Inactivation of the PBRM1 tumor suppressor gene amplifies the HIF-response in VHL-/- clear cell renal carcinoma.

Most clear cell renal carcinomas (ccRCCs) are initiated by somatic inactivation of the VHL tumor suppressor gene. The VHL gene product, pVHL, is the substrate recognition unit of an ubiquitin ligase that targets the HIF transcription factor for proteasomal degradation; inappropriate expression of HIF target genes drives renal carcinogenesis. Loss of pVHL is not sufficient, however, to cause ccRCC. Additional cooperating genetic events, including intragenic mutations and copy number alterations, are required. Common examples of the former are loss-of-function mutations of the PBRM1 and BAP1 tumor suppressor genes, which occur in a mutually exclusive manner in ccRCC and define biologically distinct subsets of ccRCC. PBRM1 encodes the Polybromo- and BRG1-associated factors-containing complex (PBAF) chromatin remodeling complex component BRG1-associated factor 180 (BAF180). Here we identified ccRCC lines whose ability to proliferate in vitro and in vivo is sensitive to wild-type BAF180, but not a tumor-associated BAF180 mutant. Biochemical and functional studies linked growth suppression by BAF180 to its ability to form a canonical PBAF complex containing BRG1 that dampens the HIF transcriptional signature.

The histone H3 Lys 27 demethylase JMJD3 regulates gene expression by impacting transcriptional elongation.

The histone H3 Lys 27 (H3K27) demethylase JMJD3 has been shown to play important roles in transcriptional regulation and cell differentiation. However, the mechanism underlying JMJD3-mediated transcriptional regulation remains incompletely understood. Here we show that JMJD3 is associated with KIAA1718, whose substrates include dimethylated H3K27 (H3K27me2), and proteins involved in transcriptional elongation. JMJD3 and KIAA1718 directly bind to and regulate the expression of a plethora of common target genes in both a demethylase activity-dependent and -independent manner in the human promyelocytic leukemia cell line HL-60. We found that JMJD3 and KIAA1718 collaborate to demethylate trimethylated H3K27 (H3K27me3) on a subset of their target genes, some of which are bivalently marked by H3K4me3 and H3K27me3 and associated with promoter-proximal, paused RNA polymerase II (Pol II) before activation. Reduction of either JMJD3 or KIAA1718 diminishes Pol II traveling along the gene bodies of the affected genes while having no effect on the promoter-proximal Pol II. Furthermore, JMJD3 and KIAA1718 also play a role in localizing elongation factors SPT6 and SPT16 to the target genes. Our results support the model whereby JMJD3 activates bivalent gene transcription by demethylating H3K27me3 and promoting transcriptional elongation. Taken together, these findings provide new insight into the mechanisms by which JMJD3 regulates gene expression.

EglN2 associates with the NRF1-PGC1α complex and controls mitochondrial function in breast cancer.

The EglN2/PHD1 prolyl hydroxylase is an important oxygen sensor contributing to breast tumorigenesis. Emerging studies suggest that there is functional cross talk between oxygen sensing and mitochondrial function, both of which play an essential role for sustained tumor growth. However, the potential link between EglN2 and mitochondrial function remains largely undefined. Here, we show that EglN2 depletion decreases mitochondrial respiration in breast cancer under normoxia and hypoxia, which correlates with decreased mitochondrial DNA in a HIF1/2α-independent manner. Integrative analyses of gene expression profile and genomewide binding of EglN2 under hypoxic conditions reveal nuclear respiratory factor 1 (NRF1) motif enrichment in EglN2-activated genes, suggesting NRF1 as an EglN2 binding partner. Mechanistically, by forming an activator complex with PGC1α and NRF1 on chromatin, EglN2 promotes the transcription of ferridoxin reductase (FDXR) and maintains mitochondrial function. In addition, FDXR, as one of effectors for EglN2, contributes to breast tumorigenesis in vitro and in vivo. Our findings suggest that EglN2 regulates mitochondrial function in ERα-positive breast cancer.

