Development and Validation of Predictive Indices for a Continuous Outcome Using Gene Expression Profiles

There have been relatively few publications using linear regression models to predict a continuous response based on microarray expression profiles. Standard linear regression methods are problematic when the number of predictor variables exceeds the number of cases. We have evaluated three linear regression algorithms that can be used for the prediction of a continuous response based on high dimensional gene expression data. The three algorithms are the least angle regression (LAR), the least absolute shrinkage and selection operator (LASSO), and the averaged linear regression method (ALM). All methods are tested using simulations based on a real gene expression dataset and analyses of two sets of real gene expression data and using an unbiased complete cross validation approach. Our results show that the LASSO algorithm often provides a model with somewhat lower prediction error than the LAR method, but both of them perform more efficiently than the ALM predictor. We have developed a plug-in for BRB-ArrayTools that implements the LAR and the LASSO algorithms with complete cross-validation

Gene expression deconvolution in clinical samples

Cell type heterogeneity may have a substantial effect on gene expression profiling of human tissue. Several in silico methods for deconvoluting a gene expression profile into cell-type-specific subprofiles have been published but not widely used. Here, we consider recent methods and the experimental validations available for them. Shen-Orr et al. recently developed an approach called cell-type-specific significance analysis of microarray for deconvoluting gene expression. This method requires the measurement of the proportion of each cell type in each sample and the expression profiles of the heterogeneous samples. It determines how gene expression varies among pre-defined phenotypes for each cell type. Gene expression can vary substantially among cell types and sample heterogeneity can mask the identification of biologically important phenotypic correlations. Consequently, the deconvolution approach can be useful in the analysis of mixtures of cell populations in clinical samples

Application of support vector machines for T-cell epitopes prediction

Motivation: The T-cell receptor, a major histocompatibility complex (MHC) molecule, and a bound antigenic peptide, play major roles in the process of antigen-specific T-cell activation. T-cell recognition was long considered exquisitely specific. Recent data also indicate that it is highly flexible, and one receptor may recognize thousands of different peptides. Deciphering the patterns of peptides that elicit a MHC restricted T-cell response is critical for vaccine development. Results: For the first time we develop a support vector machine (SVM) for T-cell epitope prediction with an MHC type I restricted T-cell clone. Using cross-validation, we demonstrate that SVMs can be trained on relatively small data sets to provide prediction more accurate than those based on previously published methods or on MHC binding. Supplementary information: Data for 203 synthesized peptides is available at http://linus.nci.nih.gov/Data/LAU203_Peptide.pd

A Universal Semantic-Geometric Representation for Robotic Manipulation

Robots rely heavily on sensors, especially RGB and depth cameras, to perceive and interact with the world. RGB cameras record 2D images with rich semantic information while missing precise spatial information. On the other side, depth cameras offer critical 3D geometry data but capture limited semantics. Therefore, integrating both modalities is crucial for learning representations for robotic perception and control. However, current research predominantly focuses on only one of these modalities, neglecting the benefits of incorporating both. To this end, we present Semantic-Geometric Representation (SGR), a universal perception module for robotics that leverages the rich semantic information of large-scale pre-trained 2D models and inherits the merits of 3D spatial reasoning. Our experiments demonstrate that SGR empowers the agent to successfully complete a diverse range of simulated and real-world robotic manipulation tasks, outperforming state-of-the-art methods significantly in both single-task and multi-task settings. Furthermore, SGR possesses the unique capability to generalize to novel semantic attributes, setting it apart from the other methods

Direct observation of ordered configurations of hydrogen adatoms on graphene

Ordered configurations of hydrogen adatoms on graphene have long been proposed, calculated and searched for. Here we report direct observation of several ordered configurations of H adatoms on graphene by scanning tunneling microscopy. On the top side of the graphene plane, H atoms in the configurations appear to stick to carbon atoms in the same sublattice. A gap larger than 0.6 eV in the local density of states of the configurations was revealed by scanning tunneling spectroscopy measurements. These findings can be well explained by density functional theory calculations based on double sided H configurations. In addition, factors that may influence H ordering are discussed

EGFR inhibitor C225 increases the radiosensitivity of human lung squamous cancer cells

