Targeting the Ets Binding Site of the HER2/neu Promoter with Pyrrole-Imidazole Polyamides

Three DNA binding polyamides (1-3) were synthesized that bind with high affinity (Ka = 8.7·10^9 M^-1 to 1.4·10^10 M^-1) to two 7-base pair sequences overlapping the Ets DNA binding site (EBS; GAGGAA) within the regulatory region of the HER2/neu proximal promoter. As measured by electrophoretic mobility shift assay, polyamides binding to flanking elements upstream (1) or downstream (2 and 3) of the EBS were one to two orders of magnitude more effective than the natural product distamycin at inhibiting formation of complexes between the purified EBS protein, epithelial restricted with serine box (ESX), and the HER2/neu promoter probe. One polyamide, 2, completely blocked Ets-DNA complex formation at 10 nM ligand concentration, whereas formation of activator protein-2-DNA complexes was unaffected at the activator protein-2 binding site immediately upstream of the HER2/neu EBS, even at 100 nM ligand concentration. At equilibrium, polyamide 1 was equally effective at inhibiting Ets/DNA binding when added before or after in vitro formation of protein-promoter complexes, demonstrating its utility to disrupt endogenous Ets-mediated HER2/neu preinitiation complexes. Polyamide 2, the most potent inhibitor of Ets-DNA complex formation by electrophoretic mobility shift assay, was also the most effective inhibitor of HER2/neu promoter-driven transcription measured in a cell-free system using nuclear extract from an ESX- and HER2/neu-overexpressing human breast cancer cell line, SKBR-3

What Can the Accretion Induced Collapse of White Dwarfs Really Explain?

The accretion induced collapse (AIC) of a white dwarf into a neutron star has been invoked to explain gamma-ray bursts, Type Ia supernovae, and a number of problematic neutron star populations and specific binary systems. The ejecta from this collapse has also been claimed as a source of r-process nucleosynthesis. So far, most AIC studies have focussed on determining the event rates from binary evolution models and less attention has been directed toward understanding the collapse itself. However, the collapse of a white dwarf into a neutron star is followed by the ejection of rare neutron-rich isotopes. The observed abundance of these chemical elements may set a more reliable limit on the rate at which AICs have taken place over the history of the galaxy. In this paper, we present a thorough study of the collapse of a massive white dwarf in 1- and 2-dimensions and determine the amount and composition of the ejected material. We discuss the importance of the input physics (equation of state, neutrino transport, rotation) in determining these quantities. These simulations affirm that AICs are too baryon rich to produce gamm-ray bursts and do not eject enough nickel to explain Type Ia supernovae (with the possible exception of a small subclass of extremely low-luminosity Type Ias). Although nucleosynthesis constraints limit the number of neutron stars formed via AICs to <0.1% of the total galactic neutron star population, AICs remain a viable scenario for forming systems of neutron stars which are difficult to explain with Type II core-collapse supernovae.Comment: Latex File, aaspp4 style, 18 pages total (5 figures), accepted by Ap

Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies

BACKGROUND: Recent studies indicate that microRNAs (miRNAs) are mechanistically involved in the development of various human malignancies, suggesting that they represent a promising new class of cancer biomarkers. However, previously reported methods for measuring miRNA expression consume large amounts of tissue, prohibiting high-throughput miRNA profiling from typically small clinical samples such as excision or core needle biopsies of breast or prostate cancer. Here we describe a novel combination of linear amplification and labeling of miRNA for highly sensitive expression microarray profiling requiring only picogram quantities of purified microRNA. RESULTS: Comparison of microarray and qRT-PCR measured miRNA levels from two different prostate cancer cell lines showed concordance between the two platforms (Pearson correlation R(2 )= 0.81); and extension of the amplification, labeling and microarray platform was successfully demonstrated using clinical core and excision biopsy samples from breast and prostate cancer patients. Unsupervised clustering analysis of the prostate biopsy microarrays separated advanced and metastatic prostate cancers from pooled normal prostatic samples and from a non-malignant precursor lesion. Unsupervised clustering of the breast cancer microarrays significantly distinguished ErbB2-positive/ER-negative, ErbB2-positive/ER-positive, and ErbB2-negative/ER-positive breast cancer phenotypes (Fisher exact test, p = 0.03); as well, supervised analysis of these microarray profiles identified distinct miRNA subsets distinguishing ErbB2-positive from ErbB2-negative and ER-positive from ER-negative breast cancers, independent of other clinically important parameters (patient age; tumor size, node status and proliferation index). CONCLUSION: In sum, these findings demonstrate that optimized high-throughput microRNA expression profiling offers novel biomarker identification from typically small clinical samples such as breast and prostate cancer biopsies

Planetary population synthesis

In stellar astrophysics, the technique of population synthesis has been successfully used for several decades. For planets, it is in contrast still a young method which only became important in recent years because of the rapid increase of the number of known extrasolar planets, and the associated growth of statistical observational constraints. With planetary population synthesis, the theory of planet formation and evolution can be put to the test against these constraints. In this review of planetary population synthesis, we first briefly list key observational constraints. Then, the work flow in the method and its two main components are presented, namely global end-to-end models that predict planetary system properties directly from protoplanetary disk properties and probability distributions for these initial conditions. An overview of various population synthesis models in the literature is given. The sub-models for the physical processes considered in global models are described: the evolution of the protoplanetary disk, the planets' accretion of solids and gas, orbital migration, and N-body interactions among concurrently growing protoplanets. Next, typical population synthesis results are illustrated in the form of new syntheses obtained with the latest generation of the Bern model. Planetary formation tracks, the distribution of planets in the mass-distance and radius-distance plane, the planetary mass function, and the distributions of planetary radii, semimajor axes, and luminosities are shown, linked to underlying physical processes, and compared with their observational counterparts. We finish by highlighting the most important predictions made by population synthesis models and discuss the lessons learned from these predictions - both those later observationally confirmed and those rejected.Comment: 47 pages, 12 figures. Invited review accepted for publication in the 'Handbook of Exoplanets', planet formation section, section editor: Ralph Pudritz, Springer reference works, Juan Antonio Belmonte and Hans Deeg, Ed

Population Synthesis for Neutron Star Systems with Intrinsic Kicks

We use a Monte Carlo binary synthesis code to model the formation and evolution of neutron star systems including high-mass X-ray binaries, low-mass X-ray binaries, double neutron star systems and radio pulsars. Our focus is on the signature imprinted on such systems due to natal kicks to neutron stars over and above that imparted by orbital motions. The code incorporates the effect of the galactic potential (including rotation) on the velocities of these systems. A comparison between our models and the observations leads us to infer mean natal kicks between 400-500 km/s. Moreover, to be consistent with all the data, we require a bimodal kick distribution with one peak in the distribution near 0 km/s and the other above 600 km/s.Comment: 41 pages total, 24 text+tables pages, 17 figures, AASTeX, Accepted for publication in Ap

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

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

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

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