Targeting BET Proteins Downregulates miR-33a To Promote Synergy with PIM Inhibitors in CMML

Mutations in genes governing epigenetic regulation are the most common alterations in myeloid malignancies. Although clinically heterogenous, mutations in epigenetic regulators such as TET2 and DNMT3a are predominantly early molecular events associated with disease initiation (1, 2). In a large subset of cases, secondary mutations in genes encoding signal transduction proteins such as N/K-RAS or FLT3 result in leukemic transformation of clones harboring pre-existing epigenetic pathway mutations in both preclinical and clinical specimens (3, 4). The frequency and clonal composition of mutations in genes that alter the epigenome have suggested that therapies aimed at targeting this pathway may be therapeutically attractive. However, although several therapies have been tested across a variety of myeloid neoplasms, only 5-azacitidine has been clinically demonstrated to alter the natural history of myelodysplastic syndromes. Therefore, there exists a critical need to identify therapies that capitalize on the epigenetic dysregulation that molecularly hallmarks these cancers.

The development of potent inhibitors that bind to the BET family of proteins by mimicking acetylated lysine and occupying tandem bromodomains conserved among BET proteins represents a powerful epigenetic therapeutic approach that has been clinically tested in myeloid malignancies (5). Occupation of tandem bromodomains prevents BET proteins from binding acetylated lysine resulting in widespread downregulation of gene expression, particularly those gene expression programs governed by super enhancers, such as NFkB signaling in myeloproliferative diseases (6, 7). Preclinical models of myeloid malignancies have suggested that bromodomain and extra-terminal protein inhibitor (BETi) may be highly effective against a variety of hematological malignancies (8–10). However, early-phase BETi clinical trials have demonstrated minimal clinical efficacy with the potential for significant side effects associated with long-term treatment (11). In addition, several persistence mechanisms to BETis have been identified such as Wnt pathway upregulation (12–14), BRD4 hyperphosphorylation (15), or transcriptional reprograming leading to alternate kinome dependencies (16). Interestingly, these can be reversed with withdrawal of BETis establishing them as persistent and not resistant mechanisms (16, 17). Given the alterations in both epigenetic and signaling mutations seen in myeloid malignancies and the recent preclinical and clinical efficacy observed with combining BETi and JAKi in myeloproliferative neoplasms, we hypothesized that novel combinations of BETi and kinase inhibitors may represent an effective therapeutic strategy for myeloid malignancies (18).

To test this hypothesis, we performed a broad in-house compound screen and identified PIM inhibitor (PIMi) as a potential synergistic combination with BETi. We validated this in vitro and in vivo using human leukemia cells, primary patient material, and isogenic BETi-persistent cells. Mechanistically, BETi-dependent PIM1 overexpression was observed in cell lines vulnerable to the combination therapy and PIM1 overexpression alone was sufficient to both induce persistence to BETi and increase sensitivity to PIMi. We additionally demonstrate that BETi-dependent PIM upregulation was secondary to downregulation of miR-33a, known post-transcriptional regulators of PIM1 via global repression of miRNA biogenesis. Last, we find that sensitivity to this combination is associated with cytokine-dependent transcriptional priming of PIM1 and validate this in human specimens of patients with chronic myelomonocytic leukemia (CMML) that are transcriptionally primed at the PIM1 loci and vulnerable to this therapeutic approach.

U-937 (CVCL_0007), MV4–11 (CVCL_0064), SKM-1 (CVCL_0098), OCI-AML-3 (CVCL_1844), HEL (CVCL_0001), HL-60 (CVCL_0002), THP-1 (CVCL_0006) and ML-1 (CVCL_0436) cells were cultured in RPMI with 10% FBS. KG-1 (CVCL_0374) cells were cultured in IMDM with 20% FBS. TF-1 (CVCL_0559) cells were cultured in RPMI with 10% FBS and 2 ng/mL GM-CSF. M-07e (CVCL_2106) cells were cultured in IMDM with 10% FBS and 10 ng/mL GM-CSF. U-937, KG-1, TF-1, THP-1, HL-60, and MV-4–11 cells were obtained from the ATCC. SKM-1, HEL, OCI-AML-3, and M07-e cells were obtained from DSMZ. ML-1 cells were obtained from ECACC. Cell lines were not authenticated, but were routinely tested for Mycoplasma by HEK Blue Detection. Cell lines were thawed from cryopreservation and used in experiments for no longer than 3 months.

Bone marrow samples were obtained from 12 patients. Patient samples were collected after obtaining written informed consent. The use of human materials was approved by the Institutional Review Board of the Moffitt Cancer Center Scientific Review Committee and the University of South Florida Institutional Review Board in accordance with the Declaration of Helsinki. Patient characteristics for patient samples used in patient-derived xenograft (PDX) studies and Colony Forming Assays are shown in Supplementary Table S3.

All animal studies were approved by the Moffitt Cancer Center Institutional Animal Care and Use Committee.

