Abstract
Chromatin-binding proteins are critical regulators of cell state in haematopoiesis1,2. Acute leukaemias driven by rearrangement of the mixed lineage leukaemia 1 gene (KMT2Ar) or mutation of the nucleophosmin gene (NPM1) require the chromatin adapter protein menin, encoded by the MEN1 gene, to sustain aberrant leukaemogenic gene expression programs3,4,5. In a phase 1 first-in-human clinical trial, the menin inhibitor revumenib, which is designed to disrupt the menin–MLL1 interaction, induced clinical responses in patients with leukaemia with KMT2Ar or mutated NPM1 (ref. 6). Here we identified somatic mutations in MEN1 at the revumenib–menin interface in patients with acquired resistance to menin inhibition. Consistent with the genetic data in patients, inhibitor–menin interface mutations represent a conserved mechanism of therapeutic resistance in xenograft models and in an unbiased base-editor screen. These mutants attenuate drug–target binding by generating structural perturbations that impact small-molecule binding but not the interaction with the natural ligand MLL1, and prevent inhibitor-induced eviction of menin and MLL1 from chromatin. To our knowledge, this study is the first to demonstrate that a chromatin-targeting therapeutic drug exerts sufficient selection pressure in patients to drive the evolution of escape mutants that lead to sustained chromatin occupancy, suggesting a common mechanism of therapeutic resistance.
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Data availability
All raw and processed sequencing data are accessible via the NCBI Gene Expression Omnibus (GEO) under the accession number: GSE196037 (GSE196036 for ChIP–seq and GSE196035 for RNA sequencing). X-ray crystal structures are publicly available via the PDB (8E90, 7UJ4 and 4GQ6, which was previously resolved by Shi et al. 25). All storage intensive files (that is, Markov state model structures, transition matrices, weights and strided trajectories, among others) can be found on OSF (https://osf.io/uge5j/). The complete dataset of trajectories (450 GB total storage needed) is available on request; owing to data size restrictions, external hard drives will be shipped to fulfil these data requests. Source data are provided with this paper.
Code availability
Scripts for structure preparation, docking and simulation can be found on GitHub (https://github.com/choderalab/men1). Software packages used for ChIP and RNA sequencing analysis are commonly used by the community and are publicly available: bcl2fastq (v.2.20.0.422), STAR (v.2.7.5a), picard (v.2.9.4), SAMtools (v.1.95), IGVtools (v.2.3.75), htseq-count (v.0.6.1pl), DESeq2 (v.1.24.0), bedtools (v.2.28.0), MACS2 (v.2.1.4), parallel (v.20061222) and snakemake (v.5.20.0).
References
Rodrigues, C. P., Shvedunova, M. & Akhtar, A. Epigenetic regulators as the gatekeepers of hematopoiesis. Trends Genet. 37, P125–P142 (2021).
Uckelmann, H. J. & Armstrong, S. A. Chromatin complexes maintain self-renewal of myeloid progenitors in AML: opportunities for therapeutic intervention. Stem Cell Rep. 15, 6–12 (2020).
Yokoyama, A. et al. The menin tumor suppressor protein is an essential oncogenic cofactor for MLL-associated leukemogenesis. Cell 123, 207–218 (2005).
Yokoyama, A. & Cleary, M. L. Menin critically links MLL proteins with LEDGF on cancer-associated target genes. Cancer Cell 14, 36–46 (2008).
Kuhn, M. W. et al. Targeting chromatin regulators inhibits leukemogenic gene expression in NPM1 mutant leukemia. Cancer Discov. 6, 1166–1181 (2016).
Issa, G. C. et al. The menin inhibitor revumenib in KMT2A-rearranged or NPM1-mutant leukaemia. Nature https://doi.org/10.1038/s41586-023-05812-3 (2023).
Huang, J. et al. The same pocket in menin binds both MLL and JUND but has opposite effects on transcription. Nature 482, 542–546 (2012).
Hughes, C. M. et al. Menin associates with a trithorax family histone methyltransferase complex and with the hoxc8 locus. Mol. Cell 13, 587–597 (2004).
Borkin, D. et al. Pharmacologic inhibition of the menin–MLL interaction blocks progression of MLL leukemia in vivo. Cancer Cell 27, 589–602 (2015).
