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

doi:10.21203/rs.3.rs-2626343/v1

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

https://doi.org/10.21203/rs.3.rs-2626343/v1

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published 19 Apr, 2024

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Markers predicting response and resistance to chimeric antigen receptor (CAR) T cells in relapsed/refractory multiple myeloma are currently missing. We subjected cells isolated from peripheral blood and bone marrow before and after the application of CAR T cells directed against B cell maturation antigen to single cell multi-omic analyses to identify markers associated with resistance and early relapse. Differences between responders and non-responders were already identified at time of leukapheresis. Non-responders showed an immunosuppressive microenvironment characterized by increased numbers of monocytes expressing the immune checkpoint molecule CD39 and suppressing CD8+ and NK cell function. The analyses of CAR T cells showed cytotoxic and exhausted phenotypes in hyperexpanded compared to low/intermediate expanded clones. We identified potential immunotherapeutic targets on CAR T cells, like PD1 and KLRB1, to improve their functionality and durability. Our work provides first evidence that an immunosuppressive microenvironment is associated with resistance to CAR T cell therapies.

Health sciences/Oncology/Cancer/Haematological cancer/Myeloma

Biological sciences/Cancer/Cancer therapy/Cancer immunotherapy

Health sciences/Oncology/Cancer/Cancer microenvironment

Health sciences/Health care/Therapeutics/Immunotherapy

Multiple myeloma

single-cell sequencing

chimeric antigen receptor T cells

B-cell maturation antigen

Chimeric antigen receptor (CAR) T cells targeting B cell maturation antigen (BCMA) revolutionized treatment of relapsed and refractory multiple myeloma (RRMM). Although the introduction of Idecabtagene vicleucel (Ide-cel) and Ciltacabtagene autoleucel (Cilta-cel) has led to unprecedented rates of deep, long-lasting remissions in heavily pretreated patients compared to conventional therapies¹, approx. 20% of patients do not respond to Ide-cel and the majority of patients with sub-optimal response relapse within the first year². While rates of sustained measurable residual disease negativity in clinical trials are higher with Cilta-cel, approx. 40% of patients show progressive disease within the first 2 years^3,4. Modes of resistance or factors predicting response to BCMA-directed CAR T cells are currently missing. Bi-allelic loss of BCMA on chromosome 16 has been described as a tumor escape mechanism in few case reports but does not explain relapse in the majority of patients^5,6. Due to the worldwide limited availability, the considerable financial toxicity and the increasing number of eligible patients for cellular therapies⁷, it is necessary to characterize modes of resistance and develop markers that predict response to CAR T cell therapy. Exhaustion of CAR T cells and non-CAR host immunity, development of anti-drug antibodies and shedding of BCMA through gamma secretases are further possible modes of resistance⁸. The dismal outcome of patients with extramedullary disease (EMD) and the increased incidence of EMD after relapse from CAR T cell therapy suggest that a non-permissive microenvironment could be another important factor associated with resistance⁹. Single cell RNA sequencing (scRNA-seq) has been successfully used to study cellular dynamics associated with response to CD19-directed CAR T cell therapy in large B cell lymphoma and acute lymphoblastic leukemia^10–12. Furthermore, single cell sequencing has shed new light on the evolution of asymptomatic plasma cell disorders, over symptomatic disease to RRMM^13–17. Single cell sequencing offers the unique opportunity to study the interplay between CAR T cells, malignant plasma cells and non-malignant cells of the host immune microenvironment. Therefore, we performed a longitudinal single cell multi-omics study to identify factors associated with response to BCMA-directed CAR T cells. By combining scRNA-seq with single cell T cell (scTCR-seq) and B cell receptor sequencing (scBCR-seq) as well as single cell surface proteomics, we aimed at identifying markers that predict response and resistance to CAR T cells in RRMM. We found significant quantitative and qualitative differences in cellular composition between responders and non-responders at the time of leukapheresis and demonstrate that response is not only associated with expansion of CAR T cells but also associated with pre-infusional changes in the host immune microenvironment.

Treatment of RRMM with anti-BCMA CAR T cells

We performed single cell RNA, T cell receptor (TCR), B cell receptor (BCR) and surface protein sequencing on mononuclear cells collected from multiple time points in 10 patients with RRMM (median prior lines: n = 4, range 2-10, Figure 1A and B) treated with approved BCMA-directed CAR T cells (Ide-cel, n = 8 and Cilta-cel, n = 2). All patients were triple-class exposed (Figure 1B). Response assessment performed at day 30 after CAR T cell infusion showed complete response (CR) in 5 patients and suboptimal response (very good partial remission or worse) in 5 patients. Patients in the latter group showed progressive disease (PD) within the first 4 months after CAR T cell therapy (Figure 1C). Cytokine release syndrome occurred 7-11 days after infusion and was predominantly not severe (grade I or II), and required application of tocilizumab in two cases.

Longitudinal single cell dissection of response and resistance to BCMA-directed CAR T cells in RRMM

To assess longitudinal cellular dynamics of (CAR) T cells, peripheral blood mononuclear cells (PBMCs) were collected at the day of leukapheresis, at day of re-infusion after lymphodepletion (d 0) as well as at days 7, 14, 30 and 100 following CAR T cell therapy. A summary of sample collection and the workflow is shown in Figure 2A. Bone marrow aspirates to isolate mononuclear cells (BMMCs) were performed on day 30 after re-infusion. Samples collected longitudinally from the date of leukaphereses to day 30 following CAR T cell infusion (n = 25) were subjected to single cell multi-omic analyses. In total, 178,142 cells were sequenced (median 7,990 cells/sample, range 1,569-10,972 cells) and 95% of cells passed quality assessment (Supplemental table 1 summarizes quality control data). For further analyses, patients were grouped based on their outcome (CR/responders versus non-CR/non-responders 30 days after CAR T cell therapy).