U-937, SKM-1 or SKM-1 P1–14 cells were resuspended in cold 0.9% sterile saline and mixed with Matrigel Matrix to a final protein concentration of 7 mg/mL. A total of 3×10⁵ U-937 or 1×10⁶ SKM-1 and SKM-1 P1–14 cells were injected into the right flank of NGS-S (RRID:IMSR_JAX:013062) mice (male, 20–30 g, 10–30-weeks-old) and allowed to reach between 100 and 150 mm³ before beginning treatment. Tumors were measured at least twice a week by caliper and tumor volume was calculated using the formula; Tumor volume = width × width × length × 0.52. Mice were randomized into 4 groups: Vehicle, INCB057643, INCB053914 or Combination. INCB057643 was administered once a day at 10 mg/kg, 5 days a week by oral gavage. INCB053914 was administered twice a day at 30 mg/kg, 5 days a week by oral gavage. Both compounds were dissolved in 5% N,N-dimethylacetimide/95% 0.5% methylcellulose.

For CMML PDX experiments, frozen bone marrow mononuclear cells (BMMC) were first thawed and treated with DNAse I for 15 minutes to create a single-cell suspension. Cells were washed once and resuspended in 0.9% sterile saline and injected via tail vein into NSG-S mice (male or female, 20–30 g, 10–30-weeks-old) sublethally irradiated the day prior. At least 2 million cells were injected into each mouse and treatment started between 2–3 weeks after injection. Mice were randomized into the same groups as the heterotopic cell line models. Treatment lasted 2 weeks and all mice were euthanized shortly after the end of treatment. The spleen, peripheral blood, and femurs were taken for analysis. One femur and a portion of the spleen were fixed in formalin and used for IHC. Another portion of the spleen, peripheral blood, and bone marrow were further processed by creating a single-cell suspension and lysing red blood cells with ACK lysis buffer. Cells were then washed with PBS and stained with zombie violet viability dye (BioLegend Cat#: 423114) before fixation in 1.6% formaldehyde and storage at 4°C.

For the drug screen, cells were plated with compounds in 384-well plates and viability was assessed after 72 hours using Cell-Titre Glo (Promega) according to the manufacturer's instructions. For all other viability assays, cells were plated with compounds in 96-well plates and viability was assessed after 72 hours using CCK8 following the manufacturer's instructions. Synergy was calculated using Zero Interaction Potential (ZIP) via SynergyFinder (19)

U-937 and TF-1 cells were grown in medium containing 500 nmol/L INCB054329 and SKM-1 cells were grown in medium containing 300 nmol/L INCB054329. Persistence was determined by significantly increased IC₅₀ value by CCK8 and steady growth in medium containing INCB054329.

Frozen BMMCs were thawed and prepared in a similar manner to PDX experiments. Cells were then resuspended in IMDM + 2% FBS at a concentration of 200,000 cells per mL. 300 |$\,\mu {\curr L}$| of cell suspension and 3 μL of each compound were added to 3-mL Methocult 4034 (StemCell Cat#: 04034) and mixed by vortexing for 1 minute. 1.1 mL of cell mixture was plated in StemCell smart dishes, incubated for 14 days and read on StemVision (Stem Cell Technologies) for the final colony count.

All cells were lysed using RIPA lysis buffer and protein quantified using BCA. SDS–PAGE was performed using 7.5, 10 or 12.5% bis-tris gels and protein was transferred to polyvinylidene difluoride membranes using a wet transfer system (Bio-Rad). Membranes were blocked with 5% milk in TBS-T and incubated overnight with primary antibody in either milk or BSA at the manufacturer's recommended concentrations. Blots were washed multiple times in TBS-T before addition of horseradish peroxidase–conjugated secondary antibody diluted in 5% milk and incubated for 1 hour at room temperature. Antibodies used: BRD2(RRID:AB_10835146), BRD4(RRID:AB_2687578), FLAG-TAG (RRID:AB_2572291), PIM1(RRID:AB_2799461), PIM2(RRID:AB_2163921), PIM3(RRID:AB_1904094), BRD3(RRID:AB_1907250), Actin (RRID:AB_476744), and Vinculin (RRID:AB_477629).

Total RNA from cultured cells was extracted using either Quick-RNA Miniprep (Zymo Research) for gene expression or miRNeasy/miRNeasy advanced (Qiagen) for miRNA detection.

RNA was converted into cDNA using iScript Reverse Transcription Supermix for RTqPCR (Bio-Rad). qRT-PCR reactions were performed for in triplicate using probes designed and ordered from IDT (PIM1, PIM2, and TBP) or off the shelf TaqMan assays (SREBF2, pri-33a, pre-33a; Thermo Fisher Scientific). For miRNA, cDNA was generated using the TaqMan Advanced miRNA cDNA Synthesis Kit. miRNA qRT-PCR reactions were also performed in triplicate using off the shelf TaqMan Advanced miRNA Assays for miR-33a, miR-33b, miR-16, and miR-26b and miR-93 (Endogenous Control miR Thermo Fisher Scientific). Primers: PIM1 forward: GCTTCGGCTCGGTCTACTCA, reverse: AGTGCCATTAGGCAGCTCTC; PIM2 forward: GGACACCGCCTCACAGATCG, reverse: TGACTGAGTCTGACAAGGGGG; TBP forward: CACGAACCACGGCACTGATT, reverse: TTTTCTTGCTGCCAGTCTGGAC.

