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      Assessment of tumor-infiltrating TCRV γ9V δ2 γδ lymphocyte abundance by deconvolution of human cancers microarrays

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          ABSTRACT

          Most human blood γδ cells are cytolytic TCRVγ9Vδ2 + lymphocytes with antitumor activity. They are currently investigated in several clinical trials of cancer immunotherapy but so far, their tumor infiltration has not been systematically explored across human cancers. Novel algorithms allowing the deconvolution of bulk tumor transcriptomes to find the relative proportions of infiltrating leucocytes, such as CIBERSORT, should be appropriate for this aim but in practice they fail to accurately recognize γδ T lymphocytes. Here, by implementing machine learning from microarray data, we first improved the computational identification of blood-derived TCRVγ9Vδ2 + γδ lymphocytes and then applied this strategy to assess their abundance as tumor infiltrating lymphocytes (γδ TIL) in ∼10,000 cancer biopsies from 50 types of hematological and solid malignancies. We observed considerable inter-individual variation of TCRVγ9Vδ2 +γδ TIL abundance both within each type and across the spectrum of cancers tested. We report their prominence in B cell-acute lymphoblastic leukemia (B-ALL), acute promyelocytic leukemia (M3-AML) and chronic myeloid leukemia (CML) as well as in inflammatory breast, prostate, esophagus, pancreas and lung carcinoma. Across all cancers, the abundance of αβ TILs and TCRVγ9Vδ2 + γδ TILs did not correlate. αβ TIL abundance paralleled the mutational load of tumors and positively correlated with inflammation, infiltration of monocytes, macrophages and dendritic cells (DC), antigen processing and presentation, and cytolytic activity, in line with an association with a favorable outcome. In contrast, the abundance of TCRVγ9Vδ2 + γδ TILs did not correlate with these hallmarks and was variably associated with outcome, suggesting that distinct contexts underlie TCRVγ9Vδ2 + γδ TIL and αβ TIL mobilizations in cancer.

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          Author and article information

          Journal
          Oncoimmunology
          Oncoimmunology
          KONI
          koni20
          Oncoimmunology
          Taylor & Francis
          2162-4011
          2162-402X
          2017
          6 February 2017
          : 6
          : 3
          : e1284723
          Affiliations
          [a ] Centre de Recherches en Cancérologie de Toulouse (CRCT) , Toulouse, France
          [b ] INSERM U1037-Université Paul Sabatier-CNRS ERL5294, Université de Toulouse , Toulouse, France
          [c ] Laboratoire d'Excellence TOUCAN , Toulouse, France
          [d ] Programme Hospitalo-Universitaire en Cancérologie CAPTOR , Toulouse, France
          [e ] Pôle Technologique du Centre de Recherches en Cancérologie de Toulouse (CRCT) , Toulouse, France
          [f ] Institut Universitaire du Cancer de Toulouse (IUCT) , Toulouse, France
          [g ] Department of Biopharmacy and Institute for Medical Immunology (IMI), Université Libre de Bruxelles , Bruxelles, Belgium
          [h ] Central Laboratory for Advanced Diagnostics and Biomedical Research (CLADIBIOR), University of Palermo , Palermo, Italy
          Author notes
          CONTACT Jean-Jacques Fournié jean-jacques.fournie@ 123456inserm.fr Centre de Recherches en Cancerologie de Toulouse (CRCT) , 2 avenue Hubert Curien, CS53717, F-31037, Toulouse, France

          Supplemental data for this article can be accessed on the publisher's website.

          Author information
          https://orcid.org/0000-0001-5278-5952
          https://orcid.org/0000-0001-6542-6908
          Article
          PMC5384348 PMC5384348 5384348 1284723
          10.1080/2162402X.2017.1284723
          5384348
          28405516
          a6c075bc-dbf2-4d05-a922-a8be71cc561d
          © 2017 Taylor & Francis Group, LLC
          History
          : 22 December 2016
          : 12 January 2017
          : 13 January 2017
          Page count
          Figures: 4, Tables: 0, Equations: 0, References: 57, Pages: 10
          Categories
          Original Research

          deconvolution,microarray,machine learning,gamma delta lymphocyte,data mining,cancer,Artificial intelligence,transcriptome

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