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      Long-term survival of patients with stage III colon cancer treated with VRP-CEA(6D), an alphavirus vector that increases the CD8+ effector memory T cell to Treg ratio

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          Abstract

          Background

          There remains a significant need to eliminate the risk of recurrence of resected cancers. Cancer vaccines are well tolerated and activate tumor-specific immune effectors and lead to long-term survival in some patients. We hypothesized that vaccination with alphaviral replicon particles encoding tumor associated antigens would generate clinically significant antitumor immunity to enable prolonged overall survival (OS) in patients with both metastatic and resected cancer.

          Methods

          OS was monitored for patients with stage IV cancer treated in a phase I study of virus-like replicon particle (VRP)-carcinoembryonic antigen (CEA), an alphaviral replicon particle encoding a modified CEA. An expansion cohort of patients (n=12) with resected stage III colorectal cancer who had completed their standard postoperative adjuvant chemotherapy was administered VRP-CEA every 3 weeks for a total of 4 immunizations. OS and relapse-free survival (RFS) were determined, as well as preimmunization and postimmunization cellular and humoral immunity.

          Results

          Among the patients with stage IV cancer, median follow-up was 10.9 years and 5-year survival was 17%, (95% CI 6% to 33%). Among the patients with stage III cancer, the 5-year RFS was 75%, (95%CI 40% to 91%); no deaths were observed. At a median follow-up of 5.8 years (range: 3.9–7.0 years) all patients were still alive. All patients demonstrated CEA-specific humoral immunity. Patients with stage III cancer had an increase in CD8 +T EM (in 10/12) and decrease in FOXP3 +Tregs (in 10/12) following vaccination. Further, CEA-specific, IFNγ-producing CD8+granzyme B+T CM cells were increased.

          Conclusions

          VRP-CEA induces antigen-specific effector T cells while decreasing Tregs, suggesting favorable immune modulation. Long-term survivors were identified in both cohorts, suggesting the OS may be prolonged.

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          Most cited references31

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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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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

              A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.
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                Author and article information

                Journal
                J Immunother Cancer
                J Immunother Cancer
                jitc
                jitc
                Journal for Immunotherapy of Cancer
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2051-1426
                2020
                11 November 2020
                : 8
                : 2
                : e001662
                Affiliations
                [1 ]departmentSurgery , Duke University School of Medicine , Durham, North Carolina, USA
                [2 ]departmentBiostatistics and Bioinformatics , Duke University School of Medicine , Durham, North Carolina, USA
                [3 ]departmentBiostatistics , Duke Cancer Institute , Durham, North Carolina, USA
                [4 ]departmentMedicine , Duke University School of Medicine , Durham, North Carolina, USA
                [5 ]HDT Bio Corp , Seattle, Washington, USA
                [6 ]VLP Therapeutics , Gaithersburg, Maryland, USA
                [7 ]departmentMedicine , University of Washington , Seattle, Washington, USA
                [8 ]departmentPathology , Duke University School of Medicine , Durham, North Carolina, USA
                [9 ]departmentImmunology , Duke University School of Medicine , Durham, North Carolina, USA
                Author notes
                [Correspondence to ] Dr Herbert Kim Lyerly; kim.lyerly@ 123456duke.edu
                Author information
                http://orcid.org/0000-0002-4872-6711
                http://orcid.org/0000-0001-6549-8207
                http://orcid.org/0000-0002-0063-4770
                Article
                jitc-2020-001662
                10.1136/jitc-2020-001662
                7661359
                33177177
                3c8926b1-5d41-44cb-9698-3971f57bbdd6
                © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See https://creativecommons.org/licenses/by/4.0/.

                History
                : 20 October 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: PO1-CA078673
                Categories
                Clinical/Translational Cancer Immunotherapy
                1506
                2435
                Original research
                Custom metadata
                unlocked

                cd4-cd8 ratio,clinical trials as topic,immunotherapy,vaccination,adaptive immunity

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