ABSTRACT
The chromatin adaptor Menin interacts with oncogenic fusion proteins encoded by MLL1- rearrangements (MLL1-r), and small molecules that disrupt these associations are currently in clinical trials for the treatment of leukemia. Here, we delineate a molecular switch between the MLL1-Menin and MLL3/4-UTX chromatin modifying complexes that dictates response to Menin-MLL inhibitors. We show that Menin safeguards leukemia cell fitness by impeding binding of the histone demethylase UTX at a subset of non-canonical target gene promoters. Disrupting the interaction between Menin and MLL1 leads to UTX-dependent transcriptional activation of genes with tumor suppressive function. We show that this epigenetic mechanism is operative in murine and human models of AML, and clinical responses to Menin-MLL inhibition in primary human leukemia are accompanied by induction of tumor suppressive gene expression at Menin-UTX targets. These findings shed light on the context-dependent and often antagonistic roles that chromatin regulators exhibit in development and disease and provide mechanistic insight for rational design of targeted epigenetic therapies.
INTRODUCTION
Menin is an evolutionarily conserved nuclear factor that associates with chromatin to recruit (adapt) interacting proteins1, including the Trithorax (Trx)-related MLL1 (KMT2A) and MLL2 (KMT2B) histone methyltransferase complexes2, 3 and the MLL1 oncogenic fusion proteins4, transcription factors (e.g., c-MYC5, JUND6, 7, SMADs8, 9), and other chromatin-bound proteins (e.g. LEDGF10 (reviewed in ref. 11)). However, the detailed underlying molecular mechanisms and functional interactions associated with Menin remain elusive.
Menin is a core subunit of the MLL1 (ref. 12) and MLL2 complexes2 and is responsible for targeting these to chromatin3. Menin is required for MLL1/MLL2-dependent H3K4 trimethylation of HOX genes and their stable long-term expression during development2, 13. Menin has context-specific functions in human diseases, acting as a tumor suppressor in neuroendocrine malignancies14, 15 and in certain skin16, lung17, and CNS tumors18, and as an oncogenic cofactor in other cancers, including hepatocellular carcinoma19 and MLL1-rearranged (MLL1-r) leukemias4, 20. Furthermore, over 1000 germline and somatic MEN1 variants have been identified, some of which are linked to cancer predisposition21. Owing to its pro-oncogenic roles in mixed lineage leukemia and other malignancies, small molecule inhibitors targeting the Menin-MLL1 and Menin-MLL2 protein-protein interactions have shown great promise for intercepting and treating different types of cancers19, 22–27. Notably, three structurally different Menin-MLL inhibitors have recently entered clinical trials (NCT04065399, NCT04067336, NCT04811560) and at least one has been granted fast track designation by the FDA for treatment of relapsed/refractory acute leukemias26–28. Thus, an understanding of the molecular mechanisms of action of these drugs would facilitate the development of biomarkers to predict therapeutic response and resistance and lead to rational design of more effective combination treatments.
RESULTS
Functional interplay between MLL1-Menin and MLL3/4-UTX chromatin modifying complexes
We performed a series of CRISPR-based genetic screens to molecularly dissect the dependency of MLL1-r leukemias to Menin and identify factors that dictate response and resistance to Menin inhibitors (see Methods). First, we screened a chromatin-focused CRISPR library in Cas9-expressing Menin-dependent mouse leukemia cells driven by a human MLL-AF9 transgene (hereafter referred to as MLL-AF9 cells)29 (Fig.1A, Extended Fig. 1A-D, Supplementary Table 1). Library-transduced cells were cultured in media with DMSO (vehicle) or the Menin-MLL inhibitor MI-503 (ref. 23) for 12 cell population doublings (CPDs), followed by screen deconvolution using next-generation sequencing (Fig.1A, Extended Fig. 1E-F). A gene score (mean log2 fold-change) was calculated for each gene included in the library by assessing the change in abundance of sgRNAs during the culture period (Fig.1B). As expected, sgRNAs targeting known MLL1-r leukemia dependencies, such as Dot1l (refs. 29-32), Brd4 (ref. 33), and Myb (ref. 34) were strongly depleted in both treatments while control sgRNAs remained largely neutral, thereby validating our screening platform (Supplementary Table 2).
Among the sgRNAs that were enriched or depleted in the absence and presence of drug, sgRNAs targeting the histone H3 lysine 27 (H3K27) demethylase Utx (Kdm6a) and the histone H3 lysine 4 (H3K4) mono-methyltransferases Mll3 (Kmt2c) and Mll4 (Kmt2d) were the most enriched in the Menin-MLL inhibitor context (Fig.1B, red circles). This result was unexpected, since the canonical activity of the MLL1/2-Menin complex (disrupted by MI-503) is to catalyze the di/tri-methylation of H3K4 (H3K4me2/3) at promoters, resulting in loading of the transcriptional machinery at such sites13, 35, 36, while the MLL3/4-UTX complex regulates enhancer states by serving as the major H3K4 mono-methyltransferase (H3K4me1) (refs. 37-41).
To assess whether these results were idiosyncratic of the cell line, library, or inhibitor used, we performed a genome-wide CRISPR screen in an independently derived MLL-AF9 cell line using VTP-50469, a more potent, selective, and orally bioavailable Menin-MLL inhibitor that led to the development of SNDX-5613, which is currently under investigation in a phase 1/2 clinical trial for treatment of relapsed/refractory acute leukemia (NCT04065399) (refs. 26-28). Remarkably, sgRNAs targeting Utx (Kdm6a), Mll3 (Kmt2c), and Mll4 (Kmt2d) were also among the most significantly enriched candidate genes identified in this genome-wide screen (Fig.1C, Extended Fig. 2A-C, Supplementary Table 2). Interestingly, shared subunits between the two types of MLL complexes scored similarly in both vehicle and Menin-MLL inhibitor conditions (Fig.1B-D, Extended Fig. 1G, Extended Fig. 2D). This suggests that core subunits of the MLL3/4-UTX complex42 modulate therapeutic responses to Menin-MLL inhibition and have context-specific tumor suppressive functions43, 44, a result that is consistent with their haploinsufficient tumor suppressor activity in some cancers45.
Utx or Mll3 loss-of-function did not affect cell proliferation in vitro, while Mll4 inactivation decreased cell growth, consistent with previous work43 (Extended Fig. 3A-D). However, in agreement with our screening results, Utx disruption using three distinct sgRNAs significantly increased the viability of MI-503-treated leukemia cells, confirming that inactivation of Utx confers resistance to Menin-MLL inhibition (Fig.1E, Extended Fig. 3E). In addition, Mll3- and Mll4- deficient cells treated with MI-503 exhibited a proliferative advantage over wild-type cells only in the treatment context (Extended Fig. 3F-G). These orthogonal results establish the MLL3/4-UTX complex as a central modulator of therapy response to Menin-MLL inhibition.
UTX was among the most significantly enriched chromatin factors in our screen (Fig.1B-C, Extended Fig. 1G, Extended Fig. 2D). As this factor is shared by both MLL3 and MLL4 complexes40, 46, 47, we focused on UTX disruption to functionally probe the molecular mechanisms linked to resistance to Menin inhibition. We first tested whether genetic Utx inactivation could rescue the effects of Menin-specific ablation in MLL-AF9 leukemia cells. While CRISPR-mediated deletion of Men1 led to robust inhibition of proliferation (Extended Fig. 4A), co-deletion of Utx suppressed this phenotype, such that Men1-deficient MLL-AF9 cells were able to robustly proliferate (Fig.1F, Extended Fig. 4B-C). Importantly, deletion of Utx, Mll3, or Mll4 in isolation did not confer any significant fitness advantage to cells, further validating our findings obtained with pharmacologic inhibition of Menin-MLL (Extended Fig. 3).
