Format

Send to

Choose Destination
Oncoimmunology. 2017 Feb 6;6(3):e1284723. doi: 10.1080/2162402X.2017.1284723. eCollection 2017.

Assessment of tumor-infiltrating TCRVγ9Vδ2 γδ lymphocyte abundance by deconvolution of human cancers microarrays.

Author information

1
Centre de Recherches en Cancérologie de Toulouse (CRCT), Toulouse, France; INSERM U1037-Université Paul Sabatier-CNRS ERL5294, Université de Toulouse, Toulouse, France; Laboratoire d'Excellence TOUCAN, Toulouse, France; Programme Hospitalo-Universitaire en Cancérologie CAPTOR, Toulouse, France; Pôle Technologique du Centre de Recherches en Cancérologie de Toulouse (CRCT), Toulouse, France; Institut Universitaire du Cancer de Toulouse (IUCT), Toulouse, France.
2
Centre de Recherches en Cancérologie de Toulouse (CRCT), Toulouse, France; INSERM U1037-Université Paul Sabatier-CNRS ERL5294, Université de Toulouse, Toulouse, France; Pôle Technologique du Centre de Recherches en Cancérologie de Toulouse (CRCT), Toulouse, France.
3
Centre de Recherches en Cancérologie de Toulouse (CRCT), Toulouse, France; INSERM U1037-Université Paul Sabatier-CNRS ERL5294, Université de Toulouse, Toulouse, France; Laboratoire d'Excellence TOUCAN, Toulouse, France; Programme Hospitalo-Universitaire en Cancérologie CAPTOR, Toulouse, France.
4
Centre de Recherches en Cancérologie de Toulouse (CRCT), Toulouse, France; INSERM U1037-Université Paul Sabatier-CNRS ERL5294, Université de Toulouse, Toulouse, France; Institut Universitaire du Cancer de Toulouse (IUCT), Toulouse, France.
5
Institut Universitaire du Cancer de Toulouse (IUCT) , Toulouse, France.
6
Central Laboratory for Advanced Diagnostics and Biomedical Research (CLADIBIOR), University of Palermo , Palermo, Italy.
7
Centre de Recherches en Cancérologie de Toulouse (CRCT), Toulouse, France; INSERM U1037-Université Paul Sabatier-CNRS ERL5294, Université de Toulouse, Toulouse, France; Programme Hospitalo-Universitaire en Cancérologie CAPTOR, Toulouse, France.
8
Department of Biopharmacy - Institute for Medical Immunology (IMI), Université Libre de Bruxelles , Bruxelles, Belgium.

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.

KEYWORDS:

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

Supplemental Content

Full text links

Icon for Taylor & Francis Icon for PubMed Central
Loading ...
Support Center