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Single-cell multiomic dissection of response and resistance to chimeric antigen receptor T cells against BCMA in relapsed multiple myeloma

Nature Cancer (2024)Cite this article

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Abstract

Markers that predict response and resistance to chimeric antigen receptor (CAR) T cells in relapsed/refractory multiple myeloma are currently missing. We subjected mononuclear cells isolated from peripheral blood and bone marrow before and after the application of approved B cell maturation antigen-directed CAR T cells to single-cell multiomic analyses to identify markers associated with resistance and early relapse. Differences between responders and nonresponders were identified at the time of leukapheresis. Nonresponders showed an immunosuppressive microenvironment characterized by increased numbers of monocytes expressing the immune checkpoint molecule CD39 and suppressed CD8+ T cell and natural killer cell function. Analysis of CAR T cells showed cytotoxic and exhausted phenotypes in hyperexpanded clones compared to low/intermediate expanded clones. We identified potential immunotherapy targets on CAR T cells, like PD1, to improve their functionality and durability. Our work provides evidence that an immunosuppressive microenvironment causes resistance to CAR T cell therapies in multiple myeloma.

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Fig. 1: Participant characteristics, summary of treatments and outcome.
Fig. 2: Summary of workflow, clustering of samples based on cell-type annotation and origin and expression of canonical marker antigens.
Fig. 3: Analysis of peripheral blood and bone marrow cellular composition following CAR T cell infusion.
Fig. 4: Analysis of peripheral blood cellular composition at leukapheresis.
Fig. 5: In vitro cytotoxicity and expansion of (CAR) T cells in responders and nonresponders.
Fig. 6: In silico extraction of Ide-cel and Cilta-cel and TCR repertoire analyses.
Fig. 7: Single-cell transcriptomes and surface proteomes of CAR T cells based on clonality, product and compartment.
Fig. 8: Surface antigen expression of druggable targets and coevolution of susceptible and resistant circulating plasma cell and T cell clones after treatment.

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Data availability

RNA-seq and scRNA-seq, BCR sequencing and TCR sequencing data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession code GSE234261. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

Processing and analysis code related to this study is deposited in the GitHub repository at https://github.com/fraunhofer-izi/Rade_Grieb_et_al_2023.

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Acknowledgements

We thank S. Scharf from the Leipzig Medical Biobank for handling cryopreserved samples and D. Bretschneider, C. Mueller, K. Wildenberger and C. Wilhelm for performing fluorescence in situ hybridization analyses on clinical samples. We express our gratitude toward the participants and their families who participated in this analysis. This work was supported by a grant from the Deutsche Forschungsgemeinschaft (SPP µbone) and EU HORIZON Project CERTAINTY (101136379), and M.M. received financial support from grants from the International Myeloma Society, SpringWorks and Janssen.

Author information

Author notes

  1. Deceased: Sebastian Schwind.

  2. These authors contributed equally: Michael Rade, Nora Grieb.

  3. These authors jointly supervised this work: Kristin Reiche, Vladan Vucinic, Maximilian Merz.

Authors and Affiliations

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

    Michael Rade, Ronald Weiss, Ulrike Köhl & Kristin Reiche

  2. Department of Hematology, Cellular Therapy and Hemostaseology, University Hospital of Leipzig, Leipzig, Germany

    Nora Grieb, Luise Fischer, Patrick Born, Christina Kloetzer, Simone Heyn, Anne Sophie Kubasch, Song Yau Wang, Enrica Bach, Sandra Hoffmann, Jule Ussmann, Birthe Schetschorke, Saskia Hell, Sebastian Schwind, Klaus H. Metzeler, Marco Herling, Madlen Jentzsch, Georg-Nikolaus Franke, Uwe Platzbecker, Vladan Vucinic & Maximilian Merz

  3. Innovation Center Computer Assisted Surgery, University Hospital of Leipzig, Leipzig, Germany

    Nora Grieb

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

    Ronald Weiss, Andreas Boldt, Stephan Fricke, Paul Franz, Ulrich Sack, Ulrike Köhl & Kristin Reiche

  5. Singleron Biotechnologies, Cologne, Germany

    Jaren Sia, Jonathan Scolnick, Lakshmi Venkatraman & Stacy Xu

  6. Institute for Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany

    Ronny Baber

  7. Leipzig Medical Biobank, University Leipzig, Leipzig, Germany

    Ronny Baber

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

    Kristin Reiche

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  1. Michael Rade
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Contributions