U-937, SKM-1, or MV411 cells were serum starved overnight. The next day, cells were stimulated with 10 ng/mL GM-CSF for 15 minutes and immediately fixed with 1% formaldehyde for 10 minutes. Formaldehyde was quenched with glycine and cells were washed 2X with cold PBS before being snap-frozen on dry ice and stored at −80°C. Fixed cells were then prepared using the SimpleChIP Magnetic Bead Kit (Cell Signaling Technology) according to the manufacturer's recommendations. DNA was sheared using a Qsonica Q800R3 with the following settings: 50% amplitude, 30 seconds pulse, 5 minutes shearing time. DNA was sheared 5 minutes, spun down, and sheared an additional 5 minutes. Antibodies for STAT5(RRID:AB_2737403), RNA PolII (RRID:AB_2732926), and IGG Negative Control (Included with kit) were incubated overnight before continuing with the protocol according to the manufacturer's recommendations. Primers for PIM1 promoter and enhancer were custom designed and ordered from IDT. PIM1 Enhancer forward: CTTCAACTGCTGTGCTGGTTC, Reverse: CGGGGTTAGTAGTGCAAGGC; PIM1 Promoter forward: GCAGCATCTGGCATCACAAC, Reverse: AAAGCACCAGCGAATCCTGA.

U-937 cells were treated with DMSO, JQ1, and INCB054329 for 24 hours in quadruplicate. Persistent cells treated with INCB054329 were also included in quadruplicate. RNA was extracted and screened for quality on an Agilent BioAnalyzer (RRID:SCR_019715). The samples were then processed for RNA-sequencing using the Nugen Universal RNA-seq kit (NuGEN). Briefly, 100 ng of RNA was used to generate cDNA and a strand-specific library following the manufacturer's protocol. Quality control steps, including BioAnalyzer library assessment and quantitative RT-PCR for library quantification, were performed. Two libraries failed QC and were excluded. The libraries were then sequenced the Illumina NextSeq 500 v2 sequencer with two high-output 75-base paired-end runs to generate approximately 25 to 30 million read pairs per sample. Sequencing data were mapped to hg38 using STAR “Spliced Transcripts Alignment to a Reference” (20). Raw data were cleaned by removing any genes with less than 10 reads or present in less than half of the samples before running differential analysis through DESeq2(RRID:SCR_015687; ref. 21). Normalized counts were run through GSEA 4.1.0(RRID:SCR_003199) with default parameters except permutation type, which was set to gene_set (22).

SKM-1 cells were transduced with a Flag-Tagged, 34kDa isoform of PIM1 in a pCDH-CMV-MCS-EF1α-GreenPuro Cloning and Expression Lentivector (System Biosciences) via the Spinfection method. Briefly, cells were resuspended in Opti-MEM and plated into 6-well plates along with fresh virus, Lipofectamine-2000 and polybrene. Cells were centrifuged for 90 minutes at 2,200 rpm in a 37°C centrifuge, and incubated at 37°C for 1 hour, after which 1 mL of normal growth medium was added and cells were incubated overnight. Cells were then centrifuged and resuspended in normal growth medium. After 1 week, cells were single-cell sorted for GFP positivity. Single-cell clones were profiled for successful transduction by western blotting for Flag-tag.

The Neon Transfection System (Thermo Fisher Scientific) with 100-μL tips was used to deliver siRNA or miRNA mimics. Cells were first washed with PBS and resuspended in R buffer at a concentration of a total of 5 × 10⁷ cells per mL. siRNA or miRNA mimics were added to a final concentration of 5 μmol/L, mixed thoroughly and cells were electroporated with the following settings: 1400V, 10 pulse width, 3 pulses. Cells were then added to 10 mL RPMI with 10% FBS and incubated for either 48 (miRNA mimics) or 72 (siRNA) hours before collection for qPCR and western blotting. For experiments with miRNA mimics, INCB054329 was added 24 hours after electroporation.

RNA was extracted from U-937 cells treated with DMSO, INBC054329 or JQ1 for 24 hours using the miRNeasy Advanced kit (Qiagen). Thermo GeneChip miRNA 4.0 arrays were processed and hybridized according to the manufacturer's protocol (Thermo Fisher Scientific). Briefly, 500 ng of RNA was processed using the FlashTag Biotin HSR RNA Labeling Kit and following poly-adenylation and ligation of a biotinylated RNA tag, the product was hybridized to GeneChip miRNA 4.0 arrays at 48°C for 16 hours at 60 RPM using the GeneChip Hybridization Oven 645. The hybridized miRNA arrays were then washed and stained using the GeneChip Fluidics Station 450, followed by scanning on the Thermo GeneChip Scanner 3000 7G. Data were reviewed for quality control and analysis was performed using the GeneChip Transcriptome Analysis Console v4.0.

SKM-1 P1–1 or P1–14 cells were plated with SKM-1 WT cells in a 1:10 ratio. The initial mixture of cells was checked before any treatment started. Cells were plated with 150 nmol/L INCB054329 and incubated for 5 days. Each day, 1 mL of cell suspension was taken out for analysis of GFP-positive cells and replaced with fresh medium with INCB054329 or DMSO.

