Response to Anti-α4β7 Blockade in Patients With Ulcerative Colitis Is Associated With Distinct Mucosal Gene Expression Profiles at Baseline

2021 ◽  
Author(s):  
Maria Gazouli ◽  
Nikolas Dovrolis ◽  
Marilena M Bourdakou ◽  
Michalis Gizis ◽  
Georgios Kokkotis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ulcerative Colitis ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Treatment ◽  
Prognostic Indicators ◽  
Cell Trafficking ◽  
Significant Differential Expression ◽  
Public Data

Abstract Background Improving treatment outcomes with biological therapy is a demanding current need for patients with inflammatory bowel disease. Discovery of pretreatment prognostic indicators of response may facilitate patient selection and increase long-term remission rates. We aimed to identify baseline mucosal gene expression profiles with predictive value for subsequent response to or failure of treatment with the monoclonal antibody against integrin α4β7, vedolizumab, in patients with active ulcerative colitis (UC). Methods Mucosal expression of 84 immunological and inflammatory genes was quantified in RNA extracted from colonic biopsies before vedolizumab commencement and compared between patients with or without response to treatment. Significantly differentiated genes were further validated in a larger patient cohort and within available public data sets, and their functional profiles were studied accordingly. Results In the discovery cohort, we identified 21 genes with a statistically significant differential expression between 54-week responders and nonresponders to vedolizumab. Our validation study allowed us to recognize a “core” mucosal profile that was preserved in both discovery and validation cohorts and in the public database. The applied functional annotation and analysis revealed candidate dysregulated pathways in nonresponders to vedolizumab, including immune cell trafficking, TNF receptor superfamily members mediating noncanonical NF-kB pathway, in addition to interleukin signaling, MyD88 signaling, and toll-like receptors (TLRs) cascade. Conclusions Nonresponse to vedolizumab in UC is associated with specific pretreatment gene-expression mucosal signatures and dysregulation of particular immunological and inflammatory pathways. Baseline mucosal and/or systemic molecular profiling may help in the optimal stratification of patients to receive vedolizumab for active UC.

scholarly journals 909 Differentiation subgroups within LKB1-deficient lung cancer influence both the immune exclusion phenotype and cellular composition of the immune microenvironment

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A954-A955
Author(s):  
Jacob Kaufman ◽  
Doug Cress ◽  
Theresa Boyle ◽  
David Carbone ◽  
Neal Ready ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Plasma Cells ◽  
Immune Cell ◽  
Expression Profiles ◽  
Neuroendocrine Differentiation ◽  
Gene Expression Profiles ◽  
Immune Microenvironment ◽  
Immune Exclusion

