Lack of interaction between ErbB2 and insulin receptor substrate signaling in breast cancer

Background: ErbB2 Receptor Tyrosine Kinase 2 (ErbB2, HER2/Neu) is amplified in breast cancer and associated with poor prognosis. Growing evidence suggests interplay between ErbB2 and insulin-like growth factor (IGF) signaling. For example, ErbB2 inhibitors can block IGF-induced signaling while, conversely, IGF1R inhibitors can inhibit ErbB2 action. ErbB receptors can bind and phosphorylate insulin receptor substrates (IRS) and this may be critical for ErbBmediated anti-estrogen resistance in breast cancer. Herein, we examined crosstalk between ErbB2 and IRSs using cancer cell lines and transgenic mouse models. Methods: MMTV-ErbB2 and MMTV-IRS2 transgenic mice were crossed to create hemizygous MMTV-ErbB2/MMTVIRS2 bigenic mice. Signaling crosstalk between ErbB2 and IRSs was examined in vitro by knockdown or overexpression followed by western blot analysis for downstream signaling intermediates and growth assays. Results: A cross between MMTV-ErbB2 and MMTV-IRS2 mice demonstrated no enhancement of ErbB2 mediated mammary tumorigenesis or metastasis by elevated IRS2. Substantiating this, overexpression or knockdown of IRS1 or IRS2 in MMTV-ErbB2 mammary cancer cell lines had little effect upon ErbB2 signaling. Similar results were obtained in human mammary epithelial cells (MCF10A) and breast cancer cell lines. Conclusion: Despite previous evidence suggesting that ErbB receptors can bind and activate IRSs, our findings indicate that ErbB2 does not cooperate with the IRS pathway in these models to promote mammary tumorigenesis

Statistical Resolution of Ambiguous HLA Typing Data

High-resolution HLA typing plays a central role in many areas of immunology, such as in identifying immunogenetic risk factors for disease, in studying how the genomes of pathogens evolve in response to immune selection pressures, and also in vaccine design, where identification of HLA-restricted epitopes may be used to guide the selection of vaccine immunogens. Perhaps one of the most immediate applications is in direct medical decisions concerning the matching of stem cell transplant donors to unrelated recipients. However, high-resolution HLA typing is frequently unavailable due to its high cost or the inability to re-type historical data. In this paper, we introduce and evaluate a method for statistical, in silico refinement of ambiguous and/or low-resolution HLA data. Our method, which requires an independent, high-resolution training data set drawn from the same population as the data to be refined, uses linkage disequilibrium in HLA haplotypes as well as four-digit allele frequency data to probabilistically refine HLA typings. Central to our approach is the use of haplotype inference. We introduce new methodology to this area, improving upon the Expectation-Maximization (EM)-based approaches currently used within the HLA community. Our improvements are achieved by using a parsimonious parameterization for haplotype distributions and by smoothing the maximum likelihood (ML) solution. These improvements make it possible to scale the refinement to a larger number of alleles and loci in a more computationally efficient and stable manner. We also show how to augment our method in order to incorporate ethnicity information (as HLA allele distributions vary widely according to race/ethnicity as well as geographic area), and demonstrate the potential utility of this experimentally. A tool based on our approach is freely available for research purposes at http://microsoft.com/science

Single molecule quantitation and sequencing of rare translocations using microfluidic nested digital PCR

Cancers are heterogeneous and genetically unstable. New methods are needed that provide the sensitivity and specificity to query single cells at the genetic loci that drive cancer progression, thereby enabling researchers to study the progression of individual tumors. Here, we report the development and application of a bead-based hemi-nested microfluidic droplet digital PCR (dPCR) technology to achieve ‘quantitative’ measurement and single-molecule sequencing of somatically acquired carcinogenic translocations at extremely low levels (<10−6) in healthy subjects. We use this technique in our healthy study population to determine the overall concentration of the t(14;18) translocation, which is strongly associated with follicular lymphoma. The nested dPCR approach improves the detection limit to 1 × 10−7 or lower while maintaining the analysis efficiency and specificity. Further, the bead-based dPCR enabled us to isolate and quantify the relative amounts of the various clonal forms of t(14;18) translocation in these subjects, and the single-molecule sensitivity and resolution of dPCR led to the discovery of new clonal forms of t(14;18) that were otherwise masked by the conventional quantitative PCR measurements. In this manner, we created a quantitative map for this carcinogenic mutation in this healthy population and identified the positions on chromosomes 14 and 18 where the vast majority of these t(14;18) events occur.Trans-National Institutes of Health Genes, Environment and Health Initiative, Biological Response Indicators of Environmental Systems Center Grant [U54 ES016115-01 to M.T.S. and R.A.M.] and National Institute of Environmental Health Sciences Superfund Basic Research Program Grant [P42 ES004705 to M.T.S.]; Canary Foundation and ACS Postdoctoral Fellowship Award in Early Detection [116373-PFTED-08-251-01-SIED to J.S.] from the American Cancer Society; New faculty start-up funds from the University of Kansas (in part to Y.Z.). National Science Foundation Graduate Research Fellowship (to R.N.). Funding for open access charge: National Institutes of Health [U54 ES016115-01]

