Targeting PRMT9-mediated arginine methylation suppresses cancer stem cell maintenance and elicits cGAS-mediated anticancer immunity

Dong, Haojie; He, Xin; Zhang, Lei; Chen, Wei; Lin, Yi-Chun; Liu, Song-Bai; Wang, Huafeng; Nguyen, Le Xuan Truong; Li, Min; Zhu, Yinghui; Zhao, Dandan; Ghoda, Lucy; Serody, Jonathan; Vincent, Benjamin; Luznik, Leo; Gojo, Ivana; Zeidner, Joshua; Su, Rui; Chen, Jianjun; Sharma, Ritin; Pirrotte, Patrick; Wu, Xiwei; Hu, Weidong; Han, Weidong; Shen, Binghui; Kuo, Ya-Huei; Jin, Jie; Salhotra, Amandeep; Wang, Jeffrey; Marcucci, Guido; Luo, Yun Lyna; Li, Ling

doi:10.1038/s43018-024-00736-x

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Published: 27 February 2024

Targeting PRMT9-mediated arginine methylation suppresses cancer stem cell maintenance and elicits cGAS-mediated anticancer immunity

Nature Cancer volume 5, pages 601–624 (2024)Cite this article

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Abstract

Current anticancer therapies cannot eliminate all cancer cells, which hijack normal arginine methylation as a means to promote their maintenance via unknown mechanisms. Here we show that targeting protein arginine N-methyltransferase 9 (PRMT9), whose activities are elevated in blasts and leukemia stem cells (LSCs) from patients with acute myeloid leukemia (AML), eliminates disease via cancer-intrinsic mechanisms and cancer-extrinsic type I interferon (IFN)-associated immunity. PRMT9 ablation in AML cells decreased the arginine methylation of regulators of RNA translation and the DNA damage response, suppressing cell survival. Notably, PRMT9 inhibition promoted DNA damage and activated cyclic GMP-AMP synthase, which underlies the type I IFN response. Genetically activating cyclic GMP-AMP synthase in AML cells blocked leukemogenesis. We also report synergy of a PRMT9 inhibitor with anti-programmed cell death protein 1 in eradicating AML. Overall, we conclude that PRMT9 functions in survival and immune evasion of both LSCs and non-LSCs; targeting PRMT9 may represent a potential anticancer strategy.

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Main

The outcomes for patients with acute myeloid leukemia (AML) remain poor¹. Allogeneic hematopoietic stem cell transplantation has emerged as the only cure. However, its applicability is restricted². Success of immunotherapies has driven interest in developing effective antileukemia drugs, including immune checkpoint inhibitors (ICIs)^3,4,5. However, translation of existing T cell-leveraging strategies to AML treatment remains challenging.

The cyclic GMP-AMP synthase (cGAS)–stimulator of interferon genes (STING) signaling triggers the type I interferon (IFN) response⁶ and can prime T cell function. Specifically, preexisting functional T cells are required for responses to ICI treatment⁶. cGAS activity is stimulated by cytosolic DNA and generates cyclic GMP-AMP (cGAMP)^7,8. Notably, cGAMP exported from tumor cells serves as an ‘immunotransmitter’ to activate the STING receptor on dendritic cells (DCs), which then activates the production of type I IFN⁸. Moreover, administration of the STING agonist blocked AML development in in vivo models⁷. However, STING agonists show only modest clinical benefits⁹. To address this challenge, we are developing a strategy based on stimulating cancer-endogenous cGAS.

Protein arginine methylation is a posttranslational modification functioning in cellular processes¹⁰. Protein arginine methyltransferases (PRMTs) have emerged as druggable targets. Accordingly, inhibitors of two major PRMTs to treat malignancies are under clinical trials. However, one concern is that PRMT1 and PRMT5 are responsible for most arginine methylation of essential histone markers¹⁰. Therefore, the relevance of other individual PRMTs to different cancers should be considered. PRMT9 is the less-known PRMT. In this study, we performed analyses that supported PRMT9 as a potential target and developed an inhibitory compound as a tool to probe PRMT9 activity.

Results

PRMT9 levels are elevated in leukemia stem cells

We assessed PRMT levels using The Cancer Genome Atlas (TCGA) program and cancer cell lines proteomic datasets^11,12. Among deadly cancers¹³, AML showed the highest PRMT9 mRNA levels (Fig. 1a,b and Extended Data Fig. 1a). Other PRMTs showed comparable levels in AML and other cancers (Fig. 1a and Extended Data Fig. 1b–n). We observed elevated PRMT9 protein levels in AML relative to other cancers (Fig. 1b and Extended Data Fig. 1o,p). We next assessed all PRMT levels using a dataset that included transcriptome from leukemia and normal hematopoietic stem and progenitor cells (HSPCs)¹⁴. Notably, PRMT9 levels were higher in leukemia stem cells (LSCs) relative to either normal hematopoietic stem cells (HSCs) or blasts (Fig. 1c). We next performed single-cell RNA sequencing (scRNA-seq) analysis on murine leukemic bone marrow from an MLL-AF9 (MA9) cKit⁺ cell transplant mouse model. Among the leukemia cells, we identified two clusters as LSCs and blasts (Fig. 1d,e and Extended Data Fig. 1q)¹⁵. Notably, Prmt9 was the only Prmt whose levels showed more than a onefold increase in LSCs relative to blasts (Fig. 1f). Next, we assessed PRMT9 levels in AML specimens and normal healthy donors (peripheral blood stem cells (PBSCs)). Notably, elevated PRMT9 protein levels were seen in an LSC-enriched (CD34⁺CD38⁻) relative to a leukemia committed progenitor (CD34⁺CD38⁺) subset or to either of the normal subsets (Fig. 1g). We also found that PRMT9 protein levels were higher in the AML CD34⁺ subsets (n = 30; Supplementary Table 1) relative to the normal counterparts (n = 10) (Fig. 1h,i). Analysis of CD34⁺ cells from another cohort with AML (n = 94; Supplementary Table 2) and normal donors (n = 19) confirmed PRMT9 upregulation (Extended Data Fig. 1r). Analysis of another dataset (n = 463) showed similar results (Extended Data Fig. 2a). PRMT9 levels were not associated with any particular cytogenetic abnormality or mutation (Extended Data Fig. 2b–d). PRMT9 protein levels were also higher in diffuse large B cell lymphoma (DLBCL) cell lines (Extended Data Fig. 1o,e)¹¹. We also found that higher PRMT9 levels predicted shorter overall survival (Fig. 1j,k). Analysis of the pediatric dataset yielded similar results (Extended Data Fig. 2f).

**Fig. 1: PRMT9 levels are elevated in AML.**

We next analyzed chromatin immunoprecipitation followed by sequencing (ChIP–seq) data in ChIPBase v.2.0 (ref. ¹⁶) and observed the binding sites of relevant transcription factors within 5 kb upstream of the PRMT9 transcription start site. CREB1, a known prognosticator¹⁷, showed the strongest correlation with PRMT9 expression in the AML and DLBCL cohorts (Extended Data Fig. 2g,h). Interestingly, we observed an increase in Creb1 in MA9 LSCs relative to blasts (Extended Data Fig. 2i). CREB1 inhibition decreased PRMT9 levels (Extended Data Fig. 2j). We next verified the significant enrichment of CREB1 in Molm13 cells at the PRMT9 promoter region relative to a control site (Extended Data Fig. 2k,l); the promoter region showed significant enrichment of H3K27Ac relative to normal cells (Extended Data Fig. 2l).

PRMT9 is dispensable for normal hematopoiesis

PRMT9 levels were higher in HSPCs than in mature lineages¹⁸ (Extended Data Fig. 2m,n). We developed a conditional knockout (KO) model by crossing Prmt9^loxP/loxP with Mx1-Cre mice (Mx1-Cre/Prmt9^loxP^/^loxP or Prmt9-cKO). In this model, the Prmt9 exon2 was flanked by loxP sites (Extended Data Fig. 2o). Sixteen weeks after pIpC administration, we observed no differences in complete blood count between Prmt9-cKO mice and littermate (Prmt9^loxP/loxP) controls. Prmt9 KO modestly increased the multipotent progenitor population and did not affect mature cells (Extended Data Fig. 2p,q). To evaluate the repopulation capacity of HSPCs with Prmt9 KO, we performed a competitive transplantation. Prmt9 KO modestly affected CD45.2 chimerism in recipients relative to controls (Extended Data Fig. 2r), suggesting that Prmt9 function is modestly required for HSPC self-renewal under transplantation stress.

PRMT9 ablation impairs cancer cell survival

We generated Prmt9-cKO/MA9 mice. We observed that Prmt9 levels are elevated in MA9 mouse bone marrow cKit⁺ cells (Fig. 2a). Given that other oncogenes cooperate with MA9 cells to induce AML^19,20,21, we assessed the effects of Prmt9 KO in both MA9 single-hit and double-hit models; for the latter, we used FLT3-internal tandem duplication (ITD) as the second hit^22,23. Specifically, we transduced Lin⁻Sca-1⁺cKit⁺ bone marrow cells from Prmt9-cKO/MA9 or control (Prmt9 WT/MA9) mice with either a lentiviral vector expressing FLT3-ITD and coexpressing green fluorescent protein (GFP). Relative to the controls, colony-forming cell (CFC) growth was inhibited in Prmt9 KO MA9⁺ bone marrow cells; more inhibition by Prmt9 KO was seen in FLT3-ITD-expressing cells than in mock cells (Fig. 2b,c and Extended Data Fig. 3a). Additionally, we also transduced a doxycycline (DOX)-inducible Prmt9 knockdown (KD) construct into MA9 cells. Those inducible constructs coexpressed red fluorescent protein (RFP) (Extended Data Fig. 3b). Consistent with the effect of Prmt9 KO, Prmt9 KD significantly inhibited CFC growth of MA9 and MA9/FLT3-ITD bone marrow cells (Extended Data Fig. 3c,d). Besides the MA9 model, we evaluated outcomes by inducing Prmt9 KD in another CBFB-MYH11 knock-in AML model plus thrombopoietin receptor transduction (namely, CMM)^24,25 resembling inv (16) AML. Prmt9 levels were significantly elevated in the AML cells (Fig. 2a). Prmt9 KD inhibited the CFC growth of CMM cells (Extended Data Fig. 3c,d). Prmt9 KD decreased LSC frequency, as shown in an in vitro limiting dilution assay^26,27,28, in all three models (Extended Data Fig. 3e–g).

We transduced MA9/FLT3-ITD double-hit Prmt9 control KO cells (or controls) with a luciferase reporter. Compared to control mice, Prmt9-deficient mice had a slower AML progression, lower tumor burden, decreased splenomegaly and survival advantage (Fig. 2d–h).

We next assessed PRMT9 function in human cancers. KD of PRMT9 (shPRMT9-1, shPRMT9-2) decreased cell growth and viability (Extended Data Fig. 3h–k). We also engineered Molm13 cells to express either a wild-type (WT) PRMT9 or corresponding catalytically dead mutant²⁹; both were designed to resist PRMT9 short hairpin RNA (shRNA) 1 (that is, PRMT9/WT-R and PRMT9/MUT-R; Fig. 2i). Notably, unlike transduction with PRMT9/MUT-R, PRMT9/WT-R reversed the inhibition seen after PRMT9 KD (Fig. 2j,k), indicating the requirement for catalysis. Moreover, PRMT9 KD decreased the viability of AML CD34⁺ cells more potently than it did with normal counterparts (Fig. 2l–o and Extended Data Fig. 3l). Molm13 cells with the DOX-inducible shPRMT9 construct were transplanted into NOD scid gamma (NSG) mice (Extended Data Fig. 3m). Once engraftment was confirmed (>1% in peripheral blood), DOX was administered (Fig. 2p). Notably, mice receiving PRMT9 KD cells exhibited decreased leukemia burden and prolonged survival, compared with control mice (Fig. 2q,r).