Topological analysis reveals a PD-L1-associated microenvironmental niche for Reed-Sternberg cells in Hodgkin lymphoma.

Signaling between programmed cell death protein 1 (PD-1) and the PD-1 ligands (PD-L1, PD-L2) is essential for malignant Hodgkin Reed-Sternberg (HRS) cells to evade antitumor immunity in classical Hodgkin lymphoma (cHL). Copy number alterations of 9p24.1/CD274(PD-L1)/PDCD1LG2(PD-L2) contribute to robust PD-L1 and PD-L2 expression by HRS cells. PD-L1 is also expressed by nonmalignant tumor-associated macrophages (TAMs), but the relationships among PD-L1+ HRS cells, PD-L1+ TAMs, and PD-1+ T cells remain undefined. We used multiplex immunofluorescence and digital image analysis to examine the topography of PD-L1+ and PD-1+ cells in the tumor microenvironment (TME) of cHL. We find that the majority of PD-L1 in the TME is expressed by the abundant PD-L1+ TAMs, which physically colocalize with PD-L1+ HRS cells in a microenvironmental niche. PD-L1+ TAMs are enriched for contacts with T cells, and PD-L1+ HRS cells are enriched for contacts with CD4+ T cells, a subset of which are PD-1+ Our data define a unique topology of cHL in which PD-L1+ TAMs surround HRS cells and implicate CD4+ T cells as a target of PD-1 blockade.

SETDB1 modulates PRC2 activity at developmental genes independently of H3K9 trimethylation in mouse ES cells.

SETDB1, a histone methyltransferase responsible for methylation of histone H3 lysine 9 (H3K9), is involved in maintenance of embryonic stem (ES) cells and early embryonic development of the mouse. However, how SETDB1 regulates gene expression during development is largely unknown. Here, we characterized genome-wide SETDB1 binding and H3K9 trimethylation (H3K9me3) profiles in mouse ES cells and uncovered two distinct classes of SETDB1 binding sites, termed solo and ensemble peaks. The solo peaks were devoid of H3K9me3 and enriched near developmental regulators while the ensemble peaks were associated with H3K9me3. A subset of the SETDB1 solo peaks, particularly those near neural development-related genes, was found to be associated with Polycomb Repressive Complex 2 (PRC2) as well as PRC2-interacting proteins JARID2 and MTF2. Genetic deletion of Setdb1 reduced EZH2 binding as well as histone 3 lysine 27 (H3K27) trimethylation level at SETDB1 solo peaks and facilitated neural differentiation. Furthermore, we found that H3K27me3 inhibits SETDB1 methyltransferase activity. The currently identified reciprocal action between SETDB1 and PRC2 reveals a novel mechanism underlying ES cell pluripotency and differentiation regulation.

Inference of transcriptional regulation in cancers.

Despite the rapid accumulation of tumor-profiling data and transcription factor (TF) ChIP-seq profiles, efforts integrating TF binding with the tumor-profiling data to understand how TFs regulate tumor gene expression are still limited. To systematically search for cancer-associated TFs, we comprehensively integrated 686 ENCODE ChIP-seq profiles representing 150 TFs with 7484 TCGA tumor data in 18 cancer types. For efficient and accurate inference on gene regulatory rules across a large number and variety of datasets, we developed an algorithm, RABIT (regression analysis with background integration). In each tumor sample, RABIT tests whether the TF target genes from ChIP-seq show strong differential regulation after controlling for background effect from copy number alteration and DNA methylation. When multiple ChIP-seq profiles are available for a TF, RABIT prioritizes the most relevant ChIP-seq profile in each tumor. In each cancer type, RABIT further tests whether the TF expression and somatic mutation variations are correlated with differential expression patterns of its target genes across tumors. Our predicted TF impact on tumor gene expression is highly consistent with the knowledge from cancer-related gene databases and reveals many previously unidentified aspects of transcriptional regulation in tumor progression. We also applied RABIT on RNA-binding protein motifs and found that some alternative splicing factors could affect tumor-specific gene expression by binding to target gene 3'UTR regions. Thus, RABIT (rabit.dfci.harvard.edu) is a general platform for predicting the oncogenic role of gene expression regulators.

