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Deep immunophenotyping at the single-cell level identifies a combination of anti-IL-17 and checkpoint blockade as an effective treatment in a preclinical model of data-guided personalized immunotherapy

BACKGROUND: Although immune checkpoint blockade is effective for several malignancies, a substantial number of patients remain refractory to treatment. The future of immunotherapy will be a personalized approach adapted to each patient’s cancer-immune interactions in the tumor microenvironment (TME)...

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Autores principales: Nagaoka, Koji, Shirai, Masataka, Taniguchi, Kiyomi, Hosoi, Akihiro, Sun, Changbo, Kobayashi, Yukari, Maejima, Kazuhiro, Fujita, Masashi, Nakagawa, Hidewaki, Nomura, Sachiyo, Kakimi, Kazuhiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583806/
https://www.ncbi.nlm.nih.gov/pubmed/33093158
http://dx.doi.org/10.1136/jitc-2020-001358
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author Nagaoka, Koji
Shirai, Masataka
Taniguchi, Kiyomi
Hosoi, Akihiro
Sun, Changbo
Kobayashi, Yukari
Maejima, Kazuhiro
Fujita, Masashi
Nakagawa, Hidewaki
Nomura, Sachiyo
Kakimi, Kazuhiro
author_facet Nagaoka, Koji
Shirai, Masataka
Taniguchi, Kiyomi
Hosoi, Akihiro
Sun, Changbo
Kobayashi, Yukari
Maejima, Kazuhiro
Fujita, Masashi
Nakagawa, Hidewaki
Nomura, Sachiyo
Kakimi, Kazuhiro
author_sort Nagaoka, Koji
collection PubMed
description BACKGROUND: Although immune checkpoint blockade is effective for several malignancies, a substantial number of patients remain refractory to treatment. The future of immunotherapy will be a personalized approach adapted to each patient’s cancer-immune interactions in the tumor microenvironment (TME) to prevent suppression of antitumor immune responses. To demonstrate the feasibility of this kind of approach, we developed combination therapy for a preclinical model guided by deep immunophenotyping of the TME. METHODS: Gastric cancer cell lines YTN2 and YTN16 were subcutaneously inoculated into C57BL/6 mice. YTN2 spontaneously regresses, while YTN16 grows progressively. Bulk RNA-Seq, single-cell RNA-Seq (scRNA-Seq) and flow cytometry were performed to investigate the immunological differences in the TME of these tumors. RESULTS: Bulk RNA-Seq demonstrated that YTN16 tumor cells produced CCL20 and that CD8(+) T cell responses were impaired in these tumors relative to YTN2. We have developed a vertical flow array chip (VFAC) for targeted scRNA-Seq to identify unique subtypes of T cells by employing a panel of genes reflecting T cell phenotypes and functions. CD8(+) T cell dysfunction (cytotoxicity, proliferation and the recruitment of interleukin-17 (IL-17)-producing cells into YTN16 tumors) was identified by targeted scRNA-Seq. The presence of IL-17-producing T cells in YTN16 tumors was confirmed by flow cytometry, which also revealed neutrophil infiltration. IL-17 blockade suppressed YTN16 tumor growth, while tumors were rejected by the combination of anti-IL-17 and anti-PD-1 (Programmed cell death protein 1) mAb treatment. Reduced neutrophil activation and enhanced expansion of neoantigen-specific CD8(+) T cells were observed in tumors of the mice receiving the combination therapy. CONCLUSIONS: Deep phenotyping of YTN16 tumors identified a sequence of events on the axis CCL20->IL-17-producing cells->IL-17-neutrophil-angiogenesis->suppression of neoantigen-specific CD8(+) T cells which was responsible for the lack of tumor rejection. IL-17 blockade together with anti-PD-1 mAb therapy eradicated these YTN16 tumors. Thus, the deep immunological phenotyping can guide immunotherapy for the tailored treatment of each individual patient’s tumor.
