Li, Y. et al. The target of the NSD family of histone lysine methyltransferases depends on the nature of the substrate. J. Biol. Chem. 284, 34283–34295 (2009).
Kuo, A. J. et al. NSD2 links dimethylation of histone H3 at lysine 36 to oncogenic programming. Mol. Cell 44, 609–620 (2011).
Garcia-Carpizo, V. et al. NSD2 contributes to oncogenic RAS-driven transcription in lung cancer cells through long-range epigenetic activation. Sci. Rep. 6, 32952 (2016).
Sengupta, D. et al. NSD2 dimethylation at H3K36 promotes lung adenocarcinoma pathogenesis. Mol. Cell 81, 4481–4492 (2021).
Lhoumaud, P. et al. NSD2 overexpression drives clustered chromatin and transcriptional changes in a subset of insulated domains. Nat. Commun. 10, 4843 (2019).
Canon, J. et al. The clinical KRAS(G12C) inhibitor AMG 510 drives anti-tumour immunity. Nature 575, 217–223 (2019).
Yuan, W. et al. H3K36 methylation antagonizes PRC2-mediated H3K27 methylation. J. Biol. Chem. 286, 7983–7989 (2011).
Schmitges, F. W. et al. Histone methylation by PRC2 is inhibited by active chromatin marks. Mol. Cell 42, 330–341 (2011).
Jaffe, J. D. et al. Global chromatin profiling reveals NSD2 mutations in pediatric acute lymphoblastic leukemia. Nat. Genet. 45, 1386–1391 (2013).
Popovic, R. et al. Histone methyltransferase MMSET/NSD2 alters EZH2 binding and reprograms the myeloma epigenome through global and focal changes in H3K36 and H3K27 methylation. PLoS Genet. 10, e1004566 (2014).
Yuan, G. et al. Elevated NSD3 histone methylation activity drives squamous cell lung cancer. Nature 590, 504–508 (2021).
Jani, K. S. et al. Histone H3 tail binds a unique sensing pocket in EZH2 to activate the PRC2 methyltransferase. Proc. Natl Acad. Sci. USA 116, 8295–8300 (2019).
Finogenova, K. et al. Structural basis for PRC2 decoding of active histone methylation marks H3K36me2/3. eLife 9, e61964 (2020).
Li, J., Ahn, J. H. & Wang, G. G. Understanding histone H3 lysine 36 methylation and its deregulation in disease. Cell. Mol. Life Sci. 76, 2899–2916 (2019).
Husmann, D. & Gozani, O. Histone lysine methyltransferases in biology and disease. Nat. Struct. Mol. Biol. 26, 880–889 (2019).
Bennett, R. L., Swaroop, A., Troche, C. & Licht, J. D. The role of nuclear receptor-binding SET domain family histone lysine methyltransferases in cancer. Cold Spring Harb. Perspect. Med. 7, a026708 (2017).
Bergsagel, P. L. & Chesi, M. Immunocompetent mouse models of multiple myeloma. Semin. Hematol. 38, 533–546 (2024).
Martinez-Garcia, E. et al. The MMSET histone methyl transferase switches global histone methylation and alters gene expression in t(4;14) multiple myeloma cells. Blood 117, 211–220 (2011).
Larrayoz, M. et al. Preclinical models for prediction of immunotherapy outcomes and immune evasion mechanisms in genetically heterogeneous multiple myeloma. Nat. Med. 29, 632–645 (2023).
Oyer, J. A. et al. Point mutation E1099K in MMSET/NSD2 enhances its methyltranferase activity and leads to altered global chromatin methylation in lymphoid malignancies. Leukemia 28, 198–201 (2014).
Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).
Hudlebusch, H. R. et al. The histone methyltransferase and putative oncoprotein MMSET is overexpressed in a large variety of human tumors. Clin. Cancer Res. 17, 2919–2933 (2011).
Aytes, A. et al. NSD2 is a conserved driver of metastatic prostate cancer progression. Nat. Commun. 9, 5201 (2018).
