KLRG1 marks tumor-infiltrating CD4 T cell subsets associated with tumor progression and immunotherapy response
© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ..
Current methods for biomarker discovery and target identification in immuno-oncology rely on static snapshots of tumor immunity. To thoroughly characterize the temporal nature of antitumor immune responses, we developed a 34-parameter spectral flow cytometry panel and performed high-throughput analyses in critical contexts. We leveraged two distinct preclinical models that recapitulate cancer immunoediting (NPK-C1) and immune checkpoint blockade (ICB) response (MC38), respectively, and profiled multiple relevant tissues at and around key inflection points of immune surveillance and escape and/or ICB response. Machine learning-driven data analysis revealed a pattern of KLRG1 expression that uniquely identified intratumoral effector CD4 T cell populations that constitutively associate with tumor burden across tumor models, and are lost in tumors undergoing regression in response to ICB. Similarly, a Helios-KLRG1+ subset of tumor-infiltrating regulatory T cells was associated with tumor progression from immune equilibrium to escape and was also lost in tumors responding to ICB. Validation studies confirmed KLRG1 signatures in human tumor-infiltrating CD4 T cells associate with disease progression in renal cancer. These findings nominate KLRG1+ CD4 T cell populations as subsets for further investigation in cancer immunity and demonstrate the utility of longitudinal spectral flow profiling as an engine of dynamic biomarker discovery.
| Errataetall: | |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
Journal for immunotherapy of cancer - 11(2023), 9 vom: 01. Sept. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ager, Casey R [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 04.09.2023 Date Revised 12.09.2023 published: Print UpdateOf: bioRxiv. 2023 Jan 02;:. - PMID 36711647 Citation Status MEDLINE |
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| doi: |
10.1136/jitc-2023-006782 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM361556055 |
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| 500 | |a published: Print | ||
| 500 | |a UpdateOf: bioRxiv. 2023 Jan 02;:. - PMID 36711647 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. | ||
| 520 | |a Current methods for biomarker discovery and target identification in immuno-oncology rely on static snapshots of tumor immunity. To thoroughly characterize the temporal nature of antitumor immune responses, we developed a 34-parameter spectral flow cytometry panel and performed high-throughput analyses in critical contexts. We leveraged two distinct preclinical models that recapitulate cancer immunoediting (NPK-C1) and immune checkpoint blockade (ICB) response (MC38), respectively, and profiled multiple relevant tissues at and around key inflection points of immune surveillance and escape and/or ICB response. Machine learning-driven data analysis revealed a pattern of KLRG1 expression that uniquely identified intratumoral effector CD4 T cell populations that constitutively associate with tumor burden across tumor models, and are lost in tumors undergoing regression in response to ICB. Similarly, a Helios-KLRG1+ subset of tumor-infiltrating regulatory T cells was associated with tumor progression from immune equilibrium to escape and was also lost in tumors responding to ICB. Validation studies confirmed KLRG1 signatures in human tumor-infiltrating CD4 T cells associate with disease progression in renal cancer. These findings nominate KLRG1+ CD4 T cell populations as subsets for further investigation in cancer immunity and demonstrate the utility of longitudinal spectral flow profiling as an engine of dynamic biomarker discovery | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a CD4-positive T-lymphocytes | |
| 650 | 4 | |a immune checkpoint inhibitors | |
| 650 | 4 | |a immunotherapy | |
| 650 | 4 | |a lymphocytes, tumor-infiltrating | |
| 650 | 4 | |a renal cell carcinoma | |
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| 700 | 1 | |a Zhang, Mingxuan |e verfasserin |4 aut | |
| 700 | 1 | |a Chaimowitz, Matthew |e verfasserin |4 aut | |
| 700 | 1 | |a Bansal, Shruti |e verfasserin |4 aut | |
| 700 | 1 | |a Tagore, Somnath |e verfasserin |4 aut | |
| 700 | 1 | |a Obradovic, Aleksandar |e verfasserin |4 aut | |
| 700 | 1 | |a Jugler, Collin |e verfasserin |4 aut | |
| 700 | 1 | |a Rogava, Meri |e verfasserin |4 aut | |
| 700 | 1 | |a Melms, Johannes C |e verfasserin |4 aut | |
| 700 | 1 | |a McCann, Patrick |e verfasserin |4 aut | |
| 700 | 1 | |a Spina, Catherine |e verfasserin |4 aut | |
| 700 | 1 | |a Drake, Charles G |e verfasserin |4 aut | |
| 700 | 1 | |a Dallos, Matthew C |e verfasserin |4 aut | |
| 700 | 1 | |a Izar, Benjamin |e verfasserin |4 aut | |
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