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Multiomic profiling of T cell lymphoma after therapy with anti-BCMA CAR T cells and GPRC5D-directed bispecific antibody

Nature Medicine volume 31, pages 1145–1153 (2025)Cite this article

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Abstract

Chimeric antigen receptor (CAR) T cells and bispecific T cell engagers have become integral components in the treatment of relapsed/refractory multiple myeloma. We report a 63-year-old male who received ciltacabtagene autoleucel CAR T cells and the GPRC5D × CD3 bispecific talquetamab for early relapse of his multiple myeloma. Nine months after CAR T therapy, he developed a symptomatic leukemic peripheral T cell lymphoma with cutaneous and intestinal involvement. Longitudinal single-cell RNA and T cell receptor sequencing of peripheral blood and bone marrow revealed two hyperexpanded CAR-carrying T cell clones. These expanded clones exhibited an exhausted effector-memory T cell transcriptional signature, and the neoplasm itself was sensitive to dexamethasone treatment. The immunophenotypic and transcriptional alterations of these abnormal T cells resembled those of T-large granular lymphocytic leukemia. Spatial transcriptomes of skin lesions confirmed the aberrant CAR-expressing T cells. Whole-genome sequencing revealed three distinct integration sites, within the introns of ZGPAT, KPNA4 and polycomb-associated noncoding RNAs. Before and after CAR T whole-genome analyses implicated clonal outgrowth of a TET2-mutated precursor propelled by additional subclone-specific loss of heterozygosity and other secondary mechanisms. This case highlights the evolution of a CAR-carrying peripheral T cell lymphoma following CAR T cell and bispecific T cell engager therapy, offering critical insights into the clonal evolution from a predisposed hematopoietic precursor to a mature neoplasm.

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Fig. 1: Persistent CAR T cell proliferation with cutaneous and gastrointestinal manifestations after cilta-cel therapy in a patient with MM.
Fig. 2: Spatially resolved single-cell multiomics analyses uncover clonal diversity and transcriptional shifts in CAR+ T cell lymphoma after steroid therapy.
Fig. 3: WGS reveals TET2 loss-of-function mutations as a common variant in the CAR+ T cell proliferations, while both CAR+ T cell clones exhibit distinct integration sites.
Fig. 4: Clonal outgrowth of a TET2-mutated precursor propelled by additional subclone-specific LOH and other secondary mechanisms.

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Data availability

The single-cell sequencing data supporting the study’s findings have been deposited in the Gene Expression Omnibus under accession codes GSE283144 (scRNA-seq) and GSE283145 (spatial transcriptome). Preprocessed Seurat Objects for the single-cell data are available via Zenodo at https://zenodo.org/records/14251447 (ref. 67). Access requests to the raw WGS data (FASTQ and BAM files) will be handled according to German law. The first author, T.B. (contact: till.braun@uk-koeln.de), will facilitate external data requests with an expected response time frame of 4 weeks. To ensure the reproducibility of the CAR integration site analysis and genomic variant calling, we provide full access to the relevant minimal datasets via the GitHub repository: https://github.com/fraunhofer-izi/Braun_et_al_2024.

Code availability

Processing and analysis code related to this study is deposited in the GitHub repository at https://github.com/fraunhofer-izi/Braun_et_al_2024.

