
==== Front
Clin Cancer Res
Clin Cancer Res
Clinical Cancer Research
1078-0432
1557-3265
American Association for Cancer Research

39024031
CCR-24-0352
10.1158/1078-0432.CCR-24-0352
Version of Record
Hematological Cancers
Myelomas
Immunotherapy
Engineered/CAR T cells
Single Cell Technologies
Tumor Microenvironment
Immune Cells and the Microenvironment
Translational Cancer Mechanisms and Therapy
Reconstitution of the Multiple Myeloma Microenvironment Following Lymphodepletion with BCMA CAR-T Therapy
Reconstitution of the Myeloma Microenvironment after CAR-T Therapy
https://orcid.org/0009-0005-9144-4292
Yang Yazi 1 #
https://orcid.org/0000-0002-1356-453X
Qin Sen 2 #
https://orcid.org/0009-0002-7837-1015
Yang Mengyu 2
https://orcid.org/0009-0001-6067-4862
Wang Ting 1
https://orcid.org/0000-0003-4585-554X
Feng Ru 1
https://orcid.org/0000-0003-0330-9567
Zhang Chunli 1
https://orcid.org/0009-0004-4547-7336
Zheng Enrun 2
https://orcid.org/0009-0003-1955-701X
Li Qinghua 2
https://orcid.org/0009-0005-7497-5649
Xiang Pengyu 2
https://orcid.org/0009-0007-8937-9908
Ning Shangyong 1
https://orcid.org/0009-0000-4219-9714
Xu Xiaodong 1
https://orcid.org/0009-0006-2722-7389
Zuo Xin 1
https://orcid.org/0000-0002-9046-2190
Zhang Shuai 1
https://orcid.org/0000-0003-2171-1082
Yun Xiaoya 1
https://orcid.org/0009-0000-6247-4189
Zhou Xuehong 2
https://orcid.org/0009-0007-0326-2873
Wang Yue 3
https://orcid.org/0000-0001-7429-8012
He Lin 24
https://orcid.org/0000-0001-5050-7625
Shang Yongfeng 23*
https://orcid.org/0000-0003-3917-473X
Sun Luyang 24*
https://orcid.org/0000-0002-8166-0216
Liu Hui 1*
1 Department of Hematology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
2 Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Peking University International Cancer Institute, Peking University Health Science Center, Beijing, China.
3 Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China.
4 Department of Integration of Chinese and Western Medicine, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University Health Science Center, Beijing, China.
* Corresponding Authors: Yongfeng Shang, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Peking University International Cancer Institute, Peking University Health Science Center, Beijing 100191, China; Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou 311121, China. E-mail: yshang@hsc.pku.edu.cn; Luyang Sun, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Peking University International Cancer Institute, Peking University Health Science Center, Beijing 100191, China; Department of Integration of Chinese and Western Medicine, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University Health Science Center, Beijing 100191, China. E-mail: luyang_sun@hsc.pku.edu.cn; and Hui Liu, Department of Hematology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China. E-mail: liuhui8140@126.com
Clin Cancer Res 2024;30:4201–14

# Y. Yang and S. Qin contributed equally to this article.

13 9 2024
18 7 2024
30 18 42014214
13 2 2024
09 4 2024
16 7 2024
©2024 The Authors; Published by the American Association for Cancer Research
2024
American Association for Cancer Research
https://creativecommons.org/licenses/by-nc-nd/4.0/ This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

Abstract

Purpose:

The purpose of this study was to investigate the remodeling of the multiple myeloma microenvironment after B-cell maturation antigen (BCMA)–targeted chimeric antigen receptor T (CAR-T) cell therapy.

Experimental Design:

We performed single-cell RNA sequencing on paired bone marrow specimens (n = 14) from seven patients with multiple myeloma before (i.e., baseline, “day −4”) and after (i.e., “day 28”) lymphodepleted BCMA CAR-T cell therapy.

Results:

Our analysis revealed heterogeneity in gene expression profiles among multiple myeloma cells, even those harboring the same cytogenetic abnormalities. The best overall responses of patients over the 15-month follow-up are positively correlated with the abundance and targeted cytotoxic activity of CD8+ effector CAR-T cells on day 28 after CAR-T cell infusion. Additionally, favorable responses are associated with attenuated immunosuppression mediated by regulatory T cells, enhanced CD8+ effector T-cell cytotoxic activity, and elevated type 1 conventional dendritic cell (DC) antigen presentation ability. DC re-clustering inferred intramedullary-originated type 3 conventional DCs with extramedullary migration. Cell–cell communication network analysis indicated that BCMA CAR-T therapy mitigates BAFF/GALECTIN/MK pathway–mediated immunosuppression and activates MIF pathway–mediated anti–multiple myeloma immunity.

Conclusions:

Our study sheds light on multiple myeloma microenvironment dynamics after BCMA CAR-T therapy, offering clues for predicting treatment responsivity.

Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation) http://dx.doi.org/10.13039/501100004826 Z200020 Sun L. Ministry of Science and Technology of the People’s Republic of China (MOST) http://dx.doi.org/10.13039/501100002855 2021YFA1300603 Shang Y. Sun L. National Natural Science Foundation of China (NSFC) http://dx.doi.org/10.13039/501100001809 32350020 32370620 Sun L. National Natural Science Foundation of China (NSFC) http://dx.doi.org/10.13039/501100001809 82188102 31991164 Shang Y. National High Level Hospital Clinical Research Funding http://dx.doi.org/10.13039/ BJ-2022-127 Liu H. Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation) http://dx.doi.org/10.13039/501100004826 7232137 Liu H. CAMS Innovation Fund for Medical Sciences http://dx.doi.org/10.13039/ 2021-I2M-C&T-A-020 Liu H.
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pmcTranslational Relevance

Despite the acknowledged effectiveness of B-cell maturation antigen (BCMA) chimeric antigen receptor T (CAR-T) cells in killing multiple myeloma cells, predicting treatment responsivity to BCMA CAR-T therapy remains a challenge. In this study, we demonstrated that the best overall responses of patients over the 15-month follow-up are positively correlated with the abundance and targeted cytotoxic activity of CD8+ effector CAR-T cells on day 28 after CAR-T cell infusion. Additionally, favorable responses are associated with attenuated immunosuppression mediated by regulatory T cells, enhanced CD8+ effector T-cell cytotoxic activity, and elevated type 1 conventional dendritic cell antigen presentation ability. Our study sheds light on multiple myeloma microenvironment dynamics after BCMA CAR-T therapy, offering clues for predicting treatment responsivity.

Introduction

Multiple myeloma is a malignancy characterized by malignant plasma cells, which accounts for more than 10% of all hematologic cancers (1), with patients typically presenting with bone marrow (BM) infiltration of multiple myeloma cells (2). Chimeric antigen receptor T (CAR-T) cell therapy is based on modifying T cells to target specific cancer cell surface antigens (3). B-cell maturation antigen (BCMA, also known as TNFRSF17) serves as a well-established target for CAR-T therapy in multiple myeloma because of its consistent expression on multiple myeloma cells (4). Clinical studies have demonstrated significant efficacy of BCMA CAR-T therapy in treating patients with multiple myeloma (5, 6). However, predicting treatment responsivity to BCMA CAR-T therapy remains a challenge.