Cellular composition of bone marrow after CAR T cell therapy in responders and non-responders

Combining single cell transcriptomic data and tags derived from a panel of 57 oligonucleotide-bound antibodies (antibody-derived tags, ADT, Supplemental table 2) using the weighted-nearest neighbor (WNN) method¹⁸ allowed the identification of distinct immune cells (Figure 2B) in pre-/post-CAR T cell infusion samples (Figure 2C) based on their expression of canonical lineage markers (Figure 2D). We used scCODA¹⁹ to assess compositional changes in the non-CAR T host immune microenvironment. Since the contact of CAR T cells with malignant plasma cells primarily takes place in the bone marrow, we first investigated if cell type frequencies were different between responders and non-responders in the respective compartment after CAR T cell infusion (Supplemental Figure 1A-C). Other than the expected higher numbers of bone marrow plasma cells in non-responders, no significant differences between both groups were identified. In malignant plasma cells from non-responders, 476 differentially expressed genes (DEGs, adjusted p-value < 0.01, Supplemental table 3) compared to responders were detected. In accordance with previous studies in Bortezomib-resistant MM cells²⁰, non-responders showed an increased expression of genes associated with oxidative phosphorylation and peptide metabolism (Supplemental Figure 1D).

Significant changes in cellular composition of peripheral blood between responders and non-responders after CAR T cell therapy

Next, we analyzed paired PBMC samples collected on the same day as bone marrow samples 30 days after CAR T cell infusion (Figure 3). Patients in CR (Figure 3A and 3B) harbored a lower proportion of NK cells and in agreement with results from CD19-directed CAR T cells¹⁰ had a reduced proportion of CD14+ monocytes compared to patients with suboptimal response (Figure 3C). To investigate whether CAR T cell therapy induced not only quantitative, but also qualitative changes in cellular composition, we compared differences in gene expression before and after CAR T cell infusion in bystander cells of the host immune system. While no significant changes in gene expression before and after CAR T cell infusion were detected in non-responders, 470 DEGs were found among 10 different cell types in patients with CR (Figure 3D, Supplemental table 3). Remarkably, most significant changes before and after infusion occurred in the subpopulations of naïve CD8+ and CD4+ non-CAR T cells. Gene set enrichment analysis (GSEA) revealed that the respective changes were associated with activation of T cell response (i.e. antigen processing and presentation by MHCII, T cell mediated cytotoxicity and regulation of T cell activation), indicating that successful CAR T cell therapy stimulates elements of the adaptive immunity, similar to previous findings in diffuse large B cell lymphoma²¹ (Figure 3D).

Differences between responders and non-responders can already be found at the time of leukapheresis

After investigating post-infusional peripheral blood and bone marrow samples and identifying significant changes especially in host immunity of patients in CR, we characterized pre-infusional PBMCs collected at the time of leukapheresis (Figure 4). Patients in CR (Figure 4A and Figure 4B) harbored more CD8+ effector memory T cells (TEM) and NK cells, but fewer CD14+ monocytes compared to non-responders (Figure 4C). Analysis of DEGs and surface protein expression showed also distinct cellular phenotypes associated with dismal outcome after CAR T cell infusion. Recurrently higher expression of Proviral Integration site for Moloney murine leukemia virus (PIM) kinases were detected in CD14+ and CD16+ monocytes as well as in dendritic and NK cells isolated from non-responders at time of leukapheresis, as compared to responders (Supplemental table 3). Compared to patients in CR after therapy, enrichment of genes connected to impaired immune function was detected in CD16+ monocytes and NK cells in non-responders (Figure 4D). Beyond altered gene expression, we detected significantly higher expression of surface immune checkpoints on monocytes (CD39) and NK cells (CD94/NKG2A) in non-responders (Figure 4E). Non-responders who showed numerically lower numbers of CD8+ TEM as well as functionally impaired monocytes and NK cells had received either lymphotoxic bendamustine or an elotuzumab-based bridging therapy (Figure 1A) that has been shown to deplete CD8+ T and NK cells in MM patients²². In non-responders, cell-cell interaction analyses revealed upregulated communication between monocytes and NK as well as CD8+ T cells via plasminogen activator urokinase receptor-ligand (PLAU/PLAUR) pairing. These results demonstrate that an immunosuppressive microenvironment at time of leukapheresis is associated with adverse outcome after CAR T cell therapy.

No differences of CAR T cell in vitro cytotoxicity between responders and non-responders

Since we detected significant immunological differences between responders and non-responders at leukapheresis, we aimed at characterizing the functionality of manufactured CAR T cells (Figure 5). Under the hypothesis that the immunosuppressive microenvironment prior to manufacturing might lead to functionally impaired CAR T cells, we isolated untransduced T cells at day 0 as well as CAR T cells 7 days after reinfusion (Figure 5A). After CD3/CD28 stimulation and expansion, (CAR) T cells were co-cultured with BCMA-expressing MM cells (U-266) for 24h (Figure 5B). In comparison to untransduced T cells, CAR T cells isolated at day 7 from both – responders and non-responders – were capable of eliminating target cells. There were no significant differences in plasma cell viability between responders and non-responders after 24h in culture (Figure 5C) underlining that not only CAR T cells from responders, but also non-responders were functionally active.

In vivo expansion of (CAR) T cells in responders and non-responders

Since there were no differences in CAR T cell in vitro functionality between both groups, we tracked (CAR) T cell dynamics for the first 100 days after re-infusion using a biotin-labeled BCMA CAR detection reagent (Figure 5D). Lymphodepletion (LDP) was followed by significant expansion of T cells in both groups that was primarily driven by expansion of CD8+ cells (Figure 5E). This marked increase of CD8+ T cells was not only observed in the CAR T cell compartment, but also in non-transduced CD8+ T cells, especially in responders (Figure 5F). Cell cycle analyses confirmed increased mitotic activity of CD8+ T cells after CAR T cell infusion especially in responders (Supplemental Figure 2) underlining the effect of CAR T cells on bystander T cells.