To determine if the genetic interaction between Menin and UTX depends on the MLL-fusion protein (MLL-FP), we ablated the human MLL1-AF9 transgene expressed in MLL-AF9 leukemia cells using sgRNAs selectively targeting exons 3 or 5 of the human MLL1 gene (preceding the breakpoint cluster region (exons 9-11) in MLL1-translocations)48, 49 (Extended Fig. 5A). Cells deficient for MLL-AF9 and Utx are equally unfit to cells lacking MLL-AF9, validating the exquisite dependency of leukemia cells to the pleiotropic gene regulatory activity of this oncogenic fusion (20) (Extended Fig. 5B-C). However, MLL-AF9 leukemia cells deficient for Men1 and Utx, or those deficient for Utx and treated with Menin-MLL inhibitor, are able to proliferate without reactivation of canonical fusion targets like Meis1 (ref. 27), (Extended Fig. 6) demonstrating that this genetic interaction is not entirely dependent on the oncogenic activity of this fusion. Similarly, co-deletion of UTX and MEN1 in a Menin-dependent non MLL1-r human leukemia cell line driven by DNMT3A and NPM1 mutations50, 51 also rescued the decreased fitness associated with loss of MEN1 alone (Extended Fig. 5D-E), suggesting that the functional Menin-UTX interactions observed in our models are conserved in human cells. Similar results were obtained in non MLL1- r AML1-ETO9a mouse leukemia cells treated with MI-503 (Extended Fig. 5F-G). These data suggest that the phenotype of Menin-MLL inhibition is evolutionarily conserved and acts in part through a pathway that involves crosstalk between Menin and UTX and is downstream or independent of MLL-FPs.
Menin is needed for expression of canonical MLL-FP target genes, such as Meis1, whose function is required for leukemia maintenance26, 35, 36, 52, 53, and Meis1 over-expression was recently shown to partially rescue the leukemic stem cell transcriptional program suppressed by Menin-MLL inhibition27. We confirmed that treatment of mouse MLL-AF9 leukemia cells with MI-503 led to robust downregulation of Meis1 (Extended Fig. 6A). However, to our surprise, cells genetically deficient for both Men1 and Utx showed a similar reduction in Meis1 levels (Extended Fig. 6B), yet were able to proliferate in vitro (Fig.1F, Extended Fig. 4B). We further confirmed these findings in an additional Menin-dependent mouse MLL-AF9 cell line (Extended Fig. 6C-D) and a human MLL-AF4-driven leukemia line54 (Extended Fig. 6E-F). These experiments reveal an epistatic relationship between Menin and UTX in Menin-dependent mammalian cells and suggest that factors beyond Meis1 can sustain the proliferative potential of MLL1-r and non MLL1- r leukemia cells.
Menin and NF-Y restrict chromatin occupancy of UTX at promoter regions
Menin is generally associated with chromatin-modifying and transcriptional complexes at promoter regions6, 55, while UTX canonically resides in complexes that regulate enhancers40. To determine the genomic distribution of Menin and UTX in the absence or presence of the Menin-MLL inhibitor, we performed chromatin immunoprecipitation followed by sequencing (ChIP-Seq). Menin showed strong enrichment at promoter regions (here defined as transcription start sites (TSSs) ± 2kb), which was substantially decreased when its canonical interactions with MLL1/2 were disrupted by MI-503 (Fig.2A). Genome-wide enrichment of Menin was also decreased, as evidenced by a >10-fold reduction in the number of ChIP peaks post MI-503 treatment (Extended Fig. 7A). Conversely, while only a very minor fraction of UTX appeared to be enriched on chromatin at basal conditions, the genome-wide enrichment of UTX dramatically increased upon treatment with MI-503, as evidenced by a ∼70-fold increase in the number of peaks (Extended Fig. 7A). UTX canonically resides in complexes that regulate enhancers40, 41 yet, contrary to expectation, more than 70% of UTX binding peaks in MI-503 treated cells were localized at promoters and proximal regions (Fig.2B, Extended Fig. 7A)56. Importantly, the enhanced enrichment of UTX on chromatin was not merely the result of increased expression and/or protein stability (Fig.2C-D). These data show that disruption of the Menin-MLL1/2 interaction leads to dynamic and robust recruitment of UTX to promoter regions (Fig.2B), suggesting a previously unrecognized role for the core MLL3/4-UTX complex in promoter regulation and gene transcription41.
Not only did Menin depletion lead to recruitment of UTX to promoter regions, the loci that accumulated UTX significantly overlapped with those where Menin was lost (Fig.2E-F, Extended Fig. 7B). Specifically, correlation analysis suggested that reduction of Menin binding by MI-503 coincided with increased UTX chromatin occupancy at the same genomic loci (Pearson coefficient (r) = -0.49) (Fig.2G). This molecular switch was specific to Menin-dependent cells, as MI-503 treatment of Menin-independent mouse fibroblasts did not lead to UTX redistribution to promoters (Extended Fig. 8A-E). Therefore, Menin excludes UTX from binding to promoters in Menin-dependent cell types, and this antagonism can be reversed by MI-503-mediated displacement of Menin-MLL1/2 complexes from chromatin.
Given that Menin lacks a defined DNA binding domain57, we hypothesized that a sequence-specific DNA binding factor regulates the switch between Menin and UTX occupancy at specific promoters. To test this hypothesis, we performed motif analysis on genomic regions bound by Menin and UTX, uncovering NF-Y sequence motifs58 as the most significantly enriched (P=1e-130) (Extended Fig. 9A). These motifs were specific to leukemia cells, as motif enrichment analysis identified a different subset of sequences in mouse fibroblasts (Extended Fig. 9B-C). ChIP-Seq of NF-YA (the DNA binding and transactivation subunit of the NF-Y complex58) confirmed these observations, showing that NF-YA co-occupies sites bound by Menin and UTX (Extended Fig. 9D-E). Consistent with our hypothesis, sites bound by UTX in the context of Menin-MLL inhibition also had lower NF-YA occupancy (Extended Fig. 9D-E), which was not due to decreased protein levels (Extended Fig. 9F). Moreover, CRISPR-mediated disruption of Nfya in MLL-AF9 leukemia cells significantly decreased the viability of MI-503-treated leukemia cells relative to control cells (Extended Fig. 10A-C). This observation led us to hypothesize that NF-YA depletion could lead to a concomitant decrease of Menin and increase of UTX occupancy at target genomic regions. Supporting this model, treatment of NfyaKO cells with MI-503 led to a substantial increase in UTX occupancy at genomic regions normally bound by Menin and NF-Y at steady-state (Extended Fig. 10D-F). These results are also consistent with co-essentiality analyses in human cells59 showing that a subset of MEN1-dependent cells are co-dependent on members of the NF-Y complex for survival (Extended Fig. 10G-J). Collectively, these results implicate NF-Y in restricting the occupancy of UTX at promoter regions and further suggest that Menin binding to these sites is mediated by the sequence-specific DNA binding activity of the NF-Y transcription factor.
