In vivo CRISPR screens identify a dual function of MEN1 in regulating tumor-microenvironment interactions.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
03 Sep 2024
03 Sep 2024
Historique:
received:
10
11
2023
accepted:
18
07
2024
medline:
4
9
2024
pubmed:
4
9
2024
entrez:
3
9
2024
Statut:
aheadofprint
Résumé
Functional genomic screens in two-dimensional cell culture models are limited in identifying therapeutic targets that influence the tumor microenvironment. By comparing targeted CRISPR-Cas9 screens in a two-dimensional culture with xenografts derived from the same cell line, we identified MEN1 as the top hit that confers differential dropout effects in vitro and in vivo. MEN1 knockout in multiple solid cancer types does not impact cell proliferation in vitro but significantly promotes or inhibits tumor growth in immunodeficient or immunocompetent mice, respectively. Mechanistically, MEN1 knockout redistributes MLL1 chromatin occupancy, increasing H3K4me3 at repetitive genomic regions, activating double-stranded RNA expression and increasing neutrophil and CD8
Identifiants
pubmed: 39227744
doi: 10.1038/s41588-024-01874-9
pii: 10.1038/s41588-024-01874-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Princess Margaret Cancer Foundation (PMCF)
ID : 886012001223
Organisme : Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada)
ID : 142246, 152863,152864,159567,438793
Organisme : Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada)
ID : FDN-148395
Organisme : Terry Fox Research Institute (Institut de Recherche Terry Fox)
ID : 1090, 1124
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 31801111
Informations de copyright
© 2024. The Author(s).
Références
Jin, M.-Z. & Jin, W.-L. The updated landscape of tumor microenvironment and drug repurposing. Signal Transduct. Target Ther. 5, 166 (2020).
pubmed: 32843638
pmcid: 7447642
doi: 10.1038/s41392-020-00280-x
Mantovani, A., Allavena, P., Sica, A. & Balkwill, F. Cancer-related inflammation. Nature 454, 436–444 (2008).
pubmed: 18650914
doi: 10.1038/nature07205
Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
pubmed: 21376230
doi: 10.1016/j.cell.2011.02.013
Balkwill, F. R., Capasso, M. & Hagemann, T. The tumor microenvironment at a glance. J. Cell Sci. 125, 5591–5596 (2012).
pubmed: 23420197
doi: 10.1242/jcs.116392
Qian, J. et al. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling. Cell Res. 30, 745–762 (2020).
pubmed: 32561858
pmcid: 7608385
doi: 10.1038/s41422-020-0355-0
Wu, S. Z. et al. A single-cell and spatially resolved atlas of human breast cancers. Nat. Genet. 53, 1334–1347 (2021).
pubmed: 34493872
pmcid: 9044823
doi: 10.1038/s41588-021-00911-1
Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e14 (2018).
pubmed: 29628290
pmcid: 5982584
doi: 10.1016/j.immuni.2018.03.023
Tang, L. et al. Nanoparticle-mediated targeted drug delivery to remodel tumor microenvironment for cancer therapy. Int. J. Nanomed. 16, 5811–5829 (2021).
doi: 10.2147/IJN.S321416
Shalem, O. et al. Genome-scale CRISPR–Cas9 knockout screening in human cells. Science 343, 84–87 (2014).
pubmed: 24336571
doi: 10.1126/science.1247005
Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR–Cas9 system. Science 343, 80–84 (2014).
pubmed: 24336569
doi: 10.1126/science.1246981
Henriksson, J. et al. Genome-wide CRISPR screens in T helper cells reveal pervasive crosstalk between activation and differentiation. Cell 176, 882–896.e18 (2019).
pubmed: 30639098
pmcid: 6370901
doi: 10.1016/j.cell.2018.11.044
Manguso, R. T. et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 547, 413–418 (2017).
pubmed: 28723893
pmcid: 5924693
doi: 10.1038/nature23270
Shifrut, E. et al. Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell 175, 1958–1971 (2018).
pubmed: 30449619
pmcid: 6689405
doi: 10.1016/j.cell.2018.10.024
Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).
pubmed: 28753430
pmcid: 5667678
doi: 10.1016/j.cell.2017.06.010
Jin, V., Wang, J. & Tang, B. Integration of Multisource Heterogenous Omics Information in Cancer (Frontiers Media SA, 2020).