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Acknowledgements

This work was partly supported by the imSAVAR project, which received funding from the Innovative Medicine Initiative 2 Joint Undertaking (JU) under grant agreement no. 853988. The JU receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA and JDRF INTERNATIONAL. Further, it was partly supported by the CERTAINTY project funded by the European Union (grant agreement no. 101136379). Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or the Health and Digital Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. T.B. is funded by the Deutsche Krebshilfe through a Mildred Scheel Nachwuchszentrum scholarship (grant no. 70113307) and, together with N.P., received research grants from the Sander Stiftung (grant no. 2023.084.1) and the DFG (seq-costs in projects, grant no. PF1028/1-1). M. Herling is supported by a grant from the German José-Carreras Leukemia Foundation (grant no. DJCLS 01 R_2023) and by the Faculty of Medicine of the University of Leipzig (endowed Professorship). T.B. and M. Herling are both part of the ImmuneT-ME consortium (EPPERMED2024-522). M.M. is supported by a Translational Research Award from the International Myeloma Society, the German Research Foundation SPP µbone, the EU Horizon Program CERTAINTY and the Deutsche Jose Carreras Leukämie Stiftung. Additionally, M.M. has received research funding from Janssen, SpringWorks and Roche/Genentech. K.R. is supported by imSAVAR (Innovative Medicine Initiative 2 Joint Undertaking grant no. 853988), CERTAINTY (European Union, grant agreement no. 101136379), T2Evolve (Innovative Medicine Initiative 2 Joint Undertaking grant no. 945393), SaxoCell (BMBF Clusters4Future, grant no. 03ZU111MB/03ZU111MD), DAAD project grant no. 57616814 (SECAI, School of Embedded Composite AI) and the German José-Carreras Leukemia Foundation (grant no. DJCLS 08 R/2023). We extend our gratitude to C.-A. Voltin (University Hospital of Cologne) for providing PET–CT scan images, G. Allo (University Hospital of Cologne) for providing endoscopy images and C. Hertel (University Hospital of Leipzig) for sample handling. We thank S. Greiser (Experimental Imaging Unit, Fraunhofer IZI) for technical support with the microscopy of the H&E slides for spatial transcriptomics. In addition, many thanks to A. Raap, W. Jahnke and J. Wiegand from the Diagnostics Department of the Fraunhofer IZI for preparing samples and conducting WGS and spatial transcriptomic analyses. Most importantly, we express our deepest thanks to the patient for his invaluable contribution.

Author information

Author notes

  1. These authors contributed equally: Till Braun, Michael Rade, Maximilian Merz.

  2. These authors jointly supervised this work: Kristin Reiche, Marco Herling, Tim Richardson.

Authors and Affiliations

  1. Department I of Internal Medicine, Center for Integrated Oncology Aachen-Bonn-Cologne-Duesseldorf (CIO ABCD), University Hospital Cologne, University of Cologne, Cologne, Germany

    Till Braun, Hanna Klepzig, Florian Kuschel, Natali Pflug, Michael Hallek, Udo Holtick, Christof Scheid & Tim Richardson

  2. Mildred Scheel School of Oncology Aachen Bonn Cologne Düsseldorf (MSSO ABCD), Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany

    Till Braun

  3. Fraunhofer Institute for Cell Therapy and Immunology IZI, Leipzig, Germany

    Michael Rade, Maximilian Merz, Florian Große, David Fandrei, Nhu-Nguyen Pham, Markus Kreuz, Christina Katharina Kuhn, Dennis Löffler, Ulrike Köhl & Kristin Reiche

  4. Department of Hematology, Cellular Therapy, Hemostaseology, Infectious Diseases, University Hospital of Leipzig, Leipzig, Germany

    Maximilian Merz, David Fandrei, Uwe Platzbecker & Marco Herling

  5. Cancer Center Central Germany (CCCG) Leipzig-Jena, University Hospital of Leipzig, Leipzig, Germany

    Maximilian Merz, David Fandrei, Uwe Platzbecker, Ulrike Köhl, Kristin Reiche & Marco Herling

  6. Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden/Leipzig, Germany

    Florian Große, Ulrike Köhl & Kristin Reiche

  7. Institute of Pathology, University Hospital Cologne, University of Cologne, Cologne, Germany

    Jörn Meinel

  8. Institute of Virology, University Hospital Cologne, University of Cologne, Cologne, Germany

    Eva Heger

  9. Department of Dermatology and Venereology, University Hospital Cologne, University of Cologne, Cologne, Germany

    Viola Schweinsberg

  10. Institute for Clinical Immunology, University Hospital of Leipzig, Leipzig, Germany

    Ulrike Köhl & Kristin Reiche

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Contributions

T.B. J.M., E.H., V.S., U.H., C.S. and T.R. were involved in diagnostic and clinical case management. T.B., M.R., K.R., M.M. and M. Herling were responsible for the experimental design. T.B., H.K., F.K. and D.L. performed the experiments. M.R., M.K., D.F., F.G., N.-N.P. and C.K.K were responsible for data analysis. T.B., N.P., U.P., M. Hallek, U.H., U.K., C.S., K.R., M.M. and M. Herling provided resources and supervision. T.B., M.R., K.R., M.M. and M. Herling prepared the manuscript. All authors revised and approved the manuscript.