Promoter regulation by the MLL3/4 complex dictates outcome of Menin inhibition
Inhibition of Menin-MLL1 leads to displacement of this complex from gene promoters, allowing UTX to bind and potentially regulate target gene expression (Fig.2). To further understand the molecular switch between Menin and UTX at promoters, we examined the chromatin-binding profiles of their cognate H3K4 methyltransferases and their enzymatic histone modifications (MLL1 and H3K4me3 for Menin, and MLL3/4 and H3K4me1 for UTX) (Fig.3A-B). Disruption of the Menin-MLL1 interaction led to a substantial decrease in MLL1 chromatin binding and a concomitant decrease of its enzymatic product, H3K4me3, (Fig.3C, Extended Fig. 11A-B). In contrast, Menin-MLL1 inhibition led to increased binding of the MLL3/4 enzymes at promoter regions co-occupied by UTX and a concomitant increase in H3K4me1 signal at these same loci (Fig.3D, Extended Fig. 11C-D). These data suggest that disruption of the MLL1-Menin complex induces targeting of the enzymatic subunits of the MLL3/4-UTX complex to non-canonical sites that are normally bound by the MLL1-Menin complex and are distinct from those bound by the MLL-FP (Extended Fig. 12A-C). These results provide additional evidence for the existence of a molecular switch between the MLL complexes that is induced by Menin-MLL inhibition.
The above results are provocative, as promoter-associated H3K4me1 facilitates transcriptional repression in other cellular settings60, suggesting that deposition of H3K4me1 at gene promoters could depend on context61. Nevertheless, our finding that UtxKO, Mll3KO, and Mll4KO cells are resistant to Menin-MLL inhibition (Fig.1, Extended Fig. 3E-G) – combined with the colocalization of UTX, MLL3, MLL4, and H3K4me1 at the promoters of target genes – led us to hypothesize that the MLL3/4-UTX complex co-regulates gene expression in the context of leukemia. To test this hypothesis, we leveraged H3 lysine-4-to-methionine (K4M) ‘oncohistone’ mutants as orthogonal molecular tools to destabilize the MLL3/4-UTX complex62 (Extended Fig. 13A). Supporting our model, expression of H3.1K4M in MLL-AF9 leukemia cells led to a substantial proliferative advantage only in the context of Menin-MLL inhibition (Extended Fig. 13B-E), demonstrating that destabilization of the MLL3/4-UTX complex (Extended Fig. 13F) can phenocopy the intrinsic resistance of Utx-, Mll3-, and Mll4-deficient cells to Menin-MLL1 inhibition (Fig.1E-F, Extended Fig. 3E-G). These results further support a model whereby the MLL3/4-UTX complex serves as a central modulator of therapy response to Menin-MLL inhibition.
Reciprocal transcriptional regulation of tumor suppressive pathways by Menin and UTX
The above results support a model whereby a molecular switch between Menin and UTX at gene promoters dictates responses to Menin-MLL inhibition. We hypothesized that this switch acts through transcriptional regulation of genes involved in pathways that control cellular proliferation and survival. To test this, we performed transcriptional profiling of MLL-AF9 leukemia cells in the absence and presence of MI-503 and identified pathways that are reciprocally regulated by Menin and UTX (Extended Fig. 14A-C). Although the catalytic activity of the MLL1/2-Menin histone methyltransferase complex is canonically associated with actively transcribed developmental genes2, 13, disruption of the complex by MI-503 resulted in both up- and down-regulation of gene expression, with slight skewing towards gene activation (Fig.4A). Remarkably, the majority of significantly upregulated genes were reciprocally bound by Menin and UTX, suggesting that their expression is tightly co-regulated by these chromatin factors and gene repression mechanisms might be operative in this context (Fig.4B). Consistent with this observation, ChIP-Seq against H4K16ac (associated with gene activation) showed that the levels of this mark increased in the context of Menin-MLL inhibition (Extended Fig. 15A-B). These results support a model whereby Menin-MLL inhibition leads to dynamic changes in gene expression due to loss of MLL-dependent repressive activity and a concomitant increase in histone acetylation marks associated with gene activation (e.g., H4K16ac)63, 64.
To determine if UTX was necessary for the activation of these genes, we generated Utx-deficient (UtxKO) MLL-AF9 leukemia cells (Extended Fig. 14D), treated these with MI-503, and profiled by RNA-Seq (Extended Fig. 14E). UTX bound genes induced by MI-503 failed to get activated in UtxKO cells, suggesting that UTX function is necessary for their transcriptional activation when Menin is displaced from chromatin (we refer to these genes as ‘Menin-UTX targets’) (Fig.4C, Supplementary Table 4). We also confirmed that transcriptional targets of the MLL-AF9 fusion were downregulated in the MI-503 context (Extended Fig. 16A-B), as reported by others23, 26, 27. As shown earlier, UtxKO leukemia cells treated with MI-503 are able to proliferate without re-expression of Meis1, a critical Menin-MLL-FP target26, 27, 52, 53 (Fig.1E, Extended Fig. 6B, Extended Fig. 16C-D). These data suggest a new paradigm whereby the effects of Menin-MLL inhibition on MLL-FP target genes are independent of its effects on Menin-UTX transcriptional targets, and that induction of a tumor suppressive gene expression program alongside repression of canonical MLL-FP targets is required for the anti-leukemic activity of Menin-MLL1 inhibitors.
To gain insight into pathways directly regulated by Menin and UTX, we performed gene ontology (GO) analysis65, 66 of Menin-UTX targets (Supplementary Table 4) and found significant evidence for their association with transcriptional programs related to proliferation, differentiation, and survival (Extended Fig. 14C). To further evaluate the relevance of these GO terms, we performed Gene Set Enrichment Analysis (GSEA)67 on RNA-Seq data from UtxWT and UtxKO cells ± MI-503 and observed a highly significant correlation between the presence of UTX and the expression of senescence-associated genes following MI-503 treatment68 (Fig.4D, Extended Fig. 17A-F, Supplementary Table 4). This correlation was independently confirmed using a curated list of genes involved in cell cycle arrest and therapy-induced senescence69 (Fig.4E) and is consistent with the fact that Menin-MLL inhibition induces a combination of cell cycle arrest, apoptosis, and differentiation23, as well as our observation that the senescence-associated H4K16ac mark increases with MI-503 treatment70 (Extended Fig. 1E-F, Extended Fig. 15). The cellular senescence program is highly complex and is characterized in part by induction of permanent cell cycle arrest and a senescence-associated secretory phenotype (SASP)71. Consistent with a direct role for the Menin-UTX switch in regulating cellular senescence, secretion of prototypical SASP cytokines like IL-6, IFNβ-1, IL-3, and IL-15 was induced upon Menin-MLL inhibition in a Utx- dependent manner (Fig.4F-G, Extended Fig. 18). These results demonstrate that Menin-MLL inhibition engages a potent tumor suppressive network that is transcriptionally regulated in large part by the MLL3/4-UTX complex.
Combinatorial targeting of Menin and CDK4/6 to overcome resistance associated with loss of MLL3/4-UTX function
Given that cellular senescence can be regulated at both transcriptional and post-transcriptional levels, and chromatin regulation has been functionally implicated in modulating this program70, 72, 73, we hypothesized that the Menin-UTX molecular switch directly regulates the expression of cell cycle arrest- and senescence-associated genes by direct binding to their promoters56, 74. Consistent with our hypothesis, we found that Menin is bound to the promoters of the cyclin-dependent kinase (CDK) inhibitors Cdkn2c/Ink4c and Cdkn2d/Ink4d at basal conditions75, and that binding is abrogated upon Menin-MLL inhibition, leading to induction of their expression (Fig.4H, Extended Fig. 19A-B, Extended Fig. 20). Conversely, we found that UTX binds to these promoters only in the context of Menin-MLL inhibition, leading to UTX-dependent upregulation of both CDK inhibitors (Fig.4H, Extended Fig. 19A-B). These data show that the Menin-UTX molecular switch regulates the expression of these CDK inhibitors by direct chromatin regulation.