Li, N. et al. AKT-mediated stabilization of histone methyltransferase WHSC1 promotes prostate cancer metastasis. J. Clin. Invest. 127, 1284–1302 (2017).
Parolia, A. et al. NSD2 is a requisite subunit of the AR/FOXA1 neo-enhanceosome in promoting prostate tumorigenesis. Nat. Genet. 56, 2132–2143 (2024).
Yuan, S. et al. Global regulation of the histone mark H3K36me2 underlies epithelial plasticity and metastatic progression. Cancer Discov. 10, 854–871 (2020).
Brown, B. A. et al. A histone methylation–MAPK signaling axis drives durable epithelial–mesenchymal transition in hypoxic pancreatic cancer. Cancer Res. 84, 1764–1780 (2024).
Bhat, K. P., Umit Kaniskan, H., Jin, J. & Gozani, O. Epigenetics and beyond: targeting writers of protein lysine methylation to treat disease. Nat. Rev. Drug Discov. 20, 265–286 (2021).
Prior, I. A., Hood, F. E. & Hartley, J. L. The frequency of Ras mutations in cancer. Cancer Res. 80, 2969–2974 (2020).
Perurena, N., Situ, L. & Cichowski, K. Combinatorial strategies to target RAS-driven cancers. Nat. Rev. Cancer 24, 316–337 (2024).
Skoulidis, F. et al. Sotorasib for lung cancers with KRAS p.G12C mutation. N. Engl. J. Med. 384, 2371–2381 (2021).
Punekar, S. R., Velcheti, V., Neel, B. G. & Wong, K. K. The current state of the art and future trends in RAS-targeted cancer therapies. Nat. Rev. Clin. Oncol. 19, 637–655 (2022).
Popow, J. et al. Targeting cancer with small-molecule pan-KRAS degraders. Science 385, 1338–1347 (2024).
Hallin, J. et al. Anti-tumor efficacy of a potent and selective non-covalent KRASG12D inhibitor. Nat. Med. 28, 2171–2182 (2022).
Oya, Y., Imaizumi, K. & Mitsudomi, T. The next-generation KRAS inhibitors…What comes after sotorasib and adagrasib? Lung Cancer 194, 107886 (2024).
Xue, J. Y. et al. Rapid non-uniform adaptation to conformation-specific KRAS(G12C) inhibition. Nature 577, 421–425 (2020).
Akhave, N. S., Biter, A. B. & Hong, D. S. Mechanisms of resistance to KRAS-targeted therapy. Cancer Discov. 11, 1345–1352 (2021).
Awad, M. M. et al. Acquired resistance to KRASG12C inhibition in cancer. N. Engl. J. Med. 384, 2382–2393 (2021).
Francis, J. W. et al. FAM86A methylation of eEF2 links mRNA translation elongation to tumorigenesis. Mol. Cell 84, 1753–1763 (2024).
Hingorani, S. R. et al. Trp53R172H and KrasG12D cooperate to promote chromosomal instability and widely metastatic pancreatic ductal adenocarcinoma in mice. Cancer Cell 7, 469–483 (2005).
Deng, H. et al. Piperidinyl-methyl-purine amines as NSD2 inhibitors and anti-cancer agents. WIPO patent WO2021028854A1 (2021).
Le, K. et al. Heterocyclesas modulators of NSD activity. WIPO patent WO2024073282 (2024).
Li, W. et al. Molecular basis of nucleosomal H3K36 methylation by NSD methyltransferases. Nature 590, 498–503 (2021).
Sato, K. et al. Structural basis of the regulation of the normal and oncogenic methylation of nucleosomal histone H3 Lys36 by NSD2. Nat. Commun. 12, 6605 (2021).
Shipman, G. A. et al. Systematic perturbations of SETD2, NSD1, NSD2, NSD3, and ASH1L reveal their distinct contributions to H3K36 methylation. Genome Biol. 25, 263 (2024).
Liu, L. et al. Discovery of LLC0424 as a potent and selective in vivo NSD2 PROTAC degrader. J. Med. Chem. 67, 6938–6951 (2024).