PRMT9-mediated methylation promotes cell growth

We performed stable isotope labeling by amino acids in cell culture (SILAC)-based proteomics analysis on inducible shPRMT9-transduced or shCtrl-transduced Molm13 cells (Fig. 3a and Supplementary Tables 3 and 4). The analysis revealed 315 unique mono-methylation arginine (MMA) and 109 dimethylation arginine (DMA) sites. PRMT9 KD resulted in marked downregulation of 16 (14.7%) DMA and 31 (9.8%) MMA sites (Fig. 3b, fold change > 1.5) in 23 unique proteins (Fig. 3c and Supplementary Table 4). However, iceLogo analysis^30,31 did not detect any consensus sequences enriched in methyl peptides (Extended Data Fig. 4a). Among 23 proteins, ten functioned in RNA translation, seven were related to the DNA damage response and six were related to RNA catabolism (Fig. 3d and Supplementary Table 5).

**Fig. 3: PRMT9-mediated methylation promotes cancer cell growth.**

We next asked whether PRMT9 regulates translation. Specifically, a sucrose density gradient assay revealed that PRMT9 KD decreased polysome-related mRNAs levels, indicating insufficient mRNA translation³² (Fig. 3e). Next, through an O-propargyl-puromycin (OPP)-based assay³³, we found decreased global protein synthesis after PRMT9 KD in cancer cells (Fig. 3f and Extended Data Fig. 4b–d), while protein synthesis in normal CD34⁺ cells was modestly altered (Fig. 3f and Extended Data Fig. 4b). PRMT9 KD downregulated the levels of short-lived proteins (Supplementary Table 3), while their mRNA levels were unchanged (Fig. 3g and Extended Data Fig. 4e). We also observed that PRMT9 KD shifted c-Myc and SAMHD1 transcripts from high-molecular-weight (HMW) to low-molecular-weight (LMW) polysomes (Fig. 3h and Extended Data Fig. 4f).

Among the downregulated methylated peptides we identified, the methylated PABPC1 peptide with dimethyl-R493 (R493me2), was most depleted by PRMT9 KD (Fig. 3i). Posttranslational modifications of PABPC1 are critical for its function^34,35. We thus defined PABPC1 as a PRMT9 substrate. Analysis of PABPC1 peptides in PRMT9 KD versus PRMT9 WT cells revealed two more sites, monomethylation at R481 (R481me) and R506 (R506me), enriched in PRMT9 WT cells (Fig. 3j and Extended Data Fig. 4g,h). To validate the modifications, we constructed a FLAG-tagged PABPC1 C-terminal fragment (amino acids 476–636, PABPC1-CT) containing R481, R493 and R506, as well as a corresponding methylation-deficient (R to K) construct for each individual residue (R481K, R493K, R506K) or for all three residues (3RK). Only the 3RK mutation depleted PABPC1-CT methylation (Extended Data Fig. 4i). We next performed an in vitro methylation assay by incubating the glutathione S-transferase (GST)-PABPC1 fragment and full-length Myc-tagged PRMT9 (Extended Data Fig. 4j) or other PRMTs with SAM. Only PRMT9 promoted PABPC1-CT methylation (Fig. 3k, Extended Data Fig. 4k,l). We confirmed the methylation by an ex vivo tritium methylation assay (Extended Data Fig. 4m). Moreover, when we incubated a synthesized peptide containing R493 with PRMT9 and S-adenosyl methionine (SAM), the signal was only seen with either anti-symmetric dimethylarginine (SDMA) or anti-MMA antibody (Fig. 3l).

To assess the function of PABPC1 methylation, we ectopically expressed full-length PABPC1 WT, PABPC1-3RK or the PABPC1-R493K mutant, which are resistant to shPABPC1, and further knock down endogenous PABPC1 (Fig. 3m). WT PABPC1 rescued the PABPC1 KD phenotypes; 3RK or R493K marginally rescued the outcomes seen after PABPC1 KD (Fig. 3n,o). Moreover, cells expressing R493K exhibited impaired protein synthesis to the same extent as cells expressing 3RK. R493 is located in the region that mediates PABPC1 self-association on the 3′ RNA poly(A) tail³⁶; thus, we asked if R493 methylation favored self-interaction (Fig. 3p). Notably, R493K pulled down less hemagglutinin (HA)-tagged PABPC1 than PABPC1 WT (Fig. 3q). Next, we performed a poly(A) agarose pull-down assay to evaluate whether R493 methylation altered PABPC1 binding to poly(A)³⁴. Less FLAG-tagged PABPC1 was pulled down from R493K-expressing cells than from PABPC1-WT-expressing cells (Fig. 3r). Furthermore, because R493 is near the PABPC1 C terminus (amino acids 541–636), we asked if R493 methylation facilitated binding of the C terminus to other translational factors, such as eRF3 (ref. ³⁷). We analyzed the interaction of FLAG-tagged PABPC1 variants with Myc-tagged eRF3 using co-immunoprecipitation (co-IP). R493K exhibited less affinity to eRF3 than PABPC1 WT (Fig. 3s). In contrast, R493K did not alter the PABPC1 interaction with eIF4G (Extended Data Fig. 4n), which binds to the N terminus³⁸.

We next generated an antibody to detect symmetrically dimethylated R493 (R493me) (Fig. 3t). Moreover, mutation of only R493, but not R481 or R506, completely abolished the methylation signals (Extended Data Fig. 4o). Knockdown of endogenous PRMT9 blocked PABPC1 R493 methylation but not R455 or R460 methylation (Fig. 3u and Extended Data Fig. 4p), while PRMT9 overexpression increased R493 methylation (Extended Data Fig. 4q). Moreover, inhibitors of other PRMTs did not alter R493 methylation levels (Extended Data Fig. 4r,s). CARM1 catalyzed PABPC1 R455 and R460 methylation did not cross-regulate R493 methylation (Supplementary Fig. 1a,b). Consistent with other reports³⁵, CARM1-dependent methylation did not alter RNA translation (Supplementary Fig. 1c,d).

We sorted the leukemia CD34⁺ subset and CD34⁻CD33⁺ blasts from specimens (n = 7; Extended Data Fig. 5a–c). Notably, CD34⁺ cells expressed higher PRMT9 and R493me levels than blasts (Fig. 3v). R493me and PRMT9 levels were positively correlated (Fig. 3w). We conducted similar analyses in MA9 and CMM cells and observed higher PRMT9 and R493me levels in cKit⁺ cells relative to cKit⁻ cells (Extended Data Fig. 5d,e).

Identification of a PRMT9 inhibitor

We performed virtual screening. Briefly, we screened compounds for binding affinity to PRMT9; compounds were from the National Cancer Institute (NCI) and ZINC library (Fig. 4a,b). According to the highest binding affinity to the catalytic pocket, we identified top candidates to assess their activity. We used doses of 1 and 5 µM in a Molm13 cell-based viability assay (Extended Data Fig. 6a). We selected the top 20 compounds exhibiting robust cell inhibitory effects for further analysis (Fig. 4c). Specifically, we assessed their effects on PRMT9 catalysis (Extended Data Fig. 6b). Of the 20 compounds tested, three structurally similar compounds showed PRMT9 inhibition (Extended Data Fig. 6c). Among the three, NSC641396 showed the highest inhibition effects (Extended Data Fig. 6d,e).

The NSC641396 docking pose indicated that the quinone ring next to the carbazole moiety extended outside the hydrophobic pocket (Extended Data Fig. 6f). We then conducted a Tanimoto-based two-dimensional similarity search after removing the quinone ring and introducing heteroatoms at different locations of the carbazole moiety (Extended Data Fig. 6g), which yielded 69 compounds. The top nine were purchased; only nos. 1, 2 and 8 showed PRMT9 inhibition efficacy superior to or similar to that of NSC641396, with no. 1 (thereafter called LD2) being the most potent (Fig. 4d,e). Next, we confirmed direct LD2 interaction with PRMT9 protein using nuclear magnetic resonance (NMR) (Fig. 4f,g and Supplementary Table 6). We also assessed the intracellular interaction of the compound and PRMT9 protein using a cellular thermal shift assay^39,40. Specifically, we engineered Molm13 cells to overexpress FLAG-tagged PRMT9 WT or PRMT9 mutant (W152A, D258A and E433A). All three residues were predicted LD2 binding sites (Fig. 4e). Notably, LD2 treatment led to substantial shifts in the thermal stability of PRMT9 WT but not PRMT9 mutant (Fig. 4h,i).

LD2 treatment preferentially inhibited the viability of cancer cells (Fig. 4j and Extended Data Fig. 6h) and their protein synthesis (Fig. 4k). LD2 at a relatively low dose decreased PRMT9 activity while sparing other PRMTs (Fig. 4l and Extended Data Fig. 6i–j). At a relatively high dose (20 µM), LD2 treatment slightly decreased PRMT5 activity (Supplementary Fig. 2). We also performed docking analysis of LD2 into CARM1, PRMT5, PRMT7 and PRMT9. The relatively lower docking score suggested that LD2 binds PRMT9 (−7.15 kcal mol⁻¹) with greater affinity than the other PRMTs tested (Extended Data Fig. 6k). Molecular dynamic simulation analyses confirmed the stronger binding of PRMT9 by LD2 (Extended Data Fig. 6l). Finally, PRMT9 KD in Molm13 cells reduced their sensitivity to LD2 (at 2.5 µM), suggesting that the LD2 effects at that dose are PRMT9-dependent (Extended Data Fig. 6m).

Next, we treated mononuclear cells (MNCs) from the AML specimens for 4 days⁴¹ with LD2 (2.5 μM) under physiological cytokine conditions⁴² and then performed cytometry by time-of-flight (CyTOF) analysis. The bone marrow subsets (Supplementary Figs. 3–5) in the vehicle group exhibited modest levels of apoptosis. AML cells from patients (n = 3) exhibited expansion of the immature CD34⁺CD45^dim subset (Fig. 4m). In these immune and leukemia cell cocultures, LD2 treatment ablated leukemia cells and relatively increased the T cell ratio (Fig. 4m, Extended Data Fig. 6n,o and Supplementary Fig. 4). Notably, LD2 treatment expanded the number of IFN-γ-expressing T cells relative to vehicle controls (Supplementary Fig. 6). We also depleted autologous CD3⁺ T cells of the AML samples (n = 3) before treatment (Fig. 4n). Notably, the inhibitory effects of LD2s were partially impaired by the depletion (Fig. 4o). T cell viability was not affected by LD2 treatment (Extended Data Fig. 6p). To analyze the correlation between PRMT9 activity and T cell function, we analyzed the RNA-seq results from GSE12417 GSE14468 and used a reported⁴³ cytotoxic T lymphocyte (CTL) score. Those scores were negatively correlated with PRMT9 levels (Fig. 4p and Extended Data Fig. 6q).

PRMT9 inhibition eradicates AML in vivo

To assess whether cancer-intrinsic PRMT9 inhibition induces immune responses, we used an MA9 AML transplant model (Fig. 5a,d). These MA9 AML cells were transduced with either a DOX-inducible shPrmt9 or shCtrl construct. Congenic WT C57BL/6 (B6) mice were used as recipients; Rag2^−/− (Fig. 5b) or NSG-SGM3 (NSGS) (Fig. 5c) mice were also used. After engraftment, DOX was administered. Rag2^−/− (Fig. 5b,e) or NSGS (Fig. 5c,f) mice bearing Prmt9 KD transplants survived significantly longer than mice with Prmt9 WT transplants, but succumbed to leukemia within 60 days (Fig. 5e,f). In contrast, five of seven B6 mice receiving Prmt9 KD transplants survived until day 120 (Fig. 5d). We established another cohort of MA9 leukemia transplants using B6 recipients and induced Prmt9 deletion on day 30. As shown (Extended Data Fig. 7a,b), Prmt9 KD modestly decreased AML progression.

We also performed antibody-based depletion of T or natural killer (NK) cells before in vivo DOX administration to KD Prmt9 (Extended Data Fig. 7c,d). CD4 and CD8 T cell depletion significantly abolished Prmt9 KD-induced AML regression, while NK depletion had minor effects (Extended Data Fig. 7c,d).

To verify the role of Prmt9 inhibition in a different AML model, we used the CMM transplant model²⁵. Thus, Prmt9 KD-mediated leukemia elimination effects are comparable in both models (Fig. 5g). Notably, Prmt9 KD remarkably decreased leukemia-initiating cell frequency in both models (Fig. 5d,g and Supplementary Table 7).