Since the proteins encoded by these two genes are natural inhibitors of the CDK4 and 6 kinases75, we hypothesized that pharmacological inhibition of CDK4/6 would bypass the intrinsic resistance of UtxKO cells to MI-503 while retaining the therapeutic effects of Menin-MLL inhibition on MLL-FP targets (Fig.4I). We therefore combined MI-503 with the FDA-approved CDK4/6 inhibitor Palbociclib76, which has been shown to trigger durable cell cycle arrest and senescence69, 77, 78. UtxKO cells were more resistant to either MI-503 or Palbociclib alone when compared to UtxWT cells, likely due to higher basal levels of both Cdk4 and Cdk6 transcripts (Fig.4J, Extended Fig. 19C-D) (refs. 74, 79). However, combined inhibition of Menin-MLL and CDK4/6 resulted in a synergistic effect (CD=0.4) (ref. 80) on inhibiting cell proliferation to similar levels achieved by MI-503 treatment of UtxWT cells (Fig.4J). These proof-of-concept data demonstrate that targeting pathways regulated by Menin and UTX can produce combinatorial therapeutic effects and further suggest that the anti-leukemic effects of MI-503 (ref. 23) are primarily through reactivation of tumor suppressor pathways and not solely through dampening transcription of MLL-FP targets like Meis1 (refs. 23, 26, 27, 53). Thus, combination of Palbociclib with Menin-MLL inhibitors may represent a novel and more effective therapeutic strategy for Menin-dependent cancers22, 24, 25, 81, 82.
Response to Menin-MLL inhibitors in primary human AML is accompanied by transcriptional induction of MLL3/4-UTX-dependent tumor suppressive programs
Small molecule inhibitors of the Menin-MLL interaction have shown promise in preclinical models of acute lymphoid and myeloid leukemias26, 27, 83 and are currently in Phase I/II clinical trials for treatment of patients with acute leukemia (SNDX-5613 (NCT04067336), KO-539 (NCT04065399), and JNJ-75276617 (NCT04811560)). Notably, SNDX-5613 was recently granted fast track designation by the FDA for treatment of relapsed/refractory acute leukemias. We hypothesized that the Menin-UTX molecular switch described above would be operative in leukemia patients treated with these Menin-MLL inhibitors. Consistent with our hypothesis, longitudinal RNA-Seq analysis of AML cells derived from two patients with NPM1c-mutated (Patient 1) and MLL-rearranged (Patient 2) leukemia treated with SNDX-5613 as part of the AUGMENT-101 clinical trial (NCT04065399) showed concomitant suppression of canonical MLL-FP target genes (e.g., MEIS1) and induction of CDK inhibitors (e.g., CDKN2C) (Fig.5A-E, Extended Fig. 21).
As an orthogonal approach, we analyzed a panel of genetically-annotated patient-derived xenografts (PDXs) of MLL-rearranged acute leukemia for response and resistance to VTP-50469 (a close analog of SNDX-5613) in vivo26 (Fig.5F, Extended Fig. 22A). Mice transplanted with an MLL3 wild type PDX showed a potent response to VTP-50469 and significant induction of Menin- UTX targets (Fig.5G-H, Extended Fig. 23A-B), reflecting the activation of the tumor suppressive gene expression program defined in this study (Fig.4). Conversely, an MLL3 mutant PDX exhibited primary resistance to VTP-50469 and failed to induce this gene expression program (Fig.5I-J, Extended Fig. 23A and S23C), thereby linking the induction of Menin-UTX-targets to preclinical drug response. DNA sequencing confirmed that the resistant PDX harbored a missense mutation in the MLL3 gene (Extended Fig. 22B). Thus, the gene expression programs regulated by the epistatic relationship between the MLL1/2-Menin and MLL3/4-UTX complexes are at play in patients and patient-derived xenografts treated with orally bioavailable Menin-MLL inhibitors that are currently under clinical investigation. Somatic mutations in members of the MLL3/4-UTX complex that impact catalytic activity or protein complex composition may impact the induction of tumor suppressive gene expression programs induced by Menin-MLL-inhibitors, thereby blunting clinical responses.
DISCUSSION
The Menin chromatin adaptor protein exhibits profound context-specific functions in different tissues, acting as a tumor suppressor gene in neuroendocrine14, 15, lung17, 84, skin16, and CNS malignancies18 and as an oncogenic cofactor in hepatocellular19 and hematologic cancers4, 20. Given that Menin can interact with similar cofactors in disparate settings, the basis for these apparently paradoxical findings has remained largely unclear. For example, Menin functionally cooperates with MLL proteins to activate transcription of the Cdkn1b/p27Kip1 and Cdkn2d/Ink4d CDK inhibitors as a tumor suppressive mechanism in neuroendocrine tumors and lung cancer3, 17, 85, 86, yet the same protein-protein interaction is critical for leukemia maintenance4, 10, 20. These contrasting observations suggest that Menin employs unique molecular mechanisms to regulate chromatin and gene expression depending on the context.
Our study sheds light on these paradoxical observations by providing the first evidence for a functional interaction between the mammalian histone methyltransferase complexes MLL1/2- Menin and MLL3/4-UTX and challenges the paradigm that these complexes are restricted to certain genomic compartments (Fig.6). We show that the Menin-MLL and NF-Y complexes coordinately inhibit the expression of a tumor suppressive network that involves the Cdkn2c/Ink4c and Cdkn2d/Ink4d tumor suppressor genes in leukemia by direct binding to gene promoters. Disruption of Menin-MLL or NF-Y complexes using genetic or pharmacological approaches triggers a molecular switch that leads to recruitment of the MLL3/4-UTX complex to the promoters of tumor suppressive genes, including the Cdkn2c/Ink4c and Cdkn2d/Ink4d CDK inhibitors75. This molecular switch leads to UTX-dependent activation of gene expression and is associated with a concomitant increase in the levels of the H4K16ac histone mark, resulting in robust induction of tumor suppressive programs like cellular senescence70, 73. Thus, the molecular choreography between the MLL1/2-Menin and MLL3/4-UTX complexes dictates cellular survival in leukemia.
Based on our observation that UTX directly binds to and activates the expression of Cdkn2c/Ink4c and Cdkn2d/Ink4d (which are natural inhibitors of the CDK4/6 kinases)75, we combined Menin-MLL inhibition with the FDA-approved CDK4/6 kinase inhibitor Palbociclib76 and showed that this combinatorial approach leads to a strong synergistic effect on inhibiting leukemia cell proliferation. As Palbociclib and other CDK4/6 inhibitors are already FDA-approved77, our proof-of-concept studies suggest that this combinatorial approach could be a viable therapeutic option to both boost the effectiveness of Menin inhibitors as monotherapies while potentially overcoming the intrinsic resistance to Menin-MLL inhibition conferred by loss-of-function of the MLL3/4-UTX complex.
Our study demonstrates –– in murine and human systems –– that the functional interplay between MLL1/2-Menin and MLL3/4-UTX is critical for Menin-inhibitor responses due to induction of Menin-UTX target genes. Notably, we were able to show that the induction of this gene expression program is at play upon oral delivery of VTP-50469 to leukemia PDX models and SDNX5613 to human AML subjects in the context of the AUGMENT-101 clinical trial (NCT04065399). Importantly, the Menin-UTX signature was absent in a non-responsive PDX harboring a missense mutation in MLL3, providing the first evidence supporting the concept that somatic variants in genes encoding for members of the MLL3/4 complex may blunt Menin-MLL inhibitor responses in the clinic.