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spelling pubmed-75838062020-10-28 Deep immunophenotyping at the single-cell level identifies a combination of anti-IL-17 and checkpoint blockade as an effective treatment in a preclinical model of data-guided personalized immunotherapy Nagaoka, Koji Shirai, Masataka Taniguchi, Kiyomi Hosoi, Akihiro Sun, Changbo Kobayashi, Yukari Maejima, Kazuhiro Fujita, Masashi Nakagawa, Hidewaki Nomura, Sachiyo Kakimi, Kazuhiro J Immunother Cancer Basic Tumor Immunology BACKGROUND: Although immune checkpoint blockade is effective for several malignancies, a substantial number of patients remain refractory to treatment. The future of immunotherapy will be a personalized approach adapted to each patient’s cancer-immune interactions in the tumor microenvironment (TME) to prevent suppression of antitumor immune responses. To demonstrate the feasibility of this kind of approach, we developed combination therapy for a preclinical model guided by deep immunophenotyping of the TME. METHODS: Gastric cancer cell lines YTN2 and YTN16 were subcutaneously inoculated into C57BL/6 mice. YTN2 spontaneously regresses, while YTN16 grows progressively. Bulk RNA-Seq, single-cell RNA-Seq (scRNA-Seq) and flow cytometry were performed to investigate the immunological differences in the TME of these tumors. RESULTS: Bulk RNA-Seq demonstrated that YTN16 tumor cells produced CCL20 and that CD8(+) T cell responses were impaired in these tumors relative to YTN2. We have developed a vertical flow array chip (VFAC) for targeted scRNA-Seq to identify unique subtypes of T cells by employing a panel of genes reflecting T cell phenotypes and functions. CD8(+) T cell dysfunction (cytotoxicity, proliferation and the recruitment of interleukin-17 (IL-17)-producing cells into YTN16 tumors) was identified by targeted scRNA-Seq. The presence of IL-17-producing T cells in YTN16 tumors was confirmed by flow cytometry, which also revealed neutrophil infiltration. IL-17 blockade suppressed YTN16 tumor growth, while tumors were rejected by the combination of anti-IL-17 and anti-PD-1 (Programmed cell death protein 1) mAb treatment. Reduced neutrophil activation and enhanced expansion of neoantigen-specific CD8(+) T cells were observed in tumors of the mice receiving the combination therapy. CONCLUSIONS: Deep phenotyping of YTN16 tumors identified a sequence of events on the axis CCL20->IL-17-producing cells->IL-17-neutrophil-angiogenesis->suppression of neoantigen-specific CD8(+) T cells which was responsible for the lack of tumor rejection. IL-17 blockade together with anti-PD-1 mAb therapy eradicated these YTN16 tumors. Thus, the deep immunological phenotyping can guide immunotherapy for the tailored treatment of each individual patient’s tumor. BMJ Publishing Group 2020-10-22 /pmc/articles/PMC7583806/ /pubmed/33093158 http://dx.doi.org/10.1136/jitc-2020-001358 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Basic Tumor Immunology
Nagaoka, Koji
Shirai, Masataka
Taniguchi, Kiyomi
Hosoi, Akihiro
Sun, Changbo
Kobayashi, Yukari
Maejima, Kazuhiro
Fujita, Masashi
Nakagawa, Hidewaki
Nomura, Sachiyo
Kakimi, Kazuhiro
Deep immunophenotyping at the single-cell level identifies a combination of anti-IL-17 and checkpoint blockade as an effective treatment in a preclinical model of data-guided personalized immunotherapy
title Deep immunophenotyping at the single-cell level identifies a combination of anti-IL-17 and checkpoint blockade as an effective treatment in a preclinical model of data-guided personalized immunotherapy
title_full Deep immunophenotyping at the single-cell level identifies a combination of anti-IL-17 and checkpoint blockade as an effective treatment in a preclinical model of data-guided personalized immunotherapy
title_fullStr Deep immunophenotyping at the single-cell level identifies a combination of anti-IL-17 and checkpoint blockade as an effective treatment in a preclinical model of data-guided personalized immunotherapy
title_full_unstemmed Deep immunophenotyping at the single-cell level identifies a combination of anti-IL-17 and checkpoint blockade as an effective treatment in a preclinical model of data-guided personalized immunotherapy
title_short Deep immunophenotyping at the single-cell level identifies a combination of anti-IL-17 and checkpoint blockade as an effective treatment in a preclinical model of data-guided personalized immunotherapy
title_sort deep immunophenotyping at the single-cell level identifies a combination of anti-il-17 and checkpoint blockade as an effective treatment in a preclinical model of data-guided personalized immunotherapy
topic Basic Tumor Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583806/
https://www.ncbi.nlm.nih.gov/pubmed/33093158
http://dx.doi.org/10.1136/jitc-2020-001358
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