Pacini, C. et al. Integrated cross-study datasets of genetic dependencies in cancer. Nat. Commun. 12, 1661 (2021).
pubmed: 33712601
pmcid: 7955067
doi: 10.1038/s41467-021-21898-7
Wang, X. et al. In vivo CRISPR screens identify the E3 ligase Cop1 as a modulator of macrophage infiltration and cancer immunotherapy target. Cell 184, 5357–5374.e22 (2021).
pubmed: 34582788
pmcid: 9136996
doi: 10.1016/j.cell.2021.09.006
Li, F. et al. In vivo epigenetic CRISPR screen identifies Asf1a as an immunotherapeutic target in Kras-mutant lung adenocarcinoma. Cancer Discov. 10, 270–287 (2020).
pubmed: 31744829
doi: 10.1158/2159-8290.CD-19-0780
Gao, S. et al. CRISPR screens identify cholesterol biosynthesis as a therapeutic target on stemness and drug resistance of colon cancer. Oncogene 40, 6601–6613 (2021).
pubmed: 34621019
pmcid: 8639446
doi: 10.1038/s41388-021-01882-7
Soares, F. et al. CRISPR screen identifies genes that sensitize AML cells to double-negative T-cell therapy. Blood 137, 2171–2181 (2021).
pubmed: 33270841
doi: 10.1182/blood.2019004108
Chen, S. et al. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160, 1246–1260 (2015).
pubmed: 25748654
pmcid: 4380877
doi: 10.1016/j.cell.2015.02.038
Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).
pubmed: 25476604
pmcid: 4290824
doi: 10.1186/s13059-014-0554-4
Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).
pubmed: 29083409
pmcid: 5709193
doi: 10.1038/ng.3984
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
pubmed: 25516281
pmcid: 4302049
doi: 10.1186/s13059-014-0550-8
Perner, F. et al. MEN1 mutations mediate clinical resistance to menin inhibition. Nature 615, 913–919 (2023).
pubmed: 36922589
pmcid: 10157896
doi: 10.1038/s41586-023-05755-9
Issa, G. C. et al. The menin inhibitor revumenib in KMT2A-rearranged or NPM1-mutant leukaemia. Nature 615, 920–924 (2023).
pubmed: 36922593
pmcid: 10060155
doi: 10.1038/s41586-023-05812-3
Sparbier, C. E. et al. Targeting Menin disrupts the KMT2A/B and polycomb balance to paradoxically activate bivalent genes. Nat. Cell Biol. 25, 258–272 (2023).
pubmed: 36635503
pmcid: 7614190
Soto-Feliciano, Y. M. et al. A molecular switch between mammalian MLL complexes dictates response to Menin-MLL inhibition. Cancer Discov. 13, 146–169 (2023).
pubmed: 36264143
doi: 10.1158/2159-8290.CD-22-0416
Lin, J. et al. Menin ‘reads’ H3K79me2 mark in a nucleosomal context. Science 379, 717–723 (2023).
pubmed: 36795828
doi: 10.1126/science.adc9318
La, P. et al. Tumor suppressor menin: the essential role of nuclear localization signal domains in coordinating gene expression. Oncogene 25, 3537–3546 (2006).
pubmed: 16449969
doi: 10.1038/sj.onc.1209400
Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife 6, e21856 (2017).
pubmed: 28079019
pmcid: 5310842
doi: 10.7554/eLife.21856
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
pubmed: 18798982
pmcid: 2592715
doi: 10.1186/gb-2008-9-9-r137
Wang, S. et al. Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat. Protoc. 8, 2502–2515 (2013).
pubmed: 24263090
pmcid: 4135175
doi: 10.1038/nprot.2013.150
Soto-Feliciano, Y. M. et al. Molecular switch between mammalian MLL complexes dictates response to Menin-MLL inhibition. Cancer Discov. 13, 146–169 (2023).
pubmed: 36264143
doi: 10.1158/2159-8290.CD-22-0416
Madani Tonekaboni, S. A., Haibe-Kains, B. & Lupien, M. Large organized chromatin lysine domains help distinguish primitive from differentiated cell populations. Nat. Commun. 12, 499 (2021).
