c/em\u003e expression on AML growth \u003cem\u003ein vivo\u003c/em\u003e using xenograft studies. sgControl, sg\u003cem\u003eHS2ST1\u003c/em\u003e, or sg\u003cem\u003eHS6ST1\u003c/em\u003e cells were intravenously transplanted into irradiated NSG mice, and AML burden was quantified using quantitative histology and flow cytometry at 2 weeks-post injection (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Bone marrow immunohistochemistry showed similar AML burden between all groups, however there was a trend towards increased burden in sg\u003cem\u003eHS2ST1-\u003c/em\u003etransplanted animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C). Spleen, liver, and peripheral blood AML burden was similar in all groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, D-G). Flow cytometry showed a significant increase in AML bone marrow burden upon transplant with sg\u003cem\u003eHS2ST1\u003c/em\u003e cells compared to sgControl cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH-I). These data reveal that depletion of \u003cem\u003eHS2ST1\u003c/em\u003e, but not \u003cem\u003eHS6ST1\u003c/em\u003e, enhances AML bone marrow burden \u003cem\u003ein vivo.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe also assessed cell homing by transplanting sgControl, sg\u003cem\u003eHS2ST1\u003c/em\u003e, and sg\u003cem\u003eHS6ST1\u003c/em\u003e into NSG mice (225 cGy) and analyzing AML burden 16 hours post-transplant (\u003cb\u003eSupplemental Fig.\u0026nbsp;3A\u003c/b\u003e). sgControl, sg\u003cem\u003eHS2ST1\u003c/em\u003e, or sg\u003cem\u003eHS6ST1\u003c/em\u003e cell homing to the bone marrow or spleen were similar (\u003cb\u003eSupplemental Fig.\u0026nbsp;3B-C\u003c/b\u003e), suggesting that \u003cem\u003eHS2ST1\u003c/em\u003e-mediated changes in AML bone marrow burden occur independently of homing.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHigher\u003c/b\u003e \u003cb\u003eHS6ST1\u003c/b\u003e \u003cb\u003eexpression correlates with increased relapse risk in\u003c/b\u003e \u003cb\u003eKMT2A\u003c/b\u003e\u003cb\u003e-r AML patients\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDisease relapse driven by chemotherapy-resistant AML cells is a major barrier to long term patient survival (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). We therefore interrogated whether heparan sulfotransferase expression correlated with relapse risk in AML. Low \u003cem\u003eHS2ST1\u003c/em\u003e expression predicted higher relapse risk across AML subtypes (\u003cb\u003eSupplemental Fig.\u0026nbsp;4A\u003c/b\u003e). There were no significant differences in relapse risk based on \u003cem\u003eHS3ST1, HS6ST1\u003c/em\u003e, and \u003cem\u003eNDST1\u003c/em\u003e expression across AML subtypes (\u003cb\u003eSupplemental Fig.\u0026nbsp;4B-D\u003c/b\u003e). \u003cem\u003eHS2ST1\u003c/em\u003e, \u003cem\u003eHS3ST1\u003c/em\u003e and \u003cem\u003eNDST1\u003c/em\u003e expression were not correlated with \u003cem\u003eKMT2A-\u003c/em\u003erearranged patient relapse risk (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, C-D). In contrast, high bone marrow \u003cem\u003eHS6ST1\u003c/em\u003e expression was associated with significantly increased relapse risk in \u003cem\u003eKMT2A-\u003c/em\u003erearranged AML patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). These data demonstrate that high \u003cem\u003eHS6ST1\u003c/em\u003e correlates with increased relapse in patients with \u003cem\u003eKMT2A\u003c/em\u003e-rearranged leukemias.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCytarabine treatment alters heparan sulfotransferase gene expression in AML cell lines\u003c/h3\u003e\n\u003cp\u003ePrevious studies show that the heparan sulfate landscape differs in therapy-refractory residual breast cancer tumor cells compared to the primary tumor (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). These findings led us to test whether therapy-refractory AML cells express distinct heparan sulfate landscapes that enable them to resist chemotherapy and promote relapse. MOLM-13 cells expressed increased \u003cem\u003eHS2ST1, HS6ST1\u003c/em\u003e, and \u003cem\u003eNDST1\u003c/em\u003e and decreased \u003cem\u003eHS3ST1\u003c/em\u003e at 24- and 72-hours post-Ara-C treatment compared to vehicle-treated controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Kasumi-1 cells had significantly increased \u003cem\u003eHS2ST1\u003c/em\u003e and \u003cem\u003eHS6ST1\u003c/em\u003e expression at 24-hours post-Ara-C treatment compared to vehicle, but \u003cem\u003eHS3ST1\u003c/em\u003e and \u003cem\u003eNDST1\u003c/em\u003e expression were unchanged (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). \u003cem\u003eHS2ST1, HS6ST1\u003c/em\u003e, and \u003cem\u003eNDST1\u003c/em\u003e expression were significantly elevated in THP-1 cells at 72 hours post-Ara-C treatment compared to vehicle controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). These data suggest that chemotherapy resistant AML cells express different heparan sulfate transcriptomic profiles compared to their chemosensitive counterparts.