Characterization of an RNA binding protein interactome reveals a context-specific post-transcriptional landscape of MYC-amplified medulloblastoma.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
06 12 2022
06 12 2022
Historique:
received:
21
05
2021
accepted:
18
11
2022
entrez:
6
12
2022
pubmed:
7
12
2022
medline:
15
12
2022
Statut:
epublish
Résumé
Pediatric medulloblastoma (MB) is the most common solid malignant brain neoplasm, with Group 3 (G3) MB representing the most aggressive subgroup. MYC amplification is an independent poor prognostic factor in G3 MB, however, therapeutic targeting of the MYC pathway remains limited and alternative therapies for G3 MB are urgently needed. Here we show that the RNA-binding protein, Musashi-1 (MSI1) is an essential mediator of G3 MB in both MYC-overexpressing mouse models and patient-derived xenografts. MSI1 inhibition abrogates tumor initiation and significantly prolongs survival in both models. We identify binding targets of MSI1 in normal neural and G3 MB stem cells and then cross referenced these data with unbiased large-scale screens at the transcriptomic, translatomic and proteomic levels to systematically dissect its functional role. Comparative integrative multi-omic analyses of these large datasets reveal cancer-selective MSI1-bound targets sharing multiple MYC associated pathways, providing a valuable resource for context-specific therapeutic targeting of G3 MB.
Identifiants
pubmed: 36473869
doi: 10.1038/s41467-022-35118-3
pii: 10.1038/s41467-022-35118-3
pmc: PMC9726987
doi:
Substances chimiques
RNA-Binding Proteins
0
MSI1 protein, human
0
Nerve Tissue Proteins
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
7506Subventions
Organisme : NCI NIH HHS
ID : R01 CA159859
Pays : United States
Organisme : NHLBI NIH HHS
ID : T32 HL086344
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA030199
Pays : United States
Organisme : NCI NIH HHS
ID : T32 CA009523
Pays : United States
Organisme : NCI NIH HHS
ID : R35 CA197699
Pays : United States
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022. The Author(s).
Références
Taylor, M. D. et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathol. 123, 465–472 (2012).
doi: 10.1007/s00401-011-0922-z
Kool, M. et al. Molecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, Group 3, and Group 4 medulloblastomas. Acta Neuropathol. 123, 473–484 (2012).
doi: 10.1007/s00401-012-0958-8
Forget, A. et al. Aberrant ERBB4-SRC signaling as a hallmark of group 4 medulloblastoma revealed by integrative phosphoproteomic profiling. Cancer Cell 34, 379–395.e377 (2018).
doi: 10.1016/j.ccell.2018.08.002
Ramaswamy, V. et al. Risk stratification of childhood medulloblastoma in the molecular era: the current consensus. Acta Neuropathol. https://doi.org/10.1007/s00401-016-1569-6 (2016).
Cho, Y. J. et al. Integrative genomic analysis of medulloblastoma identifies a molecular subgroup that drives poor clinical outcome. J. Clin. Oncol. 29, 1424–1430 (2011).
doi: 10.1200/JCO.2010.28.5148
Cavalli, F. M. G. et al. Intertumoral heterogeneity within medulloblastoma subgroups. Cancer Cell 31, 737–754.e736 (2017).
doi: 10.1016/j.ccell.2017.05.005
Bandopadhayay, P. et al. BET bromodomain inhibition of MYC-amplified medulloblastoma. Clin. Cancer Res. 20, 912–925 (2014).
doi: 10.1158/1078-0432.CCR-13-2281
Hill, R. M. et al. Combined MYC and P53 defects emerge at medulloblastoma relapse and define rapidly progressive, therapeutically targetable disease. Cancer Cell 27, 72–84 (2015).
doi: 10.1016/j.ccell.2014.11.002
Ecker, J. et al. Targeting class I histone deacetylase 2 in MYC amplified group 3 medulloblastoma. Acta Neuropathol. Commun. 3, 22 (2015).
doi: 10.1186/s40478-015-0201-7
Gottardo, N. G. et al. Medulloblastoma down under 2013: a report from the third annual meeting of the International Medulloblastoma Working Group. Acta Neuropathol. 127, 189–201 (2014).
