Suppression of Ah Receptor (AhR) increases the aggressiveness of TNBC cells and 11-Cl-BBQ-activated AhR inhibits their growth.

Triple-negative breast cancer (TNBC) represents around 15% of the 2.26 million breast cancers diagnosed worldwide annually and has the worst outcome. Despite recent therapeutic advances, there remains a lack of targeted therapies for this breast cancer subtype. The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor with biological roles in regulating development, xenobiotic metabolism, cell cycle progression and cell death. AhR activation by select ligands can promote tumor suppression in multiple cancer types. AhR can negatively regulate the activity of different oncogenic signaling pathways and can directly upregulate tumor suppressor genes such as p27Kip1. To determine the role of AhR in TNBC, we generated AhR-deficient cancer cells and investigated the impact of AhR loss on TNBC cell growth phenotypes. We found that AhR-deficient MDA-MB-468 TNBC cells have increased proliferation and formed significantly more colonies compared to AhR expressing cells. These cells without AhR expression grew aggressively in vivo. To determine the molecular targets driving this phenotype, we performed transcriptomic profiling in AhR expressing and AhR knockout MDA-MB-468 cells and identified tyrosine receptor kinases, as well as other genes involved in proliferation, survival and clonogenicity that are repressed by AhR. In order to determine therapeutic targeting of AhR in TNBC, we investigated the anti-cancer effects of the novel AhR ligand 11-chloro-7H-benzimidazo[2,1-a]benzo[de]iso-quinolin-7-one (11-Cl-BBQ), which belongs to a class of high affinity, rapidly metabolized AhR ligands called benzimidazoisoquinolines (BBQs). 11-Cl-BBQ induced AhR-dependent cancer cell-selective growth inhibition and strongly inhibited colony formation in TNBC cells.

Induction of Aryl Hydrocarbon Receptor-Mediated Cancer Cell-Selective Apoptosis in Triple-Negative Breast Cancer Cells by a High-Affinity Benzimidazoisoquinoline.

Triple-negative breast cancer (TNBC) remains a disease with a paucity of targeted treatment opportunities. The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that is involved in a wide range of physiological processes, including the sensing of xenobiotics, immune function, development, and differentiation. Different small-molecule AhR ligands drive strikingly varied cellular and organismal responses. In certain cancers, AhR activation by select small molecules induces cell cycle arrest or apoptosis via activation of tumor-suppressive transcriptional programs. AhR is expressed in triple-negative breast cancers, presenting a tractable therapeutic opportunity. Here, we identify a novel ligand of the aryl hydrocarbon receptor that potently and selectively induces cell death in triple-negative breast cancer cells and TNBC stem cells via the AhR. Importantly, we found that this compound, Analog 523, exhibits minimal cytotoxicity against multiple normal human primary cells. Analog 523 represents a high-affinity AhR ligand with potential for future clinical translation as an anticancer agent.

Systematic integration of protein-affecting mutations, gene fusions, and copy number alterations into a comprehensive somatic mutational profile.

Somatic mutations occur as random genetic changes in genes through protein-affecting mutations (PAMs), gene fusions, or copy number alterations (CNAs). Mutations of different types can have a similar phenotypic effect (i.e., allelic heterogeneity) and should be integrated into a unified gene mutation profile. We developed OncoMerge to fill this niche of integrating somatic mutations to capture allelic heterogeneity, assign a function to mutations, and overcome known obstacles in cancer genetics. Application of OncoMerge to TCGA Pan-Cancer Atlas increased detection of somatically mutated genes and improved the prediction of the somatic mutation role as either activating or loss of function. Using integrated somatic mutation matrices increased the power to infer gene regulatory networks and uncovered the enrichment of switch-like feedback motifs and delay-inducing feedforward loops. These studies demonstrate that OncoMerge efficiently integrates PAMs, fusions, and CNAs and strengthens downstream analyses linking somatic mutations to cancer phenotypes.

Revealing the biology behind MRI signatures in high grade glioma.