\u003c/p\u003e \u003cp\u003eLiquid chromatography mass spectrometry revealed significantly increased D2H6 and decreased D2A0 disaccharides in MOLM-13 cells treated with Ara-C compared to vehicle-treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). In Kasumi-1 cells, there was significantly decreased D0A0 and significantly increased D2S0 and D2S6 disaccharides upon Ara-C treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI). There were no significant changes in the heparan sulfate disaccharide composition in THP-1 cells after treatment with Ara-C (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ). These data show that Ara-C remodels the AML heparan sulfate landscape at the glycan level, suggesting that the precise structure of heparan sulfate may influence AML cells chemotherapy responses.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHS6ST1\u003c/b\u003e \u003cb\u003edepletion promotes sensitivity to cytarabine\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBecause Ara-C treatment increases heparan sulfotransferase gene expression, we investigated whether \u003cem\u003eHS2ST1\u003c/em\u003e or \u003cem\u003eHS6ST1\u003c/em\u003e regulate AML sensitivity to Ara-C. After 72 hours, Vehicle-treated sgControl, sg\u003cem\u003eHS2ST1\u003c/em\u003e, and sg\u003cem\u003eHS6ST1\u003c/em\u003e cells exhibited similar levels of live and necrotic cells, while sg\u003cem\u003eHS6ST1\u003c/em\u003e cells had slightly increased apoptotic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). However, upon Ara-C treatment, sg\u003cem\u003eHS2ST1\u003c/em\u003e cells had significantly more live cells than sgControl cells, suggesting they are more resistant to chemotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). sg\u003cem\u003eHS6ST1\u003c/em\u003e cells had significantly fewer live cells compared to sgControl cells, accompanied by increased apoptotic cells, indicating they are more chemosensitive (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Consistent with these data, there was a four-fold reduction in the Ara-C IC50 for sg\u003cem\u003eHS6ST1\u003c/em\u003e cells compared to sgControl cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-E). Together, these data show that \u003cem\u003eHS6ST1\u003c/em\u003e and \u003cem\u003eHS2ST1\u003c/em\u003e distinctly regulate AML chemotherapy sensitivity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSurfen is a small molecule antagonist of heparan sulfate (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). To evaluate whether heparan sulfate blockade is therapeutically effective for AML, we treated MOLM-13 and THP-1 cells with surfen alone and in combination with Ara-C. Compared to vehicle treatment, surfen treatment decreased cell viability in MOLM-13 and THP-1 cells, but not to the same level as Ara-C. However, combinatorial treatment of MOLM-13 or THP-1 cells with Ara-C and surfen significantly decreased cell viability compared to Ara-C treatment alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-I). These data show that heparan sulfate antagonism synergizes with Ara-C to promote AML cytotoxicity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHS6ST1\u003c/b\u003e \u003cb\u003eis promotes TGF-β1 signaling to regulate AML survival upon chemotherapy treatment\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur RNA sequencing data indicates that compared to sgControl cells, sg\u003cem\u003eHS6ST1\u003c/em\u003e cells were negatively enriched for pathways related to growth factor signaling including TGF-β1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Bone marrow TGF-β1 levels are significantly increased in relapsed/refractory AML patients, and high \u003cem\u003eTGFB1\u003c/em\u003e expression predicts adverse prognoses (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). TGF-β1 supplementation promotes AML chemoresistance \u003cem\u003ein vitro\u003c/em\u003e by inducing