Klossowski, S. et al. Menin inhibitor MI-3454 induces remission in MLL1-rearranged and NPM1-mutated models of leukemia. J. Clin. Invest. 130, 981–997 (2020).
Lei, H. et al. Recent progress of small molecule menin–MLL interaction inhibitors as therapeutic agents for acute leukemia. J. Med. Chem. 64, 15519–15533 (2021).
Perner, F. & Armstrong, S. A. Targeting chromatin complexes in myeloid malignancies and beyond: from basic mechanisms to clinical innovation. Cells 9, 2721 (2020).
Uckelmann, H. J. et al. Therapeutic targeting of preleukemia cells in a mouse model of NPM1 mutant acute myeloid leukemia. Science 367, 586–590 (2020).
Krivtsov, A. V. et al. A menin–MLL inhibitor induces specific chromatin changes and eradicates disease in models of MLL-rearranged leukemia. Cancer Cell 36, 660–673.e11 (2019).
Lemos, M. C. & Thakker, R. V. Multiple endocrine neoplasia type 1 (MEN1): analysis of 1336 mutations reported in the first decade following identification of the gene. Hum. Mutat. 29, 22–32 (2008).
Hanna, R. E. et al. Massively parallel assessment of human variants with base editor screens. Cell 184, 1064–1080.e20 (2021).
Zimmerman, M. I. et al. SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome. Nat. Chem. 13, 651–659 (2021).
Ward, M. D. et al. Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets. Nat. Commun. 12, 3023 (2021).
Sun, X., Singh, S., Blumer, K. J. & Bowman, G. R. Simulation of spontaneous G protein activation reveals a new intermediate driving GDP unbinding. eLife 7, e38465 (2018).
Grembecka, J., Belcher, A. M., Hartley, T. & Cierpicki, T. Molecular basis of the mixed lineage leukemia-menin interaction: implications for targeting mixed lineage leukemias. J. Biol. Chem. 285, 40690–40698 (2010).
Bai, H. et al. Menin–MLL protein–protein interaction inhibitors: a patent review (2014–2021). Expert Opin. Ther. Pat. 32, 507–522 (2022).
Ross, D. S. et al. Immunohistochemical analysis of estrogen receptor in breast cancer with ESR1 mutations detected by hybrid capture-based next-generation sequencing. Mod. Pathol. 32, 81–87 (2019).
Biancaniello, C. et al. Investigating the effects of amino acid variations in human menin.Molecules 27, 1747 (2022).
Nikolovska-Coleska, Z. et al. Development and optimization of a binding assay for the XIAP BIR3 domain using fluorescence polarization. Anal. Biochem. 332, 261–273 (2004).
Shi, A. et al. Structural insights into inhibition of the bivalent menin–MLL interaction by small molecules in leukemia. Blood 120, 4461–4469 (2012).
Olsen, S. N. et al. MLL::AF9 degradation induces rapid changes in transcriptional elongation and subsequent loss of an active chromatin landscape. Mol. Cell 82, 1140–1155.e1111 (2022).
Acknowledgements
We thank the patients and families for their participation in this clinical trial; J. A. Perry for her support in banking and providing PDX material that was used in this study; O. Abdel-Wahab, B. L. Bowman and L. A. Miles for their critical review of the manuscript; the citizen-scientists of Folding@home for donating their computing resources for simulations; S. Bijpuria and B. McKeever for assisting with structure determination and PDB deposition; and J. Cassel for menin dissociation measurements. Illustrations in Fig. 3c and Extended Data Fig. 3a were created with BioRender (https://biorender.com). This research was funded in part through the NIH/NCI Cancer Center support grant P30 CA008748. S.F.C. was supported by a Scholar Award from the American Society of Hematology, a Momentum Fellowship Award from The Mark Foundation for Cancer Research, a Young Investigator Award from the Edward P. Evans Foundation, and a Career Development Award from the NCI (K08 CA241371-01A1). S.A.A. was supported by NIH grants CA176745, CA206963, CA204639 and CA066996. S.A.A. and R.M.S. were supported by a SPORE grant in myeloid malignancies (P50CA206963). R.L.L. was supported by a Cycle For Survival Innovation grant, NCI grants R35 CA197594 and R01 CA173636, a grant from the Samuel Waxman Cancer Research Foundation, funding support from