We performed scRNA-seq analysis of the MA9 tumor microenvironment. A cohort of WT B6 mice transplanted with DOX-inducible Prmt9 KD AML cells was established; we evaluated the transcriptional status of all immune lineages in transplants 7 days after DOX treatment. At that time, mice receiving Prmt9 KD cells began to exhibit decreased AML engraftment (Extended Data Fig. 7e). We then collected bone marrow and spleen cells from a representative mouse in each group for scRNA-seq. Our transcriptomes include 9,741 control and 11,291 Prmt9 KD bone marrow cells. We visualized transcriptionally homogeneous cell clusters (Fig. 5h,i and Supplementary Fig. 7). Notably, Prmt9 levels were more abundant in leukemic cells than other cells; Prmt9 KD decreased tumor cell frequency relative to controls (Fig. 5j and Extended Data Fig. 7f,g) and induced T cell activation (Fig. 5k). To verify T cell function, we assessed leukemia-specific T cell responses after Prmt9 KD using major histocompatibility complex (MHC)-survivin peptide as described in Stroopinsky et al.⁴⁴. We confirmed the elevated expression of Birc5 (encoding survivin) in cancer relative to the other subsets (Supplementary Fig. 8). We also noted that Prmt9 KD mice exhibited increased CD8⁺ T cells recognizing tumor survivin relative to Prmt9 WT controls (Fig. 5l). The results were confirmed using MA9/OVA cells (Fig. 5m).

We analyzed the scRNA-seq results of spleen, where T cells are more abundant (Extended Data Fig. 7h–k). We focused on T cells (Fig. 5n,o and Extended Data Fig. 7l). Accordingly, ten distinct T cell subpopulations (c0–9) were characterized, including c0 (naive CD4⁺), c1 (naive CD8⁺), c2 (memory CD8⁺cells), c3 (effector CD4⁺cells), c8 (memory T cells), c4 and c9 (effector T (T_eff) cells), c5 (CTLs) and c6 (regulatory (T_reg) cells) (Fig. 5n,o). Notably, Prmt9 KD altered the proportions of the subpopulations (Fig. 5o–r). Prmt9 KD reduced the naive T cell subsets (c0, c1) and expanded the populations of effector and memory T cells (c2, c3, c4, c8 and c9) (Fig. 5q,r). Prmt9 KD also increased CTLs (c5) and decreased T_reg cells (c6) (Fig. 5q,r). In bone marrow, Prmt9 KD expanded Cd44-expressing T cells and decreased Foxp3-expressing T_reg cells (Extended Data Fig. 7m,n).

To assess immune memory, we selected primary B6 mice that had shown complete regression of MA9 tumors on Prmt9 KD. We rechallenged them and the naive control cohort by injecting them with comparable numbers of MA9 cells. Unlike the control cohort (Fig. 5s), tumor-free mice exhibited a survival benefit after being rechallenged (Extended Data Fig. 7o).

Prmt9 KD upregulated IFN-stimulated gene (ISG) levels in T cells (Fig. 5t). Gene set enrichment analysis (GSEA) highlighted the activation of the IFN response pathways in T cells (Fig. 5u and Extended Data Fig. 7p). Similarly, ISG upregulation, including Isg15 (Extended Data Fig. 7q,r), Ifit1 (Extended Data Fig. 7s,t) and Cxcl10 (Extended Data Fig. 7u), was seen in other immune cell types. To verify that type I IFN responses underlined the outcomes, Prmt9 KD MA9 cells were transplanted into WT recipient or type I IFN receptor KO (Ifnar1 KO) mice. After leukemia cell engraftment, we induced Prmt9 KD and monitored leukemia development. The anti-AML effects of Prmt9 KD were significantly abolished on an Ifnar1 KO background (Fig. 5v).

Immunity after PRMT9 inhibition requires cGAS activity

By analyzing the scRNA-seq transcriptomes of MA9 cells, we observed upregulation of multiple ISGs after Prmt9 KD (Fig. 6a and Extended Data Fig. 8a). GSEA showed top enrichment of the IFN-α and IFN-γ pathways after Prmt9 KD (Fig. 6b and Extended Data Fig. 8b). Consistently, transcriptome profiling of the AML lines confirmed innate immune signaling activation by targeting Prmt9 (Fig. 6c and Extended Data Fig. 8c,d). To determine if innate immune activation was associated with PRMT9 activity, we compared ISG expression in Molm13 cells overexpressed WT or catalytic mutant PRMT9 after KD of endogenous PRMT9. Notably, only expression of PRMT9 WT, but not the mutant rescued ISG upregulation (for example, ISG15, IFI44) (Fig. 6d and Extended Data Fig. 8e). LD2 treatment also stimulated ISGs expression (Fig. 6e and Extended Data Fig. 8f,g).

We used THP1-Lucia luciferase to monitor IFN regulatory factor (IRF) signaling downstream of innate immune sensors, including the double-stranded DNA (dsDNA) sensor cGAS or dsRNA sensors. PRMT9 KD or LD2 treatment of THP1-Lucia luciferase increased luciferase signals (Fig. 6f and Extended Data Fig. 8h); an increase was blocked by deletion of cGAS. PRMT9 KD also enhanced cGAS activity, as evidenced by increased cGAMP (Fig. 6g). Overexpression of ENPP1, which degrades cGAMP, abrogated this effect (Fig. 6g and Extended Data Fig. 8i). PRMT9 KD increased γH2AX levels, which is indicative of DNA damage (Fig. 6h and Extended Data Fig. 8j,l), and promoted the accumulation of cytoplasmic dsDNA (Fig. 6i and Extended Data Fig. 8k,m). To determine whether tumor-intrinsic cGAS activity was required for Prmt9 KD-mediated immunity, MA9-OVA cells with cGAS KO (Extended Data Fig. 8n) were transduced with either inducible shPrmt9 or shCtrl and implanted into WT recipients. Unlike controls, cGAS KO mice did not show the tumor-specific T cell response seen after Prmt9 KD (Fig. 5m). Survival advantages were abolished on a cGAS KO background (Fig. 6j). To test the outcomes of cGAS activation in cancer cells, we transduced cGAS KO MA9 cells with an inducible cGAS-activating mutant (ΔN)⁴⁵ or corresponding cGAS WT (Fig. 6k) and then implanted parental (cGAS KO), cGAS WT or ΔN-expressing cells into B6 mice to analyze leukemogenesis. A reduced AML burden was seen in ΔN transplants (Fig. 6l). Mice with AML cells exhibiting cGAS activation showed significantly extended survival relative to other groups (Fig. 6m). cGAS levels were remarkably high in AML relative to normal donors (Fig. 6n), while ENPP1 levels were relatively lower in AML (Extended Data Fig. 8o). Moreover, among deadly cancers, AML cells exhibited the highest cGAS and lowest ENPP1 levels (Extended Data Fig. 8p,q).

We hypothesized that the T cell priming effects seen after Prmt9 KD could be mediated by increases in the immune transmitter cGAMP. Indeed, we observed elevated cGAMP levels in bone marrow fluid from Prmt9 KD MA9 mice (Fig. 6o). Moreover, the single-cell transcriptomes of DCs and macrophages revealed increased levels of Cd86 and MHC class II (H2-ab1), suggesting activation (Fig. 6p and Extended Data Fig. 8r). To determine whether the T cell priming effects seen after Prmt9 inhibition were due to antigen cross-presentation by DCs, we cocultured LD2-pretreated cGAS KO or cGAS WT MA9/OVA cells with bone marrow-derived DCs and then purified the DCs, which were exposed to naive OT-I⁺CD8⁺ T cells. We observed increased IFN-γ production by CD8⁺ T cells after coculture with DCs purified from the LD2-pretreated AML group (Fig. 6q). cGAS KO antagonized the DC-mediated cross-priming capacity, based on decreased IFN-γ production (Fig. 6q). We then assessed IFN-β production by DCs after coculture with MA9-OVA cells pretreated with LD2 and observed increased IFN-β production (Fig. 6r), an effect abolished by cGAS KO, suggesting that tumor cGAS activation underlies type I IFN signaling in DCs. We further assessed DC function in Prmt9 KD-induced AML regression using Batf3 KO mice because Batf3 is critical to cross-prime T cells⁴⁶. Specifically, we implanted Batf3 WT or KO mice with AML cells for assessment of AML progression. Relative to Batf3 WT mice, Batf3 KO partially decreased the Prmt9 KD-induced survival advantage (Fig. 6s).

Loss of XRN2 methylation underlies cGAS activation

PRMT9 inhibition in THP1 cells via LD2 (48 h) or shRNA significantly increased Rad3-related (ATR) signaling, whereas γH2AX elevation and changes in ataxia-telangiectasia-mutated (ATM) signaling were modest (Fig. 7a,b). However, we observed remarkably increased levels of γH2AX and pCHK2 after 72 h of PRMT9 inhibition (Figs. 6h and 7a,b), corresponding with the formation of DNA double strand breaks (Fig. 7c). These findings suggest that PRMT9 inhibition triggers an early insult that selectively activates ATR.

We next asked whether any PRMT9 substrate functions in the DNA damage response and whether its loss underlies ATR activation and cGAS stimulation. SILAC showed that seven of the 23 most downregulated methylated proteins after PRMT9 KD (Fig. 3c) regulate the DNA damage response (Fig. 3d and Extended Data Fig. 8s). To determine whether KO of any of them phenocopied the PRMT9 inhibition effects, we electroporated THP1-Lucia luciferase cells with Cas9 protein and bound guide RNA (gRNA) targeting the respective candidate genes⁴⁷. Of the seven genes, KO of XRN2, DDX3X or KHDRBS1 increased reporter activity (Fig. 7d,f); an in vitro methylation assay confirmed PRMT9 catalysis (Fig. 7e). We then ectopically expressed gRNA-resistant full-length XRN2 WT or DDX3X WT complementary DNAs or corresponding methylation-deficient constructs (XRN2-R946K or DDX3X-R88K) in THP1-Lucia luciferase cells and the corresponding cGAS KO/THP1-Lucia luciferase line, then KO the corresponding endogenous genes. Notably, XRN2-R946K expression increased THP1 reporter activity, an effect blocked by cGAS deletion (Fig. 7g), while methylation-deficient DDX3X did not increase reporter activity (Fig. 7h). We confirmed that XRN2-R946K is methylated by PRMT9, based on in vitro methylation and responses to LD2 treatment (Fig. 7i,j).

We next focused on the exoribonuclease XRN2 whose C terminus interacts with p54nrb to prevent R-loop formation⁴⁸. SILAC analysis revealed that among all XRN2 R residues, only R946 methylation levels were altered by PRMT9 KD (Supplementary Table 8). To determine if R946 methylation promotes XRN2 recruitment by p54nrb, we performed co-IP analysis. FLAG-tagged XRN2 interaction with p54nrb was decreased in the presence of R946K (Fig. 7k and Extended Data Fig. 8t). Relative to XRN2 WT, expression of the gRNA-resistant XRN2-R946K mutant in THP1 cells engineered to lack endogenous XRN2 promoted R-loop formation (Fig. 7l,m), resulting in ATR activation and γH2AX elevation (Fig. 7k). Consistently, LD2 treatment or PRMT9 KD promoted excess R-loop formation and activated ATR signaling as early as 48 h after treatment (Fig. 7a,b,n,o). Cell cycle analysis after LD2 treatment showed an increased percentage of G₂/M (Fig. 7p). Finally, RNASEH1 overexpression to resolve R-loops (Fig. 7n) decreased ATR/CHK1 activation, partially rescuing the THP1-luciferase reporter activity induced by LD2 (Fig. 7q,r). Collectively, these results indicate that loss of XRN2 methylation contributes to DNA damage by PRMT9 inhibition.

Combining LD2 with an ICI ablates cancers

Based on the scRNA-seq results, among the reported relevant immune checkpoint proteins, Prmt9 KD significantly upregulated PD-L1 in cancer cells (Fig. 8a,b), although PD-L2 and CTLA-4 were also modestly upregulated (Fig. 8a,b and Extended Data Fig. 9a,b). Notably, in two of three primary AML cocultures, upregulation of PD-L1 on Prmt9 inhibition was seen (Fig. 8c and Extended Data Fig. 9c).

**Fig. 8: Combining LD2 with an ICI ablates AML.**

To determine if a PRMT9 inhibitor synergizes with PD-1 monoclonal antibody (mAb) treatment, we treated AML samples for 4 days ex vivo. The combination elicited T cell expansion and reduced tumor cell frequency (Fig. 8d,e and Extended Data Fig. 9d,e). Activation of T cells was seen among combination-treated cells (Fig. 8f,g).