For SKM-1 competition assays, live cells were washed with FACS buffer and stained with DAPI before running on a FACSCanto (BD Biosciences). For GM-CSF stimulation experiments, cells were starved overnight, incubated with zombie violet for 20 minutes, washed, and stimulated with varying concentrations of GM-CSF for 15 minutes. Immediately after stimulation, paraformaldehyde was added to a final concentration of 1.6% and cells were fixed for 10 minutes. Cells were then washed with PBS and permeabilized using 2 mL of ice-cold 95% methanol. After washing off methanol, cells were stored in FACS buffer until analysis. On the day of analysis, cells were stained with pSTAT5 antibody (BD Biosciences) for 15 minutes, washed and run on a FACSCanto. For PDX experiments, cells were resuspended in 50-μL FACS buffer with 2 μL of both human and mouse FCR-blocking antibody and incubated for 10 minutes. An antibody cocktail comprised of hCD45, mCD45, hCD3, hCD33, and hCD34 (BD Biosciences) was added to each tube, incubated for 15 minutes and washed with FACS buffer. Cells were run on an LSRII (BD Biosciences). Data were analyzed in FlowJo (RRID:SCR_008520).

Slides were stained using a Ventana Discovery XT automated system (Ventana Medical Systems). Briefly, slides were deparaffinized with Discovery Wash solution and heat-induced antigen retrieval method was used in Ribo CC. Rabbit primary antibodies for hCD45 (#ab10558, Abcam), PIM1 (#PA5–22315, Invitrogen), and PIM2 (#710504, Invitrogen) were used in Dako antibody diluent (Carpenteria) and incubated for 60, 32, and 32 minutes, respectively. Slides were then stained with anti-rabbit secondary (Ventana). Detection was performed using the Ventana ChromoMap kit and slides were counterstained Hematoxylin. Slides were then dehydrated and coverslipped as per normal laboratory protocol.

Serial slide sections stained for CD45, PIM1, and PIM2 we scanned with a Leica Aperio AT2 digital Pathology Slide Scanner (Leica Biosystems) with a 20x/0.7NA objective lens. SVS image files were imported into Visiopharm version 2022.02 (Visiopharm A/S, Denmark) where the Tissuealign tool was used to co-register images for the 3 IHC biomarkers. After alignment, the software's manual drawing tool was used to select Regions of Interest (ROI) on each aligned image set and a simple intensity threshold segmentation was applied to the ROIs to label each co-registered image pixel into staining categories. All thresholds and settings for pixel labeling were identical for each image analyzed within each experiment.

Statistical analyses and graphical representations were performed using GraphPad Prism 9.0 (RRID:SCR_002798). For comparisons involving only 2 sets of data, unpaired t tests were used. For data involving more than 2 comparisons, one-way ANOVA was used. For experiments involving tumor size measurements over time, or the SKM-1 co-culture experiment, we first measured the AUC of each individual sample to get as single data point describing that sample over time. Then, depending on the groups being compared, either unpaired t test or one-way ANOVA was used to test significance.

Raw data corresponding to the drug screen in Fig. 1A are attached as Supplementary Table S1. Raw RNA-seq data corresponding to Figs. 2C and 4D and miRNA array data corresponding to Supplementary Fig. S4F are included as Supplementary Data. ChIP-seq (chromatin immunoprecipitation sequencing), ATAC-seq, and DIP-seq data were previously deposited in the Gene Expression Omnibus database under GSE159886. For original data, please contact [email protected].

To test potential synergies between BETis and inhibitors in clinical development, we used an in-house targeted chemical screen of 300 compounds that are FDA approved or in clinical cancer development (Supplementary Table S1; ref. 23). U-937 and SKM-1 cells were incubated with the IC₂₀ (U-937:155 nmol/L, SKM-1: 30 nmol/L) of the BETi INCB054329 and two doses (0.5 and 2.5 μmol/L) of each library compound. Cell viability was evaluated 72 hours post-treatment using CellTitre-Glo. Combinations with a drug − base/drug + base ratio greater than 2 were chosen for further consideration as previously described (23). As expected, known synergies with JAK, HDAC, CDK, MEK, and PI3K inhibitors were found supporting the validity of our chemical screen to identify clinically relevant BETi combinations (24–33). After previously published interactions were filtered out (Supplementary Table S2), the only combination with a drug − base/drug + base ratio greater than 2 was with SGI-1776, a pan-PIMi (Fig. 1A). To validate therapeutic synergy between these BET and PIMis, we repeated the experiment in three human myeloid cell lines (U-937, TF1, and SKM-1) with 7 doses of INCB054329 and either pan-PIMis SGI-1776 or INCB053914. In all lines, and in both BET/PIMi combinations, in vitro synergy was observed consistent with our initial compound screen (Fig. 1B). AZD1208, another PIMi included in the screen, did not achieve the threshold. However, given that the only compound that met our threshold was a PIMi, we performed formal synergy analysis and were able to demonstrate that AZD1208 was indeed synergistic with BETis similarly to that observed with INCB054329 and SGI-1776 (Supplementary Fig. S1A). Importantly, synergy was evident in the low dose PIMi chemical screen and most models when testing low doses of both BETi and PIMi.

