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.

Summary The extent and nature of epigenomic changes associated with melanoma progression is poorly understood. Through systematic epigenomic profiling of 35 epigenetic modifications and transcriptomic analysis, we define chromatin state changes associated with melanomagenesis using a cell phenotypic model of non-tumorigenic and tumorigenic states. Computation of specific chromatin state transitions showed loss of histone acetylations and H3K4me2/3 on regulatory regions proximal to specific cancer-regulatory genes in important melanoma-driving cell signaling pathways. Importantly, such acetylation changes were also observed between benign nevi and malignant melanoma human tissues. Intriguingly, only a small fraction of chromatin state transitions correlated with expected changes in gene expression patterns. Restoration of acetylation levels on deacetylated loci by HDAC inhibitors selectively blocked excessive proliferation in tumorigenic cells and human melanoma cells suggesting functional roles of observed chromatin state transitions in driving hyper-proliferative phenotype. Taken together, we define functionally relevant chromatin states associated with melanoma progression.

The roles of Plant Homeodomain (PHD) fingers in catalysis of histone modifications are unknown. We demonstrated that the PHD finger of Ubiquitin Protein Ligase E3 Component N-Recognin7 (UBR7) harbors E3 ubiquitin ligase activity toward monoubiquitination of histone H2B at lysine120 (H2BK120Ub). Purified PHD finger or full-length UBR7 monoubiquitinated H2BK120 in vitro, and loss of UBR7 drastically reduced H2BK120Ub genome-wide binding sites in MCF10A cells. Low UBR7 expression was correlated with occurrence of triple-negative breast cancer and metastatic tumors. Consistently, UBR7 knockdown enhanced the invasiveness, induced epithelial-to-mesenchymal transition and promoted metastasis. Conversely, ectopic expression of UBR7 restored these cellular phenotypes and reduced tumor growth. Mechanistically, UBR7 loss reduced H2BK120Ub levels on cell adhesion genes, including CDH4, and upregulated the Wnt/β-Catenin signaling pathway. CDH4 overexpression could partially revert UBR7-dependent cellular phenotypes. Collectively, our results established UBR7 as a histone H2B monoubiquitin ligase that suppresses tumorigenesis and metastasis of triple-negative breast cancer.

Motivation: A major goal of biomedical research is to identify molecular features associated with a biological or clinical class of interest. Differential expression analysis has long been used for this purpose; however, conventional methods perform poorly when applied to data with high within class heterogeneity.Results: To address this challenge, we developed EMDomics, a new method that uses the Earth mover’s distance to measure the overall difference between the distributions of a gene’s expression in two classes of samples and uses permutations to obtain q-values for each gene. We applied EMDomics to the challenging problem of identifying genes associated with drug resistance in ovarian cancer. We also used simulated data to evaluate the performance of EMDomics, in terms of sensitivity and specificity for identifying differentially expressed gene in classes with high within class heterogeneity. In both the simulated and real biological data, EMDomics outperformed competing approaches for the identification of differentially expressed genes, and EMDomics was significantly more powerful than conventional methods for the identification of drug resistance-associated gene sets. EMDomics represents a new approach for the identification of genes differentially expressed between heterogeneous classes and has utility in a wide range of complex biomedical conditions in which sample classes show within class heterogeneity.Availability and implementation: The R package is available at http://www.bioconductor.org/packages/release/bioc/html/EMDomics.htmlContact: abeck2@bidmc.harvard.eduSupplementary information: supplementary data are available at Bioinformatics online.