We next investigated cooperation between PRMT9 inhibitor and αPD-1 treatment using an A20 lymphoma syngeneic model (Extended Data Fig. 9f). Once A20 tumors reached 100 mm³, we treated mice with isotype control (vehicle), anti-PD-1 mAb (10 mg kg⁻¹ intraperitoneally every other day for 2 weeks), LD2 (100 mg kg⁻¹ intratissue injection once a day for 2 weeks) or combined LD2 and anti-PD-1. Tumor volumes were monitored through the end (Extended Data Fig. 9g). A humane endpoint was reached in a vehicle group mouse on day 29. The tumor size of the combined treatment was smaller than that of the vehicle (Ctrl) group starting on day 17. LD2 administration alone significantly decreased tumor size relative to controls after day 21 (Extended Data Fig. 9g–i). We also evaluated LD2 single treatment effects in NSGS mice xenografted with A20 cells; treatment modestly decreased A20 tumor growth (Extended Data Fig. 9j,k), probably because of its effects on translation (Extended Data Fig. 9l). Notably, A20 tumor weight in BALB/c mice was reduced in the combination group versus the vehicle controls (Extended Data Fig. 9h,i). LD2 treatment upregulated ISGs and PD-L1 (Cd274) (Extended Data Fig. 9m,n). We also observed an increased number of tumor-infiltrating T cells or CD8⁺ T cells after LD2 treatment or combination treatment (Extended Data Fig. 9o–r). Moreover, a remarkable increase in the number of active CD8⁺ T cells was seen in the LD2 single treatment group; the effects were enhanced by the combination treatment (Extended Data Fig. 9s–v).

We next evaluated the combination treatment in an MA9 AML transplant model. We treated AML-bearing mice for 3 weeks with vehicle, anti-PD-1 mAb (10 mg kg⁻¹ intraperitoneally every other day), LD2 or LD2 plus anti-PD-1. LD2 was administered at a dose of 10 mg kg⁻¹ intravenously twice a day. After treatment, compared to LD2 only, combination treatment significantly decreased leukemia engraftment and expanded tumor-specific T cells (Fig. 8h–j). Notably, combination treatment extended mouse survival and decreased LSC activity (Fig. 8k,l).

We established a humanized AML model. Specifically, in a cohort of MHC class I and II double-KO (DKO) NSG mice, we implanted 2 million MNCs from an AML specimen using intrafemoral injection of each DKO mouse. DKO mice showed long-term engraftment of T and CD33⁺ cells (Extended Data Fig. 10a,b), without acute graft-versus-host disease, consistent with other reports⁴⁹. Importantly, we confirmed bone marrow engraftment of human hematopoietic subsets (Extended Data Fig. 10c) and observed selective expansion of the immature CD33⁺CD34⁺CD45^dim subset (Extended Data Fig. 10c). We then divided mice into vehicle and LD2 treatment groups. After 3 weeks of treatment, we observed decreased numbers of leukemic CD34⁺ cells (Fig. 8m,n) and increased numbers of active CD8⁺ T cells (Fig. 8o,p) in LD2 relative to the control group.

Also, we assessed the correlation between PRMT9 activity and the response of PD-1 and PD-L1 inhibitors using clinical datasets^6,50,51. To do that, we defined the PRMT9 KD gene signature established from RNA-seq analysis of PRMT9 KD versus Ctrl AML lines (Fig. 6c and Supplementary Table 9). The signature consists of 102 differentially expressed genes (DEGs) common to two AML cell lines (fold change > 2, P < 0.05). Notably, higher levels of the PRMT9 KD gene signature were positively associated with complete response (CR) to ICI versus progressive disease (PD) in two clinical cohorts (Fig. 8q,r). Relevant to AML, we failed to detect any correlation of the signature with clinical responses to PD-1 inhibitors using the only available dataset (Extended Data Fig. 10d).

Discussion

PRMT9^hi LSCs may give rise to immune-evasive leukemia blasts. Our results reveal that targeting PRMT9 not only ablates LSCs but stimulates an anticancer immune response to achieve maximal therapeutic effects. This strategy, when combined with an ICI, could approach a cure. Specifically, our approach targets the arginine methyltransferase PRMT9 to ablate AML LSCs by downregulating the synthesis of short-lived oncoproteins; targeting PRMT9 also induces DNA damage-mediated activation of cGAS and release of cGAMP, thereby cross-priming T cells via a type I IFN response. Moreover, we identified that the lead compound LD2 as a potent inhibitor of PRMT9 activity that promotes robust anti-AML activity (Extended Data Fig. 10e).

PRMT9, one of two SDMA-forming PRMTs, is characterized by a unique duplicated methyltransferase domain^29,52. In this study, we used a quantitative proteomic method to profile changes in global arginine methylation on PRMT9 knockdown and identified undefined targets. Specifically, methylation at residue R493 enables the PABPC1 protein to bind to the mRNA poly(A) tail, promoting translation. Moreover, XRN2 methylation at R946 may allow complex formation with p54nrb to prevent the DNA double-strand breaks associated with the role of XRN2 in resolving the R-loop (RNA/DNA hybrid) structure⁴⁸. Indeed, PRMT9 inhibition or expression of XRN2-R946K in AML cells promoted R-loop formation and ATR signaling, which underlies cGAS activation in cancer cells. Moreover, PRMT9 did not catalyze cGAS methylation (Supplementary Fig. 9).

Our study demonstrates that tumor elimination induced by Prmt9 deletion relies on type I IFN responses. scRNA-seq analysis revealed that the changes in T cell subpopulations seen after Prmt9 KD are associated with immune memory. Other studies used high-dose cytotoxic chemotherapies that dampen immune responses^53,54,55. Interestingly, we found that neither Prmt9 KO nor LD2 treatment perturbed T cell function.

How does PRMT9 inhibition in cancer cells elicit a distinct response in T cells? In this study, we showed that cGAS-dependent dsDNA sensing by cancer cells is critical for the effects of T cell priming. Notably, leukemia cells express higher levels of cGAS relative to normal counterparts from healthy donors. On PRMT9 KD, cancer cells accumulate cytosolic dsDNA, providing abundant substrate for cGAS catalysis (Fig. 6i). Such changes in dsDNA are partially due to DNA damage induced by the loss of XRN2 methylation seen after PRMT9 inhibition. Interestingly, GSEA of single-cell transcriptomes from PRMT9 KD versus control MA9 cells showed significant enrichment of DNA damage response gene signatures (Extended Data Fig. 10f), confirming an association between PRMT9 inhibition and DNA damage.

Moreover, PRMT9 inhibition also downregulated SAMHD1 (Fig. 3g), which antagonizes cGAS–STING activity as reported previously⁵⁶. Consequently, cGAS-activating cancer cells can produce high levels of the immunotransmitter cGAMP. Among all cancers, leukemia cells express the lowest levels of ENPP1, which hydrolyzed cGAMP (Extended Data Fig. 8o,q), allowing sustained cGAMP production in cancer cells. Extracellular cGAMP may be transferred via gap junctions from cancer cells to DCs⁵⁷. Indeed, scRNA-seq analysis of the MA9 model revealed ISG upregulation in DCs (Extended Data Fig. 10g). Moreover, subsequent GSEA analysis showed upregulation of the IFN-α response pathway in T cells from Prmt9 KD AML bone marrow (Extended Data Fig. 7p). As an outcome of the type I IFN response, T cells (Fig. 5k) exhibited IFN-γ upregulation after Prmt9 KD. Indeed, we observed significant enrichment of IFN-γ response genes in MA9 cancer cells and in T cells (Extended Data Fig. 10h,i).

Collectively, we showed a biological role for PRMT9 in cancer. We developed a small molecule inhibitor blocking PRMT9 activity. Our study also prompts an appraisal of anticancer drugs with consideration of their impact on immune cells within the tumor microenvironment and provides a rationale for further evaluation of PRMT9 inhibition combined with a PD-1/PD-L1 inhibitor against AML.

Methods

Ethics statement

This study follows ethical regulations. Experiments using patient specimens were approved in part by the institutional review boards of City of Hope Comprehensive Cancer Center (COHCCC) and conducted in accordance with the Declaration of Helsinki (2013). Samples were acquired as part of the COHCCC institutional review board-approved clinical protocol no. 18067. All mouse experiments were completed in accordance with the Guidelines for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee (IACUC) at COHCCC. Experiments were performed in accordance with a protocol approved by the COHCCC ICUC (no. 15046). The maximum tumor size (humane endpoint) permitted by IACUC is 15 mm (diameter). All animals were euthanized before tumor size reached 15 mm in diameter. Maximum tumor size did not exceed 15 mm.

Patient cells

De-identified, clinically annotated primary patient samples including those derived from peripheral blood or bone marrow were obtained from patients with AML at COHCCC. The annotations are shown in Supplementary Table 1. Normal cells derived from peripheral blood were obtained from the COHCCC. Informed written consent was completed and acquired from all involved participants before sample acquisition. MNC separation, CD34⁺ cell enrichment or CD3⁺ T cell depletion was performed as described previously⁵⁸.

Cell culture

Molm13 (catalog no. ACC 554, DSMZ), MV4-11 (catalog no. CRL-9591, ATCC), THP1 (catalog no. TIB-202, ATCC), NB4 (catalog no. ACC 207, DSMZ), U937 (catalog no. CRL-1593.2, ATCC), HL-60 (catalog no. CCL-240, ATCC), MA9.6ITD and RAJI (catalog no. ACC 319, DSMZ), UPN1 (catalog no. CVCL_A795, Cellosaurus), BL41 (catalog no. ACC 160, DSMZ), Rec1 (catalog no. ACC 584, DSMZ), OCI-Ly3 (catalog no. ACC 761, DSMZ) and A20 (a gift from Y. Fu) were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium with 10% FCS as described previously^58,59. All other cell lines, including 293FT (catalog no. R70007, Thermo Fisher Scientific), DMS273 (a gift from R. Salgia), DMS114 (a gift from R. Salgia), SW1573 (a gift from E. Wang), A549 (a gift from E. Wang), SW620 (catalog no. CCL-227, ATCC), HCT116 (catalog no. CCL-247, ATCC), HepG2 (catalog no. HB-8065, ATCC), PC3 (a gift from S. Priceman), DU145 (a gift from S. Priceman), MDA-MB-231 (catalog no. CRM-HTB-26, ATCC), HT1197 (catalog no. CRL-1473, ATCC), A172 (catalog no. CRL-1620, ATCC), MIAPACA2 (catalog no. CRM-CRL-1420, ATCC) and HT1080 (catalog no. CCL-121, ATCC) were cultured in DMEM with 10% FCS. MA9.6ITD cells (MLL-AF9 plus FLT3-ITD) were established by J. Mulloy⁶⁰. The human primary normal and AML CD34⁺ cells used for transduction were maintained as described previously⁵⁹. Specifically, as noted in that paper, the medium was StemSpan SFEM (STEMCELL Technologies) supplemented with 50 ng ml⁻¹ recombinant human stem cell factor (SCF), 100 ng ml⁻¹ Flt3 ligand (Flt3L), 100 ng ml⁻¹ thrombopoietin, 25 ng ml⁻¹ interleukin-3 (IL-3) and 10 ng ml⁻¹ IL-6 (PeproTech). Mouse AML cells were cultured in RPMI 1640 medium with cytokines (mouse IL-3, 10 ng ml⁻¹; mouse IL-6, 10 ng ml⁻¹; mouse SCF, 30 ng ml⁻¹; Supplementary Table 10) as described previously⁵⁹.

