The p21-Dependent Radiosensitization of Human Breast Cancer Cells by MLN4924, an Investigational Inhibitor of NEDD8 Activating Enzyme

Radiotherapy is a treatment choice for local control of breast cancer. However, intrinsic radioresistance of cancer cells limits therapeutic efficacy. We have recently validated that SCF (SKP1, Cullins, and F-box protein) E3 ubiquitin ligase is an attractive radiosensitizing target. Here we tested our hypothesis that MLN4924, a newly discovered investigational small molecule inhibitor of NAE (NEDD8 Activating Enzyme) that inactivates SCF E3 ligase, could act as a novel radiosensitizing agent in breast cancer cells. Indeed, we found that MLN4924 effectively inhibited cullin neddylation, and sensitized breast cancer cells to radiation with a sensitivity enhancement ratio (SER) of 1.75 for SK-BR-3 cells and 1.32 for MCF7 cells, respectively. Mechanistically, MLN4924 significantly enhanced radiation-induced G2/M arrest in SK-BR-3 cells, but not in MCF7 cells at early time point, and enhanced radiation-induced apoptosis in both lines at later time point. However, blockage of apoptosis by Z-VAD failed to abrogate MLN4924 radiosensitization, suggesting that apoptosis was not causally related. We further showed that MLN4924 failed to enhance radiation-induced DNA damage response, but did cause minor delay in DNA damage repair. Among a number of tested SCF E3 substrates known to regulate growth arrest, apoptosis and DNA damage response, p21 was the only one showing an enhanced accumulation in MLN4924-radiation combination group, as compared to the single treatment groups. Importantly, p21 knockdown via siRNA partialy inhibited MLN4924-induced G2/M arrest and radiosensitization, indicating a causal role played by p21. Our study suggested that MLN4924 could be further developed as a novel class of radiosensitizer for the treatment of breast cancer

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

The investigation of Mitogen-Activated Protein kinase Phosphatase-1 as a potential pharmacological target in non-small cell lung carcinomas, assisted by non-invasive molecular imaging

<p>Abstract</p> <p>Background</p> <p>Invasiveness and metastasis are the most common characteristics of non small cell lung cancer (NSCLC) and causes of tumour-related morbidity and mortality. Mitogen-activated protein kinases (MAPKs) signalling pathways have been shown to play critical roles in tumorigenesis. However, the precise pathological role(s) of mitogen-activated protein kinase phosphatase-1 (MKP-1) in different cancers has been controversial such that the up-regulation of MKP-1 in different cancers does not always correlate to a better prognosis. In this study, we showed that the induction of MKP-1 lead to a significant retardation of proliferation and metastasis in NSCLC cells. We also established that rosiglitazone (a PPARγ agonist) elevated MKP-1 expression level in NSCLC cells and inhibited tumour metastasis.</p> <p/> <p>Methods</p> <p>Both wildtype and dominant negative forms of MKP-1 were constitutively expressed in NSCLC cell line H441GL. The migration and invasion abilities of these cells were examined in vitro. MKP-1 modulating agents such as rosiglitazone and triptolide were used to demonstrate MKP-1's role in tumorigenesis. Bioluminescent imaging was utilized to study tumorigenesis of MKP-1 over-expressing H441GL cells and anti-metastatic effect of rosiglitazone.</p> <p>Results</p> <p>Over-expression of MKP-1 reduced NSCLC cell proliferation rate as well as cell invasive and migratory abilities, evident by the reduced expression levels of MMP-2 and CXCR4. Mice inoculated with MKP-1 over-expressing H441 cells did not develop NSCLC while their control wildtype H441 inoculated littermates developed NSCLC and bone metastasis. Pharmacologically, rosiglitazone, a peroxisome proliferator activated receptor-γ (PPARγ) agonist appeared to induce MKP-1 expression while reduce MMP-2 and CXCR4 expression. H441GL-inoculated mice receiving daily oral rosiglitazone treatment demonstrated a significant inhibition of bone metastasis when compared to mice receiving sham treatment. We found that rosiglitazone treatment impeded the ability of cell migration and invasion <it>in vitro</it>. Cells pre-treated with triptolide (a MKP-1 inhibitor), reversed rosiglitazone-mediated cell invasion and migration.</p> <p>Conclusion</p> <p>The induction of MKP-1 could significantly suppress the proliferative and metastatic abilities of NSCLC both in vitro and in vivo. Therefore, MKP-1 could be considered as a potential therapeutic target in NSCLC therapy and PPARγ agonists could be explored for combined chemotherapy.</p