doi: 10.1007/s00401-013-1213-7
Archer, T. C. et al. Proteomics, post-translational modifications, and integrative analyses reveal molecular heterogeneity within medulloblastoma subgroups. Cancer Cell 34, 396–410.e398 (2018).
doi: 10.1016/j.ccell.2018.08.004
Zomerman, W. W. et al. Identification of two protein-signaling states delineating transcriptionally heterogeneous human medulloblastoma. Cell Rep. 22, 3206–3216 (2018).
doi: 10.1016/j.celrep.2018.02.089
Grabowski, P. Alternative splicing takes shape during neuronal development. Curr. Opin. Genet. Dev. 21, 388–394 (2011).
doi: 10.1016/j.gde.2011.03.005
Miura, P., Shenker, S., Andreu-Agullo, C., Westholm, J. O. & Lai, E. C. Widespread and extensive lengthening of 3’ UTRs in the mammalian brain. Genome Res. 23, 812–825 (2013).
doi: 10.1101/gr.146886.112
Wang, E. T. et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008).
doi: 10.1038/nature07509
Xu, Q., Modrek, B. & Lee, C. Genome-wide detection of tissue-specific alternative splicing in the human transcriptome. Nucleic Acids Res. 30, 3754–3766 (2002).
doi: 10.1093/nar/gkf492
Yeo, G. W., Van Nostrand, E., Holste, D., Poggio, T. & Burge, C. B. Identification and analysis of alternative splicing events conserved in human and mouse. Proc. Natl Acad. Sci. USA 102, 2850–2855 (2005).
doi: 10.1073/pnas.0409742102
Sakakibara, S. et al. Mouse-Musashi-1, a neural RNA-binding protein highly enriched in the mammalian CNS stem cell. Dev. Biol. 176, 230–242 (1996).
doi: 10.1006/dbio.1996.0130
Sakakibara, S. & Okano, H. Expression of neural RNA-binding proteins in the postnatal CNS: implications of their roles in neuronal and glial cell development. J. Neurosci. 17, 8300–8312 (1997).
doi: 10.1523/JNEUROSCI.17-21-08300.1997
Chen, J. et al. A restricted cell population propagates glioblastoma growth after chemotherapy. Nature 488, 522–526 (2012).
doi: 10.1038/nature11287
Driessens, G., Beck, B., Caauwe, A., Simons, B. D. & Blanpain, C. Defining the mode of tumour growth by clonal analysis. Nature 488, 527–530 (2012).
doi: 10.1038/nature11344
Schepers, A. G. et al. Lineage tracing reveals Lgr5+ stem cell activity in mouse intestinal adenomas. Science 337, 730–735 (2012).
doi: 10.1126/science.1224676
Hemmati, H. D. et al. Cancerous stem cells can arise from pediatric brain tumors. Proc. Natl Acad. Sci. USA 100, 15178–15183 (2003).
doi: 10.1073/pnas.2036535100
Singh, S., Clarke, I., Terasaki, M. & Bonn, V. Identification of a cancer stem cell in human brain tumors. Cancer Res. 63, 5821–5828 (2003).
Singh, S. K. et al. Identification of human brain tumour initiating cells. Nature 432, 396–401 (2004).
doi: 10.1038/nature03128
Kanemura, Y. et al. Musashi1, an evolutionarily conserved neural RNA-binding protein, is a versatile marker of human glioma cells in determining their cellular origin, malignancy, and proliferative activity. Differentiation 68, 141–152 (2001).
doi: 10.1046/j.1432-0436.2001.680208.x
Toda, M. et al. Expression of the neural RNA-binding protein Musashi1 in human gliomas. Glia 34, 1–7 (2001).
doi: 10.1002/glia.1034
Sanchez-Diaz, P. C., Burton, T. L., Burns, S. C., Hung, J. Y. & Penalva, L. O. Musashi1 modulates cell proliferation genes in the medulloblastoma cell line Daoy. BMC Cancer 8, 280 (2008).