BackgroundLKB1 (STK11) is a commonly disrupted tumor suppressor in NSCLC. Its loss promotes an immune exclusion phenotype with evidence of low expression of interferon stimulated genes (ISG) and decreased microenvironment immune infiltration.1 2 Clinically, LKB1 loss induces primary immunotherapy resistance.3 LKB1 is a master regulator of a complex downstream kinase network and has pleiotropic effects on cell biology. Understanding the heterogeneous phenotypes associated with LKB1 loss and their influence on tumor-immune biology will help define and overcome mechanisms of immunotherapy resistance within this subset of lung cancer.MethodsWe applied multi-omic analyses across multiple lung adenocarcinoma datasets2 4–6 (>1000 tumors) to define transcriptional and genetic features enriched in LKB1-deficient lung cancer. Top scoring phenotypes exhibited heterogeneity across LKB1-loss tumors, and were further interrogated to determine association with increased or decreased markers of immune activity. Further, immune cell-types were estimated by Cibersort to identify effects of LKB1 loss on the immune microenvironment. Key conclusions were confirmed by blinded pathology review.ResultsWe show that LKB1 loss significantly affects differentiation patterns, with enrichment of ASCL1-expressing tumors with putative neuroendocrine differentiation. LKB1-deficient neuroendocrine tumors had lower expression of Interferon Stimulated Genes (ISG), MHC1 and MHC2 components, and immune infiltration compared to LKB1-WT and non-neuroendocrine LKB1-deficient tumors (figure 1).The abundances of 22 immune cell types assessed by Cibersort were compared between LKB1-deficient and LKB1-WT tumors. We observe skewing of immune microenvironmental composition by LKB1 loss, with lower abundance of dendritic cells, monocytes, and macrophages, and increased levels of neutrophils and plasma cells (table 1). These trends were most pronounced among tumors with neuroendocrine differentiation, and were concordant across three independent datasets. In a confirmatory subset of 20 tumors, plasma cell abundance was assessed by a blinded pathologist. Pathologist assessment was 100% concordant with Cibersort prediction, and association with LKB1 loss was confirmed (P=0.001).Abstract 909 Figure 1Immune-associated Gene Expression Profiles Affected by Neuroendocrine Differentiation within LKB1-Deficient Lung Adenocarcinomas. Gene expression profiles corresponding to five immune-associated phenotypes are shown with bars indicating average GEP scores for tumors grouped according to LKB1 and neuroendocrine status as indicated. P-values represent results from Student’s T-test between groups as indicated.Abstract 909 Table 1LKB1 Loss Affects Composition of Immune Microenvironment. Values indicate log10 P-values comparing LKB1-loss to LKB1-WT tumors. Positive (red) indicates increased abundance in LKB1 loss. Negative (blue) indicates decreased abundance.ConclusionsWe conclude that tumor differentiation patterns strongly influence the immune microenvironment and immune exclusion characteristics of LKB1-deficient tumors. Neuroendocrine differentiation is associated with the strongest immune exclusion characteristics and should be evaluated clinically for evidence of immunotherapy resistance. A novel observation of increased plasma cell abundance is observed across multiple datasets and confirmed by pathology. Causal mechanisms linking differentiation status to immune activity is not well understood, and the functional role of plasma cells in the immune biology of LKB1-deficient tumors is undefined. These questions warrant further study to inform precision immuno-oncology treatments for these patients.AcknowledgementsThis work was funded by SITC AZ Immunotherapy in Lung Cancer grant (SPS256666) and DOD Lung Cancer Research Program Concept Award (LC180633).ReferencesSkoulidis F, Byers LA, Diao L, et al. Co-occurring genomic alterations define major subsets of KRAS-mutant lung adenocarcinoma with distinct biology, immune profiles, and therapeutic vulnerabilities. Cancer Discov 2015;5:860–77.Schabath MB, Welsh EA, Fulp WJ, et al. Differential association of STK11 and TP53 with KRAS mutation-associated gene expression, proliferation and immune surveillance in lung adenocarcinoma. Oncogene 2016;35:3209–16.Skoulidis F, Goldberg ME, Greenawalt DM, et al. STK11/LKB1 mutations and PD-1 inhibitor resistance in KRAS-mutant lung adenocarcinoma. Cancer Discovery 2018;8:822-835.Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 2014;511:543–50.Chitale D, Gong Y, Taylor BS, et al. An integrated genomic analysis of lung cancer reveals loss of DUSP4 in EGFR-mutant tumors. Oncogene 2009;28:2773–83.Shedden K, Taylor JM, Enkemann SA, et al. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med 2008;14:822–7.

Gene Expression Profiling Reveals Fundamental Differences between Leukemic Stem Cells (Lin-CD34+CD38−) and Mature Blasts (Lin−CD34+CD38+) of Acute Myeloid Leukaemia (AML) Patients.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1377-1377
Author(s):  
Kazem Zibara ◽  
Daniel Pearce ◽  
David Taussig ◽  
Spyros Skoulakis ◽  
Simon Tomlinson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Populations ◽  
Leukemic Stem Cells ◽  
Significant Differential Expression ◽  
New Genes