pubmed: 33479238
pmcid: 7820432
doi: 10.1038/s41467-020-20830-9
Chen, R., Ishak, C. A. & De Carvalho, D. D. Endogenous retroelements and the viral mimicry response in cancer therapy and cellular homeostasis. Cancer Discov. 11, 2707–2725 (2021).
pubmed: 34649957
doi: 10.1158/2159-8290.CD-21-0506
Gao, D. et al. Cyclic GMP–AMP synthase is an innate immune sensor of HIV and other retroviruses. Science 341, 903–906 (2013).
pubmed: 23929945
doi: 10.1126/science.1240933
Roulois, D. et al. DNA-demethylating agents target colorectal cancer cells by inducing viral mimicry by endogenous transcripts. Cell 162, 961–973 (2015).
pubmed: 26317465
pmcid: 4843502
doi: 10.1016/j.cell.2015.07.056
Morel, K. L. et al. EZH2 inhibition activates a dsRNA–STING–interferon stress axis that potentiates response to PD-1 checkpoint blockade in prostate cancer. Nat. Cancer 2, 444–456 (2021).
pubmed: 33899001
pmcid: 8061902
doi: 10.1038/s43018-021-00185-w
Liu, S. et al. Phosphorylation of innate immune adaptor proteins MAVS, STING, and TRIF induces IRF3 activation. Science 347, aaa2630 (2015).
pubmed: 25636800
doi: 10.1126/science.aaa2630
Borkin, D. et al. Pharmacologic inhibition of the Menin-MLL interaction blocks progression of MLL leukemia in vivo. Cancer Cell 27, 589–602 (2015).
pubmed: 25817203
pmcid: 4415852
doi: 10.1016/j.ccell.2015.02.016
Chen, S. et al. Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression. Nat. Cell Biol. 23, 87–98 (2021).
pubmed: 33420488
doi: 10.1038/s41556-020-00613-6
Xu, W. et al. Early innate and adaptive immune perturbations determine long-term severity of chronic virus and Mycobacterium tuberculosis coinfection. Immunity 54, 526–541.e7 (2021).
pubmed: 33515487
pmcid: 7946746
doi: 10.1016/j.immuni.2021.01.003
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).
pubmed: 31821784
pmcid: 7227117
doi: 10.1016/j.ccell.2019.11.001
Grembecka, J. et al. Menin-MLL inhibitors reverse oncogenic activity of MLL fusion proteins in leukemia. Nat. Chem. Biol. 8, 277–284 (2012).
pubmed: 22286128
pmcid: 3401603
doi: 10.1038/nchembio.773
Davis, J. A. et al. Clinical-stage menin inhibitor KO-539 is synergistically active with multiple classes of targeted agents in KMT2A-r and NPM1-mutant AML models. Blood 138, 3357 (2021).
doi: 10.1182/blood-2021-149831
Al-Salameh, A., Cadiot, G., Calender, A., Goudet, P. & Chanson, P. Clinical aspects of multiple endocrine neoplasia type 1. Nat. Rev. Endocrinol. 17, 207–224 (2021).
pubmed: 33564173
doi: 10.1038/s41574-021-00468-3
Qiu, H. et al. MEN1 deficiency leads to neuroendocrine differentiation of lung cancer and disrupts the DNA damage response. Nat. Commun. 11, 1009 (2020).
pubmed: 32081882
pmcid: 7035285
doi: 10.1038/s41467-020-14614-4
Chandrasekharappa, S. C. et al. Positional cloning of the gene for multiple endocrine neoplasia-type 1. Science 276, 404–407 (1997).
pubmed: 9103196
doi: 10.1126/science.276.5311.404
Jiao, Y. et al. DAXX/ATRX, MEN1, and mTOR pathway genes are frequently altered in pancreatic neuroendocrine tumors. Science 331, 1199–1203 (2011).
pubmed: 21252315
pmcid: 3144496
doi: 10.1126/science.1200609
Yokoyama, A. & Cleary, M. L. Menin critically links MLL proteins with LEDGF on cancer-associated target genes. Cancer Cell 14, 36–46 (2008).
pubmed: 18598942
pmcid: 2692591
doi: 10.1016/j.ccr.2008.05.003
Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).
