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      Characterization of m6A regulator‐mediated methylation modification patterns and tumor microenvironment infiltration in acute myeloid leukemia

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          Abstract

          Background

          Previous studies have confirmed the existence of epigenetic regulation of immune responses in acute myeloid leukemia. However, the potential role of RNA N6‐methyladenosine (m6A) remodeling in tumor microenvironment (TME) infiltration remains unclear.

          Methods and Materials

          m6A patterns of 469 AML patients (420 of which provided survival data) based on 18 m6A regulators were systematically evaluated. Based on the expression of 18 m6A regulators, unsupervised agglomerative cluster analysis was applied to recognize the various m6A modification types and to classify patients. We linked these patterns to TME infiltration characteristics and identified three distinct populations of m6A modifications.

          Results

          These three TME cell infiltration patterns are characterized by a high degree of concordance with the three tumor immunophenotypes, which include immunoinflammatory, immunorejection, and immune inert patterns. We showed that assessment of m6A modification patterns within individually neoplasms can forecast the stage of neoplasmic inflammation, TME basal activity, subtype, hereditary mutation, and clinical patient prognosis. Limited low m6Ascore, featuring increased mutational load and immune activation, indicates an inflammatory phenotype of TME with a 5‐year survival rate at 14.4% compared to the high‐m6Ascore group (40.9%).

          Conclusions

          Data from two different cohorts demonstrated that a higher m6Ascore showed a marked therapeutic superiority as well as clinical advantage. Assessing m6A modification patterns in AML patients could improve our knowledge of the TME infiltrative profile as well as directing effective immunotherapeutic approaches.

          Abstract

          TME cell infiltration patterns are characterized by a high degree of concordance with the three tumor immunophenotypes, which include immunoinflammatory, immune rejection, and immune inert patterns. Assessment of m6A revision patterns within individual neoplasms can forecast the stage of neoplasmic inflammation, TME basal activity, subtype, hereditary mutation, and clinical patient prognosis. Limited low m6Ascore, featuring increased mutational load, and immune activation, indicates an inflammatory phenotype of TME with a 5‐year survival rate at 14.4% compared to the high‐m6Ascore group (40.9%).

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          Most cited references35

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          GSVA: gene set variation analysis for microarray and RNA-Seq data

          Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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            Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.

            The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia.at/). Cellular characterization of the immune infiltrates showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning, we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was a superior predictor of response to anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (anti-PD-1) antibodies in two independent validation cohorts. Our findings and this resource may help inform cancer immunotherapy and facilitate the development of precision immuno-oncology.
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              ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

              Summary: Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery. Availability: ConsensusClusterPlus is open source software, written in R, under GPL-2, and available through the Bioconductor project (http://www.bioconductor.org/). Contact: mwilkers@med.unc.edu Supplementary Information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                drwudepei@163.com
                hanyue@suda.edu.cn
                Journal
                Cancer Med
                Cancer Med
                10.1002/(ISSN)2045-7634
                CAM4
                Cancer Medicine
                John Wiley and Sons Inc. (Hoboken )
                2045-7634
                13 January 2022
                March 2022
                : 11
                : 5 ( doiID: 10.1002/cam4.v11.5 )
                : 1413-1426
                Affiliations
                [ 1 ] National Clinical Research Center for Hematologic Diseases Jiangsu Institute of Hematology The First Affiliated Hospital of Soochow University Suzhou China
                [ 2 ] Institute of Blood and Marrow Transplantation Collaborative Innovation Center of Hematology Soochow University Suzhou China
                [ 3 ] Institute of Blood and Marrow Transplantation Suzhou China
                [ 4 ] Key Laboratory of Thrombosis and Hemostasis of Ministry of Health Suzhou China
                [ 5 ] University of Washington Seattle Washington USA
                [ 6 ] State Key Laboratory of Radiation Medicine and Protection Soochow University Suzhou China
                Author notes
                [*] [* ] Correspondence

                Yue Han and Depei Wu, Jiangsu Institute of Hematology, the First Affiliated Hospital of Soochow University, No. 188 Shizi Street, 215000 Suzhou, China.

                Email: hanyue@ 123456suda.edu.cn and drwudepei@ 123456163.com

                Author information
                https://orcid.org/0000-0002-7560-7195
                Article
                CAM44531
                10.1002/cam4.4531
                8894699
                35023630
                13371791-441d-4970-9e58-696397ddfaf6
                © 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 03 December 2021
                : 13 October 2021
                : 04 December 2021
                Page count
                Figures: 7, Tables: 0, Pages: 14, Words: 6115
                Funding
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 81873432,
                Award ID: 82070143
                Funded by: Jiangsu Province of China
                Award ID: BE2021645
                Funded by: Jiangsu Provincial Special Program of Social Development
                Award ID: SBE2016740635
                Funded by: Translational Research Grant of NCRCH
                Award ID: 2021ZKMA01
                Award ID: 2021ZKQA01
                Funded by: Priority Academic Program Development of Jiangsu Higher Education Institutions
                Categories
                Research Article
                Research Articles
                Bioinformatics
                Custom metadata
                2.0
                March 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.2 mode:remove_FC converted:04.03.2022

                Oncology & Radiotherapy
                immunotherapy,leukemia,m6a,microenvironment,mutation burden
                Oncology & Radiotherapy
                immunotherapy, leukemia, m6a, microenvironment, mutation burden

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