Single cell characterization of T cell receptor repertoires

Expansion and persistence of CAR T cells has been associated with beneficial outcome in RRMM². Since we detected significant differences in CD8+ and CD4+ T cells dynamics before and after CAR T cell infusion, we characterized the TCR repertoire in responders and non-responders. For this purpose, sequences of Ide-cel and Cilta-cel were added to the reference genome and Ide-cel+/Cilta-cel+ CAR T cells were extracted in silico (Figure 6A). CAR+ T cells were predominantly CD8+ in both patients with hyperexpanded CAR T cells after day 30 (e.g. patient 12, Ide-cel) and in patients with only few detectable CAR T cells 30 days after infusion (e.g. patient 14, Ide-cel). Longitudinal tracking of TCRs revealed the parallel existence of CAR+ and CAR- T cells of the same clonotype regardless whether CAR+ T cells expanded beyond day 30 after infusion (Figure 6B) or not (Figure 6C). This suggests that the transduction process is rather unselective and not restricted to certain clonotypes. Analysis of TCR diversity before and after CAR T cell infusion demonstrated no significant differences between responders and non-responders (Figure 6D). However, TCRs from patients with progressive disease at first assessment after CAR T cell treatment (patient 01 and 10) were predominantly classified as singletons (Figure 6D).

Single cell transcriptomic analyses of Ide-cel and Cilta-cel

Next, we compared single cell transcriptomes of in silico extracted CAR T cells with pre-infusion T cells to characterize functional changes caused by lentiviral transduction and expression of the CAR. Compared to pre-infusional T cells, Ide-cel+ and Cilta-cel+ CAR T cells expressed significantly higher levels of genes associated with cytolysis (GZMA, GZMB, GNLY, LYZ, Figure 7A-C). Pathway analyses revealed a downregulation of pathways associated with T cell activation and differentiation in Ide-cel+ CAR T cells (Supplemental Figures 3 and 4), while transduction to Cilta-cel was associated with increased T cell function (Supplemental Figure 5). T cells ordered by inferred pseudotimes showed trajectories from T cells collected at leukapheresis over post-infusion, non-CAR T cells to CAR+ T cells alongside recurrently increased expression of cytolytic enzymes in patients treated with Ide-cel and Cilta-cel (Supplemental Figure 6). First results from heavily pre-treated RRMM patients indicate towards better outcomes after Cilta-cel compared to Ide-cel, also for patients achieving a CR. We therefore compared single cell transcriptomes of Cilta-cel+ and Ide-cel+ CAR T cell extracted in silico from patients in CR. Differentially expressed genes in Cilta-cel+ CAR T cells were associated with cell cycle (HIST1H3B, MKI67, NUSAP1, PCLAF, TYMS), exhaustion/senescence (CD160, SESN3) and chemotaxis (CCR7, Supplemental table 3).

Single cell surface proteomics identify therapeutic targets on CAR T cells

After analyzing single CAR T cell transcriptomes, we investigated surface protein expression. Hyperexpanded CAR T cells exhibited a more exhausted and senescent phenotype as indicated by higher levels of immune checkpoints and NK cell receptors (PD1, CD57 and CD94) as well as lower levels of markers associated with activation such as CD11b, CD33 and CD69 as well as IL7-R, that is linked to a naïve-like T cell state²³ (Figure 7D). Oppositely, non-hyperexpanded CAR T cell clonotypes (<10% of the entire TCR repertoire) from both - patients treated with Ide-cel (Figure 7E) and Cilta-cel (Figure 7F) - showed a surface protein expression profile characterized by the downregulation of exhaustion/senescence markers (e.g. TIM-3, KLRB1, KLRG1 and CD337, Figure 7E) and higher expression levels of activation markers that were downregulated in hyperexpanded CAR T cell clones (Figure 7E and F). Remarkably, the comparison of CAR T cells with untransduced T cells after treatment revealed no significant differences in surface protein expression (data not shown). This supports the hypothesis, that CAR T cells instruct and activate bystander cells of the surrounding microenvironment.

Comparison of CAR T cells isolated from bone marrow and peripheral blood

Although we were able to detect malignant circulating plasma cells at leukapheresis in responders and non-responders (Figure 8A), we hypothesized that the majority of CAR T cells is activated by bone marrow plasma cells. Therefore, we compared surface antigen expression of CAR T cells isolated from bone marrow aspirates with peripheral blood. Ide-cel+ and Cilta-cel+ CAR T cells from the bone marrow were characterized by higher expression levels of LeuM1 and lower levels of CD57 (Figure 7G), supporting the theory that CAR T cells act differently in distinct compartments. Thus, addressing exhaustion as well as senescence might offer the opportunity to preserve and direct CAR T cell function in patients.

Single cell multi-omics reveal tiding and co-evolution of malignant plasma cells and T cells and identifies potential treatment targets

After investigating the immune microenvironment before and after CAR T cell infusion and dissecting CAR T cell function in vitro and at single cell resolution, we aimed at studying the compartment of malignant plasma cells to understand modes of resistance.

Sequencing single circulating plasma cells at leukapheresis and after CAR T cell infusion allowed the identification of surface antigens that are druggable with currently approved therapeutics (Figure 8A-C). Responders showed higher levels of surface BCMA protein expression compared to non-responders (Figure 8A). In non-responders, circulating malignant plasma cells were also detected after CAR T cell infusion. Bi-allelic loss of BCMA has been described as mode of resistance to CAR T cell therapies and bispecific antibodies^5,6. In our cohort, no loss of BCMA expression on plasma cells was detected (Figure 8A). No correlation between response and expression of other druggable antigens, including CD38 (Figure 8B) and SLAMF7 (Figure 8C) was detected. However, a significant resurfacing of CD38 expression after relapse from BCMA CAR T cells was detected in non-responders (Figure 8B).