a quiescent-like G\u003csub\u003e0\u003c/sub\u003e shift (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). We therefore hypothesized that depleting \u003cem\u003eHS6ST1\u003c/em\u003e may impact TGF-β1 signaling in AML, rendering cells more susceptible to Ara-C. Using intracellular flow cytometry, we measured phospho-SMAD2/3, a response element downstream of TGF-β1. TGF-β1 stimulation increased phospho-SMAD2/3 levels in sgControl and sg\u003cem\u003eHS2ST1\u003c/em\u003e compared to vehicle treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). However, phospho-SMAD2/3 expression upon TGF-β1 stimulation of sg\u003cem\u003eHS6ST1\u003c/em\u003e cells was muted compared to sgControl cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). Similarly, Ara-C treatment induced SMAD2/3 phosphorylation in all cell lines relative to vehicle treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D). However, phospho-SMAD2/3 expression was significantly lower in Ara-C-treated sg\u003cem\u003eHS2ST1\u003c/em\u003e and sg\u003cem\u003eHS6ST1\u003c/em\u003e than Ara-C-treated sgControl cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D). We next measured cell divisions at baseline and upon TGF-β1 stimulation using CellTrace Violet staining. sg\u003cem\u003eHS2ST1\u003c/em\u003e cells had a lower proliferation index than sgControl cells at baseline and after stimulation with TGF-β1. sg\u003cem\u003eHS6ST1\u003c/em\u003e proliferation index was not significantly different from sgControl cells at baseline, but it was significantly higher than sgControl cells after TGF-β1 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-F). These data indicate that depletion of \u003cem\u003eHS6ST1\u003c/em\u003e decreases TGF-β1-mediated SMAD2/3 activation and supports cell proliferation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs TGF-β1 is known to promote AML chemotherapy resistance (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), we reasoned that sg\u003cem\u003eHS6ST1\u003c/em\u003e cells may exhibit reduced cell survival abilities compared to sgControl cells due to an impaired ability to respond to TGF-β1. To test this, cells were treated with Ara-C and TGF-β1 and cell death was quantified with Annexin-V/7-AAD staining. TGF-β1 treatment alone had no impact on cell survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG-H). However, TGF-β1 treatment in combination with Ara-C promoted the viability of sg\u003cem\u003eHS2ST1\u003c/em\u003e cells and sg\u003cem\u003eHS6ST1\u003c/em\u003e cells compared to Ara-C treatment alone. However, even with this increase in viability, sg\u003cem\u003eHS6ST1\u003c/em\u003e cells do not achieve the level of cell survival detected in sgControl cells. These data suggest that \u003cem\u003eHS6ST1\u003c/em\u003e cells have an impaired ability to respond to TGF-β1 signaling, which sensitizes cells to Ara-C. Taken together, our findings indicate that \u003cem\u003eHS6ST1\u003c/em\u003e regulates AML chemotherapy resistance, and depletion of \u003cem\u003eHS6ST1\u003c/em\u003e can sensitize AML cells to Ara-C, while impairing their ability to respond to TGF-β1 stimuli.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWe reported previously that the heparan sulfate proteoglycan syndecan-2 regulates normal hematopoietic stem cell functions (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e); however, the effect of heparan sulfates in malignant hematopoiesis had not been fully analyzed. Our study identified a crucial link between the heparan sulfate biosynthesis machinery and AML patient survival outcomes and chemotherapy resistance. This work adds to a growing body of literature defining the roles of heparan sulfation in cancer physiology, such as work from others showing that 6-\u003cem\u003eO\u003c/em\u003e sulfation is necessary for survival-promoting signaling in treatment refractory dormant breast cancer residual tumor cells (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Further, the presence and structures of syndecan-1 heparan sulfate chains regulate multiple myeloma proliferation and signaling (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Our findings provide a foundation to better understand the function of heparan sulfates in other hematological malignancies and hematopoietic disorders.