Magnetic resonance imaging (MRI) measurements are routinely collected during the treatment of high-grade gliomas (HGGs) to characterize tumor boundaries and guide surgical tumor resection. Using spatially matched MRI and transcriptomics we discovered HGG tumor biology captured by MRI measurements. We strategically overlaid the spatially matched omics characterizations onto a pre-existing transcriptional map of glioblastoma multiforme (GBM) to enhance the robustness of our analyses. We discovered that T1+C measurements, designed to capture vasculature and blood brain barrier (BBB) breakdown and subsequent contrast extravasation, also indirectly reveal immune cell infiltration. The disruption of the vasculature and BBB within the tumor creates a permissive infiltrative environment that enables the transmigration of anti-inflammatory macrophages into tumors. These relationships were validated through histology and enrichment of genes associated with immune cell transmigration and proliferation. Additionally, T2-weighted (T2W) and mean diffusivity (MD) measurements were associated with angiogenesis and validated using histology and enrichment of genes involved in neovascularization. Furthermore, we establish an unbiased approach for identifying additional linkages between MRI measurements and tumor biology in future studies, particularly with the integration of novel MRI techniques. Lastly, we illustrated how noninvasive MRI can be used to map HGG biology spatially across a tumor, and this provides a platform to develop diagnostics, prognostics, or treatment efficacy biomarkers to improve patient outcomes.

Nonredundant, isoform-specific roles of HDAC1 in glioma stem cells.

Glioblastoma (GBM) is characterized by an aberrant yet druggable epigenetic landscape. One major family of epigenetic regulators, the histone deacetylases (HDACs), are considered promising therapeutic targets for GBM due to their repressive influences on transcription. Although HDACs share redundant functions and common substrates, the unique isoform-specific roles of different HDACs in GBM remain unclear. In neural stem cells, HDAC2 is the indispensable deacetylase to ensure normal brain development and survival in the absence of HDAC1. Surprisingly, we find that HDAC1 is the essential class I deacetylase in glioma stem cells, and its loss is not compensated for by HDAC2. Using cell-based and biochemical assays, transcriptomic analyses, and patient-derived xenograft models, we find that knockdown of HDAC1 alone has profound effects on the glioma stem cell phenotype in a p53-dependent manner. We demonstrate marked suppression in tumor growth upon targeting of HDAC1 and identify compensatory pathways that provide insights into combination therapies for GBM. Our study highlights the importance of HDAC1 in GBM and the need to develop isoform-specific drugs.

Neural G0: a quiescent-like state found in neuroepithelial-derived cells and glioma.

Single-cell RNA sequencing has emerged as a powerful tool for resolving cellular states associated with normal and maligned developmental processes. Here, we used scRNA-seq to examine the cell cycle states of expanding human neural stem cells (hNSCs). From these data, we constructed a cell cycle classifier that identifies traditional cell cycle phases and a putative quiescent-like state in neuroepithelial-derived cell types during mammalian neurogenesis and in gliomas. The Neural G0 markers are enriched with quiescent NSC genes and other neurodevelopmental markers found in non-dividing neural progenitors. Putative glioblastoma stem-like cells were significantly enriched in the Neural G0 cell population. Neural G0 cell populations and gene expression are significantly associated with less aggressive tumors and extended patient survival for gliomas. Genetic screens to identify modulators of Neural G0 revealed that knockout of genes associated with the Hippo/Yap and p53 pathways diminished Neural G0 in vitro, resulting in faster G1 transit, down-regulation of quiescence-associated markers, and loss of Neural G0 gene expression. Thus, Neural G0 represents a dynamic quiescent-like state found in neuroepithelial-derived cells and gliomas.

N6-methyladenosine mRNA marking promotes selective translation of regulons required for human erythropoiesis.

Many of the regulatory features governing erythrocyte specification, maturation, and associated disorders remain enigmatic. To identify new regulators of erythropoiesis, we utilize a functional genomic screen for genes affecting expression of the erythroid marker CD235a/GYPA. Among validating hits are genes coding for the N6-methyladenosine (m6A) mRNA methyltransferase (MTase) complex, including, METTL14, METTL3, and WTAP. We demonstrate that m6A MTase activity promotes erythroid gene expression programs through selective translation of ~300 m6A marked mRNAs, including those coding for SETD histone methyltransferases, ribosomal components, and polyA RNA binding proteins. Remarkably, loss of m6A marks results in dramatic loss of H3K4me3 marks across key erythroid-specific KLF1 transcriptional targets (e.g., Heme biosynthesis genes). Further, each m6A MTase subunit and a subset of their mRNAs targets are required for human erythroid specification in primary bone-marrow derived progenitors. Thus, m6A mRNA marks promote the translation of a network of genes required for human erythropoiesis.

The Immune Landscape of Cancer.

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-ß dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.

