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      Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade : A Systematic Review and Meta-analysis

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          Key Points

          Question

          What is the relative diagnostic accuracy of different biomarker assay modalities in predicting clinical response to anti–PD-1/PD-L1 (programmed cell death 1/programmed cell death ligand 1) therapy?

          Findings

          In this systematic review and meta-analysis involving tumor specimens from 8135 patients, multiplex immunohistochemistry/immunofluorescence (mIHC/IF) had significantly higher diagnostic accuracy than PD-L1 IHC, tumor mutational burden, or gene expression profiling in predicting clinical response to anti–PD-1/PD-L1 therapy and was similar to multimodality cross-platform composite approaches, such as PD-L1 IHC + tumor mutational burden.

          Meaning

          Multiplex immunohistochemistry/IF facilitates quantification of protein coexpression on immune cell subsets and assessment of their spatial arrangements; initial findings suggest that mIHC/IF has diagnostic accuracy comparable to multimodality cross-platform composite approaches in predicting response to anti–PD-1/PD-L1.

          Abstract

          This systematic review and meta-analysis assesses the diagnostic accuracy of PD-L1 immunohistochemistry, tumor mutational burden, gene expression profiling, and multiplex immunohistochemistry/immunofluorescence assays for determining treatment response to PD-1/PD-L1 checkpoint blockade.

          Abstract

          Importance

          PD-L1 (programmed cell death ligand 1) immunohistochemistry (IHC), tumor mutational burden (TMB), gene expression profiling (GEP), and multiplex immunohistochemistry/immunofluorescence (mIHC/IF) assays have been used to assess pretreatment tumor tissue to predict response to anti–PD-1/PD-L1 therapies. However, the relative diagnostic performance of these modalities has yet to be established.

          Objective

          To compare studies that assessed the diagnostic accuracy of PD-L1 IHC, TMB, GEP, and mIHC/IF in predicting response to anti–PD-1/PD-L1 therapy.

          Evidence Review

          A search of PubMed (from inception to June 2018) and 2013 to 2018 annual meeting abstracts from the American Association for Cancer Research, American Society of Clinical Oncology, European Society for Medical Oncology, and Society for Immunotherapy of Cancer was conducted to identify studies that examined the use of PD-L1 IHC, TMB, GEP, and mIHC/IF assays to determine objective response to anti–PD-1/PD-L1 therapy. For PD-L1 IHC, only clinical trials that resulted in US Food and Drug Administration approval of indications for anti–PD-1/PD-L1 were included. Studies combining more than 1 modality were also included. Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines were followed. Two reviewers independently extracted the clinical outcomes and test results for each individual study.

          Main Outcomes and Measures

          Summary receiver operating characteristic (sROC) curves; their associated area under the curve (AUC); and pooled sensitivity, specificity, positive and negative predictive values (PPV, NPV), and positive and negative likelihood ratios (LR+ and LR−) for each assay modality.

          Results

          Tumor specimens representing over 10 different solid tumor types in 8135 patients were assayed, and the results were correlated with anti–PD-1/PD-L1 response. When each modality was evaluated with sROC curves, mIHC/IF had a significantly higher AUC (0.79) compared with PD-L1 IHC (AUC, 0.65, P < .001), GEP (AUC, 0.65, P = .003), and TMB (AUC, 0.69, P = .049). When multiple different modalities were combined such as PD-L1 IHC and/or GEP + TMB, the AUC drew nearer to that of mIHC/IF (0.74). All modalities demonstrated comparable NPV and LR−, whereas mIHC/IF demonstrated higher PPV (0.63) and LR+ (2.86) than the other approaches.

          Conclusions and Relevance

          In this meta-analysis, tumor mutational burden, PD-L1 IHC, and GEP demonstrated comparable AUCs in predicting response to anti–PD-1/PD-L1 treatment. Multiplex immunohistochemistry/IF and multimodality biomarker strategies appear to be associated with improved performance over PD-L1 IHC, TMB, or GEP alone. Further studies with mIHC/IF and composite approaches with a larger number of patients will be required to confirm these findings. Additional study is also required to determine the most predictive analyte combinations and to determine whether biomarker modality performance varies by tumor type.