Corresponding author

Correspondence to Maximilian Merz.

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Competing interests

M.M. gave advisory boards and received honoraria and research support from Amgen, BMS, Celgene, Gilead, Janssen, Stemline, Springworks, Sanofi and Takeda. U.H. received consultant and/or speaker fees from Bristol-Myers Squibb, Gilead, Janssen, Miltenyi Biotec and Novartis. C.S. gave advisory boards and received honoraria from Amgen, Abbvie, Bristol-Myers Squibb, Janssen, Novartis, Oncopeptides, Pfizer, Roche, Sanofi, Stemline Menarini and Takeda, and received research support from Janssen and Takeda. U.K. received consultant and/or speaker fees from AstraZeneca, Affimed, Glycostem, GammaDelta, Zelluna, CGT manufacturing: Miltenyi Biotec and Novartis Pharma GmbH, Bristol-Myers Squibb GmbH & Co. M. Herling gave advisory boards and received honoraria from Abbvie, Beigene, Jazz, Janssen, Stemline Menarini and Takeda, and received research support from EDO-Mundipharma, Janpix, Novartis and Roche. The other authors declare no competing interests.

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Nature Medicine thanks Jake Jackson, Simone Webb and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Saheli Sadanand, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Cutaneous infiltration of clonal CD30+ T cells.

Immunohistochemical analysis (n=1) of a skin biopsy obtained from the left dorsal region of the patient at nine months post CAT-T therapy. The panels display staining for H&E, CD3, CD5, CD4, Granzyme B, MUM1, and CD30, with the magnification indicated in each image. The biopsy revealed dense lymphocytic infiltrations associated with smaller blood vessels. These infiltrates were positive for CD3, MUM1, Granzyme B, and CD30, predominantly negative for CD4, and exhibited partial loss of CD5.

Extended Data Fig. 2 Quantification of CAR Transgene by Digital PCR of the Lentiviral LTR Sequence.

This figure illustrates the results of digital PCR (ddPCR) analysis for quantification of the CAR transgene. The FAM fluorescence amplitude corresponds to the quantification of the lentiviral LTR sequence, indicating CAR transgene expression. The HEX fluorescence amplitude indicates the RPP30 reference gene as a control for normalization. Red dot populations are positive partitions, blue dot populations are negative partitions, and the dashed line marks the threshold for positivity. Quantification is presented for each condition (copies/µl). Upper panel: Results are displayed for skin biopsy, both in undiluted and serially diluted formats (1:10 and 1:100), and peripheral blood (PB). Lower panel: Results are displayed for duodenal biopsy.

Extended Data Fig. 3 Flow cytometry reveals predominant memory T-cell differentiation.

(a) Flow cytometry analysis of lymphocyte populations in PB. Upper Panel: Lymphocytes were identified based on forward scatter (FS) and side scatter (SS) parameters, and subsequently divided into CD5+ and CD5- populations. Middle Panel: Analysis of the CD5+ population, and Lower Panel: Analysis of the CD5- population. In both the middle and lower panels, naïve and memory T cells were identified by CD45RA and CD45RO expression, respectively. Staining for CCR7 and CD62L allowed further subclassification. (b) Flow cytometry analysis of bone marrow 9 months post-CAR-T infusion, at the time of cutaneous lesion development, and prior to dexamethasone treatment. Upper left: Lymphocytes are identified based on side scatter (SS) and CD45 expression. Lower left: CD5+ BCMA-CAR- T cells. Upper right: CD5+ BCMA-CAR+ T cells. Lower right: CD5- BCMA-CAR+ T cells. Expression profiles of TRBC1 and TRBC2, as well as CD4 and CD8, are shown for each population. The percentage of expression for each marker and the corresponding fluorochrome are indicated in the graph. (c) Sorting strategy for flow cytometry-based cell sorting of CAR+CD5+, CAR+CD5-, and CAR- CD5+ T-cell populations. Singlets were selected based on the SS, and viable T-cells based on the expression of CD3+ and Calcein+. From the single Calcein+CD3+ T-cells, the three respective populations were sorted based on the expression of CAR and CD5.

Extended Data Fig. 4 Duodenal infiltration of clonal T cells.

