DLBCL tumor microenvironment archetype and CD4+ T cell conversion to cytotoxic cells are required for response to glofitamab

Abstract INTRODUCTION Glofitamab (glofit) is a T cell engager (TCE), which binds CD20 on the DLBCL cell and the CD3 molecule on T cells to drive their activation and killing of the tumor target cells. However, it is not known exactly which T cells are recruited into this killing nexus, the key molecular interactions associated with glofit efficacy in DLBCL, and the drivers for cytotoxic (CTL) differentiation in the DLBCL tumor microenvironment (TME). Furthermore, the pre-treatment DLBCL TME signals associated with improved vs poor clinical outcome have not been well described. METHODS Pre-treatment tissue and peripheral blood samples were collected from patients enrolled in the COALITION trial (NCT04914741). We explored the DLBCL TME pre-treatment by multiplex IHC (mIHC) and RNAseq. mIHC used a panel of markers (CD3, CD8, CD4, CD20), the digital imaging data analyzed by InForm and HALO, followed by our algorithm SPIAT (Feng Y et al Nat Comms 2023) to generate cell density, cell-cell spatial relationships and tissue archetypes. RNAseq analysis revealed differential gene expression, gene ontology, gene set enrichment analysis and focussed gene modules for specific immune TME features (cytotoxicity, T cell exhaustion, trafficking and tissue remodelling). Co-cultures of DLBCL and purified CD4+ or CD8+ T cells in the presence of glofit was used to assess target cell killing by Incucyte and the kinetics of CTL differentiation by flow cytometry. Glofit-induced changes in circulating T cells were examined in the COALITION patient PBMCs by single cell (sc)RNAseq and TCRseq (10X Genomics), as well as by spectral flow cytometry analysis (CyTEK) pre- and on-treatment. RESULTS We showed post-treatment responder (Resp) patients had significantly increased TME archetypes characterized by (CD4-CD8-lymphoma) close interactions, also increased expression of IFN-g response genes and IL2-STAT5 signalling suggesting that both CD4+ and CD8+ T cells participated in this pre-existing anti-tumor response and played a role in glofit efficacy. In addition, the DLBCL TME of resp patients had increased expression of the tissue re-modelling gene module including matrix metalloproteinases. We also explored the peripheral blood (PB) of the COALITION trial patients by scRNAseq. By cycle 5 of therapy, Resp patients had increased circulating Ki67+CD4+ T cells, these Ki67+CD4+ CTL had increased expression of PRF1, GZMB, TBX21, RUNX3, EOMES, NKG7 and reduced ZBTB7B (THPOK). Spectral flow cytometry confirmed these findings at a protein level, Ki67+CD4+ CTL cells had increased GzmB, T-bet and Runx3 expression. In addition, other Ki67+ cytotoxic lymphocytes cells included NK and CD8+ T cells with high levels of cytotoxicity genes. In vitro co-culture models using DLBCL target cells (MD901) and glofit showed healthy donor CD4+ and CD8+ T cells were equally capable of killing DLBCL target cells over 24 hrs. Interestingly, glofit binding fast-tracked CD4+ T cell CTL differentiation, within 6 hrs of co-culture both naïve and antigen-experienced CD4+ T cells had increased GzmB expression levels, formed CTLs and commenced killing tumor targets by a perforin-mediated mechanism. The untreated naïve CD4+ T cells included a subset of cells with increased expression levels for the key cytotoxicity genes RUNX3, TBX21, EOMES, GZMB, NKG7, CST7 and decreased ZBTB7B suggesting intrinsic mechanisms for the rapid CD4+ CTL ‘converters.‘ This CD4+ CTL phenotype (GzmB, ThPOK, Runx3, Eomes, T-bet) was maintained for at least 7 days. In the same co-culture system, Resp patient CD4+ T cells had a higher conversion to CTL phenotype (GzmB+) compared to early relapse patients. In addition, molecular interactions critical for this CD4+ CTL differentiation included IL2/IL2R, 41BB/41BBL, CD2/CD58 and LFA1/ICAM1. This is supported by cell-cell interactome analysis which showed the CD4+ CTL interact with the DLBCL cells via LFA-1/ICAM1. The contribution of these molecular pathways to CD4+ CTL generation was inversely correlated with CD20 expression levels on the DLBCL target line. In this co-culture model, CD4+ T cells also contributed to increased CD8+ CTL differentiation and proliferation. CONCLUSIONS In summary, CD4+ T cells play a critical direct role in glofit efficacy in DLBCL patients. This effect is dependent on two key factors including a specific DLBCL TME archetype with increased inflammation, and an intrinsic transition to CD4+ CTL differentiation.