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          Most cited references19

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          Is Open Access

          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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            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Meta-DiSc: a software for meta-analysis of test accuracy data

            Background Systematic reviews and meta-analyses of test accuracy studies are increasingly being recognised as central in guiding clinical practice. However, there is currently no dedicated and comprehensive software for meta-analysis of diagnostic data. In this article, we present Meta-DiSc, a Windows-based, user-friendly, freely available (for academic use) software that we have developed, piloted, and validated to perform diagnostic meta-analysis. Results Meta-DiSc a) allows exploration of heterogeneity, with a variety of statistics including chi-square, I-squared and Spearman correlation tests, b) implements meta-regression techniques to explore the relationships between study characteristics and accuracy estimates, c) performs statistical pooling of sensitivities, specificities, likelihood ratios and diagnostic odds ratios using fixed and random effects models, both overall and in subgroups and d) produces high quality figures, including forest plots and summary receiver operating characteristic curves that can be exported for use in manuscripts for publication. All computational algorithms have been validated through comparison with different statistical tools and published meta-analyses. Meta-DiSc has a Graphical User Interface with roll-down menus, dialog boxes, and online help facilities. Conclusion Meta-DiSc is a comprehensive and dedicated test accuracy meta-analysis software. It has already been used and cited in several meta-analyses published in high-ranking journals. The software is publicly available at .
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              Programmed death ligand-1 expression in non-small cell lung cancer.

              Recent strategies targeting the interaction of the programmed cell death ligand-1 (PD-L1, B7-H1, CD274) with its receptor, PD-1, resulted in promising activity in early phase clinical trials. In this study, we used various antibodies and in situ mRNA hybridization to measure PD-L1 in non-small cell lung cancer (NSCLC) using a quantitative fluorescence (QIF) approach to determine the frequency of expression and prognostic value in two independent populations. A control tissue microarray (TMA) was constructed using PD-L1-transfected cells, normal human placenta and known PD-L1-positive NSCLC cases. Only one of four antibodies against PD-L1 (5H1) validated for specificity on this TMA. In situ PD-L1 mRNA using the RNAscope method was similarly validated. Two cohorts of NSCLC cases in TMAs including 340 cases from hospitals in Greece and 204 cases from Yale University were assessed. Tumors showed PD-L1 protein expression in 36% (Greek) and 25% (Yale) of the cases. PD-L1 expression was significantly associated with tumor-infiltrating lymphocytes in both cohorts. Patients with PD-L1 (both protein and mRNA) expression above the detection threshold showed statistically significant better outcome in both series (log-rank P=0.036 and P=0.027). Multivariate analysis showed that PD-L1 expression was significantly associated with better outcome independent of histology. Measurement of PD-L1 requires specific conditions and some commercial antibodies show lack of specificity. Expression of PD-L1 protein or mRNA is associated with better outcome. Further studies are required to determine the value of this marker in prognosis and prediction of response to treatments targeting this pathway.
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                Author and article information