doi: 10.1186/1471-2407-8-280
Chen, H. Y. et al. Musashi-1 regulates AKT-derived IL-6 autocrinal/paracrinal malignancy and chemoresistance in glioblastoma. Oncotarget 7, 42485–42501 (2016).
doi: 10.18632/oncotarget.9890
Cox, J. L. et al. The SOX2-interactome in brain cancer cells identifies the requirement of MSI2 and USP9X for the growth of brain tumor cells. PLoS ONE 8, e62857 (2013).
doi: 10.1371/journal.pone.0062857
Dahlrot, R. H. et al. Prognostic value of Musashi-1 in gliomas. J. Neurooncol. 115, 453–461 (2013).
doi: 10.1007/s11060-013-1246-8
Dahlrot, R. H. The prognostic value of clinical factors and cancer stem cell-related markers in gliomas. Dan. Med. J. 61, B4944 (2014).
de Araujo, P. R. et al. Musashi1 impacts radio-resistance in glioblastoma by controlling DNA-protein kinase catalytic subunit. Am. J. Pathol. 186, 2271–2278 (2016).
doi: 10.1016/j.ajpath.2016.05.020
Johannessen, T. C. et al. Highly infiltrative brain tumours show reduced chemosensitivity associated with a stem cell-like phenotype. Neuropathol. Appl. Neurobiol. 35, 380–393 (2009).
doi: 10.1111/j.1365-2990.2009.01008.x
Lagadec, C. et al. The RNA-binding protein Musashi-1 regulates proteasome subunit expression in breast cancer- and glioma-initiating cells. Stem Cells 32, 135–144 (2014).
doi: 10.1002/stem.1537
Muto, J. et al. RNA-binding protein Musashi1 modulates glioma cell growth through the post-transcriptional regulation of Notch and PI3 kinase/Akt signaling pathways. PLoS ONE 7, e33431 (2012).
doi: 10.1371/journal.pone.0033431
Vo, D. T. et al. The RNA-binding protein Musashi1 affects medulloblastoma growth via a network of cancer-related genes and is an indicator of poor prognosis. Am. J. Pathol. 181, 1762–1772 (2012).
doi: 10.1016/j.ajpath.2012.07.031
Vo, D. T. et al. The oncogenic RNA-binding protein Musashi1 is regulated by tumor suppressor miRNAs. RNA Biol. 8, 817–828 (2011).
doi: 10.4161/rna.8.5.16041
Vo, D. T. et al. The oncogenic RNA-binding protein Musashi1 is regulated by HuR via mRNA translation and stability in glioblastoma cells. Mol. Cancer Res. 10, 143–155 (2012).
doi: 10.1158/1541-7786.MCR-11-0208
Uren, P. J. et al. RNA-binding protein musashi1 is a central regulator of adhesion pathways in glioblastoma. Mol. Cell Biol. 35, 2965–2978 (2015).
doi: 10.1128/MCB.00410-15
Potten, C. S. et al. Identification of a putative intestinal stem cell and early lineage marker; musashi-1. Differentiation 71, 28–41 (2003).
doi: 10.1046/j.1432-0436.2003.700603.x
Li, D. et al. Msi-1 is a predictor of survival and a novel therapeutic target in colon cancer. Ann. Surg. Oncol. 18, 2074–2083 (2011).
doi: 10.1245/s10434-011-1567-9
Ito, T. et al. Regulation of myeloid leukaemia by the cell-fate determinant Musashi. Nature 466, 765–768 (2010).
doi: 10.1038/nature09171
Van Nostrand, E. L. et al. Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods 13, 508–514 (2016).
Bao, S. et al. Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature 444, 756–760 (2006).
doi: 10.1038/nature05236
Lin, J. C. et al. MSI1 associates glioblastoma radioresistance via homologous recombination repair, tumor invasion and cancer stem-like cell properties. Radiother. Oncol. https://doi.org/10.1016/j.radonc.2018.09.014 (2018).