AUTHOR CONTRIBUTIONS
Y.M.S.F., F.J.S.R., F.P, S.A.A, S.W.L., and C.D.A. conceived the study and wrote the manuscript with input from all the authors; Y.M.S.F., F.J.S.R., F.P., Y.X., L.G., M.C.B., and D.C. conducted experiments; D.W.B., T.C., and Y.J.H. analyzed RNA-Seq and ChIP-Seq data; Y.J.H. and E.R.K. analyzed CRISPR-screen data. S.G. and X.S.L. provided genome-wide CRISPR library. A.V.K., M.M., and E.dS. provided patient-derived xenograft samples. R.M.S. provided primary AML samples from patients in the Syndax trial A.S. provided conceptual advice. S.A.A, S.W.L., and C.D.A. supervised the study and secured funding.
DECLARATION OF INTERESTS
C.D.A. is a co-founder of Chroma Therapeutics and Constellation Pharmaceuticals and a Scientific Advisory Board member of EpiCypher. S.W.L. is an advisor for and has equity in the following biotechnology companies: ORIC Pharmaceuticals, Faeth Therapeutics, Blueprint Medicines, Geras Bio, Mirimus Inc., and PMV Pharmaceuticals. S.W.L. also acknowledges receiving funding and research support from Agilent Technologies for the purposes of massively parallel oligo synthesis. S.A.A. has been a consultant and/or shareholder for Vitae/Allergan Pharmaceuticals, Epizyme Inc., Imago Biosciences, Cyteir Therapeutics, C4 Therapeutics, Syros Pharmaceuticals, OxStem Oncology, Accent Therapeutics, and Mana Therapeutics. S.A.A. has received research support from Janssen, Novartis, and AstraZeneca. The remaining authors declare no competing interests.
MATERIALS AND METHODS
Plasmids and sgRNA cloning
To generate stable Cas9-expressing cell lines, we used lentiCas9-Blast (Addgene, 52962). Human wild-type or mutant (K4M) histone H3.1 were cloned into pCDH-EF1-MCS-IRES-RFP (System Biosciences, CD531A-2). To express sgRNAs, we generated the pUSEPR (U6-sgRNA-EFS-Puro-P2A-TurboRFP) and pUSEPB (U6-sgRNA-EFS-Puro-P2A-TagBFP) lentiviral vectors by Gibson assembly of the following DNA fragments: (i) PCR-amplified U6-sgRNA (improved scaffold)87 cassette, (ii) PCR-amplified EF1s promoter, (iii) PCR-amplified Puro-P2A-TurboRFP or -TagBFP gene fragment (IDT), and (iv) BsrGI/PmeI-digested pLL3-based lentiviral backbone88. For sgRNA cloning, pUSEPR and pUSEPB vectors were linearized with BsmBI (NEB) and ligated with BsmBI-compatible annealed and phosphorylated oligos encoding sgRNAs using high concentration T4 DNA ligase (NEB). All sgRNA sequences used are listed in Supplementary Table 1.
Cell culture
Mouse MLL-AF9 leukemia cells were kindly shared by David Chen (Chun-Wei Chen) and were originally generated by transformation of female mouse bone marrow Lin-Sca1+cKit+ (LSK) cells with an MSCV-IRES-GFP (pMIG) retrovirus expressing the human MLL-AF9 fusion protein and transplanted into sub-lethally irradiated recipient mice as described previously29, 89. Leukemic blasts were harvested from moribund mice and cultured in vitro in IMDM (Gibco) supplemented with 15% FBS (Gibco), mouse IL-6 (10ng/μL, PeproTech), mouse IL-3 (10ng/μL, PeproTech), mouse SCF (20ng/μL, PeproTech), penicillin (100U/mL, Gibco), streptomycin (100μg/uL, Gibco), L-glutamine (2mM, Gibco), and plasmocin (5μg/mL, InvivoGen). Human leukemia cell lines MV4;11 and OCI-AML3 were kindly shared by Zhaohui Feng and were cultured in RPMI 1640 (Corning) supplemented with 10% FBS (Gibco), penicillin (100U/mL, Gibco), streptomycin (100μg/uL, Gibco), L-glutamine (2mM, Gibco), and plasmocin (5μg/mL, InvivoGen). Mouse NIH-3T3 cells were maintained in DMEM (Corning) supplemented with 10% FCS (ATCC), penicillin (100U/mL, Gibco), streptomycin (100μg/uL, Gibco), and plasmocin (5μg/mL, InvivoGen). Human HEK293 cells were maintained in DMEM (Corning) supplemented with 10% FBS (Gibco), penicillin (100U/mL, Gibco), streptomycin (100μg/uL, Gibco), and plasmocin (5μg/mL, InvivoGen). Cas9-expressing cells were generated by lentiviral transduction of lentiCas9-Blast followed by Blasticidin (InvivoGen) selection and validation of Cas9 expression and activity. All cells were confirmed to be free of Mycoplasma contamination and cultured at 37°C and 5% CO2.
Virus production
Lentiviruses were produced by co-transfection of HEK293T (ATCC) cells with pUSEPR-EpiV2.0 sgRNA library, individual sgRNA plasmids, or lentiCas9-Blast, and packaging vectors psPax2 (Addgene, 12260) and pMD2.G (Addgene, 12259) using Lipofectamine 2000 (Invitrogen). Viral supernatants were collected at 48 and 72 hours post transfection and stored at -80°C.
Transduction of cell lines
Leukemia cells were seeded at a density of 2.5 x 105 cells/well of a non TC-treated 12-well plate in complete medium containing polybrene (10μg/mL, EMD Millipore), and then transduced with lentivirus by centrifugation at 2,500rpm for 90 minutes at 37°C. After a 24-hour incubation, cells were transferred to a new plate containing fresh culture medium. Antibiotic selection or cell sorting was done 48 hours post transduction.
Drug treatments
For MI-503 (Active Biochem) treatments, leukemia cells were seeded at a density of 4 x 105 cells/mL, treated with limiting dilutions of the inhibitor as indicated or 0.25% DMSO (vehicle). Cells were re-plated every 4 days to the initial density and re-treated. Viability was assessed at various time points by using the CellTiter-Glo Luminescent Cell Viability assay (Promega) following the manufacturer’s guidelines. Ratio of luminescence signal from metabolically active cells in MI-503 versus DMSO were plotted to calculate IC50 values (Prism 8, GraphPad). For MI-503 (Active Biochem) and Palbociclib HCl (Selleckchem) combination treatments, 25,000 leukemia cells in 250μL of drug-containing medium were seeded in a 48-well plate and viability was assessed every 4 days by using the CellTiter-Glo Luminescent Cell Viability assay (Promega). For RNA-Seq and ChIP-Seq experiments, cells were cultured at 4 x 105 cells/mL, treated with MI-503 (concentrations as indicated in Fig.legends) or 0.25% DMSO for 4 days. Cells were collected, washed with PBS, pelleted, and flash-frozen before RNA or chromatin isolation. For VTP-50649, XYZ, chow
Flow cytometric analyses
Immunophenotyping of leukemia cells treated with MI-503 (or vehicle), was done by collecting cells post treatment and staining using the indicated conjugated primary antibodies. Stained samples were analyzed on an LSRFortessa (BD Biosciences) flow cytometer. Data analysis was performed using FlowJo (BD Biosciences) software. Intracellular antigens detection was done by using the Foxp3/Transcription Factor Staining Buffer Set (eBioscience) following the manufacturer’s guidelines. Conjugated primary antibodies used were: Pacific Blue anti-CD11b (Biolegend, 101224), Alexa Fluor 647 anti-Cas9 (CST, 48796).
