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      Anti-tumor activity of a T-helper 1 multiantigen vaccine in a murine model of prostate cancer

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          Abstract

          Prostate cancer is one of the few malignancies that includes vaccination as a treatment modality. Elements of an effective cancer vaccine should include the ability to elicit a Type I T-cell response and target multiple antigenic proteins expressed early in the disease. Using existing gene datasets encompassing normal prostate tissue and tumors with Gleason Score ≤ 6 and ≥ 8, 10 genes were identified that were upregulated and conserved in prostate cancer regardless of the aggressiveness of disease. These genes encoded proteins also expressed in prostatic intraepithelial neoplasia. Putative Class II epitopes derived from these proteins were predicted by a combination of algorithms and, using human peripheral blood, epitopes which selectively elicited IFN-γ or IL-10 dominant antigen specific cytokine secretion were determined. Th1 selective epitopes were identified for eight antigens. Epitopes from three antigens elicited Th1 dominant immunity in mice; PSMA, HPN, and AMACR. Each single antigen vaccine demonstrated significant anti-tumor activity inhibiting growth of implanted Myc-Cap cells after immunization as compared to control. Immunization with the combination of antigens, however, was superior to each alone in controlling tumor growth. When vaccination occurred simultaneously to tumor implant, multiantigen immunized mice had significantly smaller tumors than controls (p = 0.002) and a significantly improved overall survival (p = 0.0006). This multiantigen vaccine shows anti-tumor activity in a murine model of prostate cancer.

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            NCBI GEO: archive for functional genomics data sets—update

            The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.
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              Adjusting batch effects in microarray expression data using empirical Bayes methods.

              Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
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                Author and article information

                Contributors
                dcecil@uw.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 August 2022
                10 August 2022
                2022
                : 12
                : 13618
                Affiliations
                [1 ]GRID grid.34477.33, ISNI 0000000122986657, Cancer Vaccine Institute, , University of Washington, ; 850 Republican Street, Brotman Bld., 2nd Floor, Box 358050, Seattle, WA 98195-8050 USA
                [2 ]GRID grid.497530.c, ISNI 0000 0004 0389 4927, Janssen Research and Development LLC, ; Spring House, PA USA
                [3 ]Janssen Vaccines and Prevention, Leiden, The Netherlands
                Article
                17950
                10.1038/s41598-022-17950-1
                9365795
                35948756
                1fd98183-15c9-4d32-a44a-dc2fdab2c54c
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 January 2022
                : 3 August 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100008897, Janssen Pharmaceuticals;
                Funded by: Athena Professorship for Breast Cancer Research
                Funded by: American Cancer Society
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                cancer models,tumour immunology
                Uncategorized
                cancer models, tumour immunology

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