Chen, H. Y. et al. Musashi-1 promotes chemoresistant granule formation by PKR/eIF2alpha signalling cascade in refractory glioblastoma. Biochim. Biophys. Acta 1864, 1850–1861 (2018).
doi: 10.1016/j.bbadis.2018.02.017
Panosyan, E. H. et al. Clinical outcome in pediatric glial and embryonal brain tumors correlates with in vitro multi-passageable neurosphere formation. Pediatr. Blood Cancer 55, 644–651 (2010).
doi: 10.1002/pbc.22627
Kanai, R. et al. Enhanced therapeutic efficacy of G207 for the treatment of glioma through Musashi1 promoter retargeting of gamma34.5-mediated virulence. Gene Ther. 13, 106–116 (2006).
doi: 10.1038/sj.gt.3302636
Kagara, N. et al. Epigenetic regulation of cancer stem cell genes in triple-negative breast cancer. Am. J. Pathol. 181, 257–267 (2012).
doi: 10.1016/j.ajpath.2012.03.019
Yi, C. et al. Luteolin inhibits Musashi1 binding to RNA and disrupts cancer phenotypes in glioblastoma cells. RNA Biol. 15, 1420–1432 (2018).
doi: 10.1080/15476286.2018.1539607
Lan, L. et al. Natural product derivative Gossypolone inhibits Musashi family of RNA-binding proteins. BMC Cancer 18, 809 (2018).
doi: 10.1186/s12885-018-4704-z
Velasco, M. X. et al. Antagonism between the RNA-binding protein Musashi1 and miR-137 and its potential impact on neurogenesis and glioblastoma development. RNA 25, 768–782 (2019).
doi: 10.1261/rna.069211.118
Jacobs, J. J., Kieboom, K., Marino, S., DePinho, R. A. & van Lohuizen, M. The oncogene and Polycomb-group gene bmi-1 regulates cell proliferation and senescence through the ink4a locus. Nature 397, 164–168 (1999).
doi: 10.1038/16476
Jacobs, J. J. et al. Bmi-1 collaborates with c-Myc in tumorigenesis by inhibiting c-Myc-induced apoptosis via INK4a/ARF. Genes Dev. 13, 2678–2690 (1999).
doi: 10.1101/gad.13.20.2678
Leung, C. et al. Bmi1 is essential for cerebellar development and is overexpressed in human medulloblastomas. Nature 428, 337–341 (2004).
doi: 10.1038/nature02385
Toledo, C. M. et al. Genome-wide CRISPR-Cas9 screens reveal loss of redundancy between PKMYT1 and WEE1 in glioblastoma stem-like cells. Cell Rep. 13, 2425–2439 (2015).
doi: 10.1016/j.celrep.2015.11.021
Sakakibara, S. et al. RNA-binding protein Musashi family: roles for CNS stem cells and a subpopulation of ependymal cells revealed by targeted disruption and antisense ablation. Proc. Natl Acad. Sci. USA 99, 15194–15199 (2002).
doi: 10.1073/pnas.232087499
Pei, Y. et al. An animal model of MYC-driven medulloblastoma. Cancer Cell 21, 155–167 (2012).
doi: 10.1016/j.ccr.2011.12.021
Fox, R. G. et al. Image-based detection and targeting of therapy resistance in pancreatic adenocarcinoma. Nature 534, 407–411 (2016).
doi: 10.1038/nature17988
Li, Y., Choi, P. S., Casey, S. C. & Felsher, D. W. Activation of Cre recombinase alone can induce complete tumor regression. PLoS ONE 9, e107589 (2014).
doi: 10.1371/journal.pone.0107589
McFarland, J. M. et al. Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration. Nat. Commun. 9, 4610 (2018).
doi: 10.1038/s41467-018-06916-5
Ohyama, T. et al. Structure of Musashi1 in a complex with target RNA: the role of aromatic stacking interactions. Nucleic Acids Res. 40, 3218–3231 (2012).
doi: 10.1093/nar/gkr1139
Northcott, P. A. et al. Subgroup-specific structural variation across 1,000 medulloblastoma genomes. Nature 488, 49–56 (2012).
doi: 10.1038/nature11327
Kool, M. et al. Integrated genomics identifies five medulloblastoma subtypes with distinct genetic profiles, pathway signatures and clinicopathological features. PLoS ONE 3, e3088 (2008).