Application of machine learning methods to histone methylation ChIP-Seq data reveals H4R3me2 globally represses gene expression

<p>Abstract</p> <p>Background</p> <p>In the last decade, biochemical studies have revealed that epigenetic modifications including histone modifications, histone variants and DNA methylation form a complex network that regulate the state of chromatin and processes that depend on it including transcription and DNA replication. Currently, a large number of these epigenetic modifications are being mapped in a variety of cell lines at different stages of development using high throughput sequencing by members of the ENCODE consortium, the NIH Roadmap Epigenomics Program and the Human Epigenome Project. An extremely promising and underexplored area of research is the application of machine learning methods, which are designed to construct predictive network models, to these large-scale epigenomic data sets.</p> <p>Results</p> <p>Using a ChIP-Seq data set of 20 histone lysine and arginine methylations and histone variant H2A.Z in human CD4⁺T-cells, we built predictive models of gene expression as a function of histone modification/variant levels using Multilinear (ML) Regression and Multivariate Adaptive Regression Splines (MARS). Along with extensive crosstalk among the 20 histone methylations, we found H4R3me2 was the most and second most globally repressive histone methylation among the 20 studied in the ML and MARS models, respectively. In support of our finding, a number of experimental studies show that PRMT5-catalyzed symmetric dimethylation of H4R3 is associated with repression of gene expression. This includes a recent study, which demonstrated that H4R3me2 is required for DNMT3A-mediated DNA methylation--a known global repressor of gene expression.</p> <p>Conclusion</p> <p>In stark contrast to univariate analysis of the relationship between H4R3me2 and gene expression levels, our study showed that the regulatory role of some modifications like H4R3me2 is masked by confounding variables, but can be elucidated by multivariate/systems-level approaches.</p

Reversing SKI-SMAD4-mediated suppression is essential for TH17 cell differentiation

T helper 17 (TH17) cells are critically involved in host defence, inflammation, and autoimmunity. Transforming growth factor β (TGFβ) is instrumental in TH17 cell differentiation by cooperating with interleukin-6 (refs 6, 7). Yet, the mechanism by which TGFβ enables TH17 cell differentiation remains elusive. Here we reveal that TGFβ enables TH17 cell differentiation by reversing SKI-SMAD4-mediated suppression of the expression of the retinoic acid receptor (RAR)-related orphan receptor γt (RORγt). We found that, unlike wild-type T cells, SMAD4-deficient T cells differentiate into TH17 cells in the absence of TGFβ signalling in a RORγt-dependent manner. Ectopic SMAD4 expression suppresses RORγt expression and TH17 cell differentiation of SMAD4-deficient T cells. However, TGFβ neutralizes SMAD4-mediated suppression without affecting SMAD4 binding to the Rorc locus. Proteomic analysis revealed that SMAD4 interacts with SKI, a transcriptional repressor that is degraded upon TGFβ stimulation. SKI controls histone acetylation and deacetylation of the Rorc locus and TH17 cell differentiation via SMAD4: ectopic SKI expression inhibits H3K9 acetylation of the Rorc locus, Rorc expression, and TH17 cell differentiation in a SMAD4-dependent manner. Therefore, TGFβ-induced disruption of SKI reverses SKI-SMAD4-mediated suppression of RORγt to enable TH17 cell differentiation. This study reveals a critical mechanism by which TGFβ controls TH17 cell differentiation and uncovers the SKI-SMAD4 axis as a potential therapeutic target for treating TH17-related diseases