LegaardAndersson, J. et al. Discovery of NSD2-degraders from novel and selective DEL hits. ChemBioChem 24, e202300515 (2023).
Meng, F. et al. Discovery of a first-in-class degrader for nuclear receptor binding SET domain protein 2 (NSD2) and ikaros/aiolos. J. Med. Chem. 65, 10611–10625 (2022).
Nie, D. Y. et al. Recruitment of FBXO22 for targeted degradation of NSD2. Nat. Chem. Biol. 20, 1597–1607 (2024).
Hanley, R. P. et al. Discovery of a potent and selective targeted NSD2 degrader for the reduction of H3K36me2. J. Am. Chem. Soc. 145, 8176–8188 (2023).
Skene, P. J., Henikoff, J. G. & Henikoff, S. Targeted in situ genome-wide profiling with high efficiency for low cell numbers. Nat. Protoc. 13, 1006–1019 (2018).
Ponnaluri, V. K. C. et al. NicE-seq: high resolution open chromatin profiling. Genome Biol. 18, 122 (2017).
Alonso-Curbelo, D. et al. A gene–environment-induced epigenetic program initiates tumorigenesis. Nature 590, 642–648 (2021).
Grutzmann, R. et al. Meta-analysis of microarray data on pancreatic cancer defines a set of commonly dysregulated genes. Oncogene 24, 5079–5088 (2005).
Sun, Z. et al. Chromatin regulation of transcriptional enhancers and cell fate by the Sotos syndrome gene NSD1. Mol. Cell 83, 2398–2416 (2023).
Janne, P. A. et al. Adagrasib in non-Small-cell lung cancer harboring a KRASG12C mutation. N. Engl. J. Med. 387, 120–131 (2022).
Ianevski, A., Giri, A. K. & Aittokallio, T. SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples. Nucleic Acids Res. 50, W739–W743 (2022).
Yang, D. et al. Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution. Cell 185, 1905–1923 (2022).
Wei, J. et al. Discovery of a highly potent and selective inhibitor targeting protein lysine methyltransferase NSD2. J. Med. Chem. 67, 16056–16071 (2024).
Bon, C., Halby, L. & Arimondo, P. B. Bisubstrate inhibitors: the promise of a selective and potent chemical inhibition of epigenetic ‘writers’. Epigenomics 12, 1479–1482 (2020).
Ma, X. et al. Rise and fall of subclones from diagnosis to relapse in pediatric B-acute lymphoblastic leukaemia. Nat. Commun. 6, 6604 (2015).
Nguyen, B. et al. Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients. Cell 185, 563–575 (2022).
De Vito, C. et al. A TARBP2-dependent miRNA expression profile underlies cancer stem cell properties and provides candidate therapeutic reagents in Ewing sarcoma. Cancer Cell 21, 807–821 (2012).
Weinberg, D. N. et al. The histone mark H3K36me2 recruits DNMT3A and shapes the intergenic DNA methylation landscape. Nature 573, 281–286 (2019).
Mureddu, L. & Vuister, G. W. Simple high-resolution NMR spectroscopy as a tool in molecular biology. FEBS J. 286, 2035–2042 (2019).
Shen, Y. & Bax, A. Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks. J. Biomol. NMR 56, 227–241 (2013).
Honorato, R. V. et al. The HADDOCK2.4 web server for integrative modeling of biomolecular complexes. Nat. Protoc. 19, 3219–3241 (2024).
Shukla, S. et al. Small-molecule inhibitors targeting Polycomb repressive complex 1 RING domain. Nat. Chem. Biol. 17, 784–793 (2021).
Tisi, D. et al. Structure of the epigenetic oncogene MMSET and inhibition by N-alkyl sinefungin derivatives. ACS Chem. Biol. 11, 3093–3105 (2016).
Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).
Hanwell, M. D. et al. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J. Cheminform. 4, 17 (2012).
Schuttelkopf, A. W. & van Aalten, D. M. F. PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr. D 60, 1355–1363 (2004).