                Journal
                JAMA Oncol
                JAMA Oncol
                JAMA Oncol
                JAMA Oncology
                American Medical Association
                2374-2437
                2374-2445
                18 July 2019
                August 2019
                18 July 2020
                : 5
                : 8
                : 1195-1204
                Affiliations
                [1 ]Department of Dermatology, Johns Hopkins Medical Institutions, Baltimore, Maryland
                [2 ]Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
                [3 ]Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
                [4 ]Department of Medicine, Division of Hematology-Oncology, Northwestern University Medical Center, and Robert H. Lurie Cancer Center, Chicago, Illinois
                [5 ]Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, Maryland
                [6 ]Division of Biostatistics & Bioinformatics at the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins Medical Institutions, Baltimore, Maryland
                [7 ]Akoya Biosciences, Hopkinton, Massachusetts
                [8 ]Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, Maryland
                [9 ]Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins Medical Institutions, Baltimore, Maryland
                Author notes
                Article Information
                Accepted for Publication: March 15, 2019.
                Corresponding Author: Janis M. Taube, MD, Division of Dermatopathology Johns Hopkins University, 600 N Wolfe St, Blalock Building Room 907, Baltimore, MD 21287 ( jtaube1@ 123456jhmi.edu ).
                Published Online: July 18, 2019. doi:10.1001/jamaoncol.2019.1549
                Author Contributions: Dr Taube and Mr Lu 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.
                Study concept and design: Lu, Rimm, Hoyt, Pardoll, Taube.
                Acquisition, analysis, or interpretation of data: Lu, Stein, D. Wang, Bell, Johnson, Sosman, Schalper, Anders, H. Wang, Hoyt, Danilova, Taube.
                Drafting of the manuscript: Lu, Stein, D. Wang, Johnson, Sosman, Danilova, Taube.
                Critical revision of the manuscript for important intellectual content: All authors.
                Statistical analysis: Lu, Stein, D. Wang, H. Wang, Danilova.
                Administrative, technical, or material support: Lu, Stein, D. Wang, Schalper, Anders, Taube .
                Study supervision: Rimm, Hoyt, Pardoll, Danilova, Taube.
                Conflict of Interest Disclosures: Dr Rimm reports personal fees from and serves on the advisory board of Amgen, personal fees from Bristol-Myers Squibb, Merck, GlaxoSmithKline, Daiichi Sankyo, Konica Minolta, personal fees from and serves on the advisory board of Cell Signaling Technology, grants and personal fees from Cepheid, AstraZeneca, NextCure, Ultivue, Ventana, Perkin Elmer, grants from Lilly, patents including AQUA software licensing and Navigate Biopharma (Yale owned patent). Dr Johnson serves on the advisory board of Array Biopharma, Bristol-Myers Squibb, Genoptix, Incyte, Merck, and Novartis; receives grant funding from Bristol-Myers Squibb and Incyte; patent pending for using MHC-II as a biomarker for immunotherapy responses. Dr Schalper reports grant funding from Navigate Biopharma, Vasculox, Tesaro, Takeda, Surface Oncology, and Bristol-Myers Squibb; receives grant funding and consulting fees from Celgene, Shattuck Labs, Pierre Fabre, Moderna Therapeutics, AstraZeneca, AbbVie, and Merck; and receives speaking fees from Merck and Fluidigm. Dr Anders receives grant funding from FLX Bio and Five Prime Therapeutics, and is a consultant for Bristol-Myers Squibb, Merck, and AstraZeneca. Mr Hoyt is employed by Akoya Biosciences and owns Akoya Biosciences stock and stock options. Dr. Pardoll reported other support from Aduro Biotech, Amgen, Bayer, Camden Partners, DNAtrix, Dracen, Dynavax, Five Prime, FLX Bio, Immunomic, Janssen, Merck, Rock Springs Capital, Potenza, Tizona, Trieza, and WindMil during the conduct of the study; grants from Astra Zeneca, Medimmune/Amplimmune, and Compugen; grants and other support from ERvaxx and Potenza. Dr Taube reports nonfinancial support from Akoya during the conduct of the study; grants and personal fees from Bristol-Myers Squibb, personal fees from Merck, Astra Zeneca, and Amgen outside the submitted work; equipment and reagents from Akoya Biosciences, and a patent pending related to image processing of mIF/IHC images. No other disclosures were reported.
                Funding/Support: This work was supported by the Melanoma Research Alliance (Dr Taube); Harry J. Lloyd Trust (Dr Taube); the Emerson Collective (Dr Taube); Moving for Melanoma of Delaware (Dr Taube); Bristol-Myers Squibb (Drs Taube, Stein, Pardoll, and Ms Wang); Navigate BioPharma (Dr Rimm); Sidney Kimmel Cancer Center Core Grant P30 CA006973 (Drs Taube and Danilova); Yale Cancer Center P30 CA016359 (Dr Rimm); National Institutes of Health (NIH) Lung SPORE in Lung Cancer P50CA196530 (Drs Rimm and Schalper); Department of Defense Lung Cancer Research Program award W81XWH-16-1-0160 (Dr Schalper); Stand Up To Cancer/AACR SU2C-AACR-DT17-15 SU2C-AACR-DT22-17.ACS (Dr Schalper); Melanoma Professorship No. RP-14-246-06 (Dr Sosman); National Cancer Institute R01 CA142779 (Drs Taube and Pardoll); NIH T32 CA193145 (Dr Stein); P50 CA062924 (Dr Anders); K23 CA204726 (Dr Johnson); and The Bloomberg~Kimmel Institute for Cancer Immunotherapy.
                Role of Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, and approval of the manuscript; or decision to submit the manuscript for publication.
                Additional Contributions: The authors would like to acknowledge Matthew Hellmann, MD (Memorial Sloan Kettering Cancer Center), Evan Lipson, MD, and Suzanne L. Topalian, MD (both Johns Hopkins University), and Robin Edwards, MD (Bristol-Myers Squibb), for helpful discussions. These contributions were not compensated.
                Article
                PMC6646995 PMC6646995 6646995 coi190040
                10.1001/jamaoncol.2019.1549
                6646995
                31318407
                fa5cd917-3a7b-40f7-b0a3-d80cd056e213
                Copyright 2019 American Medical Association. All Rights Reserved.
                History
                : 8 December 2018
                : 14 March 2019
                : 15 March 2019
                Funding
                Funded by: Melanoma Research Alliance
                Funded by: Harry J. Lloyd Trust
                Funded by: Emerson Collective
                Funded by: Moving for Melanoma of Delaware
                Funded by: Bristol-Myers Squibb
                Funded by: Navigate BioPharma
                Funded by: Sidney Kimmel Cancer Center
                Funded by: Yale Cancer Center
                Funded by: National Institutes of Health (NIH) Lung SPORE in Lung Cancer
                Funded by: Department of Defense Lung Cancer Research Program
                Funded by: Stand Up To Cancer
                Funded by: Melanoma Professorship
                Funded by: National Cancer Institute
                Funded by: The Bloomberg~Kimmel Institute for Cancer Immunotherapy
                Categories
                Research
                Research
                Original Investigation
                Online First

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