the Martino Family Foundation, and SCOR grants from the Leukemia and Lymphoma Society. R.L.L. and S.A.A. were supported by an Alex’s Lemonade Stand Foundation Crazy 8 grant. F.P. was supported by the German Research Foundation (DFG; PE 3217/1-1), a Momentum Fellowship award by the Mark Foundation for Cancer Research, a research grant from the ‘Else Kröner-Fresenius-Stiftung’ (2021-EKEA.111), and startup funding from the University Medicine Greifswald (FOVB-2022-01). C.M. was funded by a Momentum Fellowship award by the Mark Foundation for Cancer Research. J.D.C. acknowledges support from NIH grants P30 CA008748 and R01 GM121505. J.D.C., A.V., D.S. and S.S. acknowledge funding from the Stiftung Charité, the BIH Einstein Foundation, MSKCC, NIH grant R01 GM121505 and Bayer. E.S.F. was supported by NIH grants CA214608 and CA066966. J.A.C. is supported by a Ruth L. Kirschstein Postdoctoral Individual National Research Service Award (NIH F32CA250240-02). W.X. was supported by Alex’s Lemonade Stand Foundation and the Runx1 Research Program, the Cycle for Survival’s Equinox Innovation Award in Rare Cancers, MSK Leukemia SPORE Career Enhancement Program and a career development award from the NCI (K08CA267058). H.R. was supported by a Fellow award from the Leukemia and Lymphoma Society. S.S. is a Damon Runyon Quantitative Biology Fellow supported by the Damon Runyon Cancer Research Foundation (DRQ-14-22).
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S.F.C., F.P., E.M.S., R.L.L. and S.A.A. led the conception and design of the manuscript. F.P., S.F.C., E.M.S., R.L.L., S.A.A., E.S.F. and G.M.M. wrote the manuscript. S.F.C., R.L.L. and S.A.A. supervised all studies. S.F.C., F.P., E.M.S., R.M.S., W.X., R.L.L. and S.A.A. performed clinical analysis and annotation of patient samples. F.P., D.V.W., A.A., D.A., C.M., S.M., H.A.G., A.J.S., S.P., E.E. and J.A.C. cloned mutant MEN1 constructs, expressed in cell lines, and performed functional drug resistance assays. F.P. and J.G.D. performed CRISPR screens. J.K., R.P.N., G.M.M. and E.S.F. performed biochemical binding assays (fluorescence polarization and isothermal titration calorimetry) and analysed X-ray crystallography data. S.S., D.S., A.V. and J.D.C. performed computational modelling studies. F.P., H.R., C.H., Y.W. and D.V.W. performed ChIP–PCR, ChIP–seq and RNA sequencing experiments and analysis of these data.
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S.F.C. is a consultant for and holds equity interest in Imago Biosciences. J.G.D. consults for Microsoft Research, Servier, Abata Therapeutics, Maze Therapeutics, BioNTech and Pfizer; consults for and has equity in Tango Therapeutics; and receives support via the Functional Genomics Consortium (Merck, AbbVie, Janssen, Vir and Bristol Meyers Squibb). R.L.L. is on the supervisory board of Qiagen and is a scientific advisor to Loxo, Imago, C4 Therapeutics and Isoplexis; receives research support from and consulted for Celgene and Roche; receives research support from Prelude Therapeutics; has consulted for Novartis and Gilead; and has received honoraria from Lilly and Amgen for invited lectures. E.M.S. receives research support to his institution from Agios, Amgen, Astellas, Bayer, Biotheryx, Bristol Myers Squibb, Eisai Foghorn, Servier, Syndax, Syros; receives consulting fees from Novartis, PinotBio, Janssen, Bristol Myers Squibb, Agios, Jazz, Menarini, Genentech, Genesis, AbbVie, Neoleukin, Gilead, Syndax, OnCusp, CTI Biopharma, Foghorn, Servier, Calithera, Daiichi, Aptose, Syros, Astellas, Ono Pharma, Blueprint, Kura, Epizyme and Cellectis; and also holds equity interest in Auron Therapeutics. S.A.A. has been a consultant and/or shareholder for Vitae/Allergan Pharmaceuticals, Neomorph, Inc., Imago Biosciences, Cyteir Therapeutics, C4 Therapeutics and Accent Therapeutics; and has received research support from Janssen and Syndax. Memorial Sloan Kettering Cancer Center holds a patent (WO/2017/132398A1) covering menin inhibition in NPM1-mutant AML that lists S.A.A. as an inventor. J.D.C. is