CRISPR-DO for genome-wide CRISPR design and optimization.

MOTIVATION: Despite the growing popularity in using CRISPR/Cas9 technology for genome editing and gene knockout, its performance still relies on well-designed single guide RNAs (sgRNA). In this study, we propose a web application for the Design and Optimization (CRISPR-DO) of guide sequences that target both coding and non-coding regions in spCas9 CRISPR system across human, mouse, zebrafish, fly and worm genomes. CRISPR-DO uses a computational sequence model to predict sgRNA efficiency, and employs a specificity scoring function to evaluate the potential of off-target effect. It also provides information on functional conservation of target sequences, as well as the overlaps with exons, putative regulatory sequences and single-nucleotide polymorphisms (SNPs). The web application has a user-friendly genome-browser interface to facilitate the selection of the best target DNA sequences for experimental design. AVAILABILITY AND IMPLEMENTATION: CRISPR-DO is available at http://cistrome.org/crispr/ CONTACT: qiliu@tongji.edu.cn or hanxu@jimmy.harvard.edu or xsliu@jimmy.harvard.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Enhancer RNAs participate in androgen receptor-driven looping that selectively enhances gene activation.

The androgen receptor (AR) is a key factor that regulates the behavior and fate of prostate cancer cells. The AR-regulated network is activated when AR binds enhancer elements and modulates specific enhancer-promoter looping. Kallikrein-related peptidase 3 (KLK3), which codes for prostate-specific antigen (PSA), is a well-known AR-regulated gene and its upstream enhancers produce bidirectional enhancer RNAs (eRNAs), termed KLK3e. Here, we demonstrate that KLK3e facilitates the spatial interaction of the KLK3 enhancer and the KLK2 promoter and enhances long-distance KLK2 transcriptional activation. KLK3e carries the core enhancer element derived from the androgen response element III (ARE III), which is required for the interaction of AR and Mediator 1 (Med1). Furthermore, we show that KLK3e processes RNA-dependent enhancer activity depending on the integrity of core enhancer elements. The transcription of KLK3e was detectable and its expression is significantly correlated with KLK3 (R(2) = 0.6213, P < 5 × 10(-11)) and KLK2 (R(2) = 0.5893, P < 5 × 10(-10)) in human prostate tissues. Interestingly, RNAi silencing of KLK3e resulted in a modest negative effect on prostate cancer cell proliferation. Accordingly, we report that an androgen-induced eRNA scaffolds the AR-associated protein complex that modulates chromosomal architecture and selectively enhances AR-dependent gene expression.

Long-range enhancer activity determines Myc sensitivity to Notch inhibitors in T cell leukemia.

Notch is needed for T-cell development and is a common oncogenic driver in T-cell acute lymphoblastic leukemia. The protooncogene c-Myc (Myc) is a critical target of Notch in normal and malignant pre-T cells, but how Notch regulates Myc is unknown. Here, we identify a distal enhancer located >1 Mb 3' of human and murine Myc that binds Notch transcription complexes and physically interacts with the Myc proximal promoter. The Notch1 binding element in this region activates reporter genes in a Notch-dependent, cell-context-specific fashion that requires a conserved Notch complex binding site. Acute changes in Notch activation produce rapid changes in H3K27 acetylation across the entire enhancer (a region spanning >600 kb) that correlate with Myc expression. This broad Notch-influenced region comprises an enhancer region containing multiple domains, recognizable as discrete H3K27 acetylation peaks. Leukemia cells selected for resistance to Notch inhibitors express Myc despite epigenetic silencing of enhancer domains near the Notch transcription complex binding sites. Notch-independent expression of Myc in resistant cells is highly sensitive to inhibitors of bromodomain containing 4 (Brd4), a change in drug sensitivity that is accompanied by preferential association of the Myc promoter with more 3' enhancer domains that are strongly dependent on Brd4 for function. These findings indicate that altered long-range enhancer activity can mediate resistance to targeted therapies and provide a mechanistic rationale for combined targeting of Notch and Brd4 in leukemia.