Xenograft models of AML
All animal experiments were performed with the approval of Dana-Farber Cancer Institute’s Institutional Animal Care and Use Committee (IACUC). NOD.Cg-Prkdcscid Il2rgtm1Sug/JicTac (NOG) mice were obtained from Taconic Biosciences (Rensselaer, NY, USA). Non-irradiated 8-12-week-old adult mice were transplanted with previously established patient derived xenografts (PDXs)26, 90 via tail vein injection (250,000 cells/mouse). Engraftment of human cells (hCD45+) was analyzed and monitored longitudinally by weekly bleeding to quantify hCD45+ cells in the peripheral blood by flow cytometry with human CD45-PE and anti-mouse CD45-APC-Cy7 antibodies (Biolegend, San Diego, CA, USA). Mice were monitored closely to detect disease onset and treatment started when hCD45+ cells were detectable in the peripheral blood. Mice were randomly assigned to either normal or 0.1% VTP-50469 rodent special diet26. Mice were bled weekly to monitor leukemia burden as described above and euthanized when showing clinical signs of disease (experimental endpoint). Leukemia cells from a subset of these animals were harvested after seven days of treatment to perform RNA-seq (see methods section on RNA-seq).
Longitudinal analysis of AML patient treated with SNDX-5613
Peripheral blood of patients was taken under informed consent according to the Declaration of Helsinki during routine blood draws at screening and different timepoints during the first cycle of treatment with SDNX-5613 within the AUGMENT-101 clinical trial (NCT04065399). Peripheral blood mononuclear cells (PBMCs) were subsequently isolated using Ficoll (BD Bioscience, Franklin Lakes, NJ, USA) gradient centrifugation, viably frozen, and banked at the Dana-Farber Cancer Institute, Boston, MA (approved institutional protocol, IRB: #01-206). For longitudinal analysis, samples were thawed, washed twice in PBS, and stained with anti-CD45 (PE) and anti-CD117 (APC) (Biolegend, San Diego, CA, USA). CD45-low/CD117+ leukemia cells were FACS sorted (MA900 sorter, Sony Biotechnology, San Jose, CA, USA) and subsequently processed for RNA-seq (see methods section on RNA-seq).
Immunoblotting
Whole cell lysates were separated by SDS-PAGE, transferred to a PVDF membrane (EMD Millipore), blocked in 5% non-fat milk in TBS plus 0.5% Tween-20 (Sigma-Aldrich), probed with primary antibodies, and detected with HRP-conjugated anti-rabbit or anti-mouse secondary antibodies (GE Healthcare). Primary antibodies used included: anti-Cas9 (CST, 14697), anti-UTX (CST, 33510), anti-Menin (Bethyl, A300-105A), anti-NF-YA (Santa Cruz Biotechnology, sc-17753), anti-Actin (abcam, ab8224), anti-HSP90 (BD Biosciences, 610418), anti-HA (Biolegend, 901501), anti-H3K4me1 (abcam, ab8895), anti-H3K4me3 (CST, 9751), anti-H3 (abcam, ab1791).
Locus-specific DNA sequencing
To determine the mutational status of the Men1 and Utx loci in cells targeted by CRISPR-Cas9, we performed next generation sequencing of PCR-amplified target regions. Genomic DNA (gDNA) was isolated using the DNeasy Blood & Tissue Kit (Qiagen) following the manufacturer’s guidelines. Amplification of target regions was performed from 500ng of gDNA using Q5 High-Fidelity 2X Master Mix (NEB) and primers listed in Supplementary Table 1. PCR products were purified using the QIAquick PCR Purification Kit (Qiagen) and sequenced on Illumina instruments at GENEWIZ (Amplicon-EZ service).
RNA isolation, qRT-PCR analyses, and RNA sequencing
Total RNA was isolated from cells using the RNeasy kit (Qiagen, Hilden, Germany). RNA was reverse transcribed with High-Capacity cDNA Reverse Transcription kits (Applied Biosystems) following the manufacturer’s instructions. Quantitative PCRs (qRT-PCRs) were performed using the TaqMan Gene Expression Master Mix (Applied Biosystems) with the StepOne Real-Time System (Applied Biosystems). TaqMan gene expression assays were used. ActB was used as the endogenous control for normalization and relative gene expression was calculated by using the comparative CT method. The mouse gene probes used were: ActB (Mm02619580_g1), Hoxa9 (Mm00439364_m1), Meis1 (Mm00487664_m1), Utx (Kdm6a) (Mm00801998_m1), Men1 (Mm00484957_m1). Quality of extracted RNA for sequencing was assessed by RIN using a Bioanalyzer (Agilent) and quantified by TapeStation (Agilent). Poly(A) mRNA enrichment and library preparation was performed using the NEBNext Poly(A) mRNA Magnetic Isolation Module and NEBNext Ultra II RNA Library Prep kit (NEB). Sequencing was done using the Illumina NextSeq500 to obtain >20 million 75bp single-end or 37bp paired-end reads per sample or at Genewiz (HiSeq, 150bp, paired end) (Illumina, South Plainfield, NJ, USA).
Cloning of sgRNA library targeting mouse chromatin regulators
sgRNA sequences (six per gene) targeting 616 mouse chromatin regulators (for a total of 3,696 sgRNAs) (Supplementary Table 1) were designed using the Broad Institute sgRNA Designer tool91. We also included 36 non-targeting control sgRNAs obtained from the GeCKOv2 Mouse CRISPR library92 (for a total of 3,732 sgRNAs). This library was divided into 6 pools (each composed of 616 targeting and 6 non-targeting sgRNAs), synthesized by Agilent Technologies, and cloned into the pUSEPR lentiviral vector93 using a modified version of the protocol published by Doench et al91 to ensure a library representation of >10,000X. Briefly, each sub-pool was selectively amplified using barcoded forward and reverse primers that append cloning adapters at the 5’ and 3’ ends of the sgRNA insert (Supplementary Table 1), purified using the QIAquick PCR Purification Kit (Qiagen), and ligated into BsmBI-digested and dephosphorylated pUSEPR vector using high-concentration T4 DNA ligase (NEB). A minimum of 1.2 ug of ligated pUSEPR plasmid DNA per sub-pool was electroporated into Endura electrocompetent cells (Lucigen), recovered for one hour at 37°C, plated across four 15cm LB-Carbenicillin plates (Teknova), and incubated at 37°C for 16 hours. The total number of bacterial colonies per sub-pool was quantified using serial dilution plates to ensure a library representation of >10,000X (>6.2 million colonies per sub-pool). The next morning, bacterial colonies were scraped and briefly expanded for 4 hours at 37°C in 500mL of LB-Carbenicillin. Plasmid DNA was isolated using the Plasmid Plus Maxi Kit (Qiagen).
To assess sgRNA distribution in each of the sub-pools, as well as the master pool (composed of equimolar amounts of plasmid DNA from each individual sub-pool), we amplified the sgRNA target region using primers that append Illumina sequencing adapters on the 5’ and 3’ ends of the amplicon, as well as a random nucleotide stagger and unique demultiplexing barcode on the 5’ end (Supplementary Table 1). Library amplicons were size-selected on a 2.5% agarose gel, purified using the QIAquick Gel Extraction Kit (Qiagen), and sequenced on an Illumina NextSeq instrument (75nt single end reads).