The multiple myeloma microenvironment comprises multiple myeloma cells, stromal cells, and immune cells, including lymphocytes and myeloid cells (7). To create a favorable immune microenvironment for CAR-T cells, lymphodepletion-conditioning regimens are often used. These regimens, which deplete both lymphocytes and myeloid cells, enhance CAR-T cell expansion, persistence, and clinical activity while reducing anti-CAR immune responses (8). Cyclophosphamide, a highly effective lymphodepleting agent, is commonly administered before (i.e., “day −3 to −5”) BCMA CAR-T cell infusion in multiple myeloma to boost the engraftment of transferred BCMA CAR-T cells (9, 10). This treatment induces the generation of new immune cell populations [e.g., T cells, monocytes, and dendritic cells (DC)], contributing to multiple myeloma microenvironment reconstitution (11). Despite the acknowledged effectiveness of BCMA CAR-T cells in killing multiple myeloma cells, there is limited exploration into the immune cell reconstitution following lymphodepleted BCMA CAR-T therapy.

Here, we used single-cell RNA sequencing (scRNA-seq) to examine paired BM samples (n = 14) from seven patients with multiple myeloma before (i.e., baseline, “day −4”) and after (i.e., “day 28”) BCMA CAR-T therapy. We conducted copy-number variation (CNV) and gene expression analyses to evaluate the gene expression profiles in multiple myeloma cells. The best overall responses (BOR) of patients over the 15-month follow-up are positively correlated with the abundance activity of CD8+ effector CAR-T cells on day 28 after CAR-T cell infusion, as well as reduced immunosuppression and enhanced antitumor immunity. Re-clustering of DCs inferred the intramedullary origin and extramedullary migration of type 3 conventional DCs (cDC3). An intercellular cross-talk analysis indicated that the BCMA CAR-T therapy suppresses BAFF/GALECTIN/MK pathway–mediated immunosuppression pathways and stimulates MIF pathway–mediated anti–multiple myeloma immunity. Our study provides insights into the multiple myeloma microenvironment reconstitution induced by lymphodepletion and BCMA CAR-T cell infusion, offering clues for predicting the treatment responsivity.

Materials and Methods

Ethical regulations

This study adhered to the Declaration of Helsinki, with all participants providing written informed consent. The Beijing Hospital Ethics Committee approved the study protocol (2020BJYYEC-162-03).

Patient cohort

The analysis included seven (one male and six female) patients with multiple myeloma diagnosed and treated at Beijing Hospital. The median age at diagnosis was 62 years (range 45–73). Isotype distribution showed two patients with IgG-κ, one with IgA-κ, and four with IgA-λ. Patients had undergone a median of three previous therapies (range 2–3), with four being triple-class refractory (to immunomodulatory drug, proteasome inhibitor, and anti–CD38 mAb). One patient had undergone previous autologous stem-cell transplantation.

Tissue processing

BM aspirate samples were collected during the diagnostic procedure. Bone marrow mononuclear cells were isolated using Ficoll-Paque PLUS (Cytiva) and washed three times with Hank's Balanced Salt Solution (Solarbio). Following centrifugation at 1,000 rpm for 5 minutes, the supernatant was discarded, and the sediment was resuspended in 1 mL PBS (Solarbio). To eliminate red blood cells, 1 to 2 mL red blood cell lysis buffer (BD Biosciences) was added at 4°C for 10 minutes. After centrifugation at 500 g for 5 minutes, the cells were suspended in PBS. The cell viability was assessed using trypan blue (Sigma) and examined under a microscope.

Single-cell library construction and sequencing

As previously described (12), using the 10x Genomics Chromium Single Cell 3′ v3 Library Kit and Chromium instrument, we partitioned approximately 17,500 cells into nanoliter droplets to achieve single-cell resolution, with a maximum of 10,000 individual cells per sample. The resulting cDNA was labeled with a common 16-nt cell barcode and 10-nt unique molecular identifier (UMI) during the RT reaction. Full-length cDNA from poly-A mRNA transcripts underwent enzymatic fragmentation and size selection to optimize the cDNA amplicon size (approximately 400 bp) for library construction following the 10x Genomics protocol. The concentration of the 10x single-cell library was accurately determined through qPCR (Kapa Biosystems), ensuring cluster counts suitable for the HiSeq 4000 or NovaSeq 6000 platform (Illumina). In total, 26 × 98 bp (3′ v3 libraries) sequence data were generated, targeting between 25 and 50 K read pairs/cell, providing digital gene expression profiles for each individual cell.

Generation of the custom reference genome containing the CAR sequence

We used the human genome (GRCh38) and transcriptome annotations provided by 10x Genomics. To accurately identify BCMA CAR-T cells, we supplemented our GRCh38-based reference with a segment of the CAR plasmid sequence. This modified reference genome was prepared using Cell Ranger mkref (10x Genomics, v.6.1.2) and served as the reference for preprocessing the entire dataset.

General scRNA-seq data analysis

For the analysis of scRNA-seq data, we utilized Cell Ranger (10x Genomics, v.6.1.2, RRID: SCR_023672) to preprocess the paired-end raw reads. Cell barcodes and UMIs of the library were extracted from read 1. Then, the reads were segregated based on their cell (barcode) IDs, with concurrent recording of the UMI sequences from read 2 for each cell. Quality control (QC) measures were then implemented on the raw reads to eliminate adapter contamination, duplicates, and low-quality bases. After filtering out barcodes and low-quality reads unrelated to cells, we mapped the cleaned reads to the custom reference genome we generated, retaining uniquely mapped reads for UMI counts. Next, we estimated accurate molecular counts and produced a UMI count matrix for each cell by counting UMIs for each sample. Finally, we generated a gene–barcode matrix, illustrating the barcoded cells and gene expression counts.

All subsequent analyses were conducted using the R package Seurat (v.4.1.1, RRID: SCR_016341; ref. 13). To perform QC on scRNA-seq data, various filters were applied to the data to exclude barcodes falling into specific categories: those with too few expressed genes (potentially debris), those with too many associated UMIs (potentially more than 1 cell), and those with excessively high mitochondrial gene expression (potentially dead cells). The cutoffs for these filters followed the recommendations of the Seurat package, including a minimum of 200 and a maximum of 5,000 genes for controlling the number of genes, and a maximum of 10% for mitochondrial gene expression to ensure QC. Doublets were predicted using DoubletFinder (v.2.0.3, RRID: SCR_018771; ref. 14). After discarding low-quality cells and doublets, the remaining single cells that met the QC criteria were used for subsequent analyses.