Conception and design: M.R., N.G., R.W., V.V. and M.M. Acquisition of data (acquired and managed participants, provided facilities, fluorescence in situ hybridization, flow cytometry, biobanking, in vitro studies and so on): L.F., P.B., A.B., S.F., P.F., C.K., S. Heyn, A.S.K., R.B., S.Y.W., E.B., S. Hoffmann, J.U., B.S., S. Hell, M.J., S.S., K.H.M., G.-N.F., M.H., U.S., K.R., U.K., U.P., V.V. and M.M. Analysis and interpretation of data (for example, statistical analysis, biostatistics and computational analysis): M.R., N.G., R.W., J. Sia, S.X., L.F., J. Scolnick, L.V., K.R., V. V. and M.M. Writing, review and/or revision of the manuscript: all authors.

Corresponding author

Correspondence to Maximilian Merz.

Ethics declarations

Competing interests

S.F.: consultant and/or speaker fees from Novartis Pharma, Janssen-Cilag, Vertex Pharmaceuticals (Germany), Kite/Gilead Sciences, MSGO and Bristol-Myers Squibb. U.K.: consultant and/or speaker fees from AstraZeneca, Affimed, Glycostem, GammaDelta, Zelluna, Miltenyi Biotec and Novartis Pharma and Bristol-Myers Squibb. M.M.: advisory boards/honoraria/research support from Amgen, BMS, Celgene, Gilead, Janssen, Stemline, Springworks and Takeda. K.H.M.: BMS (consultancy and honoraria), AbbVie (honoraria, research funding), Pfizer (honoraria), Otsuka (honoraria), Janssen (honoraria) and Novartis (consultancy). U.P.: Syros (consultancy, honoraria, research funding), MDS Foundation (membership on an entity’s Board of Directors or advisory committees), Silence Therapeutics (consultancy, honoraria, research funding), Celgene (honoraria), Takeda (consultancy, honoraria, research funding), Fibrogen (research funding), Servier (consultancy, honoraria, research funding), Roche (research funding), Merck (research funding), Amgen (consultancy, research funding), Novartis (consultancy, honoraria, research funding), AbbVie (consultancy), Curis (consultancy, research funding), Janssen Biotech (consultancy, research funding), Jazz (consultancy, honoraria, research funding), BeiGene (research funding), Geron (consultancy, research funding) and Bristol-Myers Squibb (consultancy, honoraria, membership on an entity’s Board of Directors or advisory committees, other, travel support, medical writing support, research funding). M.J.: Novartis (honoraria), Amgen (honoraria), Pfizer (honoraria), Blueprint Medicine (honoraria), BMS (honoraria) and Jazz (honoraria). The remaining authors declare no competing interests.

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Nature Cancer thanks Marco Davila and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Enrichment analysis for post-infusional BMMCs and PBMCs.

GO term enrichment analysis of significantly differentially expressed genes (non-responders vs. responders) for BMMCs and PBMCs. Terms are ranked by rich factor, which is the number of DE genes in the term divided by the number of background genes in that term. Dot plot depict the highest ranked significantly enriched GO terms for biological processes (FDR < 0.05). The dot size indicates the z-score, which is the number of DE genes with logFC >0 minus the number of DE genes with logFC <0 divided by the square root of the number of term-associated genes. Grey/white dots indicate the same number of genes with a logFC >0 and <0. Cell type abbreviations: NK = natural killer; Mono = monocyte; cDC = classical dendritic cell.

Source data

Extended Data Fig. 2 Comparing post- and pre-infusional BMMCs and PBMCs in nonCR and CR.