Chromatin-focused CRISPR-Cas9 genetic screening
To ensure that most cells harbor a single sgRNA integration event, we determined the volume of viral supernatant that would achieve an MOI of ∼0.3 upon spinfection of a population of Cas9- expressing leukemia cells. Briefly, cells were plated at a concentration of 2.5×105 per well in 12- well plates along with increasing volumes of master pool viral supernatant (0, 25, 100, 200, 500, 1000, and 2000μL) and polybrene (10 μg/mL, EMD Millipore). Cells were then centrifuged at 1,500rpm for 2 hours at 37°C and incubated at 37°C overnight. Viral infection efficiency was determined by the percentage of tRFP+ cells assessed by flow cytometry on an LSRFortessa (BD Biosciences) instrument 72 hours post infection.
Each step of the screen - from infection to sequencing - was optimized to achieve a minimum representation of 1000X. To ensure a representation of 1000X at the transduction step, we spinfected a total of 20 million cells across seven 12-well plates in triplicate (for a total of twenty one 12-well plates) using the volume of viral supernatant that would achieve a 30% infection rate (6 million transduced cells per technical replicate).
24 hours after infection, cells were pooled into 2 x T225 flasks (Corning) per infection replicate and selected with 2.5μg/mL puromycin (Gibco) for 4 days. Subsequently, 6 million puromycin-selected cells were pelleted and stored at -20°C (T0/Input population) while the rest were plated into either DMSO- or MI-503-containing media (at an IC50 concentration) and cultured until the population reached 12 cumulative population doublings (TF/Final). At least 6 million cells were harvested and pelleted for this final time point. Genomic DNA from MLL-AF9 cells was isolated using the DNeasy Blood & Tissue Kit (Qiagen) following the manufacturer’s guidelines.
As previously published94, we assumed that each cell contains approximately 6.6 pg of genomic DNA (gDNA). Therefore, deconvolution of the screen at 1000X required sampling ∼4 million x 6.6 pg of gDNA, or ∼26.4 ug. We employed a modified 2-step PCR version of the protocol published by Doench et al91. Briefly, we perform an initial “enrichment” PCR, whereby the integrated sgRNA cassettes are amplified from gDNA, followed by a second PCR to append Illumina sequencing adapters on the 5’ and 3’ ends of the amplicon, as well as a random nucleotide stagger and unique demultiplexing barcode on the 5’ end. Each “PCR1” reaction contains 25μL of Q5 High-Fidelity 2X Master Mix (NEB), 2.5μL of Nuc PCR#1 Fwd Primer (10μM), 2.5μL of Nuc PCR#1 Rev Primer (10μM), and 5μg of gDNA in 20μL of water (for a total volume of 50μL per reaction). The number of PCR1 reactions is scaled accordingly; therefore, we performed six PCR1 reactions per technical replicate, per time point (T0 or TF), and per condition (DMSO or MI-503). PCR1 amplicons were purified using the QIAquick PCR Purification Kit (Qiagen) and used as template for “PCR2” reactions. Each PCR2 reaction contains 25μL of Q5 High-Fidelity 2X Master Mix (NEB), 2.5μL of a unique Nuc PCR#2 Fwd Primer (10μM), 2.5μL of Nuc PCR#2 Rev Primer (10μM), and 300ng of PCR1 product in 20μL of water (for a total volume of 50μL per reaction). We performed two PCR2 reactions per PCR1 product. Library amplicons were size-selected on a 2.5% agarose gel, purified using the QIAquick Gel Extraction Kit (Qiagen), and sequenced on an Illumina NextSeq500 instrument (75nt single end reads). All primer sequences are available in Supplementary Table 1. PCR Program for PCR1 and PCR2: 1) 98°C x 30s; 2) 98°C x 10s; 3) 65°C x 30s; 4) 72°C x 30s; 5) Go to step 2 x 24 cycles; 6) 72°C x 2 min; 7) 4°C forever.
Genome-wide CRISPR-Cas9 genetic screening
Paired mouse genome-scale CRISPR-Cas9 screening libraries (M1/M2) were provided by Shengqing Gu and Xiaole Shirley Liu (Addgene Pooled Library #1000000173). The M1 and M2 libraries cover protein coding genes of the genome with a total of 10 guide RNAs per gene. Lentivirus was produced using each separate library pool and used to transduce each 5×108 MLL- AF9 cells at low MOI. 48 hours after library transduction, cells were selected with blasticidin (5ug/ml). After 5 days of antibiotic selection, a baseline (T0) sample was collected, and cells were cultured in duplicate before harvest of terminal samples after 12 days (TF). Subsequently, genomic DNA was isolated using phenol-chloroform extraction and sgRNA libraries were deconvoluted using next-generation sequencing essentially as described above.
Analysis of CRISPR-Cas9 genetic screen data
FASTQ files were processed and trimmed to retrieve sgRNA target sequences followed by alignment to the reference sgRNA library file. Sequencing read counts were summarized at gene level per sample and used as input to run differential analysis using DESeq2 package. The log2 fold change values were used as ‘Gene Score’ for the final visualization. Genome-wide screening data was analyzed using MAGeCK MLE essentially as described in the original publication95. See Supplementary Table 2 for all raw screening data.
Growth competition assays
Cas9-expressing cells were virally transduced with the designated constructs (pUSEPR-sgRNA, pUSEPB-sgRNA, pCDH-cDNA) in 12-well plates at 30-40% infection rate (three infection replicates). Cells were monitored by flow cytometry over time using an LSRFortessa (BD Biosciences) flow cytometer and relative growth of sgRNA-containing cells was assesed. Flow cytometry data was analyzed with FlowJo software (BD Biosciences). The percentage of single positive (SP) (tRFP+ or BFP+) or double positive (DP) (tRFP+/BFP+) cells was normalized to their respective “T0” time-point values (assessed on day 2 or 3 post transduction, as indicated in the Fig.legend). Normalized values were log2-transformed, and the differential fitness of cells was calculated as follow:
Differential Fitness = log2(Normalized DP) - log2(Normalized SP)
Chromatin immunoprecipitation (ChIP)
Cross-linking ChIP in mouse leukemia and NIH-3T3 cells was performed with 10-20×107 cells per immunoprecipitation. After drug (or vehicle) treatment, cells were collected, washed once with ice-cold PBS, and flash-frozen. Cells were resuspended in ice-cold PBS and cross-linked using 1% paraformaldehyde (PFA) (Electron Microscopy Sciences) for 5 minutes at room temperature with gentle rotation. Unreacted PFA was quenched with glycine (final concentration 125mM) for 5 minutes at room temperature with gentle rotation. Cells were washed once with ice-cold PBS and pelleted by centrifugation (800g for 5 minutes). To obtain a soluble chromatin extract, cells were resuspended in 1mL of LB1 (50mM HEPES pH 7.5, 140mM NaCl, 1mM EDTA, 10% glycerol, 0.5% NP-40, 0.25% Triton X-100, 1X complete protease inhibitor cocktail) and incubated at 4°C for 10 minutes while rotating. Samples were centrifuged (1400g for 5 minutes), resuspended in 1mL of LB2 (10mM Tris-HCl pH 8.0, 200mM NaCl, 1mM EDTA, 0.5mM EGTA, 1X complete protease inhibitor cocktail), and incubated at 4°C for 10 minutes while rotating. Finally, samples were centrifuged (1400g for 5 minutes) and resuspended in 1mL of LB3 (10mM Tris-HCl pH 8.0, 100mM NaCl, 1mM EDTA, 0.5mM EGTA, 0.1% sodium deoxycholate, 0.5% N- Lauroylsarcosine, 1X complete protease inhibitor cocktail). Samples were homogenized by passing 7-8 times through a 28-gauge needle and Triton X-100 was added to a final concentration of 1%. Chromatin extracts were sonicated for 14 minutes using a Covaris E220 focused ultrasonicator. Lysates were centrifuged at maximum speed for 10 minutes at 4°C and 5% of supernatant was saved as input DNA. Beads were prepared by incubating in 0.5% BSA in PBS and antibodies overnight (100μL of Dynabeads Protein A or Protein G (Invitrogen) plus 20μL of antibody). Antibodies used were: anti-Menin (Bethyl, A300-105A), anti-UTX (Bethyl, A302-374A), anti-MLL1 (N-term-specific, Bethyl, A300-086A), anti-MLL3/4 (kindly provided by the Wysocka laboratory41, anti-NF-YA (Santa Cruz Biotechnology, sc-17753), anti-H3K4me1 (abcam, ab8895), anti-H3K4me3 (Active Motif, 39159), and anti-H4K16ac (Active Motif, 39167). Antibody-Beads mixes were washed with 0.5% BSA in PBS and then added to the lysates overnight while rotating at 4°C. Beads were then washed six times with RIPA buffer (50mM HEPES pH 7.5, 500mM LiCl, 1mM EDTA, 0.7% sodium-deoxycholate, 1% NP-40) and once with TE-NaCl Buffer (10mM Tris-HCl pH 8.0, 50mM NaCl, 1mM EDTA). Chromatin was eluted from beads in Elution buffer (50mM Tris-HCl pH 8.0, 10mM EDTA, 1% SDS) by incubating at 65°C for 30 minutes while shaking, supernatant was removed by centrifugation, and crosslinking was reversed by further incubating chromatin overnight at 65°C. The eluted chromatin was then treated with RNase A (10mg/mL) for 1 hour at 37°C and with Proteinase K (Roche) for 2 hours at 55°C. DNA was purified by using phenol-chloroform extraction followed with ethanol precipitation. The NEBNext Ultra II DNA Library Prep kit was used to prepare samples for sequencing on an Illumina NextSeq500 (75bp read length, single-end, or 37bp read length, paired-end).