\u003c/p\u003e \u003cp\u003eMechanistically, we show that \u003cem\u003eHS6ST1\u003c/em\u003e regulates TGF-β1 signaling to support AML cell survival upon Ara-C. Several studies highlight critical functions for heparan sulfate proteoglycans in regulating TGF-β1 signaling (\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Work on hepatocellular carcinoma demonstrated a connection between the 6-\u003cem\u003eO\u003c/em\u003e heparan sulfate endosulfatase \u003cem\u003eSULF1\u003c/em\u003e and TGF-β1 signaling (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). However, HS6ST1 decorates heparan sulfates during the biosynthesis process in the endoplasmic reticulum, while SULF1 acts extracellularly after heparan sulfate proteoglycan trafficking. Therefore, the push and pull of heparan sulfate biosynthesis and post-synthesis processing should be carefully analyzed in the future to resolve the distinct contributions of these processes in AML and how they change during disease.\u003c/p\u003e \u003cp\u003eCorrelations between TGF-β1 and AML survival outcomes are well-characterized, but inhibiting TGF-β1 receptors in AML can induce expression of drug efflux pumps, weakening treatment efficacy (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Targeting heparan sulfate to extrinsically reprogram TGF-β1 signaling in AML cells could represent a therapeutic avenue to improve patient outcomes. In our study, we show that the heparan sulfate antagonist surfen promotes AML cell killing alone and in combination with Ara-C. Other groups showed that surfen decreases tumorigenicity of Ewing sarcoma cells by reprogramming growth factor signaling (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Surfen can also inhibit glioblastoma invasion by blocking chondroitin sulfate, another type of glycosaminoglycan (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Further, others have shown heparan sulfate mimetics can therapeutically target colorectal cancer stem cells (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Our results build on these studies by supporting the notion that targeting extracellular glycans represents a viable way to inhibit blood cancer progression, starting with therapeutic relevance in AML.\u003c/p\u003e \u003cp\u003eWe showed that AML patient peripheral blood mononuclear cells express heparan sulfate disaccharide profiles that are distinct from normal donor cells. However, open questions regarding heparan sulfate structural changes in AML remain. A landmark study using single-chain variable fragment antibodies targeting differentially modified heparan sulfate showed that distinct heparan sulfate patterns can identify hematopoietic cells primed for different lineages (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). However, heparan sulfate chains are between 40 and 300 sugar residues long, and sulfate modifications and patterning are both important to coordinate growth factor signaling (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). While disaccharide analysis can provide insight into which modifications are present, tools have yet to be created to depict heparan sulfate patterning and chain length with sufficient resolution to accurately discern complete structural motifs. The field will benefit from more robust mass spectrometry methods to characterize the structures of entire heparan sulfate chains, another layer of information that likely informs AML cell responses to chemotherapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e \u003cb\u003eCOMPETING INTERESTS\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003ch2\u003eAUTHORSHIP CONTRIBUTIONS\u003c/h2\u003e \u003cp\u003eProject conception and supervision (CMT), performed experiments (KAW, DP, NJS, MWH), analyzed data (KAW, DP, JHP, SM, CMT), wrote the manuscript with input from all authors (KAW and CMT).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENTS:\u003c/h2\u003e \u003cp\u003eWe are grateful to Cyd McKay, Christina Root, and Alex Hastie for their assistance with animal studies, Taylor Billings for performing cell line authentication, and Logan Wallace and Rhonda Ries for help procuring data for initial TARGET analyses. We would like to acknowledge the excellent assistance provided by the Fred Hutchinson Cancer Center Comparative Medicine, Experimental Histopathology, Flow Cytometry, and Genomics \u0026amp; Bioinformatics Shared Resources (Pritha Chanana). We are grateful for the technical assistance provided by Biswa Choudhury from the UCSD GlycoAnalytics Core. This investigation was supported by 5K01DK126989 from the National Institutes of Health (CMT), a pilot award from Safeway, Inc. via the Fred Hutch Cancer Consortium and a Scholar Award from the V Foundation for Cancer Research (V2024-028). KAW was supported by a Fellowship from the Fred Hutchinson Cancer Center Office of Faculty Affairs and the Graduate Research Fellowship Program from the National Science Foundation (DGE-2140004). This research was supported by NIH P30 CA015704 to the Fred Hutch/University of Washington/Seattle Children's Cancer Consortium, which includes the Comparative Medicine, Experimental Histopathology, Flow Cytometry, and Genomics \u0026amp; Bioinformatics Shared Resources.