Rapid persistence to BETi has been shown to occur through various mechanisms in both leukemia and solid tumors, likely responsible for its limited clinically efficacy as a single agent (12–16). To determine whether PIMi could overcome persistence to BETi, we generated 3 BET-persistent human leukemia cell lines. We then compared the IC₅₀ value of PIMi with that of the parental cell lines tested. Persistence was achieved by daily treatment of cell lines with 500 nmol/L INCB054329 or 300 nmol/L INCB054329 for SKM-1 cells (Fig. 1Ci). These cells were chronically exposed to INCB054329 to maintain persistence. Consistent with the notion that these cells demonstrate BETi persistence, drug withdrawal for 1 week led to resensitization to BETi (Supplementary Fig. S1B). At 60 days, all three cell lines demonstrated an increase in PIMi sensitivity compared with their parental counterparts, particularly in the human monocytic leukemia cell line, U-937 (Fig. 1Cii). To determine whether the observed in vitro synergy was present in vivo, heterotopic tumors were established in NSG-S mice (34) with either U-937 (n = 10/group) or SKM-1 cells (n = 10/group). After tumors reached between 100 and 150 mm³, drug treatment was started with 10 mpk (milligrams per kilogram) INCB057643 and 30 mpk INCB053914 via oral gavage either as single agent or in combination and continued for 2 weeks, with tumor measurements occurring twice per week and at endpoint. These experiments identified a statistically significant decrease in tumor volume using both cell line models with combination treatment, suggesting that this combination strategy may be synergistic in vivo (Fig. 1D).

PIM proteins are serine/threonine kinases with a short half-life that do not require post-translational modifications for their activation and therefore, their activity is primarily transcriptionally and post-translationally mediated (35, 36). Given the profound effects that BETis exert on the transcriptome, we first sought to examine the effect of BET inhibition on RNA and protein expression of PIM kinases. Interestingly, PIM kinase protein and RNA expression of cells treated with BETi after 24 hours revealed a significant increase in expression of PIM kinases. This increase was highest in BETi-persistent cells where significant increases in PIM1 and PIM2 were observed (Fig. 2A and B). Notably, PIM kinases have been previously implicated in drug resistance in AML (37–39). Furthermore, time course studies demonstrated that PIM mRNA upregulation occurs as early as 8 hours (Supplementary Fig. S2A). Because PIM1 was expressed in both short-term–treated and persistent cell lines, we prioritized this PIM isoform for further investigation in subsequent experiments. Differential gene expression analysis of RNA-seq data from U-937 cells identified that PIM1 was among the top 20 upregulated genes in BETi-treated cells compared with DMSO control (Enrichment score = −3.298) and that a gene set previously reported to be enriched in PIM-overexpressing myeloid cells was also upregulated in our BETi-treated cells (Fig. 2C; Supplementary Fig. S2B; ref. 40). We next confirmed the increased PIM levels after BETi in multiple myeloid leukemia cell lines. Four of nine cell lines demonstrated increased PIM kinase protein levels at 24 hours (Supplementary Fig. S2C). Although PIM upregulation was heterogeneous, the BETi-dependent increases in PIM levels correlated to increased synergy with BETi and PIMi in vitro (Fig. 2D and E). Because current inhibitors in clinical development are pan-BETis, including those tested here, we sought to investigate which BET proteins were most associated with PIM upregulation. We individually genetically depleted BRD2, 3, and 4 in U-937 and SKM-1 cells and found that only BRD4 knockdown resulted in significant upregulation of PIM1 levels (Fig. 2F and G) consistent with the known expression of BRD4 in the hematopoietic compartment (8, 41). Collectively, these data suggest that BET inhibition leads to increased PIM expression in a subset of cell lines that is associated with synergy between BET and PIMis.

We next sought to determine whether increases in PIM1 alone could drive persistence to BET inhibition as well as contribute to the observed synergy seen in vitro. To test this, single-cell PIM-overexpressing SKM-1 clones were derived by transducing a GFP-expressing lentiviral vector-encoding PIM1 (Fig. 3A; Supplementary Fig. S3A). All four SKM-1 clones engineered to overexpress PIM1 were both persistent to BETi (Fig. 3Bi), and significantly more sensitive to PIMi (Fig. 3Bii). Moreover, PIM1 levels correlated with persistence to BET inhibition (R² = 0.9925, P = 0.0037), indicating that PIM1 overexpression is sufficient for BETi persistence and sensitization to PIM inhibition in vitro (Fig. 3C). Of note, although all PIM1-overexpressing clones were more sensitive to PIM inhibition, there was no correlation between levels of PIM1 expression and PIMi sensitivity (Supplementary Fig. S3B). In addition, we performed in vitro competition assay by co-culturing SKM-1 cells with two isogenic PIM1-overexpressing clones in the presence BETi or vehicle control. After 5 days of treatment with BETi, there was a statistically significant increase in PIM-overexpressing isogenic cells, indicating that PIM1-overexpressing cells were selected in the presence of their parental counterparts (Fig. 3D). To determine whether PIM1 overexpression leads to BETi persistence and PIM sensitivity in vivo, heterotopic SKM-1 xenograft models were generated of P1–14 SKM-1 PIM-overexpressing clones and isogenic controls. As in the above in vivo experiments, flank tumors were allowed to grow until 100–150 mm³ and treatment was initiated for two weeks. These experiments demonstrated statistically significant decreases in tumor volume in PIM-overexpressing SKM-1 clones after PIM inhibition compared with parental cells, suggesting that PIM overexpression is sufficient for PIMi sensitivity in vivo (Fig. 3E).