Cell clustering and cell-type annotation

The Seurat software package (v.4.1.1) was used for cell clustering analysis to identify major cell types. All Seurat objects constructed from the filtered UMI-based gene expression matrices of the respective samples were merged. Initially, we utilized the SCTransform (v.0.3.4) function to perform normalization, variance stabilization, and feature selection through a regularized negative binomial model. Subsequently, principal component analysis was applied for linear dimensionality reduction, focusing on the top 3,000 variable genes. Following the standard procedures implemented in Seurat, a variable number of highly variable principal components ranging from 1 to 50 were selected and used for clustering, using the Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) method.

Identification of cell types within these clusters was based on the expression of canonical cell-type markers or inferred using the CellMarker database (15). Cluster markers were also validated using the FindAllMarkers function of the Seurat suite, and cell types were manually annotated based on the certified markers. The subclustering resolutions (0.2 for multiple myeloma cells, 2.5 for BCMA CAR-T cells, 0.6 for endogenous T cells, 0.5 for monocytes, and 0.5 for DCs) were selected to ensure that each subcluster can be distinguished from others and thus able to be specifically defined based on canonical marker genes. The number of multiple myeloma cells was corrected in the analysis of immune cell–type proportions among the multiple myeloma microenvironment.

Correlation analysis

After integration, we compared the gene expression of each cell cluster with others to identify significantly highly expressed genes (adjusted P value <0.05 and log fold change >0). Then the average gene expressions in each cell cluster were calculated, and pairwise correlations were estimated.

Gene set expression level analysis

The CellCycleScoring function of the Seurat suite was used to infer the cell cycle of every cell. The expression percentage of each gene set, calculated by the AddModuleScore function of the Seurat suite, was defined as the gene set expression level for every cell. Cytotoxicity and exhaustion scores were calculated using classical cytotoxicity-associated genes (GZMA, GZMB, GZMH, GZMK, NKG7, GNLY, IFNG, TNF, KLRD1, FCGR3A, PRF1, and TYROBP) and exhausted marker genes (TIGIT, LAG3, PDCD1, CD160, SLAMF6, BTLA, HAVCR2, VSIR, and CTLA4; ref. 16). HLA-I and HLA-II expression scores were calculated using major HLA-I molecules (HLA-A, HLA-B, HLA-C, HLA-E, and HLA-F) and HLA-II molecules (HLA-DRA, HLA-DRB1, HLA-DRB5, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DOA, HLA-DMA, and HLA-DMB; ref. 17).

Differentially expressed gene identification and enrichment analysis

As previously described (12), the cluster-specific genes were identified by running Seurat, including the FindAllMarkers function on a log-transformed expression matrix (min. pct = 0.25, only; Pos = TRUE, and logfc.threshold = 0.25). Differentially overexpressed genes between two clusters were identified using the Wilcoxon rank-sum test with the FindMarkers function in Seurat (adjusted P value <0.05, only.pos = T, and logfc.threshold = 0.1). The cluster-specific overrepresented Gene Ontology (GO) biological processes were calculated with the compareCluster function in the clusterProfiler package (v.4.2.2, RRID: SCR_016884) of R (18).

scRNA CNV detection and clustering

Genome-wide copy-number aberrations in multiple myeloma cells were inferred based on the genome-wide copy-number profiles computed from the gene expression UMI matrix using the Bayesian segmentation approach copy-number karyotyping of aneuploid tumors (CopyKAT; v.1.0.8, RRID: SCR_024512; ref. 19).

Pseudotime analysis

The Monocle2 packages (v.2.22.0; ref. 20) for R were used to determine the pseudotimes of the differentiation of different cell clusters. The differentialGeneTest function implemented in Monocle2 was used to identify genes that are significantly regulated as the cells differentiate along the cell-to-cell distance trajectory.

Cellular communication analysis

As previously described (12), cell–cell interactions based on the expression of known ligand–receptor pairs in different cell types were inferred using the R package CellChatDB (v.1.1.3, RRID: SCR_021946; ref. 21). In brief, we followed the official workflow and loaded the normalized counts into CellChat. We applied the preprocessing functions “identifyOverExpressedGenes,” “identifyOverExpressedInteractions,” and “projectData” with standard parameters set. As database, we selected the “Secreted Signaling” pathways and used the precompiled human “Protein–Protein Interactions” as a priori network information. For the main analyses, the core functions “computeCommunProb,” “computeCommunProbPathway,” and “aggregateNet” were applied using standard parameters and fixed randomization seeds. Finally, to determine the senders and receivers in the network, the function “netAnalysis_signalingRole” was applied on the “netP” data slot.

Statistical analysis

GraphPad Prism 9.0 (RRID: SCR_002798) was used to conduct the t tests of significance. P < 0.05 was considered statistically significant.

Data availability

All data generated or analyzed during this study are included in the article and Supplementary Material. The scRNA-seq data generated in this study and code used for analysis are publicly available in the Gene Expression Omnibus with the accession number “GSE271915” (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE271915) or Dryad repository with the accession number “44j0zpcn7” (https://doi.org/10.5061/dryad.44j0zpcn7). The previously published public scRNA-seq dataset was downloaded from the Gene Expression Omnibus with the accession number “GSE210079” (22).

Results

A landscape view of the cellular composition of patients with multiple myeloma before and after BCMA CAR-T therapy

To investigate the dynamic changes in cellular components within the BM microenvironment of patients with multiple myeloma receiving BCMA CAR-T therapy, we performed scRNA-seq on paired BM specimens (n = 14) collected from seven patients with multiple myeloma (P1–P7) before (i.e., baseline, “day −4”) and after (i.e., “day 28”) BCMA CAR-T cell infusion (i.e., “day 0”). These patients had received two or three previous lines of therapies, including two patients (P2 and P6) with extramedullary disease (EMD); the other patients did not experience extramedullary progression (Supplementary Table S1). All patients received cyclophosphamide-mediated lymphodepletion on day −3 with the aim of potentiating the expansion of CAR-T cells. Efficacy assessments based on the International Myeloma Working Group criteria (23) displayed that the patients had achieved varying degrees of remission by day 28: P3 achieved stringent complete remission (sCR); P1 and P4 achieved very good partial remission (PR); and the other patients achieved PR (Supplementary Table S2).

We collected baseline BM aspirate specimens from each patient on day −4, and the BM aspirate specimens after BCMA CAR-T therapy were collected on day 28 (Fig. 1A). The specimens were processed as single-cell suspensions of bone marrow mononuclear cells through density gradient centrifugation, and 3′-scRNA-seq (Chromium Single Cell 3′ v3 Libraries) analysis was performed for each specimen. Following QC—using Seurat (v.4.1.1), based on filtering cells with high gene detection (more than 5,000 genes) and mitochondrial gene coverage (>10%; Supplementary Fig. S1A–S1C), as well as exclusion of inferred doublets (Supplementary Fig. S1D)—we retained 87,413 individual cells. SCTransform normalization and principal component analysis were then used for unsupervised dimensionality reduction prior to clustering. UMAP was used for visualizing the distinct patients and specimens (Fig. 1B–D).