doi: 10.1371/journal.pone.0003088
Ferrucci, V. et al. Metastatic group 3 medulloblastoma is driven by PRUNE1 targeting NME1-TGF-beta-OTX2-SNAIL via PTEN inhibition. Brain 141, 1300–1319 (2018).
doi: 10.1093/brain/awy039
McCarthy, D. J. & Smyth, G. K. Testing significance relative to a fold-change threshold is a TREAT. Bioinformatics 25, 765–771 (2009).
doi: 10.1093/bioinformatics/btp053
Northcott, P. A. et al. Medulloblastoma comprises four distinct molecular variants. J. Clin. Oncol. 29, 1408–1414 (2011).
doi: 10.1200/JCO.2009.27.4324
Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017).
doi: 10.1038/nature22973
Baltz, A. G. et al. The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts. Mol. Cell 46, 674–690 (2012).
doi: 10.1016/j.molcel.2012.05.021
Castello, A. et al. System-wide identification of RNA-binding proteins by interactome capture. Nat. Protoc. 8, 491–500 (2013).
doi: 10.1038/nprot.2013.020
Keene, J. D. & Tenenbaum, S. A. Eukaryotic mRNPs may represent posttranscriptional operons. Mol. Cell 9, 1161–1167 (2002).
doi: 10.1016/S1097-2765(02)00559-2
Doma, M. K. & Parker, R. Endonucleolytic cleavage of eukaryotic mRNAs with stalls in translation elongation. Nature 440, 561–564 (2006).
doi: 10.1038/nature04530
Frischmeyer, P. A. et al. An mRNA surveillance mechanism that eliminates transcripts lacking termination codons. Science 295, 2258–2261 (2002).
doi: 10.1126/science.1067338
Schmidt, E. K., Clavarino, G., Ceppi, M. & Pierre, P. SUnSET, a nonradioactive method to monitor protein synthesis. Nat. Methods 6, 275–277 (2009).
doi: 10.1038/nmeth.1314
Lavallee-Adam, M., Rauniyar, N., McClatchy, D. B. & Yates, J. R. 3rd PSEA-Quant: a protein set enrichment analysis on label-free and label-based protein quantification data. J. Proteome Res. 13, 5496–5509 (2014).
doi: 10.1021/pr500473n
Kolde, R., Laur, S., Adler, P. & Vilo, J. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics 28, 573–580 (2012).
doi: 10.1093/bioinformatics/btr709
Futreal, P. A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).
doi: 10.1038/nrc1299
Jones, D. T. et al. Dissecting the genomic complexity underlying medulloblastoma. Nature 488, 100–105 (2012).
doi: 10.1038/nature11284
Pugh, T. J. et al. Medulloblastoma exome sequencing uncovers subtype-specific somatic mutations. Nature 488, 106–110 (2012).
doi: 10.1038/nature11329
Parsons, D. W. et al. The genetic landscape of the childhood cancer medulloblastoma. Science 331, 435–439 (2011).
doi: 10.1126/science.1198056
Northcott, P. A. et al. Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathol. 123, 615–626 (2012).
doi: 10.1007/s00401-011-0899-7
Pomeroy, S. L. et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415, 436–442 (2002).
doi: 10.1038/415436a
Robinson, G. et al. Novel mutations target distinct subgroups of medulloblastoma. Nature 488, 43–48 (2012).
doi: 10.1038/nature11213
Bowman, R. L., Wang, Q., Carro, A., Verhaak, R. G. & Squatrito, M. GlioVis data portal for visualization and analysis of brain tumor expression datasets. Neuro Oncol. 19, 139–141 (2017).
doi: 10.1093/neuonc/now247
Wu, G. & Haw, R. Functional interaction network construction and analysis for disease discovery. Methods Mol. Biol. 1558, 235–253 (2017).
doi: 10.1007/978-1-4939-6783-4_11
Reimand, J. et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat. Protoc. 14, 482–517 (2019).
doi: 10.1038/s41596-018-0103-9
Paczkowska, M. et al. Integrative pathway enrichment analysis of multivariate omics data. Nat. Commun. 11, 735 (2020).
