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      Association of Long Noncoding RNA Biomarkers With Clinical Immune Subtype and Prediction of Immunotherapy Response in Patients With Cancer

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          Abstract

          This cohort study explores long noncoding RNA–based immune subtypes associated with survival and response to cancer immunotherapy and presents a novel long noncoding RNA score for immunotherapy prediction using computational algorithms.

          Key Points

          Question

          Are long noncoding RNAs (lncRNAs) associated with immune molecular classification and clinical outcomes of cancer immunotherapy?

          Findings

          This cohort study of 348 patients with bladder cancer from the IMvigor210 trial and 71 patients with melanoma from The Cancer Genome Analysis identified 4 distinct classes with different immunotherapeutic overall survival and response. An lncRNA score was developed that also was associated with survival and immunotherapy response.

          Meaning

          This study identifies novel lncRNA-based immune classes in cancer immunotherapy and provides an lncRNA score for integration into multiomic panels for precision immunotherapy.

          Abstract

          Importance

          Long noncoding RNAs (lncRNAs) are involved in innate and adaptive immunity in cancer by mediating the functional state of immunologic cells, pathways, and genes. However, whether lncRNAs are associated with immune molecular classification and clinical outcomes of cancer immunotherapy is largely unknown.

          Objectives

          To explore lncRNA-based immune subtypes associated with survival and response to cancer immunotherapy and to present a novel lncRNA score for immunotherapy prediction using computational algorithms.

          Design, Setting, and Participants

          In this cohort study, an individual patient analysis based on a phase 2, single-arm clinical trial and multicohort was performed from June 25 through September 30, 2019. Data are from the phase 2 IMvigor210 trial and from The Cancer Genome Atlas (TCGA). The study analyzed lncRNA and genomic data of 348 patients with bladder cancer from the IMvigor210 trial and 71 patients with melanoma from TCGA who were treated with immunotherapy. In addition, a pancancer multicohort that included 2951 patients was obtained from TCGA.

          Main Outcomes and Measures

          The primary end point was overall survival (OS).

          Results

          Among 348 patients from the IMvigor210 trial (272 [78.2%] male) and 71 patients with melanoma from TCGA (mean [SD] age, 58.3 [13.4] years; 37 [52.1%] female), 4 distinct classes with statistically significant differences in OS (median months, not reached vs 9.6 vs 8.1 vs 6.7 months; P = .002) were identified. The greatest OS benefit was obtained in the immune-active class, as characterized by the immune-functional lncRNA signature and high CTL infiltration. Patients with low vs high lncRNA scores had statistically significantly longer OS (hazard ratio, 0.32; 95% CI, 0.24-0.42; P < .001) in the IMvigor210 trial and across various cancer types. The lncRNA score was associated with immunotherapeutic OS benefit in the IMvigor210 trial cohort (area under the curve [AUC], 0.79 at 12 months and 0.77 at 20 months) and in TCGA melanoma cohort (AUC, 0.87 at 24 months), superior tumor alteration burden, programmed cell death ligand 1 (PD-L1) expression, and cytotoxic T-lymphocyte (CTL) infiltration. Addition of the lncRNA score to the combination of tumor alteration burden, PD-L1 expression, and CTL infiltration to build a novel multiomics algorithm correlated more strongly with OS in the IMvigor210 trial cohort (AUC, 0.81 at 12 months and 0.80 at 20 months).

          Conclusions and Relevance

          This study identifies novel lncRNA-based immune classes in cancer immunotherapy and recommends immunotherapy for patients in the immune-active class. In addition, the study recommends that the lncRNA score should be integrated into multiomic panels for precision immunotherapy.

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          Most cited references6

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          NKILA lncRNA promotes tumor immune evasion by sensitizing T cells to activation-induced cell death

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            Quantifying tumor-infiltrating immune cells from transcriptomics data

            By exerting pro- and anti-tumorigenic actions, tumor-infiltrating immune cells can profoundly influence tumor progression, as well as the success of anti-cancer therapies. Therefore, the quantification of tumor-infiltrating immune cells holds the promise to unveil the multi-faceted role of the immune system in human cancers and its involvement in tumor escape mechanisms and response to therapy. Tumor-infiltrating immune cells can be quantified from RNA sequencing data of human tumors using bioinformatics approaches. In this review, we describe state-of-the-art computational methods for the quantification of immune cells from transcriptomics data and discuss the open challenges that must be addressed to accurately quantify immune infiltrates from RNA sequencing data of human bulk tumors. Electronic supplementary material The online version of this article (10.1007/s00262-018-2150-z) contains supplementary material, which is available to authorized users.
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              Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

              Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web based survey and revised during a three day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).To encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site (www.annals.org) and will be also published in BJOG, British Journal of Cancer, British Journal of Surgery, BMC Medicine, The BMJ, Circulation, Diabetic Medicine, European Journal of Clinical Investigation, European Urology, and Journal of Clinical Epidemiology. The authors jointly hold the copyright of this article. An accompanying explanation and elaboration article is freely available only on www.annals.org; Annals of Internal Medicine holds copyright for that article.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                7 April 2020
                April 2020
                7 April 2020
                : 3
                : 4
                : e202149
                Affiliations
                [1 ]Department of Medical Oncology, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
                [2 ]Guangdong Medical University, Zhanjiang, China
                [3 ]Department of Medical Oncology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
                [4 ]Breast Tumor Centre, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
                Author notes
                Article Information
                Accepted for Publication: February 10, 2020.
                Published: April 7, 2020. doi:10.1001/jamanetworkopen.2020.2149
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Yu Y et al. JAMA Network Open.
                Corresponding Authors: Herui Yao, MD, PhD, Department of Medical Oncology ( yaoherui@ 123456mail.sysu.edu.cn ), and Erwei Song, MD, PhD, Breast Tumor Centre ( songew@ 123456mail.sysu.edu.cn ), Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 Yanjiang W Rd, Guangzhou 510120, China.
                Author Contributions: Drs Yu, W. Zhang, Li, and Chen are co–first authors. Drs Yu and Yao had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Yu, W. Zhang, Li, Chen, Yao, Song.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Yu, W. Zhang, Li, Chen, Ou, He, Y. Zhang.
                Critical revision of the manuscript for important intellectual content: Liu, Yao, Song.
                Statistical analysis: All authors.
                Obtained funding: Li, Yao.
                Administrative, technical, or material support: Yao, Song.
                Supervision: Yao, Song.
                Conflict of Interest Disclosures: None reported.
                Funding/Support: This study was supported by grants from the National Science and Technology Major Project (2020ZX09201021), the National Natural Science Foundation of China (81572596, 81972471, U1601223), the Natural Science Foundation of Guangdong Province (2017A030313828), the Guangzhou Science and Technology Major Program (201704020131), the Sun Yat-sen University Clinical Research 5010 Program (2018007), the Sun Yat-sen Clinical Research Cultivating Program (SYS-C-201801), the Guangdong Science and Technology Department (2017B030314026), and the Special Funds for the Cultivation of Guangdong College Students’ Scientific and Technological Innovation (pdjh2019a0212).
                Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Meeting Presentation: Preliminary results of this study were presented in part as a mini–oral presentation at the ESMO Immuno-Oncology Congress 2019; December 13, 2019; Geneva, Switzerland.
                Article
                zoi200114
                10.1001/jamanetworkopen.2020.2149
                7139278
                32259264
                30a3cd11-d551-4a05-89e0-a00917d0c7ec
                Copyright 2020 Yu Y et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 9 October 2019
                : 10 February 2020
                Categories
                Research
                Original Investigation
                Online Only
                Immunology

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