Figure 1. A landscape view of the cellular composition of patients with multiple myeloma before and after BCMA CAR-T therapy. A, Schematic of the experimental pipeline. Fourteen specimens were isolated from seven patients with multiple myeloma at baseline (P1_B, P2_B, P3_B, P4_B, P5_B, P6_B, and P7_B) and on day 28 (P1_R, P2_R, P3_R, P4_R, P5_R, P6_R, and P7_R), dissociated into single-cell suspensions, and analyzed using 10x Genomics Chromium droplet scRNA-seq. B–D, UMAP plots displaying 87,413 cells isolated from 14 specimens. UMAP plots of cells colored by patients (B), specimens (C), and sampling time points (D). E, UMAP plot showing 20 cell clusters within the multiple myeloma microenvironment with a clustering resolution of 0.4. F, Heatmap plotting the correlation coefficient among 20 cell clusters. The color keys from blue to red indicate the correlation coefficient from low to high. G, UMAP plot showing nine cell types within the multiple myeloma microenvironment. H, Heatmap of representative marker genes for each cell type. The color keys from blue to red indicate the gene expression levels from low to high. I, Line chart showing the proportion of multiple myeloma cells in seven patients with multiple myeloma at baseline and on day 28. The number of multiple myeloma cells was corrected in the analysis of immune cell–type proportions among the multiple myeloma microenvironment. BMMC, bone marrow mononuclear cells; GMP, granulocyte–macrophage progenitors; MK, megakaryocytes; pDC, plasmacytoid DCs.

We classified the 20 detected clusters (Fig. 1E) into nine cell types according to their expression profiles for recognized marker genes and based on Spearman correlation analysis between clusters. The classified cell types included T cells (marked by CD3D and CD3E), NK cells (marked by GNLY and NKG7), B cells (marked by CD79A and CD79B), multiple myeloma cells (marked by MZB1 and BCMA), monocytes (MNC, marked by CD14 and LYZ), granulocyte–macrophage progenitors (marked by MPO and ELANE), cDCs (marked by CLEC10A and CD1C), plasmacytoid DCs (marked by LIRA4 and CLEC4C), and megakaryocytes (marked by PPBP and TUBB1; Fig. 1F–H; Supplementary Fig. S2A). Thus, both lymphocytes (including T/NK cells and B/multiple myeloma cells) and myeloid cells (including MNCs, DCs, and megakaryocytes) were detected within the multiple myeloma microenvironment (Supplementary Fig. S2B and S2C). The proportion of multiple myeloma cells ranged from 5% to 60% in patients with multiple myeloma (excluding P2 and P6, who suffered from extramedullary progression) at baseline, which decreased to 0% in all patients on day 28 (Fig. 1I).

Delineation of the heterogeneity in gene expression profiles among multiple myeloma cells

We next re-clustered multiple myeloma cells: the high expression of MZB1 and BCMA in all clusters confirmed the status of these as multiple myeloma cells (Fig. 2A). Analysis of immunoglobulins supported the classification of the patients without EMD as IgA-κ (P1), IgA-λ (P3, P4, and P7), and IgG-κ (P5) types (Fig. 2A and B), findings consistent with the clinical classification of these patients based on immunoglobulin-based laboratory assessment using serum immunofixation electrophoresis (Supplementary Table S1). A FISH analysis of canonical primary events in multiple myeloma cells (24) showed either 1q+ or t(11,14) in patients without EMD (Supplementary Table S1). Consistent with the lack of FISH signals for 17p-, t(4,14), and t(14,16) in these patients (Supplementary Table S1), the multiple myeloma cells of these patients showed consistently low expression of NSD2 [upregulated by t(4,14)] and MAF [upregulated by t(14,16)]; no loss of TP53 expression (downregulated by 17p-) was observed (Supplementary Fig. S3A).

Figure 2. Delineation of the heterogeneity in driver genes among multiple myeloma cells. A, Feature plots showing the expression levels of marker genes and immunoglobulins in multiple myeloma cells. B, UMAP plot of multiple myeloma cells colored by patients. C, Heatmap indicating the CNV patterns of inferred multiple myeloma cell clusters. Blue, white, and red, respectively, indicate deletion from a chromosome, normal chromosome, and amplification on a chromosome. D, UMAP plot showing the inferred multiple myeloma cell subclusters identified by CopyKAT with a subclustering resolution of 0.2. E, Dot plot of differentially expressed driver genes among patients with multiple myeloma. The color scale represents the average gene expression level; dot size represents the percentage of cells expressing a given gene. F, UMAP plot of the multiple myeloma cells colored by inferred cell cycles. G, Bar chart displaying the proportion of multiple myeloma cells at G1, S, and G2–M phases. H, Box plot depicting the cell-cycle score of multiple myeloma cells. t tests were performed. **, P < 0.01. I, Dot plot of proliferation-associated transcription factors for each patient with multiple myeloma. The color scale represents the average gene expression level; dot size represents the percentage of cells expressing a given gene.

Analysis using the CopyKAT (v.1.0.8) algorithm (19) to estimate genomic CNVs of multiple myeloma cells indicated extensive chromosomal amplification and/or deletion in multiple myeloma cells (Fig. 2C). Multiple myeloma cells within the BM of P2 and P6 who suffered from EMD were not examined; among the remaining patients, there were five distinct CNV subclusters among the examined multiple myeloma cells (one for each patient; Fig. 2D). No patients were predicted to have depletion of the short arm of chromosome 17 in the CopyKAT analysis results (Supplementary Table S1; Fig. 2C and D). With regard to patients with 1q+, the CopyKAT-inferred amplification of the long arm of chromosome 1 was observed in multiple myeloma cells from P1, P3, and P4, consistent with the FISH-based clinical diagnosis of 1q+ for these patients (Fig. 2C and D; Supplementary Table S1). Given the reports of chromosome 1 CNVs in patients with multiple myeloma (24), it was notable that multiple myeloma cells of patients with 1q+ (P1, P3, and P4) showed consistently high expression of ADAR (positioned at 1q); P1 and P4 also had high expression of ILF2, PSMD4, IL6R, ANP32E, and CKS1B; and P1 had high MCL1 expression (Fig. 2E).

For patients with t(11,14) translocation, we observed that P5 and P7 exhibited high expression of CCND1 and BCL2 (Fig. 2E; Supplementary Table S1), supporting the previously reported idea that such patients are sensitive to BCL2 inhibitors (25). Consistent with the known effect of CCND1 in promoting G1–S-phase cell-cycle transition (26), we observed that both P5 and P7 exhibited relatively high S-phase gene expression scores and S-phase cell proportions (Fig. 2F–H). Our cell-type proportion analysis of the baseline multiple myeloma microenvironment indicated that P5 had the highest multiple myeloma cell burden, and P5 exhibited aberrantly high expression of genes with the known capacity in promoting proliferation, including transcripts for activator protein 1 (AP1) complex members (FOS, FOSB, JUN, and JUNB), EGR1, and MDK (Fig. 2I; refs. 27–29). Pseudotime analysis showed that these multiple myeloma cells transitioned from the AP1_Low subcluster to AP1_High subcluster (Supplementary Fig. S3B–S3D), highlighting apparent impacts of transcription factors in multiple myeloma cell plasticity and proliferation. These results implicate heterogeneity in gene expression profiles in multiple myeloma cells, even among cells harboring the same cytogenetic abnormalities.