Abstract The identification of LSC has important implications for future research as well as for the development of novel therapies. The phenotypic description of LSC now enables their purification and should facilitate the identification of genes that are preferentially expressed in these cells compared to normal HSC. However, gene-expression profiling is usually conducted on mononuclear cells of AML patients from either peripheral blood and/or bone marrow. These samples contain a mixture of blasts cells, normal hematopoietic cells and limited number of leukemic stem cells. Thus, this results in a composite profile that obscure differences between LSC and blasts cells with low proliferative potential. The aim of this study was to compare the gene expression profile of highly purified LSC versus leukemic blasts in order to identify genes that might have important roles in driving the leukemia. For this purpose, we analyzed the gene expression profiles of highly purified LSCs (Lin−CD34+CD38−) and more mature blast cells (Lin−CD34+CD38+) isolated from 7 adult AML patients. All samples were previously tested for the ability of the Lin−CD34+CD38− cells but not the Lin−CD34+CD38+ fraction to engraft using the non-obese diabetic/severe combined immuno-deficiency (NOD-SCID) repopulation assay. Affymetrix microarrays (U133A chip), containing 22,283 genes, were used for the analysis. Comparison of Lin-CD34+CD38- cell population to the Lin−CD34+CD38+ cell fraction showed 5421 genes to be expressed in both fractions. Comparative analysis of gene-expression profiles showed statistically significant differential expression of 133 genes between the 2 cell populations. Most of the genes were downregulated in the LSC-enriched fraction, compared to the more differentiated fraction. Gene ontology was used to determine the categories of the up-regulated transcripts. These transcripts, which are selectively expressed, include a number of known genes (e.g., receptors, signalling genes, proliferation and cell cycle genes and transcription factors). These genes play important roles in differentiation, self-renewal, migration and adhesion of HSCs. Among the genes showing the highest differences in expression levels were the following: ribonucleotide reductase M2 polypeptide, thymidylate synthetase, ZW10 interactor, cathepsin G, azurocidin 1, topoisomerase II, CDC20, nucleolar and spindle associated protein 1, Rac GTPase activating protein 1, leukocyte immunoglobulin-like receptor, proliferating cell nuclear antigen, myeloperoxidase, cyclin A1 (RRM2, TYMS, ZWINT, CTSG, AZU1, TOP2A, CDC20, NUSAP1, RACGAP1, LILRB2, PCNA, MPO, CCNA1). Some transcripts detected have not been implicated in HSC functions, and others have unknown function so far. This work identifies new genes that might play a role in leukemogenesis and cancer stem cells. It also leads to a better description and understanding of the molecular phenotypes of these 2 cell populations. Hence, in addition to being a more efficient way to further understand the biology of LSC, this should also provide a more efficient way of identifying new therapeutics and diagnostic targets.

scholarly journals ImmuSort, a database on gene plasticity and electronic sorting for immune cells

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Pingzhang Wang ◽  
Yehong Yang ◽  
Wenling Han ◽  
Dalong Ma
Keyword(s):  
Gene Expression ◽  
Immune Cells ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Experimental Conditions ◽  
Evaluation Scores ◽  
Cell Groups ◽  
Differential Gene ◽  
Disease Associated Genes

Abstract Gene expression is highly dynamic and plastic. We present a new immunological database, ImmuSort. Unlike other gene expression databases, ImmuSort provides a convenient way to view global differential gene expression data across thousands of experimental conditions in immune cells. It enables electronic sorting, which is a bioinformatics process to retrieve cell states associated with specific experimental conditions that are mainly based on gene expression intensity. A comparison of gene expression profiles reveals other applications, such as the evaluation of immune cell biomarkers and cell subsets, identification of cell specific and/or disease-associated genes or transcripts, comparison of gene expression in different transcript variants and probe set quality evaluation. A plasticity score is introduced to measure gene plasticity. Average rank and marker evaluation scores are used to evaluate biomarkers. The current version includes 31 human and 17 mouse immune cell groups, comprising 10,422 and 3,929 microarrays derived from public databases, respectively. A total of 20,283 human and 20,963 mouse genes are available to query in the database. Examples show the distinct advantages of the database. The database URL is http://immusort.bjmu.edu.cn/.

scholarly journals Expression of Cytokine-Coding Genes BMP8B, LEFTY1 and INSL5 Could Distinguish between Ulcerative Colitis and Crohn’s Disease

Genes ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1477
Author(s):  
Daša Jevšinek Skok ◽  
Nina Hauptman ◽  
Miha Jerala ◽  
Nina Zidar
Keyword(s):  
Gene Expression ◽  
Ulcerative Colitis ◽  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Experimental Validation ◽  
Expression Profiles ◽  
Statistical Significance ◽  
Gene Expression Profiles ◽  
Cytokine Gene Expression ◽  
Treatment Modalities

Ulcerative colitis (UC) and Crohn’s disease (CD) are characterized by an imbalance between pro-inflammatory and anti-inflammatory cytokines, interfering with the resolution of inflammation. Due to the crucial role of cytokines, new insights into their profiles in UC and CD would help to improve our understanding of pathogenesis and enable the development of new treatment modalities. We provide an expression profile of cytokines in UC and CD, using bioinformatics approach, and experimental validation of expression of the selected genes. We retrieved data and analyzed the cytokine gene expression profiles of UC and CD. From ten genes with inverse expression, common to CD and UC, BMP8B, LEFTY1 and INSL5 were selected for gene expression experimental validation. Experimentally, BMP8B and INSL5 were down-regulated in both CD and UC but followed the bioinformatics trend. The expression of genes LEFTY1 and BMP8B was statistically significant when comparing UC and CD in colon and the expression of gene LEFTY1 showed statistical significance when CD in ileum and colon were compared. Using the bioinformatics approach and experimental validation, we found differences in expression profiles between UC and CD for INSL5, LEFTY1 and BMP8B. These three promising candidate genes need to be further explored at different levels, such as DNA methylation and protein expression, to provide more evidence on their potential diagnostic role in CD and UC.

