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KMT2D Deficiency Impairs Super-Enhancers to Confer a Glycolytic Vulnerability in Lung Cancer
Open accessAbstract
Epigenetic modifiers frequently harbor loss-of-function mutations in lung cancer, but their tumor-suppressive roles are poorly characterized. Histone methyltransferase KMT2D (a COMPASS-like enzyme, also called MLL4) is among the most highly inactivated epigenetic modifiers in lung cancer. Here, we show that lung-specific loss of Kmt2d promotes lung tumorigenesis in mice and upregulates pro-tumorigenic programs, including glycolysis. Pharmacological inhibition of glycolysis preferentially impedes tumorigenicity of human lung cancer cells bearing KMT2D-inactivating mutations. Mechanistically, Kmt2d loss widely impairs epigenomic signals for super-enhancers/enhancers, including the super-enhancer for the circadian rhythm repressor Per2. Loss of Kmt2d decreases expression of PER2, which regulates multiple glycolytic genes. These findings indicate that KMT2D is a lung tumor suppressor and that KMT2D deficiency confers a therapeutic vulnerability to glycolytic inhibitors.
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“with a gene-level analysis of each corresponding pathway unveiling driver genes that are co-altered in diverse tissue types (Fig. 4b).”
From The metabolic landscape of RAS-driven cancers from biology to therapy (2021) · cites this paper as [137]
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“Analysis of data from the PanCancer Analysis of Whole Genomes Consortium samples135 on the KEGG pathway database137 depicts oncogenic driver genes involved in various metabolism processes in many cancer tissue types (Fig. 4a)”
From The metabolic landscape of RAS-driven cancers from biology to therapy (2021) · cites this paper as [137]
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
NIH Image to ImageJ: 25 years of image analysis
Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012) · Nature Methods · doi:10.1038/nmeth.2089 · Cited by 64,638
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
Cerami, E., Gao, J., Dogrusoz, U., Gross, B. E., Sumer, S. O., Aksoy, B. A., Jacobsen, A., Byrne, C. J., Heuer, M. L., Larsson, E., et al. (2012) · Cancer Discovery · doi:10.1158/2159-8290.cd-12-0095 · Cited by 17,455
Robinson, J. T., Thorvaldsdóttir, H., Winckler, W., Guttman, M., Lander, E. S., Getz, G., & Mesirov, J. P. (2011) · Nature Biotechnology · doi:10.1038/nbt.1754 · Cited by 15,390
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Metadata
- Authors
- Alam, H., Tang, M., Maitituoheti, M., Dhar, S. S., Kumar, M., Han, C. Y., Ambati, C. R., Amin, S. B., Gu, B., Chen, T.-Y., Lin, Y.-H., Chen, J., Muller, F. L., Putluri, N., Flores, E. R., DeMayo, F. J., Baseler, L., Rai, K., & Lee, M. G.
- Affiliations
- The University of Texas MD Anderson Cancer Center (US)
- Baylor College of Medicine (US)
- Moffitt Cancer Center (US)
- National Institute of Environmental Health Sciences (US)
- Corresponding author
- Rai, Kunal
- Lee, Min Gyu
- ORCID
- Venue
- Cancer Cell
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 37
- Issue
- 4
- Pages
- 599-617.e7
- ISSN
- 1535-6108 (ISSN-L)
- PMC
- PMC7178078
- OpenAlex
- W3014830213
- Cited by
- 243
- References
- 102
- Citations by year
- 2026: 28 · 2025: 39 · 2024: 38 · 2023: 53 · 2022: 42 · 2021: 35 · 2020: 8
- FWCI
- 11.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cancer Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- University of Texas MD Anderson Cancer Center (R00CA160578)
- National Cancer Institute (P30CA016672)
- American Cancer Society (RSG-15-145-01-CDD)
- National Institutes of Health (R01CA207109, R01CA157919, R01CA207098)
- National Institute of Environmental Health Sciences (Z1AES103311-10, R35CA197452)
- Cancer Prevention and Research Institute of Texas (RP170005, 127430-RSG-15-105-01-CNE)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Hypoxia, and Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- KMT2D, histone methyltransferase, lung cancer, super-enhancers, enhancers, glycolysis, glycolysis inhibitors, Per2, circadian rhythm, tumor suppressor, epigenetic regulators, loss-of-function mutations, lung carcinogenesis
- MeSH terms
- Adenocarcinoma of Lung, Animals, Antimetabolites, Deoxyglucose, DNA-Binding Proteins, Enhancer Elements, Genetic, Glycolysis, Histones, Humans, Lung Neoplasms, Mice, Nude, Mutation, Neoplasm Proteins, Prognosis, Histone-Lysine N-Methyltransferase, Tumor Cells, Cultured, Biomarkers, Tumor, Gene Expression Regulation, Neoplastic, Apoptosis, Mice, Knockout, Xenograft Model Antitumor Assays, Cell Proliferation, Mice, Myeloid-Lymphoid Leukemia Protein, Period Circadian Proteins
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