a current member of the scientific advisory boards of OpenEye Scientific Software, Interline Therapeutics and Redesign Science. The Chodera laboratory receives or has received funding from the NIH, the National Science Foundation, the Parker Institute for Cancer Immunotherapy, Relay Therapeutics, Entasis Therapeutics, Silicon Therapeutics, EMD Serono (Merck KGaA), AstraZeneca, Vir Biotechnology, XtalPi, Interline Therapeutics, the Molecular Sciences Software Institute, the Starr Cancer Consortium, the Open Force Field Consortium, Cycle for Survival, a Louis V. Gerstner Young Investigator Award and the Sloan Kettering Institute. A complete funding history for the Chodera laboratory can be found at http://choderalab.org/funding. W.X. has received research support from Stemline Therapeutics. The spouse of A.J.S. is an employee of Bristol Myers Squibb. E.S.F. is a founder, member of the scientific advisory board and equity holder of Civetta Therapeutics, Jengu Therapeutics, Proximity Therapeutics, Neomorph Inc. (board member), Avilar Therapeutics and Photys Therapeutics; and a consultant to Astellas, Sanofi, Novartis, Deerfield and EcoR1 capital. The Fischer laboratory receives or has received research funding from Novartis, Deerfield, Ajax, Interline and Astellas. R.M.S. has received advisory or consulting fees from AbbVie, Actinium, Agios, Arog, Astellas, Biolinerx, Celgene, Daiichi Sankyo, Elevate, Gemoab, Janssen, Jazz, Macrogenics, Novartis, OncoNova, Syndax, Syntrix, Syros, Takeda, Trovagene, BergenBio, Foghorn Therapeutics, GSK, Aprea, Innate, Amgen, CTI Pharmaceuticals, Bristol Myers Squibb and Boston Pharmaceuticals. The remaining authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Novel MEN1 mutations detected in patients upon relapse on revumenib.
a—c) Tables showing the results of the IMPACT targeted DNA-sequencing panel from patient 1–4 at the time point of screening prior to enrolling on the AUGMENT-101 trial and at relapse on revumenib treatment. MEN1 mutations are highlighted in red. d) Pie charts displaying the fraction of MEN1-mutant alleles measured by droplet digital PCR (ddPCR) at the time point of relapse (or last available sampling time point before relapse) in all individual patients from the cohort shown in Fig. 1b. Relative mutation frequencies (number of MEN1-mutant / WT droplets) are labeled in white. Mutations which were detected in > 2 droplets were considered. e) Longitudinal kinetics of MEN1 mutant selection in two selected patients from the cohort shown in Fig. 1b. Mutant allele frequencies at different time points during revumenib treatment were analyzed by ddPCR.
Extended Data Fig. 2 Development of Menin-inhibitor resistance in a KMT2Ar PDX.
a) Box-plot (median, box: 25th to 75th percentile, whiskers: range) showing the percentage (%) of human leukemia cells in the bone marrow of NOG-mice transplanted with PDX3 at baseline (n = 4), 4 weeks (w) (n = 4), 8w (n = 5) and at 10–12w (n = 5; symptomatic leukemia relapse) on Menin-inhibitor treatment. Dots represent individual animals. One-way ANOVA with correction for multiple comparisons was used for statistical analysis. b) Box-plots (median, box: 25th to 75th percentile, whiskers: range) showing the mean fluorescence intensity (MFI) of the myeloid differentiation markers CD11b, CD13 and CD14 on the cell surface of human cells detected in the bone marrow of NOG-mice transplanted with PDX3 at baseline (n = 4), 4w (n = 5), 8w (n = 4) and at 10–12w (n = 4) on Menin-inhibitor treatment. Dots represent individual animals. One-way ANOVA with correction for multiple comparisons was used for statistical analysis. c) Bone marrow cytology pictures (cytospins) from each 2 representative animals at baseline, 4w, 8w and 12w on Menin-inhibitor treatment. d) Pie charts showing the fraction of MEN1-T349M (red) as compared to MEN1-WT (blue) measured by droplet digital PCR (ddPCR) at baseline, 8 weeks and 12 weeks (fulminant clinical relapse) in human cells isolated from PDX3 mice and purified using magnetic cell sorting.