BETi exert profound effects on the transcriptome but are generally thought to downregulate transcriptional activity (6, 9). Therefore, to resolve the paradoxical increase in PIM levels after treatment, we first explored BETi-dependent miRNA depletion hypothesizing that depletion of miRNAs that target PIM may lead to the observed increases in PIM levels. BETis can augment miRNAs via inhibition of miRNA biogenesis at super enhancer regions and/or via direct transcriptional repression of precursor RNA species (42, 43). We treated both U-937 and SKM-1 cells with either an Argonaute RISC Catalytic Component 2 (AGO2) inhibitor (Acriflavin) or a Dicer inhibitor (Poly-l-lysine), two central components of miRNA biogenesis, and measured protein PIM1 levels. Indeed, treatment with either AGO or Dicer inhibitors was sufficient to increase PIM1 levels across both cell lines, suggesting that inhibition of miRNA biogenesis can recapitulate BETi-induced PIM1 upregulation (Fig. 4A). To narrow down putative repressed miRNAs that may be negative regulators of PIM1, we used the computational approach outlined in Fig. 4B. Briefly, miRNAs were identified by cross referencing putative PIM1-binding miRNA from the microRNA Data Integration Portal, miRNA with super enhancers from Suzuki and colleagues (43) and published PIM1-interacting miRNA (44–48). This led to the identification of 4 putative miRNAs whose expression was evaluated after BETi treatment. Of these, miR-33a was the only miRNA with a significant time-dependent decrease after treatment with two BETi (Fig. 4C). Although this reduction in miR-33a was modest, previous literature supports modest changes in miRNA leading to large impacts on gene expression of its targets (49). This was consistent with GSEA analysis of whole-transcriptome RNA-sequencing performed in U-937 cells that demonstrated increased expression of genes targeted by miR-33a (Fig. 4D). To determine whether miR-33a depletion was necessary for BET-dependent PIM upregulation, we electroporated an miR-33a mimic into both U-937 and SKM-1 cells treated with either BETi or DMSO for 24 hours and collected pellets for both RNA and protein after 48 hours (Fig. 4E). Evaluation of PIM1 protein levels demonstrated that cells with miR-33a overexpression were protected from BETi-dependent PIM upregulation (Fig. 4F). These data suggest that reduced levels of miR-33a after BET inhibition leads to an increase in PIM1 expression.

Last, to explore whether BETi directly and specifically impact miR-33a, we profiled transcript levels of SREBF2 after BET inhibition as miR-33a is intronically located between exons 19 and 20 of this gene (Supplementary Fig. S4A). This analysis demonstrated a transient increase in SREBF2 transcript expression after 4 hours treatment, suggesting that BETis do not directly impact primary miR-33a transcription in leukemia cells (Supplementary Fig. S4B). This was observed both with primers probing the intronic region between exons 19 and 20 as well as primers measuring total SREBF2 (Supplementary Fig. S3B). Importantly, no other promoters were identified in our CMML ChIP-seq data near SREBF2 that would transcribe miRNA-33a independently (Supplementary Fig. S4D). Although the rapid turnover of miRNA precursor species precludes precise measurements of their relative abundances after treatment, we attempted to profile the range of miR-33a precursors after BETi treatment at different time points. Indeed, mature miRNA isoforms (i.e., 3p and 5p) were consistently depleted upon BETi treatment, but pre-miR-33a did not significantly decrease congruent with the postulated role of BETi repression of miRNA biogenesis (ref. 43; Supplementary Fig. S4C and S4E). Moreover, broad miRNA expression profiling in U-937 cells demonstrated a global statistical downregulation of miRNAs in 2 replicates, suggesting that miR-33a downregulation may occur through impairment of miRNA biogenesis (Supplementary Fig. S4F).

The GM-CSF/STAT5 axis is the canonical upstream signal required for PIM transcription of all isoforms (36, 50–52). Given the above mechanism of synergy with respect to PIM upregulation and our previous studies demonstrating that GM-CSF hypersensitivity is a universal feature of the monocytic leukemia CMML, we hypothesized that the subset of monocytic leukemia cell lines that upregulated PIM after BET inhibition could be identified a priori by their respective pre-treatment GM-CSF sensitivity. We posited that leukemia cells, which were responsive to GM-CSF stimulation at low doses, would be primed to upregulate PIM upon BETi-dependent downregulation of its inhibitory miRNAs. To explore this possibility, we measured pSTAT5 in the presence or absence of low dose (0.1 ng/mL) GM-CSF. Consistent with our hypothesis, cells that exhibited BET-dependent PIM protein upregulation demonstrated greater pSTAT5 activation after only 0.1 ng/mL of GM-CSF stimulation compared with cells that did not upregulate PIM kinase (Fig. 5A). In the context of PIM1 specifically, pSTAT5 enrichment was accompanied by STAT5 occupation at the PIM1 downstream enhancer in PIM1 upregulating leukemia cells at a far greater magnitude compared with those cell lines that did not upregulate PIM1 after BETi (Fig. 5B). In addition, GM-CSF stimulation led to enrichment of RNA PolII at both the PIM1 enhancer and promoter in PIM1-upregulating cell lines but not in a leukemia cell line that did not upregulate PIM1 (Fig. 5C). Collectively, our data suggest that GM-CSF hypersensitive myeloid leukemia cells are transcriptionally primed at the PIM1 loci, upregulate PIM kinases after BETi, and are associated with synergy to BET and PIM inhibition.