The BORs of patients are positively correlated with the abundance and targeted cytotoxic activity of CD8+ effector CAR-T cells on day 28 postinfusion

We subsequently analyzed the characteristics of BCMA CAR-T cells on day 28 in terms of the therapeutic responses of patients. The key indicator in reflecting the treatment responsivity to CAR-T therapies BOR was evaluated to define the therapeutic responses of patients based on analyzing the best response across all time point assessments of these patients. By the 15-month follow-up after BCMA CAR-T therapy, efficacy assessments based on the International Myeloma Working Group criteria (23) displayed that both P2 and P6 achieved only PR as the BOR, whereas the other patients all achieved sCR as the BOR (Supplementary Table S2), so we investigated potential differences in the CD8+ effector CAR-T cells—the “primary” cytotoxic cells in CAR-T treatment (30). The custom reference genome containing the CAR sequence was generated to accurately distinguish CAR-T cells from endogenous T cells. The BCMA CAR-T cells (marked by CD3D, CD3G, and CAR) were re-clustered into eight subclusters by applying Seurat (v.4.1.1; Fig. 3A and B); the subclusters were then classified as CD4+ CAR-T cells (marked by IL7R) or CD8+ CAR-T cells (marked by CD8A and CD8B; Fig. 3C and D). CD8+ effector CAR-T cells were defined from among the CD8+ CAR-T cells based on the expression of GNLY, GZMK, and KLRD1 (Fig. 3E).

Figure 3. The BORs of patients are positively correlated with the abundance and targeted cytotoxic activity of CD8+ effector CAR-T cells on day 28 postinfusion. A, UMAP plot showing eight subclusters of CAR-T cells from seven patients with multiple myeloma on day 28 with a clustering resolution of 2.5. B, Feature plots displaying the expression levels of CD3D, CD3G, CAR, IL7R, CD8A, and CD8B in CAR-T cells. C, Violin plot showing the expression levels of representative marker genes in each CAR-T cell subcluster. D, UMAP plot depicting the CAR-T cells colored by defined cell types based on the expression levels of IL7R, CD8A, and CD8B. E, UMAP plots of the CAR-T cells colored by defined cell types based on representative marker gene expression in CAR-T subclusters. F, Bar chart displaying the proportion of CD8+ effector CAR-T cells among CAR-T cells in each patient on day 28. t tests were performed. **, P < 0.01. G, Box plot showing the expression of CAR of CD8+ effector CAR-T cells in each patient on day 28. t tests were performed. ***, P < 0.001. H, Dot plot of cytotoxicity-associated genes for CD8+ effector CAR-T cells in each patient with multiple myeloma on day 28. The color scale represents the average gene expression level; dot size represents the percentage of cells expressing a given gene.

To investigate the potential differences in CD8+ effector CAR-T cells between patients with the BOR constituting sCR and PR, we evaluated the anti–multiple myeloma activity of CD8+ effector CAR-T cells on day 28. We observed higher proportion of CD8+ effector CAR-T cells among CAR-T cells in patients with the BOR constituting sCR compared with PR (Fig. 3F). We also found that the expression of CAR- and cytotoxicity-associated genes (including GZMA, GZMB, GZMH, GNLY, TYROBP, PRF1, IFNG, TNF, and KLRD1) was higher in CD8+ effector CAR-T cells of patients with the BOR constituting sCR compared with PR (Fig. 3G and H). Together, these results suggest that the abundance and targeted cytotoxic activity of CD8+ effector CAR-T cells on day 28 are positively correlated with the therapeutic responsivity in patients.

BCMA CAR-T therapy enhances the cytotoxic activity of CD8+ effector T cells and reduces the abundance of regulatory T cells, positively correlating with therapeutic responsivity

Although the effective eradication of multiple myeloma cells by CAR-T cells has been widely recognized (3), little is known about the effect of CAR-T cells on the multiple myeloma microenvironment, which is composed of newly generated immune cell populations (including lymphocytes and myeloid cells) through reconstitution (which has been reported to occur over a 31-day period) induced by lymphodepletion (31). Diverse BCMA CAR-T cell characteristics on day 28 remind us to investigate the potential differences in endogenous T cells between patients with the BOR constituting sCR and PR. Briefly, we re-clustered endogenous T cells (marked by CD3D, CD3E, and CD3G) using Seurat (v.4.1.1; Fig. 4A and B). Cells from each of the 13 subclusters were classified as either CD4+ T cells (marked by IL7R) or CD8+ T cells (marked by CD8A and CD8B; Fig. 4C and D). Among these subclusters, the “primary” cytotoxic T cells—CD8+ effector T cells—were identified among the CD8+ T cells based on the expression of GNLY, GZMK, and KLRD1, whereas the “suppressive” regulatory T cells (Treg) were identified among the CD4+ T cells based on the expression of FOXP3 and IL2RA (Fig. 4E).

Figure 4. BCMA CAR-T therapy enhances the cytotoxic activity of CD8+ effector T cells and reduces the abundance of Tregs, positively correlating with therapeutic responsivity. A, UMAP plots showing 13 subclusters of endogenous T cells from seven patients with multiple myeloma at baseline and on day 28 with a clustering resolution of 0.6. B, Feature plots displaying the expression levels of CD3D, CD3E, CD3G, IL7R, CD8A, and CD8B in endogenous T cells. C, Violin plot showing the expression levels of representative marker genes in each endogenous T-cell subcluster. D, UMAP plot depicting the endogenous T cells colored by defined cell types based on the expression levels of IL7R, CD8A, and CD8B. E, UMAP plot of the endogenous T cells colored by defined cell types based on representative marker gene expression in endogenous T-cell subclusters. F, Comparison of the proportion of CD8+ effector T cells among T cells in each patient on day 28 vs. baseline. t tests were performed. *, P < 0.05. G, Box plot showing the cytotoxicity score of CD8+ effector T cells in each patient on day 28 vs. baseline. t tests were performed. **, P < 0.01; ***, P < 0.001; n.s., not significant. H, Bar chart displaying the proportion of CD8+ effector T-cell subclusters among CD8+ effector T cells in each patient at baseline and on day 28. I, Box plot depicting the exhausted/cytotoxicity score of each CD8+ effector T-cell subcluster at baseline. t tests were performed. ***, P < 0.001. J, Comparison of the proportion of Tregs among T cells in each patient on day 28 vs. baseline. t tests were performed. *, P < 0.05.