Immunohistochemical analysis (n=1) of a duodenal biopsy performed at twelve months post CAR-T infusion. The panels display staining for H&E, CD3, CD5, CD4, Granzyme B, MUM1, and CD30, with the magnification indicated in each image. The biopsy revealed again dense lymphocytic infiltrations. These infiltrates were positive for CD3, CD5, and Granzyme B, partially positive for CD4, and negative for MUM1 and CD30.

Extended Data Fig. 5 Dimension reduction for CD8 T cells.

(a) CD8 cells were embedded into a two-dimensional space by the UMAP method. Each dot represents a single cell. (b) Cells are colored by CD45RO/RA expression using ADT (antibody-derived tag) data normalized using a centered log ratio transformation (CLR). Expression values <1 are considered as background noise.

Extended Data Fig. 6 Copy-number profile of chromosome 6 for Clone 1_3.

For each clonotype raw expression values were classified into gains (exprs>1.05) and deletions (exprs<0.95). Panel (a) shows the copy-number profile of chromosome 6 for Clone 1_3. The proportion of cells with gains (red) and deletions (blue) is presented in genomic order. (b) Differential gene expression analysis between Clone 1_3, Clone 2, and apheresis (Aph) for genes located in the region with the most recurrent gain (>75% of cells affected in Clone 1_3).

Extended Data Fig. 7 Enrichment analysis with T-cell specific gene sets.

Differences in enrichment scores for T-cell-specific gene sets after dexamethasone treatment, comparing the samples PB with PB+Dexa, by Clone 1_3 and Clone 2. Average enrichment scores were calculated for each gene set and sample, with significance (FDR < 0.05) assessed using a two-sided Wilcoxon test. (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.001).

Extended Data Fig. 8 Mutation frequencies in the different T-cell populations at the time point of lymphoma development.

All nonsynonymous somatic single nucleotide variants and indels that passed quality control (see Methods) were included. The filled colors of the bar plot segments represent genomic locations of retained variants for tumor mutational burden (TMB) analysis. TMB was calculated per hg38 effective genome size (3,049 mb; Hg38 7-way Genome size statistics - genomewiki). We detected a higher TMB in the CAR+CD5- (1.9 Mut/Mb) and the CAR+CD5+ (0.8 Mut/Mb) populations compared to the CAR-CD5+ (0.1 Mut/Mb) population. These results showed that TMB in the neoplastic CAR+ clones were within the range of a recently published (Schrader et al Nat Commun 2018) whole exome sequencing (WES, NimbleGen SeqCap EZ Exome v3: 64,000,000 bp) cohort of 17 T-cell prolymphocytic leukemia (T-PLL) patients (median TMB: 1.1 Mut/Mb; range, 0.2 – 2.3) and 2 T-cell large granular lymphocyte (LGL) patients (median TMB: 1.1 Mut/Mb). WGS TMB was consistent after filtering mutations in coding DNA sequences (CDS) and untranslated regions (UTR) of protein-coding genes divided by genome size of the respective regions (84 Mb) in hg38 build using the GENCODE v46 annotations (CAR+CD5-: 2.0 Mut/Mb, CAR+CD5+: 0.7 Mut/Mb; CAR-CD5+: 0.1 Mut/Mb).

Extended Data Fig. 9 Variant allele frequency in scRNA-seq samples.

Single-cell RNA-Seq data was analyzed for reads supporting the TET2 p.R544* variant. Samples of BM-preDexa, PB-preDexa, and PB-postDexa were merged and split into Clone 1_3, Clone 2 and other T-cells (Other clones). For the analysis of the apheresis sample no filtering for T-cells was performed. The frequency of supporting reads is shown for Clone 1_3, Clone 2, other clones and the apheresis sample. For each sample, the number of high-quality supporting reads / total reads is presented. Notably, 3 out of 109 total reads of the TET2 showed the respective variant in the apheresis sample, indicating CHIP origin of this mutation.

Extended Data Table 1 Overview of Samples Utilized in the Study
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Braun, T., Rade, M., Merz, M. et al. Multiomic profiling of T cell lymphoma after therapy with anti-BCMA CAR T cells and GPRC5D-directed bispecific antibody. Nat Med 31, 1145–1153 (2025). https://doi.org/10.1038/s41591-025-03499-9

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