We developed the transcription factor (TF)-target gene database and the Systems Genetics Network Analysis (SYGNAL) pipeline to decipher transcriptional regulatory networks from multi-omic and clinical patient data, and we applied these tools to 422 patients with glioblastoma multiforme (GBM). The resulting gbmSYGNAL network predicted 112 somatically mutated genes or pathways that act through 74 TFs and 37 microRNAs (miRNAs) (67 not previously associated with GBM) to dysregulate 237 distinct co-regulated gene modules associated with patient survival or oncogenic processes. The regulatory predictions were associated to cancer phenotypes using CRISPR-Cas9 and small RNA perturbation studies and also demonstrated GBM specificity. Two pairwise combinations (ETV6-NFKB1 and romidepsin-miR-486-3p) predicted by the gbmSYGNAL network had synergistic anti-proliferative effects. Finally, the network revealed that mutations in NF1 and PIK3CA modulate IRF1-mediated regulation of MHC class I antigen processing and presentation genes to increase tumor lymphocyte infiltration and worsen prognosis. Importantly, SYGNAL is widely applicable for integrating genomic and transcriptomic measurements from other human cohorts.

Genome-wide CRISPR-Cas9 Screens Reveal Loss of Redundancy between PKMYT1 and WEE1 in Glioblastoma Stem-like Cells.

To identify therapeutic targets for glioblastoma (GBM), we performed genome-wide CRISPR-Cas9 knockout (KO) screens in patient-derived GBM stem-like cells (GSCs) and human neural stem/progenitors (NSCs), non-neoplastic stem cell controls, for genes required for their in vitro growth. Surprisingly, the vast majority GSC-lethal hits were found outside of molecular networks commonly altered in GBM and GSCs (e.g., oncogenic drivers). In vitro and in vivo validation of GSC-specific targets revealed several strong hits, including the wee1-like kinase, PKMYT1/Myt1. Mechanistic studies demonstrated that PKMYT1 acts redundantly with WEE1 to inhibit cyclin B-CDK1 activity via CDK1-Y15 phosphorylation and to promote timely completion of mitosis in NSCs. However, in GSCs, this redundancy is lost, most likely as a result of oncogenic signaling, causing GBM-specific lethality.

cMonkey2: Automated, systematic, integrated detection of co-regulated gene modules for any organism.

The cMonkey integrated biclustering algorithm identifies conditionally co-regulated modules of genes (biclusters). cMonkey integrates various orthogonal pieces of information which support evidence of gene co-regulation, and optimizes biclusters to be supported simultaneously by one or more of these prior constraints. The algorithm served as the cornerstone for constructing the first global, predictive Environmental Gene Regulatory Influence Network (EGRIN) model for a free-living cell, and has now been applied to many more organisms. However, due to its computational inefficiencies, long run-time and complexity of various input data types, cMonkey was not readily usable by the wider community. To address these primary concerns, we have significantly updated the cMonkey algorithm and refactored its implementation, improving its usability and extendibility. These improvements provide a fully functioning and user-friendly platform for building co-regulated gene modules and the tools necessary for their exploration and interpretation. We show, via three separate analyses of data for E. coli, M. tuberculosis and H. sapiens, that the updated algorithm and inclusion of novel scoring functions for new data types (e.g. ChIP-seq and transcription factor over-expression [TFOE]) improve discovery of biologically informative co-regulated modules. The complete cMonkey2 software package, including source code, is available at https://github.com/baliga-lab/cmonkey2.

Structure-based predictions broadly link transcription factor mutations to gene expression changes in cancers.

Thousands of unique mutations in transcription factors (TFs) arise in cancers, and the functional and biological roles of relatively few of these have been characterized. Here, we used structure-based methods developed specifically for DNA-binding proteins to systematically predict the consequences of mutations in several TFs that are frequently mutated in cancers. The explicit consideration of protein-DNA interactions was crucial to explain the roles and prevalence of mutations in TP53 and RUNX1 in cancers, and resulted in a higher specificity of detection for known p53-regulated genes among genetic associations between TP53 genotypes and genome-wide expression in The Cancer Genome Atlas, compared to existing methods of mutation assessment. Biophysical predictions also indicated that the relative prevalence of TP53 missense mutations in cancer is proportional to their thermodynamic impacts on protein stability and DNA binding, which is consistent with the selection for the loss of p53 transcriptional function in cancers. Structure and thermodynamics-based predictions of the impacts of missense mutations that focus on specific molecular functions may be increasingly useful for the precise and large-scale inference of aberrant molecular phenotypes in cancer and other complex diseases.