To investigate the effect of BCMA CAR-T cells on CD8+ effector T cells, we evaluated the anti–multiple myeloma activity of CD8+ effector T cells before and after BCMA CAR-T therapy. With the exception of patients with the BOR constituting PR, the proportion of CD8+ effector T cells among T cells was increased on day 28 compared with baseline (Fig. 4F). Additionally, the increased cytotoxicity scores of CD8+ effector T cells on day 28 compared with baseline were only observed in patients with the BOR constituting sCR (Fig. 4G). These findings suggest that BCMA CAR-T therapy can amplify the abundances and cytotoxic activity of CD8+ effector T cells. Notably, the patient (P5) with the highest multiple myeloma cell burden exhibited the lowest CD8+ effector T-cell cytotoxicity scores at baseline (Fig. 4G). This observation might be linked to the P5’s exceptionally high proportion of CD8_GZMK cells among CD8+ effector T cells (Fig. 4H), as CD8_GZMK cells are recognized for higher exhaustion/cytotoxicity compared with other CD8+ effector T cells (Fig. 4I; ref. 32). Given the enhanced anti–multiple myeloma activity of CD8+ effector T cells, we investigated whether the BCMA CAR-T therapy could alleviate the immunosuppression mediated by Tregs. Evaluating the proportion of Tregs before and after BCMA CAR-T therapy, we observed a decreased Treg proportion on day 28 compared with baseline, specifically in patients with the BOR constituting sCR (Fig. 4J).

We also analyzed a previously published dataset that included scRNA-seq data of BM specimens collected from patients with multiple myeloma before and after BCMA CAR-T therapy (GSE210079; ref. 22). Based on the identical sampling time points as in our study, we assessed paired baseline and day-28 specimen data for an additional four patients with multiple myeloma. These patients achieved either CR (P01 and P19) or PR (P16 and P33) as the BORs. After filtering out low-quality cells (Supplementary Fig. S4A–S4C), the cell types within the multiple myeloma microenvironment were defined (Supplementary Fig. S4D and S4E). We then identified the CD8+ effector T cells among the CD8+ T cells based on re-clustering the T/NK cells (Supplementary Fig. S5A and S5B) and analyzing marker gene expression in each subcluster (Supplementary Fig. S5C and S5D). Consistent with the findings for our patients, enhanced CD8+ effector T-cell abundance and cytotoxic activity on day 28 (compared with baseline) were only observed for those patients with CR as the BOR (Supplementary Fig. S5E and S5F). Together, these findings collectively indicate that BCMA CAR-T therapy for multiple myeloma enhances the cytotoxic activity of CD8+ effector T cells and reduces Treg abundance, positively correlating with therapeutic responsivity.

BCMA CAR-T therapy attenuates immunosuppression mediated by myeloid-derived suppressor cells

Considering that antigen-presenting cells, including MNCs and DCs, trigger antitumor immunity by regulating the activity of T cells (33), we first investigated the effect of BCMA CAR-T therapy on MNC clusters. The MNCs (marked by LYZ and S100A9) were re-clustered into 11 subclusters by applying Seurat (v.4.1.1; Fig. 5A and B), which were then classified as either MNCs (marked by CD14 and CD16), granulocyte–macrophage progenitors (marked by MPO), or neutrophils (marked by LTF; Fig. 5C and D). Three classical MNC clusters—CD14_VCAN_LGALS1, CD14_VCAN_NAMPT, and CD14_VCAN_IFIT cells—exhibited gene expression profiles similar to myeloid-derived suppressor cells (MDSC; ref. 34), including low expression of HLA class II molecules and high expression of known MDSC marker genes such as CD14, CD33, and ITGAM (Fig. 5E and F).

Figure 5. BCMA CAR-T therapy attenuates immunosuppression mediated by MDSCs. A, UMAP plots showing 11 subclusters of MNCs from seven patients with multiple myeloma at baseline and on day 28 with a clustering resolution of 0.5. B, Feature plots displaying the expression levels of LYZ, S100A9, CD14, CD16, MPO, and LTF in MNCs. C, Violin plot showing the expression levels of representative marker genes in each MNC subcluster. D, UMAP plot depicting the MNCs colored by defined cell types based on the expression levels of CD14, CD16, MPO, and LTF. E, UMAP plot of the MNCs colored by defined cell types based on representative marker gene expression in MNC subclusters. F, Dot plot depicting the expression levels of recognized MDSC marker genes and HLA-Ⅱ molecules in each MNC subcluster. The color scale represents the average gene expression level; dot size represents the percentage of cells expressing a given gene. G, Comparison of the proportion of MDSCs among MNCs in each patient on day 28 vs. baseline. t tests were performed. **, P < 0.01. H, Box plot showing the HLA-Ⅱ molecule expression scores of MDSCs in each patient on day 28 vs. baseline. t tests were performed. **, P < 0.01; ***, P < 0.001. GMP, granulocyte–macrophage progenitors; NE, neutrophils.

We analyzed the proportion of MDSCs among MNCs before and after BCMA CAR-T therapy, observing a decreased MDSC proportion on day 28 compared with baseline in all patients with multiple myeloma (Fig. 5G). As the expression of HLA-II molecules is indicative of antigen presentation ability, we assessed the HLA-II molecule expression scores for MDSCs before and after BCMA CAR-T therapy. All patients with multiple myeloma showed higher HLA-II molecule expression scores of MDSCs on day 28 than baseline (Fig. 5H), indicating that BCMA CAR-T therapy enhances the antigen presentation ability of MDSCs. Taken together, our analyses suggest that BCMA CAR-T therapy for multiple myeloma attenuates MDSC-mediated immunosuppression.

BCMA CAR-T therapy enhances the antigen presentation ability of type 1 conventional DCs/type 2 conventional DCs and enables the inference of cDC3 developmental processes

DCs are another type of antigen-presenting cell that initiates and maintains antitumor activity of T cells (35), so we investigated the effect of BCMA CAR-T therapy on DC clusters. The DCs (marked by ID2 and LILRA4) were re-clustered into six subclusters using Seurat (v.4.1.1; Fig. 6A–C), and these subclusters were then classified as type 1 cDCs (cDC1, marked by ID2), type 2 cDCs (cDC2, marked by CD1C), cDC3s (marked by CD36), or plasmacytoid DCs (marked by LILRA4; Fig. 6D and E).

Figure 6. BCMA CAR-T therapy enhances the antigen presentation ability of cDC1s/cDC2s and enables the inference of cDC3 developmental processes. A, UMAP plots showing six subclusters of DCs from seven patients with multiple myeloma at baseline and on day 28 with a clustering resolution of 0.5. B, Feature plots displaying the expression levels of ID2, CD1C, CD36, and LILRA4 in DCs. C, Violin plot showing the expression levels of representative marker genes in each DC subcluster. D, UMAP plot depicting the DCs colored by defined cell types based on the expression levels of ID2, CD1C, CD36, and LILRA4. E, UMAP plot of the DCs colored by defined cell types based on representative marker gene expression in DC subclusters. F, Box plot depicting the HLA-I molecule expression scores of cDC1s in each patient on day 28 vs. baseline. t tests were performed. **, P < 0.01; ***, P < 0.001. G, Box plot depicting the HLA-Ⅱ molecule expression scores of cDC2s in each patient on day 28 vs. baseline. t tests were performed. ***, P < 0.001. H, Bar chart displaying the proportion of DC subclusters among DCs in each patient at baseline and on day 28. I and J, GO enrichment analysis of the upregulated genes in cDC1s/cDC2s (I) and cDC3s (J) indicating the top altered 10 terms in the biological process of GO. The x-axis specifies the number of genes enriched in the pathways. The color keys from shallow to deep indicate the P value from high to low. pDC, plasmacytoid DCs.