a, Comparison between post- and pre-infusional cell types in PBMCs from responders (CR) and non-responders (nonCR). The number of biologically independent samples for the contrasts is as follows. CR: n = 4 for post- and n = 5 for pre-infusional groups. nonCR: n = 5 for post- and n = 5 for pre-infusional groups. Significant differences (unadjusted p-values) were estimated using the empirical Bayes moderated t-statistics (two-sided) implemented in the speckle package (* p = 0.1, ** p = 0,05, *** p = 0.01, **** p = 0.001, p < 0.0001 = *****). The exact p-values for CR are as follows: NK (p = 0.00210), NK_CD56bright (p = 0.01849), B memory (p = 0.08501) and pDC (p = 0,02643), B intermediate (p = 0.074792) and HSPC (p = 0,09701). The exact p-value for nonCR, is as follows: CD14 Mono (p = 0.03526). The center of the box plots indicates the median, and the upper and lower bounds of the boxes indicate first and third interquartile ranges. Whiskers extends to 1.5 x interquartile range. b, Differential gene expression analysis comparing post- with pre-infusional BMMCs and PBMCs in CR and nonCR Only cell types with significant genes are shown (FDR < 0.05 and fold change <1.5 or >1.5). A positive log fold change indicates upregulation in post-infusional samples. c, GO term enrichment analysis of significantly differentially expressed genes (post- vs. pre-infusional). Terms are ranked by rich factor, which is the number of DE genes in the term divided by the number of background genes in that term. Dot plots depict the highest ranked significantly enriched GO terms for biological processes (FDR < 0.05). The dot size indicates the z-score, which is the number of DE genes with logFC >0 minus the number of DE genes with logFC <0 divided by the square root of the number of term-associated genes. Grey/white dots indicate the same number of genes with a logFC >0 and <0. Cell type abbreviations: NK = natural killer; gdT-Cell = gamma delta T cell; Mono = monocyte; cDC = classical dendritic cell; pDC = plasmacytoid dendritic cell; Cilta cel = Ciltacabtagene autoleucel; Ide cel = Idecabtagene vicleucel; GMP = granulocyte-monocyte progenitor, HSPC = Hematopoietic stem and progenitor cell.

Source data

Extended Data Fig. 3 Comparing post- with pre-infusional T cell subtypes in nonCR and CR.

a, Differences in T cell subtype composition between post- and pre-infusional PBMCs from CR and nonCR. For each cell type, the log fold change in mean cell fraction between post- and pre-infusional samples was calculated with the R package speckle based on arcsin square root transformation. The cell fraction calculation includes all cell types (the denominator is the sum of all cells analyzed). For clarity, only cell types with a fold change >1.5 are shown. b, GO term enrichment analysis for DEGs comparing post- with pre-infusional PBMCs in CR and nonCR. GO terms are ranked by rich factor, which is the number of DE genes in the term divided by the number of background genes in that term. Dot plot depict the highest ranked significantly enriched GO terms for biological processes (FDR < 0.05). Cell type abbreviations: CM = central memory; EM = effector memory; TEMRA = effector memory cells re-expressing CD45RA; TPEX = precursor exhausted T cells; TEX = exhausted T cell; MAIT = Mucosal-associated invariant T cell; CTL_EOMES = eomesodermin expressing cytotoxic lymphocytes; CTL_GNLY = granulysin expressing cytotoxic lymphocytes; CTL_Exh = exhausted cytotoxic lymphocytes; Tfh T follicular helper cells; Treg = regulatory T cells; gdT = gamma delta T cell.

Source data

Extended Data Fig. 4 Enrichment analysis in CAR T cells (CD4 and CD8) compared to pre-infusional T cells from patients treated with Ide-cel.

GO term enrichment analysis of significantly differentially expressed genes (CAR vs. pre-infusional T cells) in participants 12 (a) and 14 (b). Terms are ranked by rich factor, which is the number of DE genes in the term divided by the number of background genes in that term. Dot plot depict the highest ranked significantly enriched GO terms for biological processes (FDR < 0.05).

Source data

Extended Data Fig. 5 Enrichment analysis in CAR T cells (T cell subtypes) compared to pre-infusional T cells from patients treated with Ide-cel.

a, GO term enrichment analysis of significantly differentially expressed genes (CAR vs. pre-infusional T cells) in PBMCs for patient 12. No significantly enriched GO terms were found for patient 14. b, Differential cell surface protein expression for CD4 and CD8 T-cells when comparing CAR+ from PBMCs with CAR+ from BMMCs for patient 12 (BMMCs CD4 n = 41; CD8 430 // PBMCs CD4 n = 214; CD8 4699). Genes with an FDR < 0.05 and fold change <1.5 or >1.5 were considered as significantly differentially expressed. Cell type abbreviations: CM = central memory; EM = effector memory; TEMRA = effector memory cells re-expressing CD45RA; TEX = exhausted T cell.

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Extended Data Fig. 6 Analysis of CD38 surface protein expression in patient 01 with samples available after exposure/becoming refractory to a CD38 antibody as well as after discontinuing the antibody.

Patient 01 had progressed with a plasma cell leukemia after frontline treatment with 4 cycles bortezomib/cyclophosphamide/dexamethasone (VCD), high-dose melphalan and autologous transplantation (HDM + ASCT) and lenalidomide (Len) maintenance. Circulating malignant plasma cells (n = 7071) showed homogeneous CD38 protein expression, which was significantly decreased 4 days after starting Daratumumab/Carfilzomib/Dexamethasone (Dara/Carf/Dex). The patient relapsed 9 months later. CD38 expression on circulating malignant plasma cells significantly increased after 4 days of 3rd line treatment with Elotuzumab/Pomalidomide/Dexamethasone (Elo/Pom/Dex). CD38 expression levels increased further upon relapsed from treatment with Idecabtagene vicleucel (Ide-cel) 2 months after the last dose of Daratumumab.

