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Identification of human CD4+ T cell populations with distinct antitumor activity.

ABSTRACT: How naturally arising human CD4+ T helper subsets affect cancer immunotherapy is unclear. We reported that human CD4+CD26high T cells elicit potent immunity against solid tumors. As CD26high T cells are often categorized as TH17 cells for their IL-17 production and high CD26 expression, we posited these populations would have similar molecular properties. Here, we reveal that CD26high T cells are epigenetically and transcriptionally distinct from TH17 cells. Of clinical importance, CD26high and TH17 cells engineered with a chimeric antigen receptor (CAR) regressed large human tumors to a greater extent than enriched TH1 or TH2 cells. Only human CD26high T cells mediated curative responses, even when redirected with a suboptimal CAR and without aid by CD8+ CAR T cells. CD26high T cells cosecreted effector cytokines, produced cytotoxic molecules, and persisted long term. Collectively, our work underscores the promise of CD4+ T cell populations to improve durability of solid tumor therapies.

SUBMITTER: Nelson MH

PROVIDER: S-EPMC7458458 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Identification of human CD4+ T cell populations with distinct antitumor activity.

Nelson Michelle H MH Knochelmann Hannah M HM Bailey Stefanie R SR Huff Logan W LW Bowers Jacob S JS Majchrzak-Kuligowska Kinga K Wyatt Megan M MM Rubinstein Mark P MP Mehrotra Shikhar S Nishimura Michael I MI Armeson Kent E KE Giresi Paul G PG Zilliox Michael J MJ Broxmeyer Hal E HE Paulos Chrystal M CM

Science advances 20200701 27

How naturally arising human CD4+ T helper subsets affect cancer immunotherapy is unclear. We reported that human CD4+CD26high T cells elicit potent immunity against solid tumors. As CD26high T cells are often categorized as TH17 cells for their IL-17 production and high CD26 expression, we posited these populations would have similar molecular properties. Here, we reveal that CD26high T cells are epigenetically and transcriptional ...[more]

PMID: 32937437

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Project description:BackgroundPrediction of T cell immunogenicity is a topic of considerable interest, both in terms of basic understanding of the mechanisms of T cells responses and in terms of practical applications. HLA binding affinity is often used to predict T cell epitopes, since HLA binding affinity is a key requisite for human T cell immunogenicity. However, immunogenicity at the population it is complicated by the high level of variability of HLA molecules, potential other factors beyond HLA as well as the frequent lack of HLA typing data. To overcome those issues, we explored an alternative approach to identify the common characteristics able to distinguish immunogenic peptides from non-recognized peptides.MethodsSets of dominant epitopes derived from peer-reviewed published papers were used in conjunction with negative peptides from the same experiments/donors to train neural networks and generate an "immunogenicity score." We also compared the performance of the immunogenicity score with previously described method for immunogenicity prediction based on HLA class II binding at the population level.ResultsThe immunogenicity score was validated on a series of independent datasets derived from the published literature, representing 57 independent studies where immunogenicity in human populations was assessed by testing overlapping peptides spanning different antigens. Overall, these testing datasets corresponded to over 2,000 peptides and tested in over 1,600 different human donors. The 7-allele method prediction and the immunogenicity score were associated with similar performance [average area under the ROC curve (AUC) values of 0.703 and 0.702, respectively] while the combined methods reached an average AUC of 0.725. This increase in average AUC value is significant compared with the immunogenicity score (p = 0.0135) and a strong trend toward significance is observed when compared to the 7-allele method (p = 0.0938). The new immunogenicity score method is now freely available using CD4 T cell immunogenicity prediction tool on the Immune Epitope Database website (http://tools.iedb.org/CD4episcore).ConclusionThe new immunogenicity score predicts CD4 T cell immunogenicity at the population level starting from protein sequences and with no need for HLA typing. Its efficacy has been validated in the context of different antigen sources, ethnicities, and disparate techniques for epitope identification.

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Identification of human CD4+ T cell populations with distinct antitumor activity.

Publications

Identification of human CD4<sup>+</sup> T cell populations with distinct antitumor activity.

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