Estrogen receptor prevents p53-dependent apoptosis in breast cancer.

More than two-thirds of breast cancers express the estrogen receptor (ER) and depend on estrogen for growth and survival. Therapies targeting ER function, including aromatase inhibitors that block the production of estrogens and ER antagonists that alter ER transcriptional activity, play a central role in the treatment of ER+ breast cancers of all stages. In contrast to ER- breast cancers, which frequently harbor mutations in the p53 tumor suppressor, ER+ breast cancers are predominantly wild type for p53. Despite harboring wild-type p53, ER+ breast cancer cells are resistant to chemotherapy-induced apoptosis in the presence of estrogen. Using genome-wide approaches, we have addressed the mechanism by which ER antagonizes the proapoptotic function of p53. Interestingly, both ER agonists such as estradiol and the selective ER modulator (SERM) tamoxifen promote p53 antagonism. In contrast, the full ER antagonist fulvestrant blocks the ability of ER to inhibit p53-mediated cell death. This inhibition works through a mechanism involving the modulation of a subset of p53 and ER target genes that can predict the relapse-free survival of patients with ER+ breast cancer. These findings suggest an improved strategy for the treatment of ER+ breast cancer using antagonists that completely block ER action together with drugs that activate p53-mediated cell death.

Enhanced Efficacy of Simultaneous PD-1 and PD-L1 Immune Checkpoint Blockade in High-Grade Serous Ovarian Cancer.

Immune therapies have had limited efficacy in high-grade serous ovarian cancer (HGSC), as the cellular targets and mechanism(s) of action of these agents in HGSC are unknown. Here we performed immune functional and single-cell RNA sequencing transcriptional profiling on novel HGSC organoid/immune cell co-cultures treated with a unique bispecific anti-programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) antibody compared with monospecific anti-PD-1 or anti-PD-L1 controls. Comparing the functions of these agents across all immune cell types in real time identified key immune checkpoint blockade (ICB) targets that have eluded currently available monospecific therapies. The bispecific antibody induced superior cellular state changes in both T and natural killer (NK) cells. It uniquely induced NK cells to transition from inert to more active and cytotoxic phenotypes, implicating NK cells as a key missing component of the current ICB-induced immune response in HGSC. It also induced a subset of CD8 T cells to transition from naïve to more active and cytotoxic progenitor-exhausted phenotypes post-treatment, revealing the small, previously uncharacterized population of CD8 T cells responding to ICB in HGSC. These state changes were driven partially through bispecific antibody-induced downregulation of the bromodomain-containing protein BRD1. Small-molecule inhibition of BRD1 induced similar state changes in vitro and demonstrated efficacy in vivo, validating the co-culture results. Our results demonstrate that state changes in both NK and a subset of T cells may be critical in inducing an effective anti-tumor immune response and suggest that immune therapies able to induce such cellular state changes, such as BRD1 inhibitors, may have increased efficacy in HGSC. SIGNIFICANCE: This study indicates that increased efficacy of immune therapies in ovarian cancer is driven by state changes of NK and small subsets of CD8 T cells into active and cytotoxic states.

Network for Biomarker Immunoprofiling for Cancer Immunotherapy: Cancer Immune Monitoring and Analysis Centers and Cancer Immunologic Data Commons (CIMAC-CIDC).