CMML is a rare hematologic malignancy classified as a myelodysplastic/myeloproliferative overlap syndrome by the World Health Organization (53). Clinically and pathologically, this disease is characterized by bone marrow dysplasia, peripheral monocytosis, cytopenias, and a propensity for transformation to acute myelogenous leukemia, all of which contribute to a poor overall survival (OS; ref. 53). Molecularly, CMML is hallmarked by GM-CSF hypersensitivity in a mutational and subtype independent manner (54, 55). To determine whether this molecular feature was associated with transcriptionally primed PIM, we leveraged our previously published multi-omic epigenetic dataset of 16 patients with CMML that enabled us to probe chromatin accessibility and histone marks at the PIM1 loci (56). Both when viewed in aggregate (Fig. 5D) or as individual patients (Supplementary Fig. S5), the PIM1 promoter and enhancer demonstrated epigenetic marks consistent with transcriptional activity supporting the notion that CMML may represent a subtype of leukemia enriched for sensitivity to BET and PIM inhibition.

To determine whether the synergy between BET and PIMis is evident in primary CMML samples, we performed clonogenicity assays with BMMCs from 10 unique patients with CMML (Supplementary Table S3), in duplicate, treated with 100 nmol/L INCB054329 and 500 nmol/L INCB053914 or the combination. Although single-agent INCB054329 and INCB053914 demonstrated significant reductions of clonogenicity, combination therapy was significantly more efficient at reducing clonogenicity compared with all groups (Fig. 6A and B). To determine whether combination therapy was a viable therapeutic approach in vivo, we generated CMML PDXs as previously described (Supplementary Table S3; ref. 57). After engraftment was established in each model, mice were randomized (3–5 mice per group) and treated with BETi, PIMi, or the combination for 2 weeks using the same doses as heterotopic cell line xenograft experiments (Fig. 6C). Initially, mice treated with the MTD of BETi and PIMi rapidly lost weight and had unacceptable toxicity (data not shown). However, given that in vitro synergy optimally occurred at lower doses of both inhibitors and nanomolar levels of BETi were sufficient to induce PIM upregulation (Supplementary Fig. S6), PDX experiments were repeated with low-dose BET and PIM inhibition. After 2 weeks of treatment, all mice were simultaneously euthanized to compare engraftment, consistent with other preclinical myeloid studies on drug efficacy (58, 59). Mice treated with either low-dose INCB057643 or INCB053914 alone showed a variable response to treatment, similar to in vitro experiments, whereas the combination was consistently able to reduce leukemic engraftment as evidenced by a reduced percentage of human CD45⁺ cells in the bone marrow (58–60) by both flow (Fig. 6D; BETi vs. Combo mean rank diff. = 13.49. PIMi vs. Combo mean rank diff. = 11.30) and IHC (Fig. 6E and F). Finally, we profiled PIM expression in our PDX models to determine whether the postulated mechanism of synergy occurred in primary patient samples. IHC was performed on spleen sections using rabbit anti-PIM1 and anti-PIM2. Our anti-PIM1 and anti-PIM2 antibodies cross-react with human and mouse tissues. To overcome this limitation, we computationally overlaid PIM IHC with that of human CD45 to quantitate human-specific PIM expression (see Materials and Methods; Fig. 6G). This analysis demonstrated that PIM upregulation occurred after BETi treatment in vivo in primary samples.

Despite advances in the molecular pathobiology and genetics of myeloid malignancies, no targeted therapeutics have demonstrated an impact on OS or augment natural history. This is especially evident in CMML where there are no CMML-specific approved therapies and the vast majority of patients will succumb to disease within 5 years (61). To address this therapeutic gap, we used a targeted chemical screen and identified BETi and PIMi as a synergistic combination in preclinical models in vitro and in vivo. Although this synergy has not been previously reported, it is consistent with recent studies, suggesting that PIM kinase upregulation may be associated with disease progression and resistance to cytotoxic therapies in AML (37–39). It is also consistent with the notion that BETi and kinase inhibitor combination therapy may be an attractive therapeutic strategy in hematologic malignancies (62–66).

