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.