pubmed: 11237011
doi: 10.1038/35057062
Payer, L. M. & Burns, K. H. Transposable elements in human genetic disease. Nat. Rev. Genet. 20, 760–772 (2019).
pubmed: 31515540
doi: 10.1038/s41576-019-0165-8
Babaian, A. & Mager, D. L. Endogenous retroviral promoter exaptation in human cancer. Mob. DNA 7, 24 (2016).
pubmed: 27980689
pmcid: 5134097
doi: 10.1186/s13100-016-0080-x
Deblois, G. et al. Epigenetic switch-induced viral mimicry evasion in chemotherapy-resistant breast cancer. Cancer Discov. 10, 1312–1329 (2020).
pubmed: 32546577
doi: 10.1158/2159-8290.CD-19-1493
Sheng, W. et al. LSD1 ablation stimulates anti-tumor immunity and enables checkpoint blockade. Cell 174, 549–563.e19 (2018).
pubmed: 29937226
pmcid: 6063761
doi: 10.1016/j.cell.2018.05.052
Chiappinelli, K. B. et al. Inhibiting DNA methylation causes an interferon response in cancer via dsRNA including endogenous retroviruses. Cell 162, 974–986 (2015).
pubmed: 26317466
pmcid: 4556003
doi: 10.1016/j.cell.2015.07.011
Linares-Saldana, R. et al. BRD4 orchestrates genome folding to promote neural crest differentiation. Nat. Genet. 53, 1480–1492 (2021).
pubmed: 34611363
pmcid: 8500624
doi: 10.1038/s41588-021-00934-8
Wei, Z. et al. MYC reshapes CTCF-mediated chromatin architecture in prostate cancer. Nat. Commun. 14, 1787 (2023).
pubmed: 36997534
pmcid: 10063626
doi: 10.1038/s41467-023-37544-3
Law, V. et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 42, D1091–D1097 (2014).
pubmed: 24203711
doi: 10.1093/nar/gkt1068
Ma, J. et al. CRISPR-DO for genome-wide CRISPR design and optimization. Bioinformatics 32, 3336–3338 (2016).
pubmed: 27402906
pmcid: 6095119
doi: 10.1093/bioinformatics/btw476
Wang, T., Lander, E. S. & Sabatini, D. M. Viral packaging and cell culture for CRISPR-based screens. Cold Spring Harb. Protoc. 2016, db.prot090811 (2016).
doi: 10.1101/pdb.prot090811
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).
pubmed: 22743226
doi: 10.1093/bioinformatics/bts356
Wang, L. et al. Measure transcript integrity using RNA-seq data. BMC Bioinformatics 17, 58 (2016).
pubmed: 26842848
pmcid: 4739097
doi: 10.1186/s12859-016-0922-z
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
pubmed: 25260700
doi: 10.1093/bioinformatics/btu638
Tonekaboni, S. A. M., Mazrooei, P., Kofia, V., Haibe-Kains, B. & Lupien, M. Identifying clusters of cis-regulatory elements underpinning TAD structures and lineage-specific regulatory networks. Genome Res. 29, 1733–1743 (2019).
doi: 10.1101/gr.248658.119
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
pubmed: 22455463
pmcid: 3339379
doi: 10.1089/omi.2011.0118
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
pubmed: 20110278
pmcid: 2832824
doi: 10.1093/bioinformatics/btq033
Chen, H. et al. Cytofkit: a bioconductor package for an integrated mass cytometry data analysis pipeline. PLoS Comput. Biol. 12, e1005112 (2016).
pubmed: 27662185
pmcid: 5035035
doi: 10.1371/journal.pcbi.1005112
Levine, J. H. et al. Data-driven phenotypic dissection of aml reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
pubmed: 26095251
pmcid: 4508757
doi: 10.1016/j.cell.2015.05.047
McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).
doi: 10.21105/joss.00861
Weber, L. M., Nowicka, M., Soneson, C. & Robinson, M. D. diffcyt: differential discovery in high-dimensional cytometry via high-resolution clustering. Commun. Biol. 2, 183 (2019).
pubmed: 31098416
pmcid: 6517415
doi: 10.1038/s42003-019-0415-5