Mice

In all experiments, male and female, 6–10-week-old, WT C57BL/6J (strain no. 000664, The Jackson Laboratory), B6(Cg)-Rag2^tm1.1Cgn/J (strain no. 008449, Rag2^−/−, The Jackson Laboratory), B6(Cg)-Ifnar1^tm1.2Ees/J (strain no. 028288, Ifnar1^−/−, The Jackson Laboratory), Kmt2a^{tm2(MLLT3)Thr}/KsyJ (strain no. 009079, MLL-AF9 knock-in, The Jackson Laboratory), B6.129S(C)-Batf3^tm1Kmm/J (strain no. 013755, Batf3^−/−, The Jackson Laboratory), NOD.Cg-Prkdc^scid Il2rg^tm1Wjl/SzJ (strain no. 005557, NSG, The Jackson Laboratory), NOD.Cg-Prkdc^scid Il2rg^tm1Wjl Tg(CMV-IL3,CSF2,KITLG)1Eav/MloySzJ (strain no. 013062, NSGS, The Jackson Laboratory) and NOD.Cg-Prkdc^scid H2-K1^b-^tm1Bpe H2-Ab1^em1Mvw H2-D1^tm1Bpe Il2rg^tm1Wjl/SzJ (strain no. 025216, NSG-MHC I/II DKO, The Jackson Laboratory) mice were used. B6-Ly5.1 (CD45.1, NCI 564) and BALB/c (NCI 028) mice were available from an outside vendor. Male and female mice were housed at the COH Animal Resource Center. All care and experimental procedures followed established institutional guidelines. The mouse room is conditioned with a 14 h light–10 h dark cycle, temperatures of 65–75 °F and 40–60% humidity. The procedure was run in accordance with a protocol approved by the IACUC at COHCCC.

Mouse experiments were performed once: Fig. 2d,e,h (male and female; five WT B6 mice per group); in Fig. 2f,g (male and female; five WT B6 mice per group); Fig. 2q (male and female; eight NSGS mice per group); Fig. 2r (male and female; eight NSGS mice for Ctrl, seven NSGS mice for Prmt9 KD); Extended Data Fig. 2p (male and female; six Prmt9^loxP/loxP/Mx1Cre⁻ mice for Prmt9 WT, nine Prmt9^l^oxP/loxP/Mx1Cre⁺mice for Prmt9 KD); Extended Data Fig. 2q (male and female; eight Prmt9^loxP/loxP/Mx1Cre⁻ mice for Prmt9 WT, 15 mice (Prmt9^loxP/loxP/Mx1Cre⁺) for Prmt9 KD); Extended Data Fig. 2r (male and female; seven B6-Ly5.1 mice per group); Fig. 5d (male and female; seven WT B6 mice per group); Fig. 5e (male and female; five Rag2^−/− mice per group); Fig. 5f(male and female; five NSGS mice per group); Fig. 5g (male and female; seven WT B6 mice per group); Fig. 5s (male and female; five WT B6 mice for naive mice, four survival mice from Fig. 5d for survivors); Fig. 5v (male and female; five Ifnar1^−/− mice for Ifnar1 KO, six WT B6 mice for Ifnar1 WT); Fig. 6j (male and female; seven WT B6 mice for the Prmt9 KD group, five WT B6 mice for each of the other three groups); Fig. 6m (male and female; seven WT B6 mice for cGAS KO + cGASΔN group, five WT B6 mice for each of the other two groups); Fig. 6s (seven WT B6 mice for each Batf3 WT group, five Batf3^−/− mice for the Batf3 KO group); Extended Data Fig. 7c (seven WT B6 mice for the Ctrl and Prmt9 KD groups, five WT B6 mice for the T and NK cell depletion groups); Extended Data Fig. 9g–i (five BALB/c mice per group); and Extended Data Fig. 9j,k (five NSGS mice per group). scRNA-seq and bulk RNA-seq were performed once per sample and are shown in Figs. 1e, 5h and 6c. If not otherwise specified, in vitro experiments were repeated at least three times.

DNA constructs and oligonucleotides

The CD530-EF1A-IRES-GFP vectors were purchased from System Biosciences. The CD530-EF1A-T2A-GFP vectors were modified from CD530-EF1A-IRES-GFP, replacing IRES with T2A sequences. Full-length WT or LDIG-to-AAAA mutant PRMT9 (ref. ²⁹) were cloned into CD530-EF1A-IRES-GFP vectors. FLAG-tagged XRN2 and FLAG-tagged DDX3X variants, and FLAG-tagged either full-length WT or C-terminal (amino acids 436–636) PABPC1 or R493K, R481K, R506K or 3RK mutants were cloned into the CD530-EF1A-T2A-GFP vector. All plasmids were synthesized by Genscript. shRNAs targeting human PRMT9, mouse Prmt9, PABPC1 and CREB1 were purchased from Sigma-Aldrich (MISSION shRNA) and cloned into pLKO-SFFV-RFP, as described elsewhere⁵⁸. cGAS WT and the activation mutant ΔN were purchased from Addgene and constructed into a DOX-inducible expression vector. SMARTvectors with shPRMT9 were purchased from Dharmacon (Horizon Discovery). The oligonucleotides used are listed in Supplementary Table 11.

Compounds

Compounds were sourced from the NCI Developmental Therapeutics Program (DTP), ZINC libraries or MolPort. The PEGylated liposome packaging of LD2 used for animal treatment was prepared using the thin film hydration method. Lipids (distearoylphosphatidylcholine, cholesterol and DSPE-PEG(2000) at a ratio of 3:1:0.2) plus compound were dissolved in chloroform; then, organic solvent was separated in a vacuum to form a thin film. Subsequently, lipids were hydrated in PBS, pH 7.4, at 60 °C to form liposomes.

Lentiviral transduction

Virus production was as described previously⁶¹. HEK 293T cells were transfected with pMD2.G and psPAX2 packaging vectors plus lentivectors designed to overexpress or knock down genes using the calcium phosphate method as described previously⁶¹. Supernatants containing virus particles were filtered and concentrated. Viral infection was performed as described previously⁶¹.

qPCR

RNA was prepared according to the TRIzol reagent protocol. After generation of complementary DNA, qPCR with reverse transcription was performed as described previously⁵⁹. The primers used are listed in Supplementary Table 11.

IP and immunoblotting

Cell lysates were prepared in a buffer containing 50 mM Tris, pH 7.4, 150 mM NaCl and 1 mM EDTA supplemented with protease inhibitors. Cell lysates were incubated with anti-FLAG beads or interested primary antibody (Sigma-Aldrich) overnight and denatured for immunoblotting. Proteins of interest were probed with primary and secondary antibodies. Signals were detected using the SuperSignal West Pico or Femato kits. All immunoblots were imaged using the G:BOX Chemi XX6 gel doc system and quantified with the ImageJ software (NIH).

ChIP–qPCR

Samples were prepared according to the protocol of the SimpleChIP Plus Enzymatic Chromatin IP Kit (catalog no. 9005, Cell Signaling Technology). Immunoprecipitates were exposed to anti-CREB1 (catalog no. SC-240, Santa Cruz Biotechnology) and anti-H3K27Ac antibodies, plus Protein G magnetic beads. After reversing, DNA was enriched; this was followed by qPCR.

Flow cytometry

Cells derived from the bone marrow or spleen samples were washed with PBS containing 1% FCS and then passed through a single-cell strainer and subjected to lysis of red cells. Before flow cytometry, cells were stained with the indicated antibodies in the same buffer. Flow cytometry analysis was performed. Data analysis was performed using FlowJo v.10. Molm13 cell engraftment in mice was determined using an anti-human CD45 antibody. CD45.2⁺ donor cells from transplants were determined using anti-mouse CD45.1 and CD45.2 antibodies. Mouse HSPCs were determined by staining with anti-mouse lineage antibody, including cKit, Sca-1, CD16 and CD32, and CD34 antibodies and a lineage antibody cocktail, including anti-mouse CD3, CD4, CD8, CD11b, CD11c, CD19, CD41, Ter119, B220, IgM, NK1.1, Gr-1 and interleukin-7 receptor subunit alpha (IL-7Rα). Anti-mouse Mac1, Gr-1, B220 and Ter119 were used to define mouse bone marrow differentiation. We also detected antigen-specific T cells in tumors as described previously⁴⁴. For intracellular staining, fixed cells were incubated once with antibodies against IFN-γ (clone XMG1.2) and granzyme B (clone QA16A02). To define the human primary samples, we used the following markers: T cells (CD3⁺), B cells (CD19⁺/CD20⁺), monocytes (CD14⁺) and DCs (HLA-DR⁺CD34⁻CD33⁻CD3⁻CD19⁻CD20⁻CD14⁻CD56⁻), as well as the immature CD33⁺CD34⁺CD45^dim subset. CD69 and IFN-γ staining was used to determine T cell status. For the cell cycle studies, fixed cells were stained with 4,6-diamidino-2-phenylindole (DAPI).

Competitive transplantation

Bone marrow cells (0.5 × 10⁶per transplant) from CD45.2⁺ Prmt9^loxP^/l^oxPMxCre⁺ or Prmt9^loxP/loxPMxCre⁻ mice were combined with CD45.1⁺ bone marrow cells (at 1:1 ratio) and then implanted into lethally irradiated (900 cGy) B6-Ly5.1 mice by intravenous injection. Peripheral blood samples were collected and assessed with CD45.1 and CD45.2 antibodies. Mouse recipients were induced with pIpC (InvivoGen) intraperitoneally 15 mg kg⁻¹ every other day for 7 days; CD45.2⁺ chimerism in peripheral blood was assessed every 4 weeks.

Limiting dilution assays

For the limiting dilution assays, to evaluate LSC frequencies, AML cells were suspended in Colony Forming Cell growth medium with DOX to induce Prmt9 KD and plated in multi-well plates. To evaluate the frequency of leukemia-initiating cells in vivo, bone marrow cells isolated from Ctrl or Prmt9 KDMA9 AML mice were injected intravenously into sublethally conditioned recipient mice, as described in Supplementary Table 7. The number of recipient mice with leukemia development was determined in each group. The frequency of LSCs and LICs was determined using the ELDA software.

AML mouse model and in vivo bioluminescence imaging

To assess the effect of Prmt9 KO and KD in vivo, MA9 or CMM cells were transduced with lentiviral vectors harboring a luciferase reporter. Cells were used for intravenous inoculation into sublethally irradiated CD45.1 B6 mice or WT B6, Rag2^−/− or NSGS mice. As for bioluminescence imaging, mice were administered 150 mg kg⁻¹ d-luciferin (GoldBio) within PBS, followed by analysis using Lago X. Bioluminescent signals were quantified using the Aura imaging software (Spectral Instruments Imaging). Total values were determined using the regions of interest and photons s cm² sr. To identify the immune subsets contributing to leukemia regression after Prmt9 KD, we performed antibody-based depletion with an initial dose of combined anti-CD4 and anti-CD8 treatment or anti-NK1.1 treatment administered 1 day before in vivo DOX administration to Prmt9 KD mice. Antibodies (400 μg) were injected intraperitoneally twice the first week, and then at 200 μg twice weekly to maintain NK or T cell depletion. To assess DC function in Prmt9 KD outcomes, we implanted Batf3 WT or Batf3 KO mice with AML cells for further evaluation.

Assessment of cell growth, apoptosis and colony formation

Cell growth was assessed using the CellTiter-Glo Assay Kit (Promega Corporation). Apoptosis was determined using annexin V or DAPI. Colony formation capacity was determined as described previously^58,59.

SILAC-based quantitative proteomics analysis

Proteomics sample preparation

For SILAC, Molm13 cells were cultured in SILAC RPMI 1640 medium (catalog no. 88365, Thermo Fisher Scientific) with 10% FCS (catalog no. A3382001, Thermo Fisher Scientific) and either light l-lysine (catalog no. 89987, Thermo Fisher Scientific) and l-arginine (catalog no. 89989, Thermo Fisher Scientific) for control cells, or heavy lysine (catalog no. 88209, Thermo Fisher Scientific) and l-arginine (catalog no.89990, Thermo Fisher Scientific) for inducible PRMT9 KD cells, for at least ten passages to ensure full incorporation of light or heavy l-lysine and l-arginine.