\u003c/p\u003e \u003cp\u003eThe results published here are in part based upon data generated by the Therapeutically Applicable Research to Generate Effective Treatments (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/ccg/research/genome-sequencing/target\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/ccg/research/genome-sequencing/target\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) initiative, phs000218. The data used for this analysis are available at the Genomic Data Commons (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis manuscript is the result of funding in whole or in part by the National Institutes of Health (NIH). It is subject to the NIH Public Access Policy. Through acceptance of this federal funding, NIH has been given a right to make this manuscript publicly available in PubMed Central upon the Official Date of Publication, as defined by NIH.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eForsberg M, Konopleva M. AML treatment: conventional chemotherapy and emerging novel agents. 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Journal of Experimental Medicine. 2022;219(11):e20212552.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8725671/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8725671/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite therapeutic advances, relapse remains the leading cause of death in patients with acute myeloid leukemia (AML). Growth factor signaling controls AML survival, proliferation, relapse, and chemotherapy resistance. Here, we studied heparan sulfate proteoglycans, a class of molecules that bind growth factors via their heparan sulfate chains to change their signaling ability. Heparan sulfate-growth factor interactions are controlled by the addition of sulfate groups catalyzed by heparan sulfotransferases, such as those encoded by \u003cem\u003eHS2ST1\u003c/em\u003e and \u003cem\u003eHS6ST1\u003c/em\u003e. Using AML patient cohort analyses, we demonstrate that increased \u003cem\u003eHS6ST1\u003c/em\u003e expression is associated with worse survival and increased relapse risk for AML patients harboring \u003cem\u003eKMT2A\u003c/em\u003e-rearrangements. Using cell line derived xenografts, we show that AML cells depleted of \u003cem\u003eHS2ST1\u003c/em\u003e, but not \u003cem\u003eHS6ST1\u003c/em\u003e, have increased bone marrow leukemic burden. Further, AML cells depleted of \u003cem\u003eHS6ST1\u003c/em\u003e are more sensitive to cytarabine than Control cells, suggesting that \u003cem\u003eHS6ST1\u003c/em\u003e regulates AML chemotherapy resistance. Heparan sulfate antagonism with surfen synergized with cytarabine to further support AML cell death compared to cytarabine alone. Mechanistically, we demonstrate that \u003cem\u003eHS6ST1\u003c/em\u003e depletion in AML cells reduces TGF-β1-mediated signaling, which diminishes cell survival upon cytarabine treatment. Together, our data show that \u003cem\u003eHS6ST1\u003c/em\u003e promotes AML cell chemotherapy resistance by supporting TGF-β1 signaling.\u003c/p\u003e","manuscriptTitle":"HS6ST1 regulates acute myeloid leukemia chemotherapy resistance via TGF-β1 signaling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 12:23:51","doi":"10.21203/rs.3.rs-8725671/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b63d62d-a39f-4d03-8f62-1f29efd9765a","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62618818,"name":"Biological sciences/Cancer/Haematological cancer/Leukaemia/Acute myeloid leukaemia"},{"id":62618819,"name":"Biological sciences/Cancer/Cancer therapy/Cancer therapeutic resistance"},{"id":62618820,"name":"Biological sciences/Cell biology/Cell signalling"}],"tags":[],"updatedAt":"2026-04-08T11:37:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 12:23:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8725671","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8725671","identity":"rs-8725671","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}