A miRNA-regulatory network explains how dysregulated miRNAs perturb oncogenic processes across diverse cancers.

Genes regulated by the same miRNA can be discovered by virtue of their coexpression at the transcriptional level and the presence of a conserved miRNA-binding site in their 3' UTRs. Using this principle we have integrated the three best performing and complementary algorithms into a framework for inference of regulation by miRNAs (FIRM) from sets of coexpressed genes. We demonstrate the utility of FIRM by inferring a cancer-miRNA regulatory network through the analysis of 2240 gene coexpression signatures from 46 cancers. By analyzing this network for functional enrichment of known hallmarks of cancer we have discovered a subset of 13 miRNAs that regulate oncogenic processes across diverse cancers. We have performed experiments to test predictions from this miRNA-regulatory network to demonstrate that miRNAs of the miR-29 family (miR-29a, miR-29b, and miR-29c) regulate specific genes associated with tissue invasion and metastasis in lung adenocarcinoma. Further, we highlight the specificity of using FIRM inferences to identify miRNA-regulated genes by experimentally validating that miR-767-5p, which partially shares the miR-29 seed sequence, regulates only a subset of miR-29 targets. By providing mechanistic linkage between miRNA dysregulation in cancer, their binding sites in the 3'UTRs of specific sets of coexpressed genes, and their associations with known hallmarks of cancer, FIRM, and the inferred cancer miRNA-regulatory network will serve as a powerful public resource for discovery of potential cancer biomarkers.

Identification of two common variants contributing to serum apolipoprotein B levels in Mexicans.

BACKGROUND AND PURPOSE: Although the Mexican population has a high predisposition to dyslipidemias and premature coronary artery disease, this population is underinvestigated for the genetic factors conferring the high susceptibility. This study attempted to determine these genetic factors. METHODS AND RESULTS: First, we investigated apolipoprotein B (apoB) levels in Mexican extended families with familial combined hyperlipidemia using a two-step testing strategy. In the screening step, we screened 5721 single-nucleotide polymorphisms (SNPs) for linkage signals with apoB. In the test step, we analyzed the 130 SNPs residing in regions of suggestive linkage signals for association with apoB. We identified significant associations with two SNPs (ie, rs1424032 [P=6.07x10(-6)] and rs1349411 [P=2.72x10(-4)]) that surpassed the significance level for the number of tests performed in the test step (P<3.84x10(-4)). Second, these SNPs were tested for replication in Mexican hyperlipidemic case-control samples. The same risk alleles as in the families with familial combined hyperlipidemia were significantly associated (P<0.05) with apoB in the case-control samples. The rs1349411 resides near the apoB messenger RNA editing enzyme (APOBEC1) involved in the processing of APOB messenger RNA in the small intestine. The rs1424032 resides in a highly conserved noncoding region predicted to function as a regulatory element. CONCLUSIONS: We identified two novel variants, rs1349411 and rs1424032, for serum apoB levels in Mexicans.

WW-domain-containing oxidoreductase is associated with low plasma HDL-C levels.

Low serum HDL-cholesterol (HDL-C) is a major risk factor for coronary artery disease. We performed targeted genotyping of a 12.4 Mb linked region on 16q to test for association with low HDL-C by using a regional-tag SNP strategy. We identified one SNP, rs2548861, in the WW-domain-containing oxidoreductase (WWOX) gene with region-wide significance for low HDL-C in dyslipidemic families of Mexican and European descent and in low-HDL-C cases and controls of European descent (p = 6.9 x 10(-7)). We extended our investigation to the population level by using two independent unascertained population-based Finnish cohorts, the cross-sectional METSIM cohort of 4,463 males and the prospective Young Finns cohort of 2,265 subjects. The combined analysis provided p = 4 x 10(-4) to 2 x 10(-5). Importantly, in the prospective cohort, we observed a significant longitudinal association of rs2548861 with HDL-C levels obtained at four different time points over 21 years (p = 0.003), and the T risk allele explained 1.5% of the variance in HDL-C levels. The rs2548861 resides in a highly conserved region in intron 8 of WWOX. Results from our in vitro reporter assay and electrophoretic mobility-shift assay demonstrate that this region functions as a cis-regulatory element whose associated rs2548861 SNP has a specific allelic effect and that the region forms an allele-specific DNA-nuclear-factor complex. In conclusion, analyses of 9,798 subjects show significant association between HDL-C and a WWOX variant with an allele-specific cis-regulatory function.