Williams, C. J. et al. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018).
Laskowski, R. A., MacArthur, M. W., Moss, D. S. & Thornton, J. M. PROCHECK: a program to check the stereochemical quality of protein structures. J. Appl. Crystallogr. 26, 283–291 (1993).
Sidoli, S., Bhanu, N. V., Karch, K. R., Wang, X. & Garcia, B. A. Complete workflow for analysis of histone post-translational modifications using bottom-up mass spectrometry: from histone extraction to data analysis. J. Vis. Exp. 111, 54112 (2016).
Bhanu, N. V., Sidoli, S. & Garcia, B. A. A workflow for ultra-rapid analysis of histone post-translational modifications with direct-injection mass spectrometry. Bio. Protoc. 10, e3756 (2020).
Yuan, Z. F. et al. EpiProfile quantifies histone peptides with modifications by extracting retention time and intensity in high-resolution mass spectra. Mol. Cell. Proteomics 14, 1696–1707 (2015).
Marunde, M. R. et al. Nucleosome conformation dictates the histone code. eLife 13, e78866 (2024).
Yusufova, N. et al. Histone H1 loss drives lymphoma by disrupting 3D chromatin architecture. Nature 589, 299–305 (2021).
Shah, R. N. et al. Examining the roles of H3K4 methylation states with systematically characterized antibodies. Mol. Cell 72, 162–177 (2018).
Vishnu, U. S., Esteve, P. O., Chin, H. G. & Pradhan, S. One-pot universal NicE-seq: all enzymatic downstream processing of 4% formaldehyde crosslinked cells for chromatin accessibility genomics. Epigenetics Chromatin 14, 53 (2021).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Ewels, P., Magnusson, M., Lundin, S. & Kaller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).
Curras-Alonso, S. et al. An interactive murine single-cell atlas of the lung responses to radiation injury. Nat. Commun. 14, 2445 (2023).
Korotkevich, G., Sukhov, V. & Sergushichev, A. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2019).
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).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Fishilevich, S. et al. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database 2017, bax028 (2017).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 3 (2011).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE Blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
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).
Ramirez, F., Dundar, F., Diehl, S., Gruning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Orlando, D. A. et al. Quantitative ChIP-Seq normalization reveals global modulation of the epigenome. Cell Rep. 9, 1163–1170 (2014).
Cunningham, F. et al. Ensembl 2015. Nucleic Acids Res. 43, D662–D669 (2015).
Lopez-Delisle, L. et al. pyGenomeTracks: reproducible plots for multivariate genomic datasets. Bioinformatics 37, 422–423 (2021).
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).
Jonkers, J. et al. Synergistic tumor suppressor activity of BRCA2 and p53 in a conditional mouse model for breast cancer. Nat. Genet. 29, 418–425 (2001).
Kawaguchi, Y. et al. The role of the transcriptional regulator Ptf1a in converting intestinal to pancreatic progenitors. Nat. Genet. 32, 128–134 (2002).
Baranczewski, P. et al. Introduction to in vitro estimation of metabolic stability and drug interactions of new chemical entities in drug discovery and development. Pharmacol. Rep. 58, 453–472 (2006).
Park, J. et al. SMYD5 methylation of rpL40 links ribosomal output to gastric cancer. Nature 632, 656–663 (2024).
Lu, X., Lofgren, S. M., Zhao, Y. & Mazur, P. K. Multiplexed transcriptomic profiling of the fate of human CAR T cells in vivo via genetic barcoding with shielded small nucleotides. Nat. Biomed. Eng. 7, 1170–1187 (2023).
Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).
McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 (2019).
Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).
Gavish, A. et al. Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours. Nature 618, 598–606 (2023).
Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016).
Cillo, A. R. et al. Immune landscape of viral- and carcinogen-driven head and neck cancer. Immunity 52, 183–199 (2020).
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).
Andreatta, M. & Carmona, S. J. UCell: robust and scalable single-cell gene signature scoring. Comput. Struct. Biotechnol. J. 19, 3796–3798 (2021).