Our study identified that PIM protein and RNA levels were paradoxically upregulated after BETi treatment in multiple cell lines and that PIM1 upregulation was necessary for sensitivity to PIM inhibition. Although no correlation was observed between PIM1 protein levels and PIMi sensitivity, all PIM1-overexpressing cells were significantly more sensitive to PIM inhibition than their parental control perhaps due to a threshold effect that was met in all of our PIM1 overexpression cell lines. That PIM kinase upregulation was sufficient to induce this phenotype, without upstream activation, is consistent with its known mechanism of phosphorylation. Unlike many serine threonine kinases that require a secondary phosphorylation event to become active, PIM kinases are constitutively active after translation (67). Although we fully investigated the mechanism of BETi-dependent PIM1 upregulation that was increased in all BETi-persistent cells generated and our PDX models, our data provide a mechanism of synergy likely consistent in the context of other PIM isoforms given their overlapping function and known role in chemotherapy resistance (35).

Given the profound downregulation of transcriptional activity and the paradoxical increase in PIM kinase RNA in our leukemia models, we hypothesized that BETis may downregulate a post-transcriptional repressor of PIM. Indeed miR-33a, a known regulator of PIM kinase, was downregulated and necessary for the observed BETi-dependent PIM upregulation (44, 68, 69). Furthermore, our data strongly suggest that BETi-dependent impairments in miRNA biogenesis, and not direct transcriptional repression of mir-33a precursors, underlies the mechanism of miR-33a downregulation making this proposed combination therapy mechanistically novel.

Last, we demonstrated that GM-CSF–hypersensitive cells were transcriptionally primed at the PIM1 loci and were associated with sensitivity to combination therapy. We therefore used our CMML PDX models, which recapitulate many features of the human condition (57), to credential this therapy in a randomized murine clinical trial and identified statistically superior repression of human leukemia engraftment with combination therapy across all models. Our data provide strong evidence to clinically test low-dose BET and PIM inhibition in CMML, which we predict would be most responsive to this combination based on its hypersensitivity to GM-CSF (54, 57). Future clinical studies are necessary to identify the biologically effective dose of BETi that upregulates PIM kinase in humans so that responses are enriched and toxicity minimized.

J. Lancet reports personal fees from Novartis, Bristol Myers Squibb, Takeda, and Daiichi Sankyo outside the submitted work. R.S. Komrokji reports personal fees from AbbVie, grants and personal fees from BMS, and personal fees from CTI BioPharma, Geron, Jazz Pharma, Novartis, PharmaEssentia, Servier, Taiho, and Rigel outside the submitted work. D.A. Sallman reports Advisory Board/Consultancy at AbbVie, Affimed Gmbh, AvenCell, BlueBird Bio, BMS, Gilead, Incyte, Intellia, Intellisphere, LLC, Jasper Therapeutics, Kite, Magenta Therapeutics, Molecular Partners AG, NKARTA, Novartis, PGEN Therapeutics, Inc., Shattuck Laboratories, Servier, Syndax, Syros, Takeda, Zentalis, Research Funding, Aprea, and Jazz. G.W. Reuther reports grants from Incyte Corporation and Revolution Medicines, Inc. outside the submitted work; as well as reports a patent for US10596161B2 issued, US10526291B2 issued, US20210355088A1 pending, and US20220119370A1 pending. A. Karsan reports grants from Canadian Institutes of Health Research and Terry Fox Research Institute during the conduct of the study. M.M. Patnaik reports grants from Kura Oncology and Stem Line Pharma and other support from CTI Pharma outside the submitted work. E. Padron reports grants from Incyte during the conduct of the study; as well as personal fees from Blueprint, Kura, Stem Line, Pharmessentia, and GSK and grants from BMS outside the submitted work. No disclosures were reported by the other authors.

C.T. Letson: Methodology, writing–original draft, writing–review and editing. M.E. Balasis: Supervision, investigation. H. Newman: Investigation. M. Binder: Investigation, visualization. A. Vedder: Investigation. F. Kinose: Investigation. M. Ball: Data curation, formal analysis. T. Kruer: Supervision, investigation. A. Quintana: Supervision, investigation. T.L. Lasho: Investigation, visualization. C.M. Finke: Investigation, visualization. L.L. Almada: Investigation, visualization. J.M. Grants: Investigation, visualization. G. Zhang: Investigation. M.E. Fernandez-Zapico: Supervision, visualization, writing–review and editing. A. Gaspar-Maia: Investigation, visualization, writing–review and editing. J. Lancet: Project administration, writing–review and editing. R. Komrokji: Supervision, writing–review and editing. E. Haura: Investigation, writing–review and editing. D.A. Sallman: Resources, supervision, writing–review and editing. G.W. Reuther: Resources, writing–review and editing. A. Karsan: Investigation, visualization, writing–review and editing. U. Rix: Investigation, visualization. M.M. Patnaik: Supervision, visualization, writing–review and editing. E. Padron: Conceptualization, resources, formal analysis, supervision, funding acquisition, writing–original draft, project administration, writing–review and editing.

This study was supported (in part) by research funding from Incyte to C.T. Letson and E. Padron, and the National Institutes of Health, National Cancer Institute (R37CA234021) and by the Flow Cytometry, Microscopy, Histology, Genomics cores, and Vivarium at the H. Lee Moffitt Cancer and Research Institute, a Comprehensive Cancer Center designated by the National Cancer Institute (P30-CA076292).

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.