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      The immune response‐related mutational signatures and driver genes in non‐small‐cell lung cancer

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          Abstract

          Immune checkpoint blockade ( ICB) therapy has achieved remarkable clinical benefit in non‐small‐cell lung cancer ( NSCLC), but our understanding of biomarkers that predict the response to ICB remain obscure. Here we integrated somatic mutational profile and clinicopathologic information from 113 NSCLC patients treated by ICB ( CTLA‐4/ PD‐1). High tumor mutation burden ( TMB) and neoantigen burden were identified significantly associated with improved efficacy in NSCLC immunotherapy. Furthermore, we identified apolipoprotein B mRNA editing enzyme, catalytic polypeptide‐like (APOBEC) mutational signature was markedly associated with responding of ICB therapy (log‐rank test, P = .001; odds ratio ( OR), 0.18 [95% CI, 0.06‐0.50], P < .001). The association with progression‐free survival remained statistically significant after controlling for age, sex, histological type, smoking, PD‐L1 expression, hypermutation, smoking signature and mismatch repair (MMR) ( HR, 0.30 [95% CI, 0.12‐0.75], P = .010). Combined high TMB with APOBEC signature preferably predict immunotherapy responders in NSCLC cohort. The CIBERSORT algorithm revealed that high APOBEC mutational activity samples were associated with increased infiltration of CD4 memory activated T cells, CD8 + T cells and natural killer (NK) cells, but reduced infiltration of regulatory T cells. Besides, individual genes mutation of IFNGR1 or VTCN1 were only found in responders; however, the PTEN mutation was only found in non‐responders (Fisher's exact test, all P < .05). These findings may be applicable for guiding immunotherapy for patients with NSCLC.

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          Most cited references15

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          Oncotator: cancer variant annotation tool.

          Oncotator is a tool for annotating genomic point mutations and short nucleotide insertions/deletions (indels) with variant- and gene-centric information relevant to cancer researchers. This information is drawn from 14 different publicly available resources that have been pooled and indexed, and we provide an extensible framework to add additional data sources. Annotations linked to variants range from basic information, such as gene names and functional classification (e.g. missense), to cancer-specific data from resources such as the Catalogue of Somatic Mutations in Cancer (COSMIC), the Cancer Gene Census, and The Cancer Genome Atlas (TCGA). For local use, Oncotator is freely available as a python module hosted on Github (https://github.com/broadinstitute/oncotator). Furthermore, Oncotator is also available as a web service and web application at http://www.broadinstitute.org/oncotator/.
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            Loss of PTEN Is Associated with Resistance to Anti-PD-1 Checkpoint Blockade Therapy in Metastatic Uterine Leiomyosarcoma.

            Response to immune checkpoint blockade in mesenchymal tumors is poorly characterized, but immunogenomic dissection of these cancers could inform immunotherapy mediators. We identified a treatment-naive patient who has metastatic uterine leiomyosarcoma and has experienced complete tumor remission for >2 years on anti-PD-1 (pembrolizumab) monotherapy. We analyzed the primary tumor, the sole treatment-resistant metastasis, and germline tissue to explore mechanisms of immunotherapy sensitivity and resistance. Both tumors stained diffusely for PD-L2 and showed sparse PD-L1 staining. PD-1(+) cell infiltration significantly decreased in the resistant tumor (p = 0.039). Genomically, the treatment-resistant tumor uniquely harbored biallelic PTEN loss and had reduced expression of two neoantigens that demonstrated strong immunoreactivity with patient T cells in vitro, suggesting long-lasting immunological memory. In this near-complete response to PD-1 blockade in a mesenchymal tumor, we identified PTEN mutations and reduced expression of genes encoding neoantigens as potential mediators of resistance to immune checkpoint therapy.
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              Predictive Markers for the Efficacy of Anti-PD-1/PD-L1 Antibodies in Lung Cancer.

