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      Differential Activity of Nivolumab, Pembrolizumab and MPDL3280A according to the Tumor Expression of Programmed Death-Ligand-1 (PD-L1): Sensitivity Analysis of Trials in Melanoma, Lung and Genitourinary Cancers

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          Abstract

          Background

          The potential predictive role of programmed death-ligand-1 (PD-L1) expression on tumor cells in the context of solid tumor treated with checkpoint inhibitors targeting the PD-1 pathway represents an issue for clinical research.

          Methods

          Overall response rate (ORR) was extracted from phase I-III trials investigating nivolumab, pembrolizumab and MPDL3280A for advanced melanoma, non-small cell lung cancer (NSCLC) and genitourinary cancer, and cumulated by adopting a fixed and random-effect model with 95% confidence interval (CI). Interaction test according to tumor PD-L1 was accomplished. A sensitivity analysis according to adopted drug, tumor type, PD-L1 cut-off and treatment line was performed.

          Results

          Twenty trials (1,475 patients) were identified. A significant interaction ( p<0. 0001) according to tumor PD-L1 expression was found in the overall sample with an ORR of 34.1% (95% CI 27.6-41.3%) in the PD-L1 positive and 19.9% (95% CI 15.4-25.3%) in the PD-L1 negative population. ORR was significantly higher in PD-L1 positive in comparison to PD-L1 negative patients for nivolumab and pembrolizumab, with an absolute difference of 16.4% and 19.5%, respectively. A significant difference in activity of 22.8% and 8.7% according to PD-L1 was found for melanoma and NSCLC, respectively, with no significant difference for genitourinary cancer.

          Conclusion

          Overall, the three antibodies provide a significant differential effect in terms of activity according to PD-L1 expression on tumor cells. The predictive value of PD-L1 on tumor cells seems to be more robust for anti-PD-1 antibody (nivolumab and pembrolizumab), and in the context of advanced melanoma and NSCLC.

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          Most cited references12

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          Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints.

          Meta-analyses aim to provide a full and comprehensive summary of related studies which have addressed a similar question. When the studies involve time to event (survival-type) data the most appropriate statistics to use are the log hazard ratio and its variance. However, these are not always explicitly presented for each study. In this paper a number of methods of extracting estimates of these statistics in a variety of situations are presented. Use of these methods should improve the efficiency and reliability of meta-analyses of the published literature with survival-type endpoints.
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            Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing.

            Human tumours typically harbour a remarkable number of somatic mutations. If presented on major histocompatibility complex class I molecules (MHCI), peptides containing these mutations could potentially be immunogenic as they should be recognized as 'non-self' neo-antigens by the adaptive immune system. Recent work has confirmed that mutant peptides can serve as T-cell epitopes. However, few mutant epitopes have been described because their discovery required the laborious screening of patient tumour-infiltrating lymphocytes for their ability to recognize antigen libraries constructed following tumour exome sequencing. We sought to simplify the discovery of immunogenic mutant peptides by characterizing their general properties. We developed an approach that combines whole-exome and transcriptome sequencing analysis with mass spectrometry to identify neo-epitopes in two widely used murine tumour models. Of the >1,300 amino acid changes identified, ∼13% were predicted to bind MHCI, a small fraction of which were confirmed by mass spectrometry. The peptides were then structurally modelled bound to MHCI. Mutations that were solvent-exposed and therefore accessible to T-cell antigen receptors were predicted to be immunogenic. Vaccination of mice confirmed the approach, with each predicted immunogenic peptide yielding therapeutically active T-cell responses. The predictions also enabled the generation of peptide-MHCI dextramers that could be used to monitor the kinetics and distribution of the anti-tumour T-cell response before and after vaccination. These findings indicate that a suitable prediction algorithm may provide an approach for the pharmacodynamic monitoring of T-cell responses as well as for the development of personalized vaccines in cancer patients.
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              Enhancement of antitumor immunity by CTLA-4 blockade.

              One reason for the poor immunogenicity of many tumors may be that they cannot provide signals for CD28-mediated costimulation necessary to fully activate T cells. It has recently become apparent that CTLA-4, a second counterreceptor for the B7 family of costimulatory molecules, is a negative regulator of T cell activation. Here, in vivo administration of antibodies to CTLA-4 resulted in the rejection of tumors, including preestablished tumors. Furthermore, this rejection resulted in immunity to a secondary exposure to tumor cells. These results suggest that blockade of the inhibitory effects of CTLA-4 can allow for, and potentiate, effective immune responses against tumor cells.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                18 June 2015
                2015
                : 10
                : 6
                : e0130142
                Affiliations
                [1 ]Medical Oncology, University of Verona, Azienda Ospedaliera Universitaria Integrata, Verona, Italy
                [2 ]Medical Oncology, Regina Elena National Cancer Institute, Roma, Italy
                [3 ]Department of Pathology and Diagnostic, University of Verona, Azienda Ospedaliera Universitaria Integrata, Verona, Italy
                [4 ]Agenzia Italiana del Farmaco (AIFA), Roma, Italy
                [5 ]Biostatistics, Regina Elena National Cancer Institute, Roma, Italy
                [6 ]ARC-NET Center for Applied Research on Cancer, Verona, Italy
                University Campus Bio-Medico, ITALY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: LC SP EB. Performed the experiments: LC EB. Analyzed the data: LC SP MM VV MB AC FC IS DG MC VB AS EB GT. Contributed reagents/materials/analysis tools: LC SP MM VV MB AC FC IS DG MC VB AS EB GT. Wrote the paper: LC SP EB.

                Article
                PONE-D-15-16314
                10.1371/journal.pone.0130142
                4472786
                26086854
                acee4bdf-5e0e-4e15-9a65-822d8700c700
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 20 April 2015
                : 18 May 2015
                Page count
                Figures: 5, Tables: 1, Pages: 16
                Funding
                This work was supported by a grant of the Italian Association for Cancer Research (AIRC-MFAG 14282, and AIRC-5X1000 12182 and 12214) without any implication in study design, data collection, data analysis, data interpretation or writing of the paper.
                Categories
                Research Article
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                All relevant data are within the paper.

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