PURPOSE: Immunoprofiling to identify biomarkers and integration with clinical trial outcomes are critical to improving immunotherapy approaches for patients with cancer. However, the translational potential of individual studies is often limited by small sample size of trials and the complexity of immuno-oncology biomarkers. Variability in assay performance further limits comparison and interpretation of data across studies and laboratories. EXPERIMENTAL DESIGN: To enable a systematic approach to biomarker identification and correlation with clinical outcome across trials, the Cancer Immune Monitoring and Analysis Centers and Cancer Immunologic Data Commons (CIMAC-CIDC) Network was established through support of the Cancer MoonshotSM Initiative of the National Cancer Institute (NCI) and the Partnership for Accelerating Cancer Therapies (PACT) with industry partners via the Foundation for the NIH. RESULTS: The CIMAC-CIDC Network is composed of four academic centers with multidisciplinary expertise in cancer immunotherapy that perform validated and harmonized assays for immunoprofiling and conduct correlative analyses. A data coordinating center (CIDC) provides the computational expertise and informatics platforms for the storage, integration, and analysis of biomarker and clinical data. CONCLUSIONS: This overview highlights strategies for assay harmonization to enable cross-trial and cross-site data analysis and describes key elements for establishing a network to enhance immuno-oncology biomarker development. These include an operational infrastructure, validation and harmonization of core immunoprofiling assays, platforms for data ingestion and integration, and access to specimens from clinical trials. Published in the same volume are reports of harmonization for core analyses: whole-exome sequencing, RNA sequencing, cytometry by time of flight, and IHC/immunofluorescence.

Regulation of human 3 alpha-hydroxysteroid dehydrogenase (AKR1C4) expression by the liver X receptor alpha.

Type I human hepatic 3alpha-hydroxysteroid dehydrogenase (AKR1C4) plays a significant role in bile acid biosynthesis, steroid hormone metabolism, and xenobiotic metabolism. Utilization of a hidden Markov model for predictive modeling of nuclear hormone receptor response elements coupled with chromatin immunoprecipitation/microarray technology revealed a putative binding site in the AKR1C4 promoter for the nuclear hormone receptor known as liver X receptor alpha, (LXRalpha [NR1H3]), which is the physiological receptor for oxidized cholesterol metabolites. The putative LXRalpha response element (LXRE), identified by chromatin immunoprecipitation, was approximately 1.5 kilobase pairs upstream of the transcription start site. LXRalpha was shown to bind specifically to this LXRE and mediate transcriptional activation of the AKR1C4 gene, leading to increased AKR1C4 protein expression. These data suggest that LXRalpha may modulate the bile acid biosynthetic pathway at a unique site downstream of CYP7A1 and may also modulate the metabolism of steroid hormones and certain xenobiotics.

Genome-scale deletion screening of human long non-coding RNAs using a paired-guide RNA CRISPR-Cas9 library.

CRISPR-Cas9 screens have been widely adopted to analyze coding-gene functions, but high-throughput screening of non-coding elements using this method is more challenging because indels caused by a single cut in non-coding regions are unlikely to produce a functional knockout. A high-throughput method to produce deletions of non-coding DNA is needed. We report a high-throughput genomic deletion strategy to screen for functional long non-coding RNAs (lncRNAs) that is based on a lentiviral paired-guide RNA (pgRNA) library. Applying our screening method, we identified 51 lncRNAs that can positively or negatively regulate human cancer cell growth. We validated 9 of 51 lncRNA hits using CRISPR-Cas9-mediated genomic deletion, functional rescue, CRISPR activation or inhibition and gene-expression profiling. Our high-throughput pgRNA genome deletion method will enable rapid identification of functional mammalian non-coding elements.

Targeting epigenetic regulators for cancer therapy.