Longitudinal tracking demonstrated clonal tiding and co-evolution of malignant plasma cells and T cells (Figure 8D and E). Based on inferred copy number variations (CNV) from single cell transcriptomes, we were able to track down daratumumab resistant clones (Figure 8F) that could be addressed with Elotuzumab/Pomalidomide/Dexamethasone, but resurged after Ide-cel treatment. Longitudinal TCR tracking showed expansion of T cells during the first four days of Daratumumab/Carfilzomib/Dexamethasone but diminished TCR richness caused by Elotuzumab/Pomalidomide/Dexamethasone prior to leukapheresis (Figure 8E). Although manufacturing was successful in the respective patient (P01: 437 x 10⁶ CAR T cells/kg infused, not out of specification, Figure 1A early progression into secondary plasma cell leukemia occurred within the first 100 days. Based on the detected CD38-positivity and presence of t(11;14), treatment with Isatuximab/Venetoclax/Bortezomib/Dexamethasone was initiated and resulted in a significant reduction of circulating plasma cells (data not shown).

CAR T cell therapies have led to unprecedented rates of deep and long-lasting remissions in heavily pretreated patients with triple-class exposed MM. However, most patients relapse within two years after treatment, especially if residual disease can be detected in the months following CAR T cell infusion^{2–4, 24}. In our current study, we used single cell multi-omics to identify markers associated with response and resistance to CAR T cell therapy in MM.

Immunoparesis is a cornerstone in the pathogenesis of MM and pronounced in RRMM^25–27. We found that an immunosuppressive microenvironment at leukapheresis characterized by monocytes expressing inhibitory immune checkpoints as well as numerically reduced and functionally impaired CD8 + T cells and NK cells were associated with adverse outcome after CAR T cell therapy. In line with this, we observed an upregulated communication between monocytes and NK as well as CD8 + T cells via PLAU-PLAUR. Expression of PLAUR has been associated with progression, decreased NK cell invasion and expression of immune checkpoints in renal cell carcinoma²⁸ and increased monocyte infiltration as well as decreased CD8 + T cells in glioma patients²⁹. In addition, genes encoding PIM kinases were recurrently upregulated in NK cells, monocytes and dendritic cells. PIM kinases promote immune evasion and have been targeted in RRMM^30,31. Combining PIM inhibition with an anti-PD1 antibody led to long-term disease control by tumor-reactive T cells in a melanoma mouse model underlining the potential of targeting PIM kinases to improve CAR T cell therapies³². Furthermore, we identified CD39 on monocytes as a potential target to modulate host immunity before and after CAR T cell therapy³³. Avoiding exposure to lymphotoxic substances like bendamustine might be another strategy to avert an immunosuppressive microenvironment at leukapheresis³⁴. These results demonstrate that patients with dismal outcome after anti-BCMA CAR T cell therapies could be identified prior to manufacturing and are characterized by an impaired immune system at apheresis. Choosing the right bridging therapy and combining it with agents that modulate immunosuppressive capabilities, especially of monocytes, might pave the way for successful treatment.

After identifying decreased T and NK cell activity in future non-responders at leukapheresis, we hypothesized that CAR T cells manufactured from the respective impaired immune microenvironment are not functional compared to responders. However, in vitro analyses of CAR T cell cytotoxicity showed no significant differences between responders and non-responders. This supports the hypothesis that CAR T cells are functional outside a non-permissive microenvironment and modulating host immunity after CAR T cell infusion might contribute to increased efficacy of cellular immunotherapies.

Another finding that emphasizes the role of bystander non-CAR T cells was the expansion and activation of CD4 + and CD8 + non-CAR T cells in responders. Stimulation of bystander cells has been described after anti-CD19 CAR T cells²¹ and in a study of an academic anti-BCMA CAR T cell product in RRMM³⁵. In line with the respective study, we also found that hyperexpanded CAR T cell clones after therapy exhibited a terminally differentiated phenotype characterized by expression of co-inhibitory markers such as PD1³⁵. In contrast to our findings, the authors described a correlation between pre-treatment TCR diversity and outcome³⁵. This difference might be explained by the fact that we analyzed peripheral blood samples instead of bone marrow samples prior to CAR T cell infusion. Furthermore, samples in the respective study were collected after application of an academic anti-BCMA CAR T cell and effects might be different compared to the currently approved CAR T cell products studied in our analysis.

In addition to hyperexpanded CAR T cell clones that were characterized by an exhausted and senescent phenotype, we dissected CAR T cells with low/intermediate clonality. While the respective CD8 + CAR T cells (Ide-cel and Cilta-cel) also expressed a cytotoxic phenotype, they showed lower expression levels of markers associated with exhaustion (e.g. TIM-3, KLRG1, KLRB1). NK cell receptors like KLRG1/CD166 on cytotoxic T cells have been successfully targeted to improve T cell immunity³⁶. Furthermore, the respective CAR T cells showed higher levels of activation markers like CD69 that was associated with improved outcome in the aforementioned study³⁵. Unlike in other hematological malignancies, where CAR T cells can be detected up to decades after therapy and are applied in a curative intention³⁷, CAR T cell persistence is limited to 6–12 months after infusion in MM^2,24. Our results support the post-infusional use of inhibitors of immune checkpoints that were expressed on exhausted CAR T cells to prolong their activity. Since we were able to collect paired peripheral blood and bone marrow samples following treatment, we compared CAR T cells isolated from the two compartments. Ide-cel + and Cilta-cel + CAR T cells from bone marrow had a more activated and less senescent phenotype compared to peripheral blood, emphasizing that CAR T cell functionality depends on the compartment they act in. This might have negative consequences for patients with plasma cell leukemia and/or EMD which becomes more prevalent after relapsed from CAR T cell therapy³⁸.