Regulation of Inflammatory Gene Expression in PBMCs by Immunostimulatory Botanicals

Many hundreds of botanicals are used in complementary and alternative medicine for therapeutic use as antimicrobials and immune stimulators. While there exists many centuries of anecdotal evidence and few clinical studies on the activity and efficacy of these botanicals, limited scientific evidence exists on the ability of these botanicals to modulate the immune and inflammatory responses. Using botanogenomics (or herbogenomics), this study provides novel insight into inflammatory genes which are induced in peripheral blood mononuclear cells following treatment with immunomodulatory botanical extracts. These results may suggest putative genes involved in the physiological responses thought to occur following administration of these botanical extracts. Using extracts from immunostimulatory herbs (Astragalus membranaceus, Sambucus cerulea, Andrographis paniculata) and an immunosuppressive herb (Urtica dioica), the data presented supports previous cytokine studies on these herbs as well as identifying additional genes which may be involved in immune cell activation and migration and various inflammatory responses, including wound healing, angiogenesis, and blood pressure modulation. Additionally, we report the presence of lipopolysaccharide in medicinally prepared extracts of these herbs which is theorized to be a natural and active component of the immunostimulatory herbal extracts. The data presented provides a more extensive picture on how these herbs may be mediating their biological effects on the immune and inflammatory responses

A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of Compounds

BACKGROUND: Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. METHODOLOGY: We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. CONCLUSIONS: This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking

Using Pre-existing Microarray Datasets to Increase Experimental Power: Application to Insulin Resistance

Although they have become a widely used experimental technique for identifying differentially expressed (DE) genes, DNA microarrays are notorious for generating noisy data. A common strategy for mitigating the effects of noise is to perform many experimental replicates. This approach is often costly and sometimes impossible given limited resources; thus, analytical methods are needed which increase accuracy at no additional cost. One inexpensive source of microarray replicates comes from prior work: to date, data from hundreds of thousands of microarray experiments are in the public domain. Although these data assay a wide range of conditions, they cannot be used directly to inform any particular experiment and are thus ignored by most DE gene methods. We present the SVD Augmented Gene expression Analysis Tool (SAGAT), a mathematically principled, data-driven approach for identifying DE genes. SAGAT increases the power of a microarray experiment by using observed coexpression relationships from publicly available microarray datasets to reduce uncertainty in individual genes' expression measurements. We tested the method on three well-replicated human microarray datasets and demonstrate that use of SAGAT increased effective sample sizes by as many as 2.72 arrays. We applied SAGAT to unpublished data from a microarray study investigating transcriptional responses to insulin resistance, resulting in a 50% increase in the number of significant genes detected. We evaluated 11 (58%) of these genes experimentally using qPCR, confirming the directions of expression change for all 11 and statistical significance for three. Use of SAGAT revealed coherent biological changes in three pathways: inflammation, differentiation, and fatty acid synthesis, furthering our molecular understanding of a type 2 diabetes risk factor. We envision SAGAT as a means to maximize the potential for biological discovery from subtle transcriptional responses, and we provide it as a freely available software package that is immediately applicable to any human microarray study

Small molecules, big targets: drug discovery faces the protein-protein interaction challenge.

Protein-protein interactions (PPIs) are of pivotal importance in the regulation of biological systems and are consequently implicated in the development of disease states. Recent work has begun to show that, with the right tools, certain classes of PPI can yield to the efforts of medicinal chemists to develop inhibitors, and the first PPI inhibitors have reached clinical development. In this Review, we describe the research leading to these breakthroughs and highlight the existence of groups of structurally related PPIs within the PPI target class. For each of these groups, we use examples of successful discovery efforts to illustrate the research strategies that have proved most useful.JS, DES and ARB thank the Wellcome Trust for funding.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nrd.2016.2