Authors

Institutions

Publication Details

Journal
Blood
Published
2025-11-03
DOI
https://doi.org/10.1182/blood-2025-557
Primary Topic
CAR-T cell therapy research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

DLBCL tumor microenvironment archetype and CD4+ T cell conversion to cytotoxic cells are required for response to glofitamab

Thiago M. Steiner, Niko Thio, Adrian Minson, Joseph A. Trapani et al.
Blood
CAR-T cell therapy research
article

DLBCL tumor microenvironment archetype and CD4+ T cell conversion to cytotoxic cells are required for response to glofitamab

Thiago M. Steiner, Niko Thio, Adrian Minson, Joseph A. Trapani, Sean Macdonald, Paul J. Neeson, Sean Harrop, Vivien R. Sutton, Lucas Tobar, L. S. Lau, Bao T. Le, Michael John Dickinson, Ruiqing Zhu, Faiza ZuairiaTabassum
article en

Abstract

Abstract INTRODUCTION Glofitamab (glofit) is a T cell engager (TCE), which binds CD20 on the DLBCL cell and the CD3 molecule on T cells to drive their activation and killing of the tumor target cells. However, it is not known exactly which T cells are recruited into this killing nexus, the key molecular interactions associated with glofit efficacy in DLBCL, and the drivers for cytotoxic (CTL) differentiation in the DLBCL tumor microenvironment (TME). Furthermore, the pre-treatment DLBCL TME signals associated with improved vs poor clinical outcome have not been well described. METHODS Pre-treatment tissue and peripheral blood samples were collected from patients enrolled in the COALITION trial (NCT04914741). We explored the DLBCL TME pre-treatment by multiplex IHC (mIHC) and RNAseq. mIHC used a panel of markers (CD3, CD8, CD4, CD20), the digital imaging data analyzed by InForm and HALO, followed by our algorithm SPIAT (Feng Y et al Nat Comms 2023) to generate cell density, cell-cell spatial relationships and tissue archetypes. RNAseq analysis revealed differential gene expression, gene ontology, gene set enrichment analysis and focussed gene modules for specific immune TME features (cytotoxicity, T cell exhaustion, trafficking and tissue remodelling). Co-cultures of DLBCL and purified CD4+ or CD8+ T cells in the presence of glofit was used to assess target cell killing by Incucyte and the kinetics of CTL differentiation by flow cytometry. Glofit-induced changes in circulating T cells were examined in the COALITION patient PBMCs by single cell (sc)RNAseq and TCRseq (10X Genomics), as well as by spectral flow cytometry analysis (CyTEK) pre- and on-treatment. RESULTS We showed post-treatment responder (Resp) patients had significantly increased TME archetypes characterized by (CD4-CD8-lymphoma) close interactions, also increased expression of IFN-g response genes and IL2-STAT5 signalling suggesting that both CD4+ and CD8+ T cells participated in this pre-existing anti-tumor response and played a role in glofit efficacy. In addition, the DLBCL TME of resp patients had increased expression of the tissue re-modelling gene module including matrix metalloproteinases. We also explored the peripheral blood (PB) of the COALITION trial patients by scRNAseq. By cycle 5 of therapy, Resp patients had increased circulating Ki67+CD4+ T cells, these Ki67+CD4+ CTL had increased expression of PRF1, GZMB, TBX21, RUNX3, EOMES, NKG7 and reduced ZBTB7B (THPOK). Spectral flow cytometry confirmed these findings at a protein level, Ki67+CD4+ CTL cells had increased GzmB, T-bet and Runx3 expression. In addition, other Ki67+ cytotoxic lymphocytes cells included NK and CD8+ T cells with high levels of cytotoxicity genes. In vitro co-culture models using DLBCL target cells (MD901) and glofit showed healthy donor CD4+ and CD8+ T cells were equally capable of killing DLBCL target cells over 24 hrs. Interestingly, glofit binding fast-tracked CD4+ T cell CTL differentiation, within 6 hrs of co-culture both naïve and antigen-experienced CD4+ T cells had increased GzmB expression levels, formed CTLs and commenced killing tumor targets by a perforin-mediated mechanism. The untreated naïve CD4+ T cells included a subset of cells with increased expression levels for the key cytotoxicity genes RUNX3, TBX21, EOMES, GZMB, NKG7, CST7 and decreased ZBTB7B suggesting intrinsic mechanisms for the rapid CD4+ CTL ‘converters.‘ This CD4+ CTL phenotype (GzmB, ThPOK, Runx3, Eomes, T-bet) was maintained for at least 7 days. In the same co-culture system, Resp patient CD4+ T cells had a higher conversion to CTL phenotype (GzmB+) compared to early relapse patients. In addition, molecular interactions critical for this CD4+ CTL differentiation included IL2/IL2R, 41BB/41BBL, CD2/CD58 and LFA1/ICAM1. This is supported by cell-cell interactome analysis which showed the CD4+ CTL interact with the DLBCL cells via LFA-1/ICAM1. The contribution of these molecular pathways to CD4+ CTL generation was inversely correlated with CD20 expression levels on the DLBCL target line. In this co-culture model, CD4+ T cells also contributed to increased CD8+ CTL differentiation and proliferation. CONCLUSIONS In summary, CD4+ T cells play a critical direct role in glofit efficacy in DLBCL patients. This effect is dependent on two key factors including a specific DLBCL TME archetype with increased inflammation, and an intrinsic transition to CD4+ CTL differentiation.

BloodVol. 146(Supplement 1)
Peter MacCallum Cancer Centre (AU)
Openalex Percentile: Top 47%
CAR-T cell therapy research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.