Background: The purpose of the present study is to investigate the direct biological effects of the epidermal growth factor receptor (EGFR) inhibitor C225 on the radiosensitivity of human lung squamous cancer cell-H520. H520 cells were treated with different dosage of (60)Co gamma ray irradiation (1.953 Gy/min) in the presence or absence of C225. The cellular proliferation, colony forming capacity, apoptosis, the cell cycle distribution as well as caspase-3 were analyzed in vitro. Results: We found that C225 treatment significantly increased radiosensitivity of H-520 cells to irradiation, and led to cell cycle arrest in G(1) phase, whereas (60)Co gamma ray irradiation mainly caused G(2) phase arrest. H-520 cells thus displayed both the G(1) and G(2) phase arrest upon treatment with C225 in combination with (60)Co gamma ray irradiation. Moreover, C225 treatment significantly increased the apoptosis percentage of H-520 cells (13.91% +/- 1.88%) compared with the control group (5.75% +/- 0.64%, P < 0.05). Conclusion: In this regard, C225 treatment may make H-520 cells more sensitive to irradiation through the enhancement of caspase-3 mediated tumor cell apoptosis and cell cycle arrest.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000284001800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701OncologySCI(E)PubMed2ARTICLE391

Analysis of Gene Expression Data Using BRB-Array Tools

BRB-ArrayTools is an integrated software system for the comprehensive analysis of DNA microarray experiments. It was developed by professional biostatisticians experienced in the design and analysis of DNA microarray studies and incorporates methods developed by leading statistical laboratories. The software is designed for use by biomedical scientists who wish to have access to state-of-the-art statistical methods for the analysis of gene expression data and to receive training in the statistical analysis of high dimensional data. The software provides the most extensive set of tools available for predictive classifier development and complete cross-validation. It offers extensive links to genomic websites for gene annotation and analysis tools for pathway analysis. An archive of over 100 datasets of published microarray data with associated clinical data is provided and BRB-ArrayTools automatically imports data from the Gene Expression Omnibus public archive at the National Center for Biotechnology Information

Angelica sinensis polysaccharide promotes apoptosis by inhibiting JAK/STAT pathway in breast cancer cells

Purpose: To determine whether Angelica polysaccharide (APS) induced apoptosis via regulation of the Janus kinase (JAK)/signal transducers and activators of transcription (STAT) pathway in breast cancer cells. Methods: Human MCF-7 cells were treated with APS. Cell proliferation, apoptosis, expression of apoptotic proteins, and the phosphorylation level of Janus kinase (JAK) and STAT were measured, respectively. For further analysis, MCF-7 cells were transfected with a JAK2 overexpression plasmid or treated with a classical JAK inhibitor, ruxolitinib. Results: Treatment with APS dose-dependently reduced cell proliferation, induced apoptosis, and downregulated the levels of phosphorylated JAK and STAT in MCF-7 cells. JAK inhibitor, ruxolitinib, blocked JAK/STAT pathway and induced cell apoptosis in MCF-7 cells. Besides, JAK2 overexpression reversed the effects of APS on cell viability and apoptosis. Conclusion: The results indicate that polysaccharide isolated from Angelica sinensis promotes apoptosis by inhibiting JAK/STAT pathway in breast cancer cells. Thus, APS may be useful as a potential therapeutic agent for breast cancer

A multi-factorial genetic model for prognostic assessment of high risk melanoma patients receiving adjuvant interferon

Purpose: IFNa was the first cytokine to demonstrate anti-tumor activity in advanced melanoma. Despite the ability of high-dose IFNa reducing relapse and mortality by up to 33%, large majority of patients experience side effects and toxicity which outweigh the benefits. The current study attempts to identify genetic markers likely to be associated with benefit from IFN-a2b treatment and predictive for survival. Experimental design: We tested the association of variants in FOXP3 microsatellites, CTLA4 SNPs and HLA genotype in 284 melanoma patients and their association with prognosis and survival of melanoma patients who received IFNa adjuvant therapy. Results: Univariate survival analysis suggested that patients bearing either the DRB1*15 or HLA-Cw7 allele suffered worse OS while patients bearing either HLA-Cw6 or HLA-B44 enjoyed better OS. DRB1*15 positive patients suffered also worse RFS and conversely HLA-Cw6 positive patients had better RFS. Multivariate analysis revealed that a five-marker genotyping signature was prognostic of OS independent of disease stage. In the multivariate Cox regression model, HLA-B38 (p = 0.021), HLA-C15 (p = 0.025), HLA-C3 (p = 0.014), DRB1*15 (p = 0.005) and CT60*G/G (0.081) were significantly associated with OS with risk ratio of 0.097 (95% CI, 0.013-0.709), 0.387 (95% CI, 0.169-0.889), 0.449 (95% CI, 0.237-0.851), 1.948 (95% CI, 1.221-3.109) and 1.484 (95% IC, 0.953-2.312) respectively. Conclusion: These results suggest that gene polymorphisms relevant to a biological occurrence are more likely to be informative when studied in concert to address potential redundant or conflicting functions that may limit each gene individual contribution. The five markers identified here exemplify this concept though prospective validation in independent cohorts is needed