After 3 days of DOX induction in both control and PRMT9 KD cells, light-labeled and heavy-labeled cells were combined at 1:1 ratio. Cells were washed and centrifuged at 300g for 5 min. Cell pellets were lysed in 9 M urea with protease and phosphatase inhibitors in HEPES (pH 8.0) buffer. Samples underwent four cycles of sonication for 30 s each using a microtip sonicator (VibraCell VCX130, Sonics & Materials) operating at 50% amplitude. Lysates were centrifuged at 20,000g for 15 min; protein quantification was performed by using a bicinchoninic acid (BCA) assay. An equal amount of extracted protein from heavy and light SILAC culture was mixed for further digestion. The sample was first reduced by incubation with dithiothreitol (DTT) (5 mM, 55 °C) and then alkylated by incubation with iodoacetamide (10 mM) in the dark. The sample was diluted fourfold before sequential digestion first with LysC (2 h) and then overnight with Trypsin Gold. Digestion was quenched using trifluoroacetic acid and the sample was desalted using 0.7 ml of a Sep-Pak Classic C18 column (Waters). Eluted peptides were speedvac’d to dryness and reconstituted in 1.4 ml immunoaffinity purification buffer followed by peptide quantification using a BCA assay. We subjected 5% of peptides to global quantitative proteomics analysis and 95% of the rest to methyl-R peptide enrichment. This consisted of sequential incubation of peptides with anti-MMA antibody beads (catalog no. 12235, Cell Signaling Technology) and anti-SDMA antibody beads (catalog no. 13563, Cell Signaling Technology). Enriched peptides were reconstituted in 10 µl loading solvent (98% water, 2% acetonitrile, 0.1% formic acid); 1 µg of nonenriched peptides was used for global protein identification.

Results acquisition

Data were obtained on an Orbitrap Fusion Lumos mass spectrometer (methylated peptides) or Orbitrap Eclipse with FAIMS Pro interface (unmodified peptides) coupled to a U3000 RSLCnano LC system with running binary solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in acetonitrile) at 300 nl min⁻¹. Methylated peptides (5 µl per injection) were directly loaded on a 25 cm EasySpray C18 column and eluted over a 120-min gradient as follow: 80 min with 2–19% B, 20 min with 19–30% B, 5 min with 30–98% B, followed by 2 min of high organic wash and return to initial conditions in 1 min. Unmodified peptides (1 µg peptides, 5 µl per injection) were directly loaded on a 50-cm EasySpray C18 column and eluted over 240 min using the following gradient: 12 min with 2–5% B, 158 min with 5–19% B, 40 min with 19–30% B, 9 min with 30–90% B, followed by 4 min of high organic wash and return to initial conditions in 2 min. Using a duty cycle of 3 s (Lumos) or 1 s (Eclipse) per FAIMS CV (−40/−60/−80), most abundant precursors were fragmented using higher-energy collisional dissociation (32% normalized collisional energy on Eclipse and 35% normalized collisional energy on Lumos) and measured in the ion trap. Dynamic exclusion was set to 60 s to prevent resampling of previously analyzed precursors.

Proteomics data analysis

MS raw files were searched against the human UniProt protein database (downloaded in 2020, 42,373 entries) and a common contaminant database using MaxQuant v.1.6.17.0. The results were filtered to 1% protein and site false discovery rate (FDR). The resulting methyl peptide SILAC ratios obtained from the MaxQuant evidence.txt output file were normalized to their protein SILAC ratios before further analyses⁶².

R-methyl analysis

Motif analysis was performed using the iceLogo web application as described previously³⁰.

Polysome profiling

We performed polysome profiling as described previously²⁸. Engineered Molm13 cells were DOX-induced for 3 days to delete PRMT9 expression and then treated for 5 min with 100 μg ml⁻¹ cycloheximide. After treatment, cells were collected and lysed. We prepared sucrose density gradients (15–45% w/v) using a Gradient Master (BioComp Instruments). Then, the supernatant from the cell lysates was separated using centrifugation and fractionation. The collected RNA was further assessed in the qPCR analysis.

OPP protein synthesis assay

Protein synthesis was assessed by using the Click-iT Plus OPP Assay Kit (Thermo Fisher Scientific), with modifications. Briefly, treated cells were exposed to Click-iT OPP, then washed with PBS and fixed. After permeabilization for 15 min, cells were reacted with cocktail, then analyzed using flow cytometry.

In vitro methylation assay

The assay was performed in a 30-µl reaction with 50 mM Tris HCl, pH 7.4, 50 mM NaCl, 50 mM KCl, 1 mM MgCl₂ and 1 mM DTT buffer. Specifically, 1 µg purified PABPC1-CT protein or synthesized peptides, 1 µg purified PRMT9 protein and 5 µM of SAM (Cayman Chemical) were combined. Methylated proteins and peptides were detected with immunoblot or dot blot assays using anti-pan-SDMA, anti-pan-MMA, anti-pan-ADMA or our in-house PABPC1 R493me antibody. The R493me antibody was created by Genemed Synthesis. For the ex vivo tritium labeling of the methylation assay, 1 µg purified PRMT9 protein, 1 µg HA-tagged PABPC1 WT or corresponding PABPC1-R481K/R493K/R506K (3RK) protein, which were immunoprecipitated from 293T cells, and 1 µl S-adenosyl-l-[methyl-3H] methionine (78 Ci mmol⁻¹) was added to a 30 µl reaction mixture at 30 °C for 1 h. Samples were separated and transferred to polyvinylidene membranes for further assessment.

PRMT9 structure-based virtual screening

The crystal structure of human PRMT9 (Protein Data Bank (PDB) ID 6PDM; 2.45 A resolution) was used for virtual screening. Missing loops were added using a molecular operating environment loop modeler. A box size of 25 × 21 × 27 Å³ centered around the cocrystalized chemical probe was used for screening, which includes both the SAM pocket and catalytic pocket in the N-terminal methyltransferase domain (amino acids 150–520). To rank the binding affinity, parallel AutoDock Vina^63,64 runs were conducted on a local computer cluster. Seven hundred thousand compounds from the ZINC library were selected using the following criteria: molecular weight 350–450, log P < 3, total charge −2e to +2e and availability. In addition, we also screened the NCI library (NCI DTP 260,000 compounds). Each ligand was docked ten times and ranked according to the lowest binding energy score. After screening, we purchased the top 300 candidates (142 of them were available) from the NCI DTP and the top 100 candidates (70 of them were available) from the ZINC library to assess anti-AML activity. To estimate lead compound selectivity, we also performed Vina docking of LD2 into human CARM1 (PDB ID 5U4X), PRMT5 (PDB ID 4X61), PRMT7 (PDB ID 4M38) and PRMT9. To compare LD2 binding to PRMT5 versus PRMT9, we carried out two replicas of 100-ns molecular dynamics simulation of LD2 docked into each.

Saturation transfer difference and Carr–Purcell–Meiboom–Gill NMR assays

Maltose binding protein (MBP)-tagged PRMT9 core methyltransferase domain (150–474) protein was expressed and purified by Genscript. Briefly, the PRMT9 core methyltransferase domain sequence was inserted into the pMAL-c5X vector between the Nde I and EcoR I sites. Tagged protein was expressed in BL21 and purified on an MBP column, followed by Superdex 200 and Q Sepharose columns. Proteins were sterile-filtered and lyophilized after extensive dialysis against the NMR buffer (50 mM NaH₂PO₄, pH 7.5). Deuterium oxide-based sodium phosphate buffer was used with 5% DMSO-d₅. For the STD NMR assay, the molar ratio of LD2 to PRMT9 was 60:1 in which the concentration of PRMT9 was 0.67 μM; 50 μM trimethylsilylpropanoic acid-d₄ was used as the internal reference. The molar ratios between PMRT9 and LD2 were 1:20, 1:40 and 1:60, in addition to a control sample with free LD2. LD2 concentration in the Carr–Purcell–Meiboom–Gill (CPMG) experiments was 40 µM. The NMR saturation transfer difference (STD) experiments were carried out at 25 °C on a 700-MHz Bruker Ascend system equipped with a 5-mm triple resonance cryogenic probe as described previously⁶⁵. The CPMG experiment was performed as described previously⁶⁶. Data were analyzed using Bruker TopSpin v.3.6.

Thermal shift assay

We also assessed whether LD2 binds to PRMT9 directly in vivo; to do so, a cellular thermal shift assay was performed as described previously^39,40. We first engineered Molm13 cells to overexpress FLAG-tagged PRMT9 WT or PRMT9 mutant (W152A, D258A and E433A; all three residues are predicted drug and PRMT9 binding sites). Five million cells were pretreated with 2.5 µM LD2 overnight. DMSO was used as the control. Cells were aliquoted in each tube and heat-shocked using Thermal Cycler at the indicated temperatures. Cells were then lysed for the immunoblot assay. Experiments were performed using three biological replicates.

Primary AML MNC culture, mass cytometry staining, acquisition and analysis

Two million MNCs from AML bone marrow specimens were cultured per well in 24-well plates in IMDM plus 20% FCS under physiological cytokine conditions as described previously^41,42 (granulocyte-macrophage colony-stimulating factor in 200 pg ml⁻¹, granulocyte colony-stimulating factor in 1 ng ml⁻¹, SCF in 200 pg ml⁻¹, IL-6 in 1 ng ml⁻¹, macrophage inflammatory protein-1 alpha in 200 pg ml⁻¹ and leukemia inhibitory factor in 50 pg ml⁻¹). We then used the EasySep Dead Cell Removal Kit (STEMCELL Technologies) to ensure more than 95% living cells before culture. Cells were treated with vehicle (dimethylsulfoxide), 2.5 μM LD2, anti-PD-1 (pembrolizumab, 10 µg ml⁻¹, SIM0010, Bio X Cell) or LD2 plus anti-PD-1 for 4 days at 37 °C. On day 4, cells were pretreated for 6 h with brefeldin A and subjected to CyTOF immunostaining with customized surface or intracellular marker antibodies, according to Fluidigm CyTOF protocols (PN400279A4). An untreated peripheral blood mononuclear cell sample from a healthy donor served as a control for phenotyping. Samples were acquired on a Fluidigm Helios. Data were normalized and saved as FCS files before analysis using the Cytobank software (https://premium.cytobank.org/). After data were cleaned up, spanning-tree progression analysis for density-normalized events was used to cluster AML cells and immune cell subpopulations based on the median level of each.

In silico analysis of CTL levels in primary AML samples

For CD8A, CD8B, GZMA, GZMB and PRF1, the average expression levels of these genes were used to estimate CTL levels in AML samples^43,67. We carried out in silico tests to calculate the ratio of PRMT9^hi and PRMT9^lo patients exhibiting high versus low CTL scores using both GSE144688, which includes 526 samples of patients with AML, and GSE12417, which includes 163 patient samples. For each patient, high versus low CTL scores were decided according to cutoff of 0.5 for the z-score. A Fisher’s exact test was used to assess significance.

scRNA-seq

Library preparation

Bone marrow cells in MA9-transplanted mice, and bone marrow and spleen cells in Ctrl and Prmt9 KD mice administered DOX in drinking water over 7 days, were collected for analysis. Single cells were resuspended in 0.4% BSA and loaded to generate an emulsion of single-cell gel beads. Approximately 5,000–10,000 cells were loaded per channel. Libraries were prepared using the Single Cell 3′ Library & Gel Bead, Single Cell 3′ Chip and i7 Multiplex Kits, according to the Single Cell 3′ Reagent Kits v2 User Guide (part no. CG00052 Rev A). Libraries were sequenced on an Illumina HiSeq 4000 system.

Data processing

We used the Cell Ranger Single Cell Software Suite to perform single-cell 3′ gene counting and aggregation of multiple samples to generate raw counts, cell barcodes and gene features. The R package Seurat was run as the platform to implement all data processing procedures⁶⁸.

Quality control, normalization and batch removal

Cell quality control was executed as follows: the minimum detected genes (3) in each cell; the minimum number of cells (200) related to each gene; and the maximum fraction (0.2%) of counts from mitochondrial genes per cell barcode. The high-count depth threshold (2,000) was used to filter out potential doublets. Then, the count matrix was normalized to obtain the correct relative gene expression abundance between cells⁶⁹. Then, the R package Harmony was applied to remove batch effects due to biological differences between cell types or states.

Feature selection, dimension reduction and visualization

To retain informative genes with high variability, genes with small variations (below 2) among all cells were filtered out. Then, the dimensions of count matrices were reduced using dedicated dimension reduction algorithms, such as UMAP and t-distributed stochastic neighbor embedding (t-SNE). Two-dimensional visualization outputs were then generated using the leading reduced components in the UMAP and t-SNE plots.