Furthermore, we compared single cell transcriptomes of Ide-cel + and Cilta-cel + CAR T cells from patients in CR. Phase I/II clinical trial data and first real-world experiences indicate towards more sustained CRs in patients treated with Cilta-cel⁴. Currently, the two BCMA-binding single chain variable fragment domains of Cilta-cel compared to the single domain of Ide-cel are possible explanations for these differences. However, we were also able to identify DEG between both products. Cilta-cel + CAR T cells showed significantly higher expression of genes associated with proliferation as well as CCR7 which is associated with a naïve or central memory phenotype³⁹. An explanation might be that Ide-cel + and Cilta-cel + CAR T cells were isolated during a different life cycle of the respective product. However, samples were collected around the same day (d30) after infusion and T cell kinetics after infusion were comparable between Ide-cel and Cilta-cel treated patients. Further studies in earlier lines of therapy and experience with the respective products outside clinical trials will follow to confirm the observed differences.

Lastly, we analyzed the compartment of malignant plasma cells. Bi-allelic loss of the BCMA locus on chromosome 16 has been described as possible mode of resistance to anti-BCMA CAR T cells in RRMM ^5,6. This mechanism occurs only in a subset of patients and was not observed in our cohort despite the detection of pre-existent del16p. However, responders had significantly higher baseline levels of surface BCMA expression compared to non-responders. In line with this observation, a recent preclinical study found out that increasing BCMA surface density by augmenting its expression with all-trans retinoic acid and preventing its shedding through gamma secretase inhibition improves efficacy of anti-BCMA CAR T cells⁴⁰. Longitudinal single cell sequencing of peripheral blood allowed a detailed characterization of tiding malignant plasma cell clones and T cells. In the past, we were able to demonstrate a negative impact of cytogenetically defined subclones in asymptomatic and newly diagnosed disease^41,42. In our current analysis, we connected the existence of subclones to response and resistance to antibody-based triplet therapies and CAR T cells. The identification of druggable antigens before and after CAR T cell therapy might support therapeutic decision-making to optimize bridging therapy and treatment of relapse after CAR T cell infusion. Furthermore, analyzing the co-evolution of the T cell compartment might identify the right moment to collect T cells for manufacturing to reduce the risk of out of specification productions.

Taken together, we identified an immunosuppressive microenvironment at time of leukapheresis being connected to adverse outcome after treatment with approved anti-BCMA CAR T cells for RRMM. Our single cell multi-omic analysis identified potential targets on non-CAR T cells, CAR T cells and malignant plasma cells to optimize activity and durability of CAR T cells for future studies.

Treatment and sample collection

Patients with RRMM were treated with commercial CAR T cell products (Ide-cel, Abecma® or Cilta-cel, Carvykti®) at the Department of Hematology, Cellular Therapy and Hemostaseology of the University Hospital in Leipzig, Germany. Patients were treated in an inpatient setting and lymphodepletion (LDP) was performed according to the KarMMa² and CARTITUDE-1^3,4 studies that lead to the approval of the respective agents. Data and sample collection were approved by the local ethics committee (biobank vote 311-15-24082015 and project vote 361/22) and performed in accordance with the Declaration of Helsinki. Peripheral blood was collected on the day of leukapheresis, the day of CAR T cell infusion (d0) as well as on days 7, 14, 30 and 100 after treatment. Bone marrow biopsies were performed on day 30 after CAR T cell therapy. Patients were followed up in our outpatient clinic and response was assessed according to International Myeloma Working Group (IMWG) recommendations⁴³. For single cell sequencing, mononuclear cells from bone marrow (BMMCs) and peripheral blood (PBMCs) were isolated following standardized operating procedures by trained staff of the Leipzig Medical Biobank using Leucosep tubes (Greiner Bio One, Frickenhausen, Germany) with a Ficoll gradient (Ficoll-Paque™ PLUS, VWR Avantor, Spring House, Pennsylvania). Cells were frozen using a CoolCell LX container (Corning, USA) and stored in the vapour phase of liquid nitrogen in an Askion HS200S freezer (Askion, Gera, Germany). For flow cytometry and cell culture experiments, fresh samples were handled as described below.

Single Cell Rna, T Cell Receptor, B Cell Receptor And Surface Proteome Sequencing

For single cell multi-omic analyses, PBMCs and BMMCs collected at day of leukapheresis and approximately 30 days after CAR T cell infusion were thawed and quality was assessed by acridine orange and propidium iodide (AO/PI) staining using automated cell counting. Prior to single cell staining and library construction, samples with initial viability < 80% were subjected to dead cell removal using Viahance™ from BioPAL, Inc. following the manufacturer’s instructions.

A custom panel of 57 oligonucleotide-coupled antibodies (Singleron Biotechnologies, Singapore) directed against cell surface proteins was used for surface proteome analyses (Supplemental table 2). The oligonucleotides consist of a linker to the antibody, a polymerase chain reaction (PCR) handle, an antibody-specific barcode and a binding site for the cell identifying capture oligonucleotide. The antibody-oligonucleotide conjugate was afterwards incubated with the cells followed by the removal of unbound antibody. After single cell droplet encapsulation, the antibody-bound oligonucleotides act as synthetic transcripts and were then processed with 10x Genomics 5’ v2 chemistry to generate libraries for BCR, TCR, gene expression and surface protein expression. Libraries were sequenced on an Illumina NovaSeq using a S4 flowcell and v1.5 PE150 chemistry, targeting a mean coverage of 40,000 reads/cell for gene expression and 5,000 reads/cell for protein expression, T cell receptor and B cell receptor libraries.

Single Cell Sequencing Data Analyses

The CAR sequences for Ide-cel⁴⁴ and Cilta-cel⁴⁵ were obtained from the respective patents and added to the GRCh38 based reference genome using Cellranger mkref v6.0.2. Sequencing data was processed with CellRanger v6.0.2 using this custom reference. Cells with fewer than 250 genes, more than 20% mitochondrial gene, more than 8,000 genes and more than 100,000 reads were filtered to exclude low quality cells and doublets. Clonotyping of BCR and TCR at patient level was performed with Enclone from 10x genomics using default parameters.