ChIP-Seq analysis
ChIP-sequencing samples were sequenced using the Illumina NextSeq500. ChIP-seq reads were aligned using Rsubread’s align method and predicted fragment lengths calculated by the ChIPQC R Bioconductor package96, 97. Normalized, fragment extended signal bigWigs were created using the rtracklayer R Bioconductor package. Peak calls were made in MACS2 software98. Read counts in peaks were calculated using the featureCounts method in the Rsubread library97. Differential ChIP-seq signal were identified using the binomTest from the edgeR R Bioconductor package99. Annotation of genomic regions to genes, biological functions, and pathways were performed using the ChIPseeker R Bioconductor package100. Meta-peak plots were produced using the soGGi package and ChIP-seq signal heatmaps generated using the Deeptools and profileplyr software101. Plots showing ChIP-Seq read signal over transcription start sites (TSSs) were made with the ngs.plot software package (v2.61) (ref. 102). Overlaps between peak sets were determined using the ChIPpeakAnno R Bioconductor package with a maximum gap between peaks set to 1kb (ref. 103). Peaks were annotated with both genes and the various types of genomic regions using the ChIPseeker R Bioconductor package100. Range-based heatmaps showing signal over genomic regions were generated using the soGGi and profileplyr R Bioconductor package to quantify read signal and group the peak ranges and the deepTools software package (v3.3.1) to generate the heatmaps101. Any regions included in the ENCODE blacklisted regions of the genome were excluded from all region-specific analyses104. For some ChIP-seq experiments, raw Illumina NextSeq BCL files were converted to FASTQs using Illumina bcl2fastq v02.14.01.07 and reads trimmed using Trimmomatic v0.36 (phred quality threshold 33) and uploaded to the to the Basepair-server (basepairtech.com). Alignment and ChIP-seq QC was performed on the basepair platform (Bowtie2). Peak calling was performed using MACS (v.1.4) within the basepair platform utilizing the default parameters.
RNA-Seq analysis
RNA-Seq samples were sequenced using the Illumina NextSeq500. Transcript abundance was computed from FASTQ files using Salmon and the GENCODE reference transcript sequences, transcript counts were imported into R with the tximport R Bioconductor package, and differential gene expression was determined with the DESeq2 R Bioconductor package105–107. The data was visualized using the ggplot2 R package. Normalized counts were extracted from the DESeq2 results and z-scores for the indicated gene sets were visualized using both heatmaps and boxplots. Heatmaps showing gene expression changes across samples were generated using the pheatmap R package and boxplots were made with the ggplot2 R package. Gene ontology analysis using the KEGG 2019 database was performed using the Enrichr tool56, 65.
Statistical analyses
Statistical tests were used as indicated in Figure legends. Generation of plots and statistical analyses were performed using Prism 8 (GraphPad). Error bars represent standard deviation, unless otherwise noted. We used Student’s t-test (unpaired, two-tailed) to assess significance between treatment and control groups, and to calculate P values. P<0.05 was considered statistically significant.
Source data availability
Data supporting the findings of this study are reported in Extended Figures 1-23 and Supplementary Tables 1-4. All raw data corresponding to high-throughput approaches (CRISPR screens, RNA-Seq, and ChIP-Seq) will be available through NCBI GEO. All reagents and materials generated in this study will be available to the scientific community through Addgene and/or MTAs. Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contacts: C. David Allis (alliscd{at}rockefeller.edu), Scott W. Lowe (lowes{at}mskcc.org), and Scott. A. Armstrong (Scott_Armstrong{at}dfci.harvard.edu).
ACKNOWLEDGEMENTS
We thank all members of the Allis, Lowe, and Armstrong laboratories for their help and support; Richard Phillips, Robert G. Roeder, Tom W. Muir, Benjamin A. Garcia, Ross Levine, Sheng Cai, and Charles J. Sherr for scientific discussions; David Chen (Chun-Wei Chen) for mouse MLL-AF9 cells, Zhaohui Feng for human leukemia cells, and Laura Whitman (Agilent) for OLS support. C.D.A. was supported by US National Institutes of Health (NIH) grants (P01CA196539 and 5R01CA204639-03), the Leukemia and Lymphoma Society (LLS-SCOR 7006-13), and the Rockefeller University and St. Jude Children’s Research Hospital Collaborative on Chromatin Regulation in Pediatric Cancer. S.W.L was supported by a NIH/NCI grant (R01 CA190261) and an Agilent Thought Leader Award, as well as the MSKCC cancer center support grant (P30 CA008748). S.W.L. is the Geoffrey Beene Chair of Cancer Biology and a Howard Hughes Medical Institute Investigator. S.A.A was supported by NIH grants CA176745, CA206963, CA204639 and CA066996. Y.M.S.F was supported by the Damon Runyon-Sohn Pediatric Cancer Fellowship (DRSG-21-17) and NIGMS-MOSAIC K99/R00 Career Development Award (1K99GM140265-01). F.J.S.R. was partially supported by the MSKCC TROT program (5T32CA160001), a MSKCC GMTEC Postdoctoral Researcher Innovation Grant, and is a HHMI Hanna Gray Fellow. F.P. was supported by the German Research Foundation (DFG, PE 3217/1-1) and a Momentum Fellowship award by the Mark Foundation for Cancer Research. D.W.B. was supported by a Ruth L. Kirschstein National Research Service Award (5F32CA217068). E.R.K. was supported by an F31 NRSA predoctoral fellowship from the NIH/NCI (F31CA192835).
Footnotes
↵† Co-first authors
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