Petralia, F. et al. Integrated proteogenomic characterization across major histological types of pediatric brain cancer. Cell 183, 1962–1985.e1931 (2020).
doi: 10.1016/j.cell.2020.10.044
Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).
doi: 10.1038/nature10098
Vogel, C. et al. Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line. Mol. Syst. Biol. 6, 400 (2010).
doi: 10.1038/msb.2010.59
Castello, A. et al. Insights into RNA biology from an atlas of mammalian mRNA-binding proteins. Cell 149, 1393–1406 (2012).
doi: 10.1016/j.cell.2012.04.031
Katz, Y. et al. Musashi proteins are post-transcriptional regulators of the epithelial-luminal cell state. Elife 3, e03915 (2014).
doi: 10.7554/eLife.03915
Fan, X. et al. Notch1 and notch2 have opposite effects on embryonal brain tumor growth. Cancer Res. 64, 7787–7793 (2004).
doi: 10.1158/0008-5472.CAN-04-1446
Garzia, L. et al. MicroRNA-199b-5p impairs cancer stem cells through negative regulation of HES1 in medulloblastoma. PLoS ONE 4, e4998 (2009).
doi: 10.1371/journal.pone.0004998
Zhong, W., Feder, J. N., Jiang, M. M., Jan, L. Y. & Jan, Y. N. Asymmetric localization of a mammalian numb homolog during mouse cortical neurogenesis. Neuron 17, 43–53 (1996).
doi: 10.1016/S0896-6273(00)80279-2
Qin, H. et al. A novel transmembrane protein recruits numb to the plasma membrane during asymmetric cell division. J. Biol. Chem. 279, 11304–11312 (2004).
doi: 10.1074/jbc.M311733200
Petersen, P. H., Zou, K., Hwang, J. K., Jan, Y. N. & Zhong, W. Progenitor cell maintenance requires numb and numblike during mouse neurogenesis. Nature 419, 929–934 (2002).
doi: 10.1038/nature01124
Petersen, P. H., Zou, K., Krauss, S. & Zhong, W. Continuing role for mouse Numb and Numbl in maintaining progenitor cells during cortical neurogenesis. Nat. Neurosci. 7, 803–811 (2004).
doi: 10.1038/nn1289
Rasin, M. R. et al. Numb and Numbl are required for maintenance of cadherin-based adhesion and polarity of neural progenitors. Nat. Neurosci. 10, 819–827 (2007).
doi: 10.1038/nn1924
Imai, T. et al. The neural RNA-binding protein Musashi1 translationally regulates mammalian numb gene expression by interacting with its mRNA. Mol. Cell Biol. 21, 3888–3900 (2001).
doi: 10.1128/MCB.21.12.3888-3900.2001
Zearfoss, N. R. et al. A conserved three-nucleotide core motif defines Musashi RNA binding specificity. J. Biol. Chem. 289, 35530–35541 (2014).
doi: 10.1074/jbc.M114.597112
Rentas, S. et al. Musashi-2 attenuates AHR signalling to expand human haematopoietic stem cells. Nature 532, 508–511 (2016).
doi: 10.1038/nature17665
Hashimoto, K. & Tsuji, Y. Arsenic-induced activation of the homeodomain-interacting protein kinase 2 (HIPK2) to cAMP-response element binding protein (CREB) axis. J. Mol. Biol. 429, 64–78 (2017).
doi: 10.1016/j.jmb.2016.11.015
Blough, R. I. et al. Variation in microdeletions of the cyclic AMP-responsive element-binding protein gene at chromosome band 16p13.3 in the Rubinstein-Taybi syndrome. Am. J. Med. Genet. 90, 29–34 (2000).
doi: 10.1002/(SICI)1096-8628(20000103)90:1<29::AID-AJMG6>3.0.CO;2-Z
Bourdeaut, F. et al. Rubinstein-Taybi syndrome predisposing to non-WNT, non-SHH, group 3 medulloblastoma. Pediatr. Blood Cancer 61, 383–386 (2014).