We tested SingleR⁴⁶, multimodal WNN¹⁸ and Mapcell⁴⁷ for unsupervised annotation of cell types and found that the WNN approach has the best concordance with results from the TCR and BCR assay. Hence, cell types were annotated with WNN using Seurat v4.0.6. We used PBMC and bone marrow⁴⁸ reference datasets to annotate the PBMC and BMMC samples respectively. The query datasets were dimensionally reduced by applying the supervised PCA transformation calculated with the reference datasets. The first 50 dimensions were then used to define the neighbor search space.

For identifying differentially expressed genes (DEGs) between CAR and non-CAR T cells within the same patient, Wilcoxon rank-sum test was performed at the single cell level. For identifying DEGs between groups of patients, a pseudobulk approach was used. UMI counts were summed across a cell type in a patient at one time point to yield one pseudobulk sample. Differential expression testing was performed with DESeq2⁴⁹ on these pseudobulk samples with the necessary blocking variables. This approach was used for comparing pre versus post CAR T cell infusion and responding vs nonresponding patients. Gene set over-representation analysis was performed using clusterProfiler⁵⁰ on the DEGs.

scCODA¹⁹ was used to identify change in cell type composition. False discovery rate (FDR) was set at 0.05 and reference cell type was determined automatically by the algorithm.

InferCNV⁵¹ package was used to identify CNVs in malignant plasma cells, with non-malignant plasma cells and B cells as reference. Cells were first clustered at patient level with Leiden algorithm (resolution of 0.5) before CNV probabilities were estimated in each clusters using Markov Chain Monte Carlo. A cell cluster is considered to harbor CNV for a particular locus if the CNV probability is more than 0.95 or if another cluster has a CNV probability of more than 0.95 while this cluster has a probability of more than 0.8 for that locus.

Ligand-receptor signaling was inferred with iTalk⁵² and CellPhoneDB⁵³ for each time point sample by identifying concomitant elevated expression of ligand-receptor pairs. 300 cells were sampled from each cell type to speed up the analysis and cell labels were permuted 1000 times to estimate a statistical significance level.

Monocle3⁵⁴ was used for plotting pseudotime trajectories. The node in the trajectory with the largest number of cells with the earliest time point in the vicinity was assigned pseudotime 0. Moran's I test was used to identify genes that vary with trajectory.

Immunophenotyping By Flow Cytometry

Blood samples of MM patients taken before CAR T cell treatment (day 0) as well as 7, 14, 30 and 100 days after treatment were analyzed by flow cytometry. For analysis of the CAR immune status, biotin labeled BCMA CAR Detection Reagent (2 µl; Miltenyi Biotec) was added directly to fresh 100 µl EDTA-anti-coagulated blood and incubated for 5 min at RT. After addition of 2 ml PBS and one washing step (500g, 5 min, RT), cell pellets were resuspended in 100 µl PBS and following antibodies were added: 2 µl anti-biotin antibody APC (Miltenyi Biotec), 1 µl CD4 BV786; 2.5 µl (each) CD45 V500, CD8 APC-H7, (all from Becton Dickinson).

After incubation for 15 min at RT, red blood cells were lysed by addition of 2 ml of lyse solution (Becton Dickinson) for 10 min. The cells were centrifuged (500g, 5 min, RT) and cell pellets were resuspended, washed with 2 ml PBS (500g, 5 min, RT) and analyzed using a FACSLyric flow cytometer (Becton Dickinson). Data analysis was performed using the FACSSuite software (Becton Dickinson).

Cell separation and cell culture for in vitro analysis

PBMCs of MM patients samples (EDTA-anti-coagulated blood) taken before CAR T cell treatment at day 0 and 7 days thereafter were isolated by either LymphoPrep™ (Progen) or Ficoll-Paque™ PLUS (GE Healthcare) density centrifugation. PBMCs, resuspended in X-Vivo medium (Lonza) supplemented with 200 U/ml IL-2 (Peprotech) and 2% AB-Sera (Sigma-Aldrich) were transferred to CD3 antibody (5 µg/ml, Invitrogen) coated culture plates and co-stimulated with CD28 antibody (2 µg/ml, Invitrogen) up to 3 weeks to induce T cell proliferation. The multiple myeloma cell line U266 was maintained in RPMI1640 medium (Gibco) supplemented with 10% FCS (Sigma-Aldrich). Culture expansion and media change were performed twice a week for T cells and U266.

Detection Of Car Expression In Cell Culture

Cultured (CAR) T cells (100.000) were washed twice in 100 µl PBS + 2% FCS (400g, 2 min, 4°C) before addition of 1 µl biotin labeled BCMA CAR Detection Reagent (Miltenyi Biotec) to 50 µl PBS + 2% FCS. After 10 min, two further washing steps in PBS + 2% FCS (400g, 2 min, 4°C) were carried out. Subsequently, 2.5 µl anti-biotin antibody PE (Miltenyi Biotec) and 2.5 µl CD3 V450 antibody (Becton Dickinson) in 50 µl PBS + 2% FCS were added for 10 min. Further two washing steps were performed (100 µl PBS + 2% FCS; 400g, 2 min, 4°C) and cells were resuspended in 100 µl PBS + 2% FCS before addition of 5 µl viability dye 7-AAD (Becton Dickinson) for 10 min. Samples were diluted with 200 µl PBS + 2% FCS and cells were immediately analyzed using a FACSCanto II flow cytometer (Becton Dickinson). Data analysis was performed using FlowJo 10.8.1 (FlowJo).

Cytotoxicity Assay

The target cell line U266 (1×10⁶/mL) was labeled with 1 µM VPD450 (Beckton Dickinson) and incubated for 15 min at 37°C. After three washing steps in PBS, cells were resuspended (1x10⁶/ml) in X-Vivo medium (Lonza) supplemented with 2% AB-Serum (Sigma-Aldrich) and proceeded to co-culture with the effector cells.