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Extended Data Fig. 7 Marker genes for T cell identities.

DE genes for each T cell identity were determined using the Wilcoxon rank-sum test. Only CAR negative cells were analyzed. Genes with an FDR < 0.05 and an absolute fold change >1.5 were considered statistically significant. Genes are ranked according to FDR. For each cell identity, 5 significant genes are shown. The color intensity indicates the standardized average expression level in a cell identity. The dot size indicates the percentage of expressing cells within each cell identity of the corresponding genes.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–8 (pseudotime trajectory analysis of preinfusional and postinfusional CD4+CD8+ T cells and CAR T cells) and Supplementary Fig. 9 (gating strategy).

Reporting Summary

Supplementary Tables

Supplementary Table 1. Summary of ten individuals treated with Ide-cel or Cilta-cel (participants P7 and P8). Except for participants P8 and P10, all individuals had at least one high-risk cytogenetic feature detected by fluorescence in situ hybridization (mostly chromosome 1 abnormalities). Bridging therapy consisted mainly of antibody-based three-drug combinations. Participant P9 was treated with high-dose chemotherapy and autologous stem cell transplantation before CAR T cell infusion. Abbreviations: m, male; f, female; FISH, fluorescence in situ hybridization; Elo/Pom/Dex, elotuzumab/pomalidomid/dexamethasone; Benda/Carf/Dex, bendamustine/carfilzomib/dexamethasone; Dara/Pom/Dex, daratumumab/pomalidomide/dexamethasone; HD-Melphalan, high-dose melphalan; ASCT, autologous stem cell transplantation; Benda/Dex, bendamustine/dexamethasone; IsaPomDex, isatuximab/pomalidomide/dexamethasone; Dara/Len/Dex, daratumumab/lenalidomide/dexamethasone; Isa/Carf/Dex, isatuximab/carfilzomib/dexamethasone. Supplementary Table 2. Summary of samples and quality control data from scRNA-seq, TCR and BCR sequencing and surface proteomics. Abbreviations: PR, partial remission; PD, progressive disease; UMI, unique molecular identifier. Supplementary Table 3. Feature reference .csv file declaring antibody capture constructs and associated barcodes. The CellRanger multipipeline requires this configuration .csv file as input. Supplementary Table 4. Summary of differential gene expression analyses. Testing for differential gene expression was performed using the function FindMarkers (Wilcoxon rank-sum test) implemented in Seurat. Genes with an FDR of <0.05 (Benjamini–Hochberg correction) and fold change of <1.5 or >1.5 were considered DEGs. Summaries include comparisons of different cell types between nonresponders and responders from PBMCs after CAR T cell therapy (post-PBMC; non-CR versus CR), BMMCs after CAR T cell therapy (post-BMMC; non-CR versus CR), PBMCs collected at apheresis (pre-PBMC; non-CR versus CR) and comparisons of PBMCs after CAR T cell therapy versus PBMCs at apheresis in responders (CR; post versus pre) and nonresponders (non-CR; post versus pre). Supplementary Table 5. Summary of differential gene expression analyses for T cell identities. Testing for differential gene expression was performed using the function FindMarkers (Wilcoxon rank-sum test) implemented in Seurat. Genes with an FDR of <0.05 (Benjamini–Hochberg correction) and fold change of <1.5 or >1.5 were considered DEGs. Summaries include comparisons of T cell identities between nonresponders and responders from PBMCs at apheresis (pre-PB; T cell; non-CR versus CR), PBMCs after CAR T cell therapy (post-PB; T cell; non-CR versus CR), BMMCs after CAR T cell therapy (post-BM; T cell; non-CR versus CR) and comparisons of PBMCs after CAR T cell therapy versus PBMCs at apheresis in responders (CR; PB; T cell; post versus pre) and nonresponders (non-CR; PB; T cell post versus pre), CAR T cells versus preinfusional T cells in participant P12 (CAR versus pre-T cell) and CAR T cells versus preinfusional T cells in participant P14 (CAR versus pre-T cell).

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Rade, M., Grieb, N., Weiss, R. et al. Single-cell multiomic dissection of response and resistance to chimeric antigen receptor T cells against BCMA in relapsed multiple myeloma. Nat Cancer (2024). https://doi.org/10.1038/s43018-024-00763-8

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