Clustering and annotation

UMAP-related processed data were regarded as the input of cell clustering. Neighborhood distances among all cells were determined to infer the identity of each cell. Then, clusters were acquired via specified distance metrics (Euclidean distance). Furthermore, for each cluster, the R package MAST was used to deduce significant DEGs. These DEGs were considered markers of a cluster and were used for annotation purposes. Annotations were conducted manually by comparing marker genes with the literature and arranging cell categories. In addition, automatic annotation of cell clusters was done using the R package SingleR, as described previously⁷⁰. By combining both annotation styles, the final cell type labels of each cluster were acquired.

GSEA (for T cell and MA9 clusters)

For the cell type clusters of interest, GSEA was performed based on preordered genes ranked using MAST-derived (−log₁₀(P_adj) × sign (log fold change)) with 1,000 permutations⁷¹. The gene sets of the Hallmark, Kyoto Encyclopedia of Genes and Genomes, chemical genetic perturbation and Gene Ontology-Biological Process categories of the Molecular Signatures Database were considered as the signatures. Finally, specific enriched genes within a cluster were visualized by averaging their expression among all cells in that cluster. Key enriched gene expression was rescaled by z-scores and visualized in the heatmap.

T cell subset identification

scRNA-seq uncovered ten distinct T cell clusters (c0–c9). c0 cells expressed Cd4 and CD62L, but not the effector and memory T cell marker Cd44 or T cell activation genes. Thus, c0 was defined as naive CD4⁺ T cells. Similarly, c1 cells expressed Cd8a and CD62L but not Cd44 or other T cell activation markers and were defined as naive CD8⁺ T cells. c2 cells expressed Cd8a, Cd44 and Sell, and intermediate levels of Tbx21 (T-bet) and Eomes, and represented a memory CD8⁺ T cell population. c3 cells expressed high Cd4, Cd44 and Icos, Ctla4, Tnfrsf4 and Pdcd1, but did not express CD62L and were defined as activated and effector CD4⁺ T cells. c5 cells expressed Cd44 and showed the highest levels of Ifng, Gzmb, Icos, Tim-3, Il2ra, Tnfrsf18 and Lag3, considered as differentiated CTLs. c6 cells were defined as T_reg cells because they express Cd4, Il2ra (Cd25) and Foxp3. c4, c7, c8 and c9 cells contained both CD4⁺ and CD8⁺ T cells. c4 and c9 showed lower levels of activation markers, and lower CD62L and higher Cd44, suggesting that they represent T_eff cell populations. c7 expressed only the naive T cell marker CD62L, indicating a naive population, while c8 expressed lower CD62L and higher Cd44, but did not express other T cell activation markers, suggesting it represents a memory T cell population.

Bulk RNA-seq analysis

Total RNA was prepared using the TRIzol reagent (Thermo Fisher Scientific). RNA quality (RNA integrity number) was assessed and sequenced on an Illumina HiSeq 2500 system. RNA-seq reads were aligned with default settings. Count data were normalized. Genes were defined as differentially expressed if the fold change was less than 1.5 or less than 0.67, with an FDR less than 0.05, and at least one sample showing reads per kilobase per million mapped reads greater than 1. We performed hierarchical clustering of DEGs using Cluster v.3.0 with Pearson correlation distance and average linkage, and visualized them with Java TreeView. Enrichment analysis on the pathways of Hallmark, Kyoto Encyclopedia of Genes and Genomes and chemical genetic perturbation was performed using GSEA.

Detection of cGAMP

cGAMP levels were detected as reported elsewhere^72,73. THP1 cells were DOX-treated to induce PRMT9 KD for 2 days; serum-free Phenol Red RPMI (Thermo Fisher Scientific) medium was replaced for another 24 h. Conditioned medium was collected and cGAMP levels were detected using the Enzyme Immunoassay Kit (Arbo Assays). To determine cGAMP levels in the bone marrow microenvironment of control and Prmt9 KD mice, bone marrow fluid was collected by centrifuging tibias and femurs at 8,000 rpm for 15 s; then, cGAMP levels were assessed.

Lucia luciferase reporter assay

WT (catalog no. thpd-nfis, InvivoGen), cGAS KO (catalog no. thpd-kocgas, InvivoGen) and MAVS KO (catalog no. thpd-komavs, InvivoGen) THP1-Dual cells were used for the reporter assay. The purchased THP1-Dual cells (InvivoGen) were derived from the human THP1 monocyte line harboring the Lucia gene. Reporter cells were further engineered with inducible PRMT9 shRNA or control shRNA. After DOX treatment to PRMT9 KD or LD2 to inhibit PRMT9 in these cells, Lucia luciferase activity was determined as described by the manufacturer (InvivoGen) by adding QUANTI-Luc reagents and read with a FilterMax F5 microplate reader (Molecular Devices).

Immunofluorescence microscopy

Cells were spun onto glass coverslips, fixed and incubated with primary anti-dsDNA (AE-2), γH2AX or S9.6 antibodies, then with secondary antibody. Slides were then mounted in 90% glycerol solution containing DAPI (Thermo Fisher Scientific) and examined under a ZEISS LSM 880 confocal microscope.

Comet assays

We used the OxiSelect Comet Assay Kit (Cell Biolabs). Briefly, after PRMT9 KD, THP1 cells were mixed with prewarmed (37 °C) Comet agarose at a 1:10 ratio (v/v), then loaded onto the top of the Comet agarose base layer. Slides were immersed for 60 min in lysis buffer at 4 °C, which was washed with prechilled alkaline solution. After three washes with prechilled Tris/Borate/EDTA buffer, slides were subjected to electrophoresis at 1 V cm⁻¹ for 15 min, and then rinsed twice with deionized water. Comets were examined under a widefield ZEISS Axio Observer 7 fluorescence microscope. Approximately 50 cells were determined using the OpenComet software in Image J and shown as olive tail moments^74,75.

Gene editing in THP1 cells

THP1 reporter cells were electroporated with ribonucleoprotein complexes consisting of Cas9 protein and sgRNAs in the Neon Transfection System; 20 μmol l⁻¹ guide RNA (gRNA) (as listed in Supplementary Table 11) were mixed at a 1:1 ratio. KO efficiency was assessed using immunoblot analysis.

Cocultures of bone marrow DCs and T cells

As described previously⁵⁷, bone marrow cells were cultured with complete RPMI medium containing 20 ng ml⁻¹ granulocyte-macrophage colony-stimulating factor (PeproTech). Fresh medium was added on days 3 and 6. CD8⁺ T cells were isolated from the spleens of OT-1 transgenic mice. MA9-OVA cells were pretreated for 2 days with LD2 and then cocultured overnight with collected bone marrow-derived DCs. Supernatants were collected for IFN-β assessment. Bone marrow-derived DCs were selected using a CD11c⁺ selection kit (STEMCELL Technologies) and cocultured for 48 h with OT-1 CD8⁺ T cells. IFN-γ supernatants were assayed using a mouse IFN-γ Flex Set Cytometric Bead Array.

MA9 AML model in vivo treatment and assessment of leukemia-specific immunity

Once leukemia cells were engrafted, MA9 syngeneic transplant mice were treated for 3 weeks with vehicle control, LD2, single anti-PD-1 mAb (catalog no. BE0146, Bio X Cell, 10 mg kg⁻¹ intraperitoneally every other day) or LD2 plus anti-PD-1 antibody. LD2 was administered at 10 mg kg⁻¹ intravenously twice a day, based on the preliminary pharmacokinetic and pharmacodynamic results. Mice were assessed for overall survival or killed directly to assess MA9 cell engraftment in bone marrow and perform staining with survivin-specific pentamers to assess MA9-specific immunity as described elsewhere⁴⁴. Briefly, the bone marrow of MA9 mice was stained with anti-CD8 together with survivin-specific pentamers. CMV-specific pentamers were the negative controls. The percentage of survivin or CMV pentamer-positive CD8 T cells was assessed using flow cytometry. Secondary transplantations were performed to evaluate LSC activity in each group by assessing MA9 cell engraftment in the bone marrow.

Humanized model

The model was established using MHC class I and II DKO NSG mice⁴⁹. To do so, we implanted 2 million MNCs from AML specimens intrafemorally into an irradiated DKO NSG mouse. After transplantation, MHC-deficient mice showed long-term (approximately 12 weeks in peripheral blood) engraftment of T and CD33⁺ cells without developing acute graft-versus-host disease. A panel of human lineage and progenitor cell markers (CD45, CD33, CD34, CD14, CD19, CD20, CD3, CD56, HLA-DR) was used to define T cells, B cells, monocytes, DCs and immature CD33⁺CD34⁺CD45^dim cells. Mice were divided into two groups and treated with vehicle or LD2. Three weeks later, the number and frequency of leukemic CD34⁺ cells and the number of CD8⁺ T cells expressing CD69 and IFN-γ were assessed.

Non-Hodgkin lymphoma tumor models

A20 cells (3 × 10⁶) were subcutaneously implanted into syngeneic BALB/c mice. When tumor volume reached 100 mm³, mice were randomized into treatment groups. Tumor-bearing mice were treated with isotype control (vehicle), anti-PD-1 mAb (10 mg kg⁻¹ intraperitoneally every other day for 2 weeks), LD2 (100 mg kg⁻¹ intratissue injection daily for 2 weeks) or a combination of LD2 with anti-PD-1. Tumor volume was monitored through the end of the study when a humane endpoint was reached. The maximum tumor size (humane endpoint) permitted by the IACUC is 15 mm (diameter). All animals were euthanized before tumor size reached 15 mm in diameter. The microenvironmental components of tumors were analyzed using immunohistochemistry (IHC) and intracellular staining followed by flow cytometry.

IHC

Fixed A20 tumors were embedded in paraffin. Four-micrometer-thick sections on slides were incubated for 1 h at 60 °C, deparaffinized and then rehydrated before IHC staining. Slides were blocked with 3% H₂O₂. Slides were subjected to antigen retrieval for 15 min at 120 °C in citrate buffer, treated with Tris-buffered saline and incubated for 1 h with anti-mouse CD3 or anti-mouse CD8 antibody. After washing, slides were incubated with secondary antibody. Slides were developed and counterstained with Mayer’s hematoxylin solution. Slides were scanned using whole slide imaging and analyzed using the NDP.view2 software (Hamamatsu).

Analysis of tumor-infiltrating cells

Portions of fresh A20 tumors were cut into small pieces, then dissociated with type IV collagenase, type IV DNase and type V hyaluronidase at 37 °C for 30 min. Cell suspensions were passed through a 70-μm strainer and centrifuged at 300g for 5 min. Cells were stained for 30 min using a Live-or-Dye Fixable Viability Stain Kit (catalog no. 32018, Biotium). Next, cells were stained with immune cell surface markers (mouse CD45-allophycocyanin, mouse CD3-allophycocyanin/cyanine 7, mouse CD4-Alexa Fluor 700 and mouse CD8-Brilliant Violet 605). After two washes, cells were fixed and permeabilized, then intracellularly stained with mouse IFN-γ-phycoerythrin and granzyme B-fluorescein isothiocyanate antibodies in the permeabilization for the flow analysis. Results were analyzed with FlowJo v.10 (FlowJo LLC).

Statistics and reproducibility

Studies involving independent cohorts of mice were typically performed once, with several exceptions stated in the figure legends. No specific statistical tests were applied to determine sample size; size was established according to our previous experience with the models used. Accordingly, we typically used experimental cohorts of 5–7 mice. The experiments were not randomized. Investigators were not blinded to allocation during the experiments and outcome assessments. Data collection and analysis by all investigators were not performed blinded to the conditions of the experiments. No data were excluded from the analyses.

