KLRG1 marks tumor-infiltrating CD4 T cell subsets associated with tumor progression and immunotherapy response

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 analy...

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Published inJournal for immunotherapy of cancer Vol. 11; no. 9; p. e006782
Main Authors Ager, Casey R, Zhang, Mingxuan, Chaimowitz, Matthew, Bansal, Shruti, Tagore, Somnath, Obradovic, Aleksandar, Jugler, Collin, Rogava, Meri, Melms, Johannes C, McCann, Patrick, Spina, Catherine, Drake, Charles G, Dallos, Matthew C, Izar, Benjamin
Format Journal Article
LanguageEnglish
Published England BMJ Publishing Group Ltd 01.09.2023
BMJ Publishing Group LTD
BMJ Publishing Group
Subjects
Antibodies
Biomarkers
Cancer
Cancer therapies
Carcinoma, Renal Cell
CD4-Positive T-Lymphocytes
Cells
Dimensional analysis
Flow cytometry
Humans
Immune checkpoint inhibitors
Immunotherapy
Immunotherapy Biomarkers
Kidney cancer
Kidney Neoplasms
Laboratory animals
Lectins, C-Type
Lymphatic system
lymphocytes, tumor-infiltrating
Medical research
Penicillin
Receptors, Immunologic
renal cell carcinoma
Stains & staining
T-Lymphocyte Subsets
Tumors
New York
United States > US
Basel Switzerland
California
Switzerland
Maryland
New Jersey
renal cell carcinoma
immune checkpoint inhibitors
CD4-positive T-lymphocytes
lymphocytes, tumor-infiltrating
immunotherapy
Online AccessGet full text
ISSN2051-1426
2051-1426
DOI10.1136/jitc-2023-006782

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Abstract 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.
AbstractList 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.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.
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.
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.
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.
Author Dallos, Matthew C
Jugler, Collin
Rogava, Meri
Spina, Catherine
McCann, Patrick
Bansal, Shruti
Melms, Johannes C
Drake, Charles G
Ager, Casey R
Zhang, Mingxuan
Obradovic, Aleksandar
Izar, Benjamin
Chaimowitz, Matthew
Tagore, Somnath
AuthorAffiliation 7 Department of Systems Biology , Columbia University Irving Medical Center , New York , New York , USA
11 Columbia University Vagelos College of Physicians and Surgeons , New York , New York , USA
10 Department of Urology , Columbia University Irving Medical Center , New York , New York , USA
2 Department of Immunology , Mayo Clinic Arizona , Scottsdale , Arizona , USA
5 Department of Molecular Pathology and Therapeutics , Columbia University Irving Medical Center , New York , New York , USA
6 Department of Radiation Oncology , Columbia University Irving Medical Center , New York , New York , USA
1 Department of Medicine, Division of Hematology and Oncology , Columbia University Irving Medical Center , New York , New York , USA
4 Department of Urology , Mayo Clinic Arizona , Scottsdale , Arizona , USA
3 Columbia Center for Translational Immunology , Columbia University Irving Medical Center , New York , New York , USA
8 Janssen Research and Development , Janssen Pharmaceuticals , Spring House
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/37657842$$D View this record in MEDLINE/PubMed
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Issue 9
Keywords renal cell carcinoma
immune checkpoint inhibitors
CD4-positive T-lymphocytes
lymphocytes, tumor-infiltrating
immunotherapy
Language English
License 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/.
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36711647 - bioRxiv. 2023 Jan 02:2023.01.01.522340. doi: 10.1101/2023.01.01.522340
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Snippet Current methods for biomarker discovery and target identification in immuno-oncology rely on static snapshots of tumor immunity. To thoroughly characterize the...
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SubjectTerms Antibodies
Biomarkers
Cancer
Cancer therapies
Carcinoma, Renal Cell
CD4-Positive T-Lymphocytes
Cells
Dimensional analysis
Flow cytometry
Humans
Immune checkpoint inhibitors
Immunotherapy
Immunotherapy Biomarkers
Kidney cancer
Kidney Neoplasms
Laboratory animals
Lectins, C-Type
Lymphatic system
lymphocytes, tumor-infiltrating
Medical research
Penicillin
Receptors, Immunologic
renal cell carcinoma
Stains & staining
T-Lymphocyte Subsets
Tumors
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Title KLRG1 marks tumor-infiltrating CD4 T cell subsets associated with tumor progression and immunotherapy response
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