Human gene expression patterns are controlled and coordinated by the activity of a diverse array of epigenetic regulators, including histone methyltransferases, acetyltransferases, and chromatin remodelers. Deregulation of these epigenetic pathways can lead to genome-wide changes in gene expression, with serious disease consequences. In recent years, research has suggested that cross talk between genomic (i.e., for example, mutations, translocations) and epigenomic factors may drive the etiology of both hematologic malignancies and solid tumors. Current work in translational research seeks to identify epigenetic regulators whose aberrant activity contributes to oncogenesis, including the histone methyltransferases DOT1L and EZH2 and the bromodomain-containing BET family, and to develop drugs that inhibit the aberrant activity of these regulators. Preclinical and clinical studies using small-molecule inhibitors of epigenetic regulators have underscored their value for therapeutic intervention, and these inhibitors can also be used to drive further studies into dissecting the functions of epigenetic factors in normal and cancer cells.

MethylPurify: tumor purity deconvolution and differential methylation detection from single tumor DNA methylomes.

We propose a statistical algorithm MethylPurify that uses regions with bisulfite reads showing discordant methylation levels to infer tumor purity from tumor samples alone. MethylPurify can identify differentially methylated regions (DMRs) from individual tumor methylome samples, without genomic variation information or prior knowledge from other datasets. In simulations with mixed bisulfite reads from cancer and normal cell lines, MethylPurify correctly inferred tumor purity and identified over 96% of the DMRs. From patient data, MethylPurify gave satisfactory DMR calls from tumor methylome samples alone, and revealed potential missed DMRs by tumor to normal comparison due to tumor heterogeneity.

Protein kinase C α is a central signaling node and therapeutic target for breast cancer stem cells.

The epithelial-mesenchymal transition program becomes activated during malignant progression and can enrich for cancer stem cells (CSCs). We report that inhibition of protein kinase C α (PKCα) specifically targets CSCs but has little effect on non-CSCs. The formation of CSCs from non-stem cells involves a shift from EGFR to PDGFR signaling and results in the PKCα-dependent activation of FRA1. We identified an AP-1 molecular switch in which c-FOS and FRA1 are preferentially utilized in non-CSCs and CSCs, respectively. PKCα and FRA1 expression is associated with the aggressive triple-negative breast cancers, and the depletion of FRA1 results in a mesenchymal-epithelial transition. Hence, identifying molecular features that shift between cell states can be exploited to target signaling components critical to CSCs.

Identifying ChIP-seq enrichment using MACS.

Model-based analysis of ChIP-seq (MACS) is a computational algorithm that identifies genome-wide locations of transcription/chromatin factor binding or histone modification from ChIP-seq data. MACS consists of four steps: removing redundant reads, adjusting read position, calculating peak enrichment and estimating the empirical false discovery rate (FDR). In this protocol, we provide a detailed demonstration of how to install MACS and how to use it to analyze three common types of ChIP-seq data sets with different characteristics: the sequence-specific transcription factor FoxA1, the histone modification mark H3K4me3 with sharp enrichment and the H3K36me3 mark with broad enrichment. We also explain how to interpret and visualize the results of MACS analyses. The algorithm requires ∼3 GB of RAM and 1.5 h of computing time to analyze a ChIP-seq data set containing 30 million reads, an estimate that increases with sequence coverage. MACS is open source and is available from http://liulab.dfci.harvard.edu/MACS/.

MM-ChIP enables integrative analysis of cross-platform and between-laboratory ChIP-chip or ChIP-seq data.

The ChIP-chip and ChIP-seq techniques enable genome-wide mapping of in vivo protein-DNA interactions and chromatin states. The cross-platform and between-laboratory variation poses a challenge to the comparison and integration of results from different ChIP experiments. We describe a novel method, MM-ChIP, which integrates information from cross-platform and between-laboratory ChIP-chip or ChIP-seq datasets. It improves both the sensitivity and the specificity of detecting ChIP-enriched regions, and is a useful meta-analysis tool for driving discoveries from multiple data sources.