In general, data from independent experiments are shown as the mean ± s.d. or s.e.m. Statistics were determined using an unpaired, two-tailed Student’s t-test, a two-way ANOVA, a one-way ANOVA and a two-sided Fisher’s exact test. Survival results were analyzed with a log-rank (Mantel–Cox) test and expressed as Kaplan–Meier survival curves. Prism (GraphPad Software) was used for the statistics; the detailed methods are described in each individual figure legend.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The scRNA-seq and bulk RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession nos. GSE217195 and GSE217396. The PRMT9 SILAC proteomics data have been deposited in PRIDE under accession no. PXD039441. The human cancer PRMT9 expression data were derived from the TCGA Research Network (http://cancergenome.nih.gov/). The dataset derived from this resource that supports the findings of this study is available in the Source data and Supplementary information. A previously published reference dataset of bone marrow samples from newly diagnosed patients with AML and healthy age-matched controls, the BEAT AML dataset, can be found at http://vizome.org/aml/. The TARGET-AML data can be found under accessions nos. GSE14468, GSE12417, GSE63270 and GSE183415. Other published datasets and information are also available at https://DepMap.org/portal/, https://www.cbioportal.org/ (TCGA PanCancer Atlas Studies), https://www.fobinf.com/ (BloodSpot) and GEPIA http://gepia.cancer-pku.cn/ (GEPIA). Source data for all the main figures, extended data figures and supplementary information have been provided as source data files or supplementary files. All other data supporting the findings of this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

No custom code was created. All the packages used in this study are open-source R packages. Briefly, Seurat v.5.0.0 was used for scRNA-seq data quality control, normalization, feature selection, dimension reduction and visualization. Harmony v.1.1.0 was used for batch removal between different scRNA-seq samples. MAST v.1.26.0 was used for the differential gene expression analysis between scRNA-seq UMAP clusters. SingleR v.2.2.0 was used for the scRNA-seq cell type annotations. edgeR v.3.42.4 was used for differential gene expression analyses between bulk RNA-seq samples. In addition, GSEA v.4.0.3 was implemented for all the pathway enrichment work.

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Acknowledgements

We thank the COH Comprehensive Cancer Center, and the patients, donors and their physicians for providing primary specimens for this study. This work was supported in part by National Institutes of Health grant nos. R01 HL141336, R01 CA248149, R01 CA279595 and ACS RSG-19-036001-LIB; the When Everyone Survives Leukemia Research Award; the Tower Cancer Research Foundation Scientific Innovations Award for Senior Investigator the St. Baldrick’s Foundation Research Grant Award the Alex’s Lemonade Stand Foundation R Accelerated Award; the Vera and Joseph Dresner Foundation Established Investigator Award and the CURE Childhood Cancer Award, all to L. Li, who was also supported by the Gehr Family Center for Leukemia Research. We thank J. Yu and M. Kortylewski (both from City of Hope Medical Center) for providing the OT-I mice; Y. Fu (UT Southwestern Medical Center) for providing the A20 cell line; E. Wang (City of Hope Medical Center) for providing the A549 and SW1573 cell lines; R. Salgia and A. Mohanty (both from City of Hope Medical Center) for providing the DMS273 and DMS114 cell lines; and S. Priceman (City of Hope Medical Center) for providing the PC3 and DU145 cell lines. The MA9.6ITD cells (MLL-AF9 plus FLT3-ITD) were established by J. Molley (Cincinnati Children’s Hospital Medical Center). We thank Y. Yang (City of Hope Medical Center) for providing the pcDNA3.1-myc-PRMT9 plasmid. We also acknowledge the Animal Resources Center, the Analytical Cytometry Core, Bioinformatics, Light Microscopy, the Integrative Genomics Core, Pathology (Hematopoietic Tissue Biorepository), Integrated Mass Spectrometry, all shared resources from the COHCCC, in part by the NIH/NCI Award no. P30CA33572. We also thank E. Lamar for editing and proofreading. The content of the paper is solely the responsibility of the authors and does not necessarily represent official views of the NIH. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

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These authors contributed equally: Haojie Dong, Xin He, Lei Zhang.

Authors and Affiliations

Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, Hematologic Malignancies and Stem Cell Transplantation Institute, Beckman Research Institute, City of Hope Medical Center, Duarte, CA, USA
Haojie Dong, Xin He, Lei Zhang, Le Xuan Truong Nguyen, Yinghui Zhu, Dandan Zhao, Lucy Ghoda, Ya-Huei Kuo, Guido Marcucci & Ling Li
Integrative Genomics Core, Beckman Research Institute, City of Hope Medical Center, Duarte, CA, USA
Wei Chen & Xiwei Wu
Department of Pharmaceutical Sciences, College of Pharmacy, Western University of Health Sciences, Pomona, CA, USA
Yi-Chun Lin, Jeffrey Wang & Yun Lyna Luo
Suzhou Key Laboratory of Medical Biotechnology, Suzhou Vocational Health College, Suzhou, People’s Republic of China
Song-Bai Liu
Department of Hematology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China
Huafeng Wang & Jie Jin
Division of Biostatistics, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope Medical Center, Duarte, CA, USA
Min Li
Department of Medicine, Division of Hematology, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
Jonathan Serody, Benjamin Vincent & Joshua Zeidner
Department of Microbiology and Immunology and Department of Pathology and Laboratory Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, USA
Jonathan Serody
Department of Microbiology and Immunology, Computational Medicine Program, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
Benjamin Vincent
Department of Oncology and Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
Leo Luznik & Ivana Gojo
Department of Systems Biology, Beckman Research Institute, City of Hope Medical Center, Duarte, CA, USA
Rui Su & Jianjun Chen
Cancer & Cell Biology Division, The Translational Genomics Research Institute, Phoenix, AZ, USA
Ritin Sharma & Patrick Pirrotte
Integrated Mass Spectrometry Shared Resource, City of Hope Medical Center, Duarte, CA, USA
Ritin Sharma & Patrick Pirrotte
Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope Medical Center, Duarte, CA, USA
Xiwei Wu
Department of Immunology and Theranostics, Beckman Research Institute, City of Hope Medical Center, Duarte, CA, USA
Weidong Hu
Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, People’s Republic of China
Weidong Han
Department of Cancer Genetics and Epigenetics, Beckman Research Institute, City of Hope Medical Center, Duarte, CA, USA
Binghui Shen
Department of Hematology and HCT, City of Hope Medical Center, Duarte, CA, USA
Amandeep Salhotra & Guido Marcucci
Department of Pediatrics, Beckman Research Institute, City of Hope Medical Center, Duarte, CA, USA
Ling Li

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Haojie Dong
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Xin He
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Lei Zhang
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Wei Chen
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Song-Bai Liu
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Huafeng Wang
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Min Li
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Yinghui Zhu
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Contributions

H.D. designed and performed the experiments, interpreted the results and wrote the paper. X.H. performed the experiments, including the immunofluorescence and CRISPR–Cas9 experiments, and reviewed the paper. L.Z. assisted with the animal experiments, immunoblots and reviewed the paper. W.C. performed the bioinformatic data mining, RNA-seq and scRNA-seq data analysis. Y.-C.L. performed the compound docking and virtual screen. S.-B.L. designed the animal experiments and performed the data analysis. H.W. collected the clinical sample, performed the qPCR detection and carried out the overall survival analysis. L.X.T.N. performed the lentivirus, retrovirus packaging and transduction, and the animal experiments. M.L. performed the statistical analysis. Y.Z. performed the quantitative MS analyses. D.Z. assisted with the animal experiments. R. Sharma performed the LC–tandem MS data acquisition and analysis and drafted the Methods. P.P. reviewed the SILAC quantitative proteomics analysis and the paper. X.W. reviewed the RNA-seq results. W. Hu performed the NMR experiments. J.W. performed the compound experiments. R. Su provided the mouse and human AML cell lines. J.S., B.V., L. Luznik and I.G. performed the bioinformatics analysis. J.Z., L.G., A.S., J.C., W. Han, B.S., Y.-H.K., J. J., G.M. and Y. Luo reviewed and edited the paper. L. Li designed the study, explained the data and wrote and revised the paper with input from the other authors.

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Correspondence to Ling Li.

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Extended Data Fig. 10 Humanized AML mouse model establishment.

a, b, Two million AML MNCs were implanted intra-femorally into an irradiated MHCI/II1/2 double-KO (DKO) NSG or regular NSG mice (n = 3 mice/group). After transplant, engraftment of CD3 + T cells, CD33+ cells in PB were monitored for 12 weeks. Then, BM engraftment of human hematopoietic subsets including T cells, monocytes and DCs, as well as the immature CD33 + CD34+CD45dim subset were assessed. Data were mean ± SEM. c, Representative gating of hematopoietic subsets from DKO mouse BM. d, Single sample gene set enrichment analysis (ssGSEA) of AML trial dataset GSE183415 which contains samples with clinical responses to PD-1 inhibitors (Complete Response [CR, n = 10] vs. No Response [NR, n = 12]) using the following gene signature: PRMT9 KD signature, 390 ISG signature, Reactome DNA repair signature, Reactome Double strand break repair signature, and Reactome G2M DNA damage checkpoint signature. ‘n’ represents patient numbers in indicated group. Data are presented as mean ± SD, statistical comparisons were performed using unpaired two-sided t-test, ‘ns’ indicates no significance. e, Proposed model. f, ‘REACTOME’ signature ‘DNA Repair’, ‘DNA Double Strand Break Repair’, ‘G2M DNA Damage Checkpoint’ plots of GSEA of MA9 AML cells (Prmt9 KD vs. Ctrl). g, 390 ISGs genes signature plot of GSEA of BM DCs (Prmt9 KD vs. Ctrl). h, ‘Hallmarks’ signature Interferon Gamma Response plot of GSEA of BM T cells (Prmt9 KD vs. Ctrl). i, ‘Hallmarks’ signature Interferon Gamma Response plot of GSEA of BM MA9 AML cells (Prmt9 KD vs. Ctrl). For GSEA analysis (g-i), Normalized enrichment scores and family-wise error rate P-value was determined by the GSEA permutation method. The Normalized Enrichment Score is calculated by dividing the Enrichment Score from the actual ranking by the means of the random permutations. An enrichment P-value is calculated by comparing the observed frequency of an annotation term with the frequency expected by chance; individual terms beyond cut-off (p-value ≤ 0.05) are deemed enriched.

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Supplementary Figs. 1–9 and Tables 1–11.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–11.

Supplementary Data 1

Numerical source data for Supplementary Figs. 1c,d and 8b.

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Dong, H., He, X., Zhang, L. et al. Targeting PRMT9-mediated arginine methylation suppresses cancer stem cell maintenance and elicits cGAS-mediated anticancer immunity. Nat Cancer 5, 601–624 (2024). https://doi.org/10.1038/s43018-024-00736-x

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Received: 16 October 2022
Accepted: 26 January 2024
Published: 27 February 2024
Issue Date: April 2024
DOI: https://doi.org/10.1038/s43018-024-00736-x

Subjects

Abstract

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Main

Results

PRMT9 levels are elevated in leukemia stem cells

PRMT9 is dispensable for normal hematopoiesis

PRMT9 ablation impairs cancer cell survival

PRMT9-mediated methylation promotes cell growth

Identification of a PRMT9 inhibitor

PRMT9 inhibition eradicates AML in vivo

Immunity after PRMT9 inhibition requires cGAS activity

Loss of XRN2 methylation underlies cGAS activation

Combining LD2 with an ICI ablates cancers

Discussion

Methods

Ethics statement

Patient cells

Cell culture

Mice

DNA constructs and oligonucleotides

Compounds

Lentiviral transduction

qPCR

IP and immunoblotting

ChIP–qPCR

Flow cytometry

Competitive transplantation

Limiting dilution assays

AML mouse model and in vivo bioluminescence imaging

Assessment of cell growth, apoptosis and colony formation

SILAC-based quantitative proteomics analysis

Proteomics sample preparation

Results acquisition

Proteomics data analysis

R-methyl analysis

Polysome profiling

OPP protein synthesis assay

In vitro methylation assay

PRMT9 structure-based virtual screening

Saturation transfer difference and Carr–Purcell–Meiboom–Gill NMR assays

Thermal shift assay

Primary AML MNC culture, mass cytometry staining, acquisition and analysis

In silico analysis of CTL levels in primary AML samples

scRNA-seq

Library preparation

Data processing

Quality control, normalization and batch removal

Feature selection, dimension reduction and visualization

Clustering and annotation

GSEA (for T cell and MA9 clusters)

T cell subset identification

Bulk RNA-seq analysis

Detection of cGAMP

Lucia luciferase reporter assay

Immunofluorescence microscopy

Comet assays

Gene editing in THP1 cells

Cocultures of bone marrow DCs and T cells

MA9 AML model in vivo treatment and assessment of leukemia-specific immunity

Humanized model

Non-Hodgkin lymphoma tumor models

IHC

Analysis of tumor-infiltrating cells

Statistics and reproducibility

Reporting summary

Data availability

Code availability

References

Acknowledgements

Author information

Authors and Affiliations

Contributions

Corresponding author

Ethics declarations

Competing interests

Peer review

Peer review information

Additional information

Extended data