The expanded (CAR) T cells (effector cells) at day 0 before CAR T cell treatment and 7 days thereafter were harvested and resuspended (1x10⁶/ml) in X-Vivo medium (Lonza) supplemented with 2% AB-Serum (Sigma-Aldrich). Subsequently, 200.000 effector cells (T cells from day 0 and day 7) and 30.000 target cells (U266) were co-cultured in two 96-well U-bottom plates. U266 target cells (30.000) cultured without effector cells serves as controls.

Cell were either incubated for 24h at 37°C and 5% CO₂ or placed on ice and analyzed immediately to avoid unwanted target cell killing. To quantify the number of cells 5000 flow cytometry count beads (Invitrogen) were added to each well.

Cells were washed twice with 100 µl PBS (400g, 2 min, 4°C) before Fixable Viability Dye FVD eFlour® 780 (1:500; Thermo Fisher Scientific) was added for 15 min at 4°C. After three further washing steps in PBS + 2% FCS (100 µl), 2.5 µl of CD3 FITC antibody (Biolegend) in 50 µl PBS + 2% FCS was added for 10 min at 4°C. Cells were washed twice in PBS + 2% FCS (400g, 2 min, 4°C), fixed with 1% formaldehyde and analyzed by a FACSCanto II flow cytometer (Becton Dickinson) by counting 2500 bead events. Cells co-cultured for 24h or 0h were treated the same way. Data analysis was performed using FlowJo 10.8.1 (FlowJo).

Financial support

This work is supported by a grant from the Deutsche Forschungsgemeinschaft (SPP µbone) and Maximilian Merz received financial support by grants from SpringWorks and Janssen.

Parts of this work were awarded with a best abstract award at the 5^th EHA EBMT CAR meeting in Rotterdam in 2023.

Conflict of interests:

MM: Advisory Boards/ Honoraria/Research support: Amgen, BMS Celgene, Gilead, Janssen, Stemline, Springworks and Takeda

R.W. is involved in collaborations with Novartis regarding the analysis of CAR T cells.

VV: Advisory Boards/Honoraria: Amgen, BMS Celgene, Novartis, Gilead Kite, Janssen, Takeda.

MH: Consultancies/Honoraria/Research Supprt: AbbVie, BeiGene, Jazz, Janssen, Janpix, Mundipharma EDO, Novartis, Roche, Stemline Menarini, Takeda

SSch: Honoraria: Pfizer

MJ: Advisory Board: Novartis, BMS, Honoraria: Novartis, BMS, JAZZ Pharmaceuticals, Pfizer.

UK: Advisory Boards/Honaria: AstraZeneca, Affimed, Glycostem, GammaDelta, Zelluna as well collaborations with Novartis and Miltenyi Biotec regarding the production of CAR T cells

UP: Abbvie: Honoraria; BMS/Celgene: Honoraria; Takeda: Honoraria; Novartis: Honoraria; Janssen: Honoraria; Jazz: Honoraria; Silence Therapeutics: Honoraria; Geron: Honoraria.

KM: Metzeler: Novartis: Consultancy; Abbvie: Consultancy; Celgene: Consultancy; Curis Inc: Research Funding; Jazz: Consultancy; Pfizer: Consultancy.

ASK: Janssen: Honoraria, Research Funding; CURIS: Research Funding; Novartis: Honoraria, Research Funding.

JSi, JSc, LV and SX are amployess of Singleron Biotechnologies

Acknowledgement:

Authors thank Simone Scharf and Astrid Rohmer from the Leipzig Medical Biobank for handling cryopreserved samples and Daniela Bretschneider, Christel Mueller, Kathrin Wildenberger and Christina Wilhelm for performing FISH analyses on clinical samples. Authors express their gratitude towards patients and their families who participated in this analysis.

Authors' Contributions

Conception and design: Nora Grieb, Ronald Weiss, Vladan Vucinic, Maximilian Merz

Acquisition of data (acquired data, clinical management of patients, provided facilities, FISH, Flow cytometry, biobanking, in vitro studies etc.): Luise Fischer, Patrick Born, Andreas Boldt, Christina Kloetzer, Simone Heyn, Jule Usmann, Anne Sophie Kubasch, Ronny Baber, Song Yau Wang, Enrica Bach, Sandra Hoffmann, Birthe Schetschorke, Saskia Hell, Madlen Jentzsch, Sebastian Schwind, Klaus H. Metzeler, Georg-Nikolaus Franke, Marco Herling, Stephan Fricke, Paul Franz, Ulrich Sack, Kristin Reiche, Ulrike Köhl, Uwe Platzbecker, Vladan Vucinicand Maximilian Merz

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Nora Grieb, Ronald Weiss, Jaren Sia, Stacy Xu, Luise Fischer, Jonathan Scolnick, Lakshmi Venkatraman, Vladan Vucinicand Maximilian Merz

Writing, review, and/or revision of the manuscript: All authors

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Yes there is potential Competing Interest. MM: Advisory Boards/ Honoraria/Research support: Amgen, BMS Celgene, Gilead, Janssen, Stemline, Springworks and Takeda R.W. is involved in collaborations with Novartis regarding the analysis of CAR T cells. VV: Advisory Boards/Honoraria: Amgen, BMS Celgene, Novartis, Gilead Kite, Janssen, Takeda. MH: Consultancies/Honoraria/Research Supprt: AbbVie, BeiGene, Jazz, Janssen, Janpix, Mundipharma EDO, Novartis, Roche, Stemline Menarini, Takeda SSch: Honoraria: Pfizer MJ: Advisory Board: Novartis, BMS, Honoraria: Novartis, BMS, JAZZ Pharmaceuticals, Pfizer. UK: Advisory Boards/Honaria: AstraZeneca, Affimed, Glycostem, GammaDelta, Zelluna as well collaborations with Novartis and Miltenyi Biotec regarding the production of CAR T cells UP: Abbvie: Honoraria; BMS/Celgene: Honoraria; Takeda: Honoraria; Novartis: Honoraria; Janssen: Honoraria; Jazz: Honoraria; Silence Therapeutics: Honoraria; Geron: Honoraria. KM: Metzeler: Novartis: Consultancy; Abbvie: Consultancy; Celgene: Consultancy; Curis Inc: Research Funding; Jazz: Consultancy; Pfizer: Consultancy. ASK: Janssen: Honoraria, Research Funding; CURIS: Research Funding; Novartis: Honoraria, Research Funding. JSi, JSc, LV and SX are amployess of Singleron Biotechnologies

