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      Mutational burden and chromosomal aneuploidy synergistically predict survival from radiotherapy in non-small cell lung cancer

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          Abstract

          Therapeutic radiation can result in substantially different survival outcomes for patients with non-small cell lung cancer (NSCLC). Measures for identification of patients who can benefit most throughout radiotherapy remain limited. In this retrospective study, survival analysis was performed based on a discovery cohort from TCGA and a validation cohort from three independent hospitals. Tumor mutational burden (TMB) and chromosomal aneuploidy (ANE) were derived from the whole exome sequencing (WES) data from treatment-naïve tumors. Integrated risk scores were derived from TMB and ANE by a multivariate Cox proportional hazards model. TCGA reveal that TMB and ANE are associated positively and negatively, respectively, with survival throughout radiotherapy. Additionally, the synergistically predictive significance of these two genomic alterations, in differing responders and non-responders to radiotherapy is identified. These biomarkers may have clinical potential to improve personalized treatment management by rationally identifying highly likely responders to therapeutic radiation in patients with NSCLC.

          Abstract

          Here, the authors use whole exome sequencing to determine the effect of tumor mutational burden (TMB) and chromosomal aneuploidy (ANE) on survival outcomes of non-small cell lung cancer patients undergoing radiotherapy. The authors find that a higher TMB and lower ANE were correlated with better survival outcomes and that these factors interact synergistically.

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          Most cited references29

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          ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

          High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
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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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              The Immune Landscape of Cancer

              We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.
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                Author and article information

                Contributors
                wany@email.unc.edu
                chxie_65@whu.edu.cn
                bo.zhu@tmmu.edu.cn
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                29 January 2021
                29 January 2021
                2021
                : 4
                : 131
                Affiliations
                [1 ]GRID grid.417298.1, ISNI 0000 0004 1762 4928, Department of Oncology, Xinqiao Hospital, , Army Medical University, ; Chongqing, 400037 China
                [2 ]GRID grid.410570.7, ISNI 0000 0004 1760 6682, Key Laboratory of Immunotherapy, Xinqiao Hospital, , The Third Military Medical University, ; Chongqing, 400037 China
                [3 ]GRID grid.33199.31, ISNI 0000 0004 0368 7223, Department of Oncology, Tongji Hospital, , Huazhong University of Science and Technology, ; Wuhan, Hubei China
                [4 ]GRID grid.413247.7, Department of Radiation and Medical Oncology, , Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological Behaviors, Hubei Cancer Clinical Study Center, ; Wuhan, 430071 Hubei China
                [5 ]GRID grid.413247.7, Department of Biological Repositories, , Zhongnan Hospital of Wuhan University, ; Wuhan, China
                [6 ]GRID grid.33199.31, ISNI 0000 0004 0368 7223, Department of Thoracic Surgery, Tongji Hospital, , Huazhong University of Science and Technology, ; Wuhan, 430071 Hubei China
                [7 ]GeneCast Biotechnology Co., Ltd., 35 North Haidian Rd, HealthWork Suite 901, Beijing, China
                [8 ]GRID grid.10698.36, ISNI 0000000122483208, Department of Microbiology and Immunology, Lineberger Comprehensive Cancer Centre, , University of North Carolina at Chapel Hill, ; NC, USA
                Author information
                http://orcid.org/0000-0001-8192-7630
                http://orcid.org/0000-0003-4153-1265
                http://orcid.org/0000-0003-1377-9685
                http://orcid.org/0000-0003-4346-9717
                http://orcid.org/0000-0001-6623-9864
                http://orcid.org/0000-0003-0224-8512
                Article
                1657
                10.1038/s42003-021-01657-6
                7846582
                33514859
                90a092f2-401f-4d68-a0ca-d0a029a310f9
                © The Author(s) 2021

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 4 March 2020
                : 4 January 2021
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                predictive markers,translational immunology
                predictive markers, translational immunology

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