Extended Data Fig. 3 Base-editor screening as a tool to identify point mutants in MEN1.
a) Schematic depicting the workflow of the MEN1-base editor screen performed in MOLM13 (MLL::AF9) and MV4;11 (MLL::AF4) cells. The schematic in a was created using BioRender (https://biorender.com). b) Dot-plot showing the results of a CRISPR-Cas9 base-editor screen in MV4;11 cells aiming to identify point mutations that cause resistance to Menin inhibitor treatment. Each dot represents a single guide RNA. Along the x-axis guide RNAs are sorted by their targeting location relative to the Menin-coding sequence. The y-axis shows differential CRISPR-beta-scores (DMSO-score subtracted from the VTP-50469-treatment score). Outstanding hits are marked in red and targeted amino acid residues are labeled. c) X-ray co-crystal structure of revumenib bound to WT-Menin (PDB: 7UJ4). The hydrogen bonds between sulfonamide oxygen of revumenib and indole nitrogen of W346 or sulfonamide nitrogen of revumenib and backbone carbonyl oxygen of M327 are indicated with black dashed lines. Non polar hydrogens are shown for revumenib and the W346. d) X-ray co-crystal structure of Menin in complex with MLL14–15 peptide (PDB: 4GQ6). View corresponds to Extended Data Fig. 3c. Recurrently mutated amino acids are labeled in red. The W346 residue that builds up a strong hydrogen bond with revumenib to stabilize binding of the molecule is marked in blue. e) Alignment of the Menin bound revumenib (PDB: 7UJ4) with Menin bound MLL14–15 peptide (PDB: 4GQ6). Recurrently mutated amino acids are labeled in red. The W346 residue that builds up a strong hydrogen bond with revumenib to stabilize binding of the molecule is marked in blue.
Extended Data Fig. 4 Atomic modeling of Menin and its mutations using equilibrium simulations.
a) Trajectory length distributions for equilibrium simulations of Menin wild-type and mutants (rows). Simulations were all run simultaneously on Folding@home and the frequency distribution of their simulation lengths is indicated for each construct, both with and without revumenib (left and right columns, respectively. b) Equilibrium molecular dynamics simulations and Markov models reveal that helices contacting revumenib separate upon mutation. Distance distributions between the sulfonamide contacting helices were computed for WT Menin and each mutant. Error bands are computed by bootstrapping the markov state model using 10 random samples with replacement, generating standard errors for the Markov State Model populations. These errors were used to compute the histogram standard error ranges as shown above using 100 bins. Results are insensitive to changing the number of bootstrapped samples from 5 to 30. c) DiffNets analysis comparing WT to mutant Menin using backbone features showing helical separation (blue lines) around revumenib (magenta). Dashed lines indicate helical motion as a structural feature that significantly differs between WT and mutant Menin. Blue lines indicate that helices move further apart and separate upon mutation, while red dashed lines indicate that helices come closer together. d) Implied timescales after clustering all Folding@home simulations. Based on this plot, a lag time of 7 nanoseconds was chosen for MSM construction to ensure Markovanaity.
Extended Data Fig. 5 MEN1 mutations impact binding affinity of revumenib to the MLL1/2 binding pocket.
a) Titration curves of WT-, M327I- and T349M-mutant Menin against a FITC-conjugated MLL1 4–43(C-A) peptide probe (N = 3, each data point represents the mean of 3 technical triplicates +/− SD) for determination of equilibrium dissociation constant (Kd). b) Curves depicting the fraction of revumenib (left) or MLL1 (right) bound to Menin (WT or mutant) over time determining the molecule’s dissociation rates (off-rates) over time (N = 8). Data point represent the mean +/− SD. c) Isothermal titration calorimetry assay measuring the binding of revumenib to WT-, M327I and T349M-mutant Menin confirming the mutation inflicted shift in affinity detected using the fluorescence polarization assay (Fig. 2e). d) Titration curves of WT-, M327I- and T349M-mutant Menin against a FITC-conjugated MLL2 (15–48) peptide probe at three peptide concentrations 0.5, 1 and 2 nM. Data is presented as fraction bound of three independent replicates (N = 3). Data point represent the mean +/− SD. e) Fluorescence polarization assay measuring dose-dependent displacement of an MLL2 peptide from WT, M327I- and T349M-mutant Menin under treatment with revumenib or MI-3454. Data is presented as fraction bound of three independent replicates (N = 3). Data point represent the mean +/− SD.
