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      Immune dysfunction signatures predict outcomes and define checkpoint blockade–unresponsive microenvironments in acute myeloid leukemia

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          Abstract

          Background

          Immune exhaustion and senescence are dominant dysfunctional states of effector T cells and major hurdles for the success of cancer immunotherapy. In the current study, we characterized how acute myeloid leukemia (AML) promotes the generation of senescent-like CD8 + T cells and whether they have prognostic relevance.

          METHODS

          We analyzed NanoString, bulk RNA-Seq and single-cell RNA-Seq data from independent clinical cohorts comprising 1,896 patients treated with chemotherapy and/or immune checkpoint blockade (ICB).

          Results

          We show that senescent-like bone marrow CD8 + T cells were impaired in killing autologous AML blasts and that their proportion negatively correlated with overall survival (OS). We defined what we believe to be new immune effector dysfunction (IED) signatures using 2 gene expression profiling platforms and reported that IED scores correlated with adverse-risk molecular lesions, stemness, and poor outcomes; these scores were a more powerful predictor of OS than 2017-ELN risk or leukemia stem cell (LSC17) scores. IED expression signatures also identified an ICB-unresponsive tumor microenvironment and predicted significantly shorter OS.

          Conclusion

          The IED scores provided improved AML-risk stratification and could facilitate the delivery of personalized immunotherapies to patients who are most likely to benefit.

          TRIAL REGISTRATION

          ClinicalTrials.gov; NCT02845297.

          FUNDING

          John and Lucille van Geest Foundation, Nottingham Trent University’s Health & Wellbeing Strategic Research Theme, NIH/NCI P01CA225618, Genentech-imCORE ML40354, Qatar National Research Fund (NPRP8-2297-3-494).

          Abstract

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          Most cited references102

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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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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              Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response

              Cancer treatment by immune checkpoint blockade (ICB) can bring long-lasting clinical benefits, but only a fraction of patients respond to treatment. To predict ICB response, we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and the prevention of T cell infiltration in tumors with low CTL level. We identified signatures of T cell dysfunction from large tumor cohorts by testing how the expression of each gene in tumors interacts with the CTL infiltration level to influence patient survival. We also modeled factors that exclude T cell infiltration into tumors using expression signatures from immunosuppressive cells. Using this framework and pre-treatment RNA-Seq or NanoString tumor expression profiles, TIDE predicted the outcome of melanoma patients treated with first-line anti-PD1 or anti-CTLA4 more accurately than other biomarkers such as PD-L1 level and mutation load. TIDE also revealed new candidate ICB resistance regulators, such as SERPINB9 , demonstrating utility for immunotherapy research.
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                Author and article information

                Contributors
                Journal
                J Clin Invest
                J Clin Invest
                J Clin Invest
                The Journal of Clinical Investigation
                American Society for Clinical Investigation
                0021-9738
                1558-8238
                1 November 2022
                1 November 2022
                1 November 2022
                : 132
                : 21
                : e159579
                Affiliations
                [1 ]John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, United Kingdom.
                [2 ]Department of Oncology and Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
                [3 ]Department of Medicine, Universitätsklinikum Carl Gustav Carus, Technische Universität (TU) Dresden, Dresden, Germany.
                [4 ]Department of Medicine, Medical University of Vienna, Vienna, Austria.
                [5 ]Masonic Cancer Center and Department of Pediatrics, Division of Blood & Marrow Transplant and Cellular Therapy, University of Minnesota, Minneapolis, Minnesota, USA.
                [6 ] Division of Hematology and Hematologic Malignancies, Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA.
                [7 ]Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA.
                [8 ]Division of Hematology, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
                [9 ]Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada.
                [10 ]Department of Leukemia, Division of Cancer Medicine and
                [11 ]Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
                [12 ]Department of Pediatrics, Division of Oncology and Centre for Childhood Cancer Research, Children’s Hospital of Philadelphia and University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA.
                [13 ]National Center for Tumor Diseases and German Cancer Consortium, Partner Site Dresden, Dresden, Germany.
                [14 ]German Cancer Research Centre, Heidelberg, Germany.
                Author notes
                Address correspondence to: Sergio Rutella, John van Geest Cancer Research Centre, Nottingham Trent University, Clifton Campus, NG11 8NS, Nottingham, United Kingdom. Phone: +44.115.8483205; Email: sergio.rutella@ 123456ntu.ac.uk . Or to: Leo Luznik, Johns Hopkins Sidney Kimmel Comprehensive Cancer Center, 401 N. Broadway, Harry and Jeanette Weinberg Building, Baltimore, Maryland 21231, USA. Phone: 410.955.8964; Email: LUZNILE@ 123456jhmi.edu .

                Authorship note: SR and LL contributed equally to this work.

                Author information
                http://orcid.org/0000-0003-1970-7375
                http://orcid.org/0000-0002-3283-0370
                http://orcid.org/0000-0002-4120-2966
                http://orcid.org/0000-0002-0866-6232
                http://orcid.org/0000-0002-9715-5054
                http://orcid.org/0000-0002-9608-9841
                http://orcid.org/0000-0002-9699-1157
                http://orcid.org/0000-0002-9014-1514
                http://orcid.org/0000-0003-2516-8005
                http://orcid.org/0000-0003-3651-6802
                http://orcid.org/0000-0003-1327-1662
                http://orcid.org/0000-0002-5916-3029
                http://orcid.org/0000-0003-0312-3412
                Article
                159579
                10.1172/JCI159579
                9621145
                36099049
                bbf80400-6649-4f89-967c-165439703aff
                © 2022 Rutella et al.

                This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 February 2022
                : 6 September 2022
                Funding
                Funded by: Qatar National Research Fund
                Award ID: NPRP8-2297-3-494
                Funded by: NIH/NCI
                Award ID: NIH/NCI 1U01CA232486
                Award ID: U01CA243072
                Funded by: Department of Defense Translational Team Science Award
                Award ID: CA180683P1
                Funded by: NIH/NCI
                Award ID: NIH/NCI P01CA225618
                Funded by: Nottingham Trent University, https://doi.org/10.13039/100010016;
                Award ID: Health
                Award ID: Wellbeing Strategic Research Theme
                Categories
                Clinical Medicine

                hematology,cancer immunotherapy,cellular senescence,leukemias

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