We evaluated the HLA-I molecule expression scores of cDC1s and HLA-II molecule expression scores of cDC2s based on the previously reported DC antigen presentation mechanisms. With the exception of patients with the BOR constituting PR, the HLA-I molecule expression score of cDC1s was increased on day 28 compared with baseline (Fig. 6F). With regard to cDC2s, all patients with multiple myeloma exhibited higher HLA-II molecule expression scores on day 28 than baseline (Fig. 6G). After re-clustering the DC data of the additional four patients with multiple myeloma in the previously published dataset (GSE210079; Supplementary Fig. S6A and S6B; ref. 22), the DC subclusters were defined based on the identical marker genes used for our data (Supplementary Fig. S6B and S6C). Notably, a patient with PR as the BOR exhibited lower cDC1 HLA-I molecule expression on day 28 than baseline (Supplementary Fig. S6D). All patients showed higher cDC2 HLA-II molecule expression on day 28 than baseline (Supplementary Fig. S6E). These findings suggest that BCMA CAR-T therapy can enhance the antigen presentation ability of cDC1s/cDC2s, with the enhanced cDC1 antigen presentation positively correlated with the therapeutic responsivity in patients.

Compared with previously reported intramedullary origin and extramedullary migration of cDC1s/cDC2s (36, 37), less is known about the developmental processes of recently defined cDC3s (38). By evaluating the proportion of DCs before and after BCMA therapy, we discovered that all cDC types exhibited higher proportion on day 28 than baseline (Fig. 6H). These results indicate the post–lymphodepleted intramedullary origin and following extramedullary migration of cDC3s, consistent with the developmental processes of cDC1s/cDC2s. We then performed functional enrichment analysis to investigate the less understood biological functions of cDC3s (38). A GO analysis indicated functional enrichment in the cDC1s/cDC2s for GO terms associated with “immune cell activation,” whereas the cDC3s were enriched for terms including both the “immune cell activation” and “mononuclear cell responses” (Fig. 6I and J). Combined with the observed high expression of MNC-associated genes (VCAN, CD14, FCGR1A, and CD163) in cDC3s, these findings indicate that cDC3s exhibited both the functions of DCs and MNCs. Together, our analysis results suggest that BCMA CAR-T therapy enhances the antigen presentation ability of cDC1s/cDC2s and enables the inference of cDC3 developmental processes.

BCMA CAR-T therapy attenuates BAFF/GALECTIN/MK pathway–mediated immunosuppression and activates MIF pathway–mediated anti–multiple myeloma immunity

We subsequently expanded our investigation on cell–cell communication to explore whether the BCMA CAR-T therapy can affect the intercellular interactions within the multiple myeloma microenvironment. We used CellChat (v.1.1.3) to quantitatively analyze intercellular communication networks (21) and first analyzed the signaling pathways and ligand–receptor pairs apparently participating in the intercellular communication within the multiple myeloma microenvironment at baseline (Supplementary Fig. S7A). Notably, there was a lack of inferred ligand–receptor communication between T cells, NK cells, B cells, and multiple myeloma cells (Supplementary Fig. S7B), indicating a low intercellular communication probability among these cells. In contrast, intense cross-talk was inferred between MDSCs and multiple myeloma cells via the BAFF pathway based on their high expression of BAFF or BCMA (Supplementary Fig. S7C), suggesting the participation of suppressor cells in BAFF pathway–mediated multiple myeloma cell proliferation (39). We also observed that MNCs and multiple myeloma cells showed high expression of LGALS9 or MDK, whereas CD45 and NCL were generally highly expressed in immune cell types (Supplementary Fig. S7C), indicating the involvement of GALECTIN and MK pathways in suppressing immune cell activation (40, 41). Together, these findings support an immunosuppressive multiple myeloma microenvironment before BCMA CAR-T therapy.

We then analyzed signaling pathways and ligand–receptor pairs on day 28 to investigate the effect of BCMA CAR-T therapy on intercellular communication within the multiple myeloma microenvironment (Supplementary Fig. S7D). Notably, no intercellular communication mediated by the BAFF pathway was observed on day 28 due to the elimination of multiple myeloma cells (Supplementary Fig. S7D). In addition, the reduction of GALECTIN and MK pathways was inferred based on the decreased expression of LGALS9 in MNCs and the lower expressed NCL in immune cell types on day 28 compared with baseline (Supplementary Fig. S7E). These findings suggest that BCMA CAR-T therapy can reduce BAFF/GALECTIN/MK pathway–mediated immunosuppression. We also observed the inferred cross-talk between CAR-T cells/lymphocytes and myeloid cells based on their high expression of MIF or CD44 (Supplementary Fig. S7E), supporting the role of BCMA CAR-T therapy in activating MIF pathway–mediated myeloid cell activation (42). Together, our cell–cell communication analysis thus indicates that BCMA CAR-T therapy attenuates BAFF/GALECTIN/MK pathway–mediated immunosuppression and activates MIF pathway–mediated anti–multiple myeloma immunity.

Discussion

Immune system dysfunction is a characteristic feature in multiple myeloma (43). Disruption in the immune microenvironment of multiple myeloma confers immunosuppression (7), leading to evasion of immune recognition of multiple myeloma cells and promoting multiple myeloma cell growth (44). Our observations of samples from patients with multiple myeloma at baseline are consistent with this understanding. Investigating the paired specimens before and after BCMA CAR-T therapy, our study indicates that CAR-T therapy attenuated immunosuppression mediated by Tregs and MDSCs, enhanced CD8+ effector T-cell cytotoxic activity, and antigen presentation ability of cDC1s/cDC2s, contributing to enhancing the antitumor responses. In addition to the elimination of tumor cells, the benefits of CAR-T therapy for patients can also arise from the reconstitution of the compromised tumor immune microenvironment. Thus, in considering the mechanisms of CAR-T therapy in treating malignancies, both of these effects should be appraised.