Extended Data Fig. 6 Lentiviral expression of MEN1 mutants confers resistance in cell lines.
a) Western blot in MOLM13 cells showing expression of HA-tagged MEN1-WT and M327I-mutant construct. Representative Western Blot of 3 independent replicates. b) Dose-response curves of MOLM13 cells to revumenib upon expression of MEN1 mutants compared to -WT. Cell counts were measured by flow cytometry and displayed relative to the DMSO control (mean +/− SEM, n = 4, each 3 technical replicates). c) Dose-response curves of MV4;11 and OCI-AML3 cells to revumenib upon expression of MEN1 mutants or -WT measured by Cell-titerGlo (mean +/− SD, n = 3). d) Induction of differentiation marker expression by revumenib in OCI-AML3 cells expressing MEN1 mutants compared to WT measured by flow cytometry (MFI CD11b) (n = 3, mean +/− SD). An unpaired, two-tailed t-test was used for statistical analysis. e) Quantification of the cytological assessment for blast morphology by a hematopathologist (n = 5, median, box: 25th to 75th percentile, whiskers: range). One-way ANOVA with correction for multiple comparisons was used for statistical analysis. f) Dose-response curves of OCI-AML3 cells to revumenib (top panel) or MI-3454 (bottom panel) upon expression of MEN1 mutants compared to -WT. Cell counts were measured by flow cytometry and displayed relative to the DMSO (mean +/− SEM, n = 4, each 3 technical replicates). g) Western blot in MV4;11 cells showing expression of HA-tagged MEN1-WT and -mutant constructs. Representative Western Blot of 2 independent replicates. h) Dose-response curves of MV4;11 cells to revumenib upon expression of MEN1 mutants compared to -WT. Cell counts were measured by flow cytometry and displayed relative to the DMSO (mean +/− SEM, n = 4, each 3 technical replicates). b, f, h) Statistical analysis was performed using an unpired t-test, two tailed, multiple comparisons. *** p < 0.001; **p < 0.01; *p < 0.05.
Extended Data Fig. 7 MEN1-M327I endogenous gene-editing induces drug resistance to different Menin-inhibitors in leukemia cell lines.
a) Sanger-sequencing tracks showing gene-editing in MV4;11 and OCI-AML3 cells generating stable cell lines harboring the mutations indicated above the respective plots at the endogenous MEN1-locus. b) Dose-response curves of M327I homozygous or -WT MV4;11 cells to a high-dose range of revumenib. c) Dose-response curves of M327I heterozygous or -WT MV4;11 cells to MI-3454. d) Dose-response curves showing the sensitivity of OCI-AML3 (NPM1) cells harboring the MEN1-M327I mutation to revumenib, MI-3454 and the Daiichi-Sankyo compound. e) Dose-response curves showing the sensitivity of OCI-AML3 (NPM1) cells harboring the MEN1-T349M mutation to revumenib, MI-3454 and the Daiichi-Sankyo compound. f) Dose-response curves showing the sensitivity of MV4;11 cells harboring homozygous or heterozygous MEN1-M327I mutations to the covalent binder MI-89. g) Dose-response curves of S160C or -WT MV4;11 cells to revumenib. b—g) Cell counts were measured by flow cytometry and displayed relative to the DMSO control (mean +/− SEM, n = 4, each 3 technical replicates). h) Fluorescence-based cell competition assay measuring relative cell fitness of MV4;11-MEN1-WT, -M327I or -T349M mutant cells in the presence or absence of revumenib (100nM) over the course of 21 days by flow cytometry (N = 4, mean +/− SD). b–g) Statistical analysis was performed using an unpired t-test, two tailed, multiple comparisons. *** p < 0.001; **p < 0.01; *p < 0.05.