scholarly journals Transcriptome profiling of ulcerative colitis mouse model suggests biomarkers and therapeutic targets for human colitis

2020 ◽  
Author(s):  
Reza Yarani ◽  
Oana Palasca ◽  
Nadezhda T. Doncheva ◽  
Christian Anthon ◽  
Bartosz Pilecki ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ulcerative Colitis ◽  
Mouse Model ◽  
High Throughput ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Unknown Etiology ◽  
Small Rna Sequencing ◽  
Complex Nature

1.AbstractBACKGROUND & AIMSUlcerative colitis (UC) is an inflammatory bowel disorder with unknown etiology. Given its complex nature, in vivo studies to investigate its pathophysiology is vital. Animal models play an important role in molecular profiling necessary to pinpoint mechanisms that contribute to human disease. Thus, we aim to identify common conserved gene expression signatures and differentially regulated pathways between human UC and a mouse model hereof, which can be used to identify UC patients from healthy individuals and to suggest novel treatment targets and biomarker candidates.METHODSTherefore, we performed high-throughput total and small RNA sequencing to comprehensively characterize the transcriptome landscape of the most widely used UC mouse model, the dextran sodium sulfate (DSS) model. We used this data in conjunction with publicly available human UC transcriptome data to compare gene expression profiles and pathways.RESULTSWe identified differentially regulated protein-coding genes, long non-coding RNAs and microRNAs from colon and blood of UC mice and further characterized the involved pathways and biological processes through which these genes may contribute to disease development and progression. By integrating human and mouse UC datasets, we suggest a set of 51 differentially regulated genes in UC colon and blood that may improve molecular phenotyping, aid in treatment decisions, drug discovery and the design of clinical trials.CONCLUSIONGlobal transcriptome analysis of the DSS-UC mouse model supports its use as an efficient high-throughput tool to discover new targets for therapeutic and diagnostic applications in human UC through identifying relationships between gene expression and disease phenotype.

scholarly journals BayICE: A hierarchical Bayesian deconvolution model with stochastic search variable selection

2019 ◽  
Author(s):  
An-Shun Tai ◽  
George C. Tseng ◽  
Wen-Ping Hsieh
Keyword(s):  
Gene Expression ◽  
Variable Selection ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
R Package ◽  
Stochastic Search ◽  
Hierarchical Bayesian ◽  
Stochastic Search Variable Selection ◽  
Search Variable

AbstractGene expression deconvolution is a powerful tool for exploring the microenvironment of complex tissues comprised of multiple cell groups using transcriptomic data. Characterizing cell activities for a particular condition has been regarded as a primary mission against diseases. For example, cancer immunology aims to clarify the role of the immune system in the progression and development of cancer through analyzing the immune cell components of tumors. To that end, many deconvolution methods have been proposed for inferring cell subpopulations within tissues. Nevertheless, two problems limit the practicality of current approaches. First, all approaches use external purified data to preselect cell type-specific genes that contribute to deconvolution. However, some types of cells cannot be found in purified profiles and the genes specifically over- or under-expressed in them cannot be identified. This is particularly a problem in cancer studies. Hence, a preselection strategy that is independent from deconvolution is inappropriate. The second problem is that existing approaches do not recover the expression profiles of unknown cells present in bulk tissues, which results in biased estimation of unknown cell proportions. Furthermore, it causes the shift-invariant property of deconvolution to fail, which then affects the estimation performance. To address these two problems, we propose a novel deconvolution approach, BayICE, which employs hierarchical Bayesian modeling with stochastic search variable selection. We develop a comprehensive Markov chain Monte Carlo procedure through Gibbs sampling to estimate cell proportions, gene expression profiles, and signature genes. Simulation and validation studies illustrate that BayICE outperforms existing deconvolution approaches in estimating cell proportions. Subsequently, we demonstrate an application of BayICE in the RNA sequencing of patients with non-small cell lung cancer. The model is implemented in the R package “BayICE” and the algorithm is available for download.

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