SCCARMMSupplementalFigure1.pdf
Supplemental figure 1: Analysis of bone marrow cellular composition following CAR T cell infusion. Uniform Manifold Approximation and Projection (UMAP) plots of bone marrow mononuclear cells (BMMC) isolated on day 30 after CAR T cell infusion color-coded by (A) response (CR = yellow, nonCR = blue) and (B) cell type annotation. (C) Comparison of different cell types in BMMC of responders (CR) and non-responders (nonCR) using scCODA demonstrated significantly lower numbers of plasma cells in responders (indicated in red). (D) Differential gene expression analysis showed enriched gene sets associated with oxidative phosphorylation and peptide metabolism in plasma cells from non-responders compared to responders.
SCCARMMSupplementalFigure2.pdf
Supplemental figure 2: Cell cycle analysis of pre- and post-infusional CD8+ T cells in responders and non-responders (A) Cell cycle phases of CD8+ T cells before (Pre) and after (Post) CAR T cell infusion based on single cell transcriptomes in responders (CR = yellow) and non-responders (nonCR = blue). Cell cycle scores for G1 (blue), G2M (red) and S-Phases (grey) were calculated using the preloaded gene sets in Seurat. (B) Boxplots of CD8+ T cells according to cell cycle phases before and after CAR T cell infusion in responders and non-responders. Significantly more CD8+ T cells were proliferatively active in responders after CAR T cell therapy.
SCCARMMSupplementalFigure3.pdf
Supplemental figure 3: Enriched pathways in CAR T cells compared to pre-infusional T cells from P12 treated with Idecabtagene vicleucel (Ide-cel) P12 exhibited hyperexpanded CAR+ TCR clones after infusion.
SCCARMMSupplementalFigure4.pdf
Supplemental figure 4: Enriched pathways in CAR T cells compared to pre-infusional T cells from P14 treated with Idecabtagene vicleucel (Ide-cel) P14 exhibited low-intermediate expanded CAR+ TCR clones after infusion.
SCCARMMSupplementalFigure5.pdf
Supplemental figure 5: Enriched pathways in CAR T cells compared to pre-infusional T cells from patients treated with Ciltacabtagene autoleucel (Cilta-cel)
SCCARMMSupplementalFigure6.pdf
Supplemental figures 6: Pseudotime trajectory analysis of pre-infusional and post-infusional CD4+/CD8+ T cells as well as CAR T cells Supplemental figure 6.1.: CD4+ T cell trajectories before/after Cilta-cel in patient 7 Supplemental figure 6.2.: CD8+ T cell trajectories before/after Cilta-cel in patient 7 Supplemental figure 6.3.: CD4+ T cell trajectories before/after Cilta-cel in patient 8 Supplemental figure 6.4.: CD8+ T cell trajectories before/after Cilta-cel in patient 8 Supplemental figure 6.5.: CD4+ T cell trajectories before/after Ide-cel in patient 12 Supplemental figure 6.6.: CD8+ T cell trajectories before/after Ide-cel in patient 12 Supplemental figure 6.7.: CD4+ T cell trajectories before/after Ide-cel in patient 14 Supplemental figure 6.8.: CD8+ T cell trajectories before/after Ide-cel in patient 14 The node in the trajectory with the largest number of cells at the earliest time point in the vicinity was assigned pseudotime 0. Moran's I test was used to identify genes that vary with trajectory. Analyses were performed with Monocle 3. Subplot A: Trajectory annotated by timepoint of sample acquisition (Apheresis, Post CAR- = post-infusional, untransduced T cells, Post CAR+ = post-infusional, CAR T cells). Subplot B: Trajectory annotated by pseudotime. Subplot C: Relative expression levels of top 6 genes ranked by Moran’s I test statistic overlaid onto trajectory. Ribosomal genes were excluded.
SCCARMMSupplementaltable1.xlsx
Supplemental table 1: Summary of samples and quality control data from single cell RNA, protein, T cell and B cell receptor sequencing CAR = Chimeric antigen receptor, PB = peripheral blood, BM = bone marrow, CR = complete response, PR = partial remission, PD = progressive disease, UMI = Unique molecular identifier.
SCCARMMSupplementaltable2.xlsx
Supplemental table 2: Custom panel of 57 oligonucleotide-bound antibodies for single cell surface proteomics
SCCARMMSupplementaltable3.xlsx
Supplemental table 3: Summary of differential gene expression analyses Summaries include comparisons of different cell types between non-responders versus responders from:
- peripheral blood mononuclear cells collected at leukapheresis (pre PBMC)
- PBMCs after CAR T cell therapy (post PBMC)
- bone marrow mononuclear cells after CAR T cell therapy (post BMMC) and comparisons of
- PBMCs at leukapheresis versusPBMCs after CAR T cell therapy in responders (pre_vs_post_CR)
- CAR T cells versuspre-infusional T cells in patient 12 (CAR_vs_pre_T_P12)
- CAR T cells versuspre-infusional T cells in patient 14 (CAR_vs_pre_T_P14)
- Ciltacabtagene autoleucel versusIdecabtagene vicleucel (Cilta_vs_Ide)

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

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Introduction

Results

Discussion

Patients And Methods

Treatment and sample collection

Single Cell Rna, T Cell Receptor, B Cell Receptor And Surface Proteome Sequencing

Single Cell Sequencing Data Analyses

Immunophenotyping By Flow Cytometry

Detection Of Car Expression In Cell Culture

Cytotoxicity Assay

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