Extended Data Fig. 8 ChIPseq of Menin and MLL1 in MEN1-WT and -M327I-mutant cells.
a) ChIPseq tracks of Menin and MLL1 at the PBX3, MEF2C, JMJD1C-loci and the HOXA-cluster in MV4;11 cells under revumenib treatment (representative example of 3 replicates). b) Torpedo-plots of total Menin signal intensity around transcription start sites (TSS) from ChIP-sequencing (ChIPseq) in OCI-AML3-MEN1-WT and -M327I-mutant cells treated with revumenib (0.1μM, 1μM) or DMSO as control (N = 2). Shown is one representative example. c) ChIPseq tracks of Menin at the MEIS1, PBX3, MEF2C, JMJD1C-loci and the HOXA-cluster in OCI-AML3 cells under revumenib treatment. d) Bar graphs showing Menin-ChIP-qPCR results at the MEIS1, MEF2C and HOXA10 transcription start sites after treatment with 100nM revumenib or DMSO as control (4 days treatment) (N = 3, mean +/− SD). An unpaired, two-tailed t-test was used for statistical analysis. e) Torpedo-plots of Menin signal intensity around TSS from ChIPseq in MEN1-WT and -T349M-mutant PDX3 treated with VTP-50469 for 14 days. f) Menin-TSS-signal at MLL1-target genes in PDX3 (mean +/− SD, 3000 TSS data points per condition). Two-tailed Mann-Whitney-Tets was used for statistical analysis. g) Read-normalized MLL1-TSS-signal at sites that lose >80% of Menin in WT cells treated with VTP-50469 (mean +/− SD, 293 data points per condition). Two-tailed Mann-Whitney-Tets was used for statistical analysis. h) ChIPseq tracks of Menin and MLL1 at the MEIS1-locus and HOXA-cluster in PDX3.
Extended Data Fig. 9 MEN1 mutations abrogate changes in gene expression signatures in MV4;11 cells upon revumenib treatment.
a) Geneset-enrichment analysis (GSEA) from revumenib (100nM, 1μM or 5μM) vs. DMSO treated MV4;11 cells harboring the MEN1-M327I mutation or -WT as control. Plotted are the False-discovery rate (FDR) q-values (y-axis) over the normalized enrichment scores (x-axis). Each dot represents a gene set. Relevant genesets covering MLL/HOX-related or myeloid differentiation associated terms were chosen for the analysis and selected terms are annotated. b) GSEA plots from revumenib (100nM, 1μM or 5μM) vs. DMSO treated MV4;11 cells harboring the MEN1-M327I mutation or -WT as control. GSEA was performed for MLL-fusion targets26 and the BROWN_MYELOID_CELL_CEVELOPMENT_UP geneset. Normalized enrichment scores and FDR q-values are indicated below each plot.
Extended Data Fig. 10 MEN1 mutations blunt repression of key MLL-target genes upon revumenib treatment.
a) Bar graphs showing relative gene expression of MEIS1 and MEF2C in MEN1-M327I homozygous (left) and heterozygous (right) cells under treatment with a wide range of revumenib doses (mean +/− SD, n = 3, each measured in triplicates) measured by quantitative real-time PCR using pre-validated Taqman probes. b) Bar graphs showing relative gene expression of MEIS1 (left) and MEF2C (right) in MEN1-M327I and MEN1-T349M mutant cells under treatment with a wide range of revumenib doses (mean +/− SD, n = 3, each measured in triplicates) measured by quantitative real-time PCR using pre-validated Taqman probes. c) Bar graphs showing relative gene expression of MEIS1, PBX3 and HOXA7 in MEN1-T349M-mutant or WT PDX2 treated for 12 days with VTP-50469 (mean +/− SD, n = 3, each measured in triplicates, replicates represent individual mice) measured by quantitative real-time PCR using pre-validated Taqman probes. d) Graphical depiction of the percentage (%) of leukemic blasts in the peripheral blood and bone marrow of a patient that developed resistance without MEN1-mutations (or other somatic mutations detected by IMPACT-sequencing) during revumenib treatment on the AUGMENT-101 clinical trial. e) Cytology pictures (May-Grünwald/Giemsa staining) showing blast morphology of leukemia cells at screening and relapse under revumenib treatment of the same patient as shown in d). Representative cytology pictures from one individual patient sample. f) Volcano-plots showing gene expression changes in resistant leukemia cells from a patient (same as in d/e) and PDX-4 which developed non-genetic Menin-inhibitor resistance. Statistical determination of differentially expressed genes was performed using DESeq2.
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Perner, F., Stein, E.M., Wenge, D.V. et al. MEN1 mutations mediate clinical resistance to menin inhibition. Nature 615, 913–919 (2023). https://doi.org/10.1038/s41586-023-05755-9
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DOI: https://doi.org/10.1038/s41586-023-05755-9
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