A major question currently facing the CAR-T therapy research field concerns predicting therapeutic responsivity at early treatment stages (45). Baseline characteristics associated with durable remission after CAR-T therapy in treating hematologic malignancies have been reported, including an absence of EMD, persistence of CAR-T cells, and relatively low tumor burden (46). In our study, a higher abundance and targeted cytotoxic activity of CD8+ effector CAR-T cells reflect better therapeutic responsivity in patients. In addition, evaluation of endogenous CD8+ effector T-cell activity, Treg abundance, and antigen presentation ability of cDC1s can contribute to predicting the treatment responsivity. These were discovered by comparing the day-28 characteristics of CAR-T cells and immune microenvironment between patients achieving different BORs over the 15-month follow-up, combined with the analysis of previously published dataset (GSE210079; ref. 22). Considering that the patient with the highest multiple myeloma cell burden took the longest time to achieve sCR compared with others, whether higher levels of CAR-T cell infusion can benefit the patient with high tumor burden could be further studied. Together, a combined consideration of CAR-T cell function, the relative ratio of CAR-T cells to tumor cells, along with the immune microenvironment, will be beneficial for predicting individualized treatment responsivity to CAR-T therapies. Notably, both patients who achieved poor BOR suffered from EMD, indicating the need for considering EMD as a potential confounder in predicting treatment responsivity.

Lymphodepletion-conditioning regimens are generally administered prior to CAR-T therapy to enable better expansion and engraftment of the infused CAR-T cells (10). In addition to creating a favorable immune environment for BCMA CAR-T cells by reducing the potential for anti-CAR immune responses (31), lymphodepletion can also trigger the generation of new immune cell populations (11), thus contributing to investigating the developmental processes of immune components within the reconstituted BM niche. The intensively investigated classical cDCs (including cDC1s and cDC2s) are short-lived cells that are constantly generated by BM and undergo terminal differentiation in the periphery (36, 37), whereas the developmental processes of recently described unclassical cDC3s remain less investigated (38, 47). In our study, the proportions of both cDC3s and cDC1s/cDC2s were increased on day 28 compared with baseline, which can at least support the post–lymphodepleted intramedullary origin and subsequent extramedullary migration of cDC3s. However, the differences in the rate of generation and migration between cDC3s and cDC1s/cDC2s, beyond other immune cell differentiations, need to be further explored.

Several retrospective studies of BCMA-targeted therapies for patients with multiple myeloma have included scRNA-seq data. One study conducted longitudinal analyses of paired baseline, day-28, or month-3 BM specimens, demonstrating that a higher proportion of immune-suppressive myeloid cells on day 28 correlated with the shorter progression-free survival, whereas a higher proportion of DCs at month 3 were associated with longer progression-free survival (22). Another study found no correlation between baseline T-cell characteristics and prognosis (48). Consistently, our study showed that treatment responsivity was positively correlated with attenuated immunosuppression and enhanced antitumor responses triggered by post–lymphodepleted BCMA CAR-T therapy, with no correlation between treatment responses and the baseline proportions or activities of MNCs or DCs.

A major weakness of our study is the limited number of cases. In addition, the absence of residual multiple myeloma cells on day 28 precluded exploration of multiple myeloma cell plasticity that previous studies have suggested may affect relapse with BCMA CAR-T therapy (49). Given a previous study reporting abundant residual multiple myeloma cells at month 3 after BCMA CAR-T therapy (22), it would likely be informative to design studies that collect and assess paired baseline and month-3 BM specimens (ostensibly with residual multiple myeloma cells). It also bears emphasis that the extramedullary specimens were not included in our study, thus precluding investigations into how cellular components and transcriptomic profiles within the extramedullary microenvironment may be influencing treatment responses; in this regard, it would likely be productive to use alternative single-cell technologies capable of more efficiently analyzing fewer cells (e.g., MARS-seq and Smart-seq2; ref. 50), and spatial transcriptomics could also be informative in studies examining EMD. These considerations underscore the necessity for additional research with an expanded cohort of patients with multiple myeloma receiving BCMA CAR-T therapy both with and without EMD.

Supplementary Material

Supplementary Figure S1 Supplementary Figure S1. Quality Control of the scRNA-seq Data.

Supplementary Figure S2 Supplementary Figure S2. Clustering of the Cells within the MM Microenvironment.

Supplementary Figure S3 Supplementary Figure S3. Cytogenetic Abnormality-associated Gene Expression and Clonal Evolution in MM Cells.

Supplementary Figure S4 Supplementary Figure S4. Quality Control of the Public scRNA-seq Dataset and Clustering of the Cells within the MM Microenvironment.

Supplementary Figure S5 Supplementary Figure S5. BCMA CAR-T Therapy Enhances the Proportion and Cytotoxic Activity of CD8+ Effector T Cells, Positively Correlating with Therapeutic Responsivity.

Supplementary Figure S6 Supplementary Figure S6. BCMA CAR-T Therapy Enhances the Proportion and Cytotoxic Activity of CD8+ Effector T Cells, Positively Correlating with Therapeutic Responsivity.

Supplementary Figure S7 Supplementary Figure S7. BCMA CAR-T Therapy Attenuates BAFF/GALECTIN/MK Pathway-mediated Immunosuppression and Activates MIF Pathway-mediated Anti-MM Immunity.

Supplementary Table S1 Supplementary Table S1. Baseline Characteristics of MM Patients before BCMA CAR-T Therapy.

Supplementary Table S2 Supplementary Table S2. Clinical Assessment of MM Patients after BCMA CAR-T Therapy.

Acknowledgments

This work was supported by a grant (Z200020 to L. Sun) from the Natural Science Foundation of Beijing, a grant (2021YFA1300603 to L. Sun and Y. Shang) from the Ministry of Science and Technology of China, grants (32350020 and 32370620 to L. Sun; 82188102 and 31991164 to Y. Shang) from the National Natural Science Foundation of China, a grant (BJ-2022-127 to H. Liu) from the National High Level Hospital Clinical Research Funding, a grant (7232137 to H. Liu) from the Beijing Natural Science Foundation, and a grant (2021-I2M-C&T-A-020 to H. Liu) from the CAMS Innovation Fund for Medical Sciences. We thank the National Center for Protein Sciences at Peking University (Beijing, China) and Dr. Yanping Ding (Beijing Imunopharm Technology) for providing technical support.

Authors’ Disclosures

No disclosures were reported.

Authors’ Contributions

Y. Yang: Resources, data curation, investigation. S. Qin: Data curation, software, formal analysis, investigation, visualization, methodology, writing–original draft. M. Yang: Validation, methodology. T. Wang: Investigation, methodology. R. Feng: Visualization, methodology. C. Zhang: Methodology. E. Zheng: Software. Q. Li: Validation. P. Xiang: Investigation. S. Ning: Methodology. X. Xu: Visualization. X. Zuo: Investigation. S. Zhang: Methodology. X. Yun: Validation. X. Zhou: Supervision, project administration. Y. Wang: Supervision, methodology, project administration. L. He: Supervision, investigation, visualization, methodology. Y. Shang: Conceptualization, supervision, funding acquisition, project administration, writing–review and editing. L. Sun: Conceptualization, supervision, funding acquisition, writing–original draft, project administration, writing–review and editing. H. Liu: Conceptualization, supervision, funding acquisition, project administration, writing–review and editing.

Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).
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