doi: 10.1002/pbc.24765
Zhang, J. et al. Essential function of HIPK2 in TGFbeta-dependent survival of midbrain dopamine neurons. Nat. Neurosci. 10, 77–86 (2007).
doi: 10.1038/nn1816
Chalazonitis, A. et al. Homeodomain interacting protein kinase 2 regulates postnatal development of enteric dopaminergic neurons and glia via BMP signaling. J. Neurosci. 31, 13746–13757 (2011).
doi: 10.1523/JNEUROSCI.1078-11.2011
Kondo, S. et al. Characterization of cells and gene-targeted mice deficient for the p53-binding kinase homeodomain-interacting protein kinase 1 (HIPK1). Proc. Natl Acad. Sci. USA 100, 5431–5436 (2003).
Milde, T. et al. HD-MB03 is a novel Group 3 medulloblastoma model demonstrating sensitivity to histone deacetylase inhibitor treatment. J. Neurooncol. 110, 335–348 (2012).
doi: 10.1007/s11060-012-0978-1
Subapanditha, M. K., Adile, A. A., Venugopal, C. & Singh, S. K. Flow cytometric analysis of brain tumor stem cells. Methods Mol. Biol. 1869, 69–77 (2019).
doi: 10.1007/978-1-4939-8805-1_6
Robertson, D., Savage, K., Reis-Filho, J. S. & Isacke, C. M. Multiple immunofluorescence labelling of formalin-fixed paraffin-embedded (FFPE) tissue. BMC Cell Biol. 9, 13 (2008).
doi: 10.1186/1471-2121-9-13
Hart, T. et al. Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens. G3 (Bethesda) 7, 2719–2727 (2017).
doi: 10.1534/g3.117.041277
Mair, B. et al. High-throughput genome-wide phenotypic screening via immunomagnetic cell sorting. Nat. Biomed. Eng. 3, 796–805 (2019).
doi: 10.1038/s41551-019-0454-8
Brinkman, E. K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014).
doi: 10.1093/nar/gku936
Lamb, J. The Connectivity Map: a new tool for biomedical research. Nat. Rev. Cancer 7, 54–60 (2007).
doi: 10.1038/nrc2044
Smirnov, P. et al. PharmacoGx: an R package for analysis of large pharmacogenomic datasets. Bioinformatics 32, 1244–1246 (2016).
doi: 10.1093/bioinformatics/btv723
Corsello, S. M. et al. The Drug Repurposing Hub: a next-generation drug library and information resource. Nat. Med. 23, 405–408 (2017).
doi: 10.1038/nm.4306
Van Nostrand, E. L. et al. A large-scale binding and functional map of human RNA-binding proteins. Nature 583, 711–719 (2020).
Lovci, M. T. et al. Rbfox proteins regulate alternative mRNA splicing through evolutionarily conserved RNA bridges. Nat. Struct. Mol. Biol. 20, 1434–1442 (2013).
doi: 10.1038/nsmb.2699
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
doi: 10.1038/nbt.1511
Vizcaino, J. A. et al. 2016 update of the PRIDE database and its related tools. Nucleic Acids Res. 44, 11033 (2016).
doi: 10.1093/nar/gkw880
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
doi: 10.1073/pnas.0506580102
Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinforma. 14, 128 (2013).
doi: 10.1186/1471-2105-14-128
Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).
doi: 10.1093/nar/gkw377
Reimand, J., Kull, M., Peterson, H., Hansen, J. & Vilo, J. g:Profiler-a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 35, W193–W200 (2007).
doi: 10.1093/nar/gkm226
P’ng, C. et al. BPG: Seamless, automated and interactive visualization of scientific data. BMC Bioinforma. 20, 42 (2019).
doi: 10.1186/s12859-019-2610-2
Wickham, H. ggplot2: elegant graphics for data analysis. 2nd edn. (Springer International Piublishing, 2016).
Wu, G., Feng, X. & Stein, L. A human functional protein interaction network and its application to cancer data analysis. Genome Biol. 11, R53 (2010).
doi: 10.1186/gb-2010-11-5-r53