              Blockade of the programmed death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) axis using antibodies against the associated receptors and ligands has yielded good clinical responses and improved overall survival in patients with non-small cell lung cancer (NSCLC). Once patients show a response to anti-PD-1/PD-L1 antibody, the median duration of response is often longer than that achieved using existing cytotoxic agents and even some molecular targeted agents. However, the response rates to these antibodies are only 15% to 20% in unselected patients with NSCLC and the cost of this therapy is high. Therefore, there is an urgent need for effective predictive biomarkers to identify patients likely to benefit. PD-L1 expression, which can be detected by immunohistochemical analysis, is a rational biomarker for selecting responders to anti-PD-1/PD-L1 antibody treatments, and this selection method has been introduced into clinical practice. However, the response rate to anti-PD-1/PD-L1 antibody in PD-L1-expressing patients with NSCLC is only 15% to 45%, response can occur in PD-L1-negative patients, and predictability based on PD-L1 expression may differ between nonsquamous NSCLC and squamous cell NSCLC. In addition, the methods of immunohistochemical analysis and evaluation of its results differ for different anti-PD-1/PD-L1 agents. This article reviews the existing data on predictive markers for the efficacy of anti-PD-1/PD-L1 antibodies in NSCLC.
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                Author and article information

                Contributors
                wangxinmarine@126.com
                yolandadalee@yahoo.com
                Journal
                Cancer Sci
                Cancer Sci
                10.1111/(ISSN)1349-7006
                CAS
                Cancer Science
                John Wiley and Sons Inc. (Hoboken )
                1347-9032
                1349-7006
                23 July 2019
                August 2019
                : 110
                : 8 ( doiID: 10.1111/cas.v110.8 )
                : 2348-2356
                Affiliations
                [ 1 ] Clinical Epidemiology Unit Qilu Hospital of Shandong University Jinan China
                [ 2 ] Key Laboratory of Cancer Prevention and Therapy of Tianjin Department of Epidemiology and Biostatistics National Clinical Research Center for Cancer Tianjin Medical University Cancer Institute and Hospital Tianjin China
                [ 3 ] Key Laboratory of Cancer Prevention and Therapy Department of Breast Cancer Pathology and Research Laboratory National Clinical Research Center for Cancer Tianjin Medical University Cancer Institute and Hospital Tianjin China
                [ 4 ] Clinical Laboratory Affiliated Hospital of Binzhou Medical College Binzhou China
                [ 5 ] Institute of Pathology and Southwest Cancer Center Southwest Hospital Army Medical University (Third Military Medical University) Chongqing China
                [ 6 ] Department of Epidemiology and Biostatistics First Affiliated Hospital Army Medical University Chongqing China
                [ 7 ] Department of Food safety and Toxicology Shanghai Jiao Tong University School of Medicine Shanghai China
                Author notes
                [*] [* ] Correspondence

                Xin Wang, Department of Epidemiology and Biostatistics, First Affiliated Hospital, Army Medical University, 30 Gaotanyan Street Shapingba District, Chongqing, China.

                Email: wangxinmarine@ 123456126.com

                Xue Li, Department of Food Safety and Toxicology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200240, China.

                Email: yolandadalee@ 123456yahoo.com

                Author information
                https://orcid.org/0000-0001-9325-3194
                Article
                CAS14113
                10.1111/cas.14113
                6676111
                31222843
                2daa428a-a990-4211-a3b9-3035dffa7caf
                © 2019 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 17 February 2019
                : 01 June 2019
                : 01 June 2019
                Page count
                Figures: 4, Tables: 0, Pages: 9, Words: 5557
                Funding
                Funded by: Science and technology planning project of Binzhou Medical college
                Award ID: BY2017KJ02
                Funded by: Top talent training program of the first affiliated hospital of PLA Army Medical University
                Award ID: SWH2018BJKJ‐12
                Funded by: National Natural Science Foundation of China
                Award ID: 81803215
                Categories
                Original Article
                Original Articles
                Basic and Clinical Immunology
                Custom metadata
                2.0
                cas14113
                August 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.7 mode:remove_FC converted:02.08.2019

                Oncology & Radiotherapy
                apobec signature,immune checkpoint blockade,neoantigen burden,non‐small‐cell lung cancer,tumor mutation burden

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