Comprehensive Single-Cell Immune Profiling Defines the Patient Multiple Myeloma Microenvironment Following Oncolytic Virus Therapy in a Phase Ib Trial.

Translational Relevance

Pelareorep (PELA) is an oncolytic virus formulation that is currently being investigated for multiple myeloma therapy. Our study shows that combination PELA, bortezomib, and dexamethasone therapy is safe, well-tolerated, and induces an inflammatory response in patients with multiple myeloma characterized by significant T and natural killer cell activation, enhanced chemokine release, bone marrow T-cell infiltration, and increased programmed death-ligand 1 expression in multiple myeloma cells. Multiplexed immune cell profiling of the tumor immune microenvironment (TiME) defined a novel immune-primed multiple myeloma phenotype (CD138 + IDO1 + HLA-ABC High ) and revealed increased interactions between multiple myeloma and immune cell subsets in bone marrow from responders compared with nonresponders. Our findings support further investigation of oncolytic virotherapy-driven immune reprogramming as a potential strategy for sensitizing immunologically “cold” tumors to immune checkpoint inhibitor therapies. Additionally, our study demonstrates that TiME immune cell profiling should be explored as a potential clinical tool to distinguish early responders and nonresponders.

Introduction

Multiple myeloma is the second most common hematologic malignancy (1% of all cancers) in the United States, with approximately 30,000 new cases diagnosed in 2017 ( 1 ). Multiple myeloma is characterized by clonal plasma cell proliferation, leading to excess paraprotein production, progressive bone marrow failure, pathologic fractures, and immunoparesis ( 2 ). Although novel agents have improved survival, multiple myeloma remains incurable, and most patients become refractory to available therapies ( 3 ). New approaches featuring novel mechanisms of action are essential for improving long-term survival in patients with relapsed/refractory multiple myeloma (RRMM).

Many recent advances in multiple myeloma treatment have involved novel immunotherapies, including monoclonal antibodies, immunomodulating agents, chimeric antigen receptor (CAR) T cells, and T cell–redirecting bispecific antibodies ( 4 ). Oncolytic viral therapy represents another promising avenue being explored in ongoing clinical trials ( 5 ). Reovirus is a double-stranded oncolytic RNA virus found ubiquitously in the environment that typically causes asymptomatic respiratory and gastrointestinal tract infections in humans. Pelareorep (PELA, formerly Reolysin) is a proprietary clinical reovirus formulation currently under investigation as an oncolytic viral therapy in multiple myeloma. We previously showed that reovirus infection increases viral protein accumulation, endoplasmic reticulum stress, apoptosis, and significantly augments the antimyeloma effects of bortezomib (BZ) in a therapeutically selective manner ( 6 ). PELA also stimulates an anticancer immune response, associated with significantly increased programmed death-ligand 1 (PD-L1) expression in multiple myeloma cell lines and patient-derived specimens but not in normal cells ( 7 ). Reovirus treatment induces direct cell lysis and promotes tumor-associated antigen presentation, associated with a less-tolerant tumor immune microenvironment (TiME) and enhanced inflammatory cytokine expression ( 8 ).

The complex TiME changes induced by oncolytic viral therapy remain incompletely understood due, in part, to limitations of available visualization methods. For example, IHC is only able to concurrently label up to three markers per section. Flow cytometry can identify complex immune cell subsets but requires tissue disruption, resulting in the loss of spatial information and precluding analyses of complex cell–cell interactions. Highly multiplexed techniques enable the simultaneous detection of multiple markers in a single section, facilitating spatial analyses through genomics-based (e.g., Visium spatial transcriptomics) or proteomics-based approaches [e.g., PhenoCycler repetitive imaging cycling, multiplexed ion beam imaging by time of flight, and imaging mass cytometry (IMC); refs. 9–11 ].

Our collective preclinical findings informed the design of this investigator-initiated phase Ib study to determine the safety and recommended phase II dose (RP2D) of combination PELA, BZ, and dexamethasone (Dex) therapy in patients with RRMM. We report that treatment with the PELA/BZ/Dex regimen robustly induced natural killer (NK) and T-cell activation. To explore the spatial relationships between multiple myeloma and various immune cells within the multiple myeloma TiME, we mapped the multiple myeloma–immune cell interactome using IMC, which revealed significant inter-patient heterogeneity. Bone marrow samples from responding patients showed higher levels of reovirus protein, cytotoxic T-cell infiltration, and immune-rich microenvironments than those from nonresponding patients. In posttreatment samples, we identified an immune-primed (IP) multiple myeloma phenotype (CD138 + IDO1 + HLA-ABC High ) associated with prolonged response to treatment and shorter distances to immune cells. Our findings support further investigation of the therapeutic immune reprogramming effects of PELA and suggest that mapping the multiple myeloma–immune interactome may have potential clinical applications for the early stratification of responders and nonresponders.

Patients and Methods

Patients

Patients with RRMM, including those refractory to BZ, were enrolled. Additional eligibility requirements included measurable disease (defined as ≥0.5 g/dL immunoglobulin G or ≥0.25 g/dL IgA paraprotein in serum electrophoreses; ≥ 100 mg/L light chain in serum; or urinary excretion of ≥200 mg monoclonal light chain per 24 hours); Eastern Cooperative Oncology Group performance status of 0 to 2; and adequate bone marrow (absolute neutrophil count ≥1,000 cells/mm 3 ; platelet count ≥ 50,000/mm 3 ), hepatic [total bilirubin ≤ 1.5 × upper limit of normal (ULN); serum alanine or aspartate aminotransferase ≤3 × ULN], and renal (serum creatinine ≤2 × ULN) function. Patients were excluded if they had current evidence of intracranial disease; were diagnosed or treated for any malignancy other than myeloma within 2 years prior to the first dose; received autologous stem cell transplantation within 3 months prior to enrollment or allogeneic stem cell transplantation at any time; or had grade 2 or higher peripheral neuropathy. All patients provided written informed consent.

Study design

This single-arm, open-label, phase Ib study (ClinicalTrials.gov: NCT02514382 ) was conducted at the University of Southern California Norris Comprehensive Cancer Center with approval from the institutional review board in accordance with ethical principles founded in the Declaration of Helsinki, including the International Conference on Harmonization, Good Clinical Practice regulations and guidelines, and all applicable local regulations. Patient enrollment followed a standard 3+3 design using three escalating PELA doses [Cohort 1: 3×10 10 median tissue culture infectious dose (TCID 50 ); Cohort 2: 4.5×10 10 TCID 50 ; and Cohort 3: 9×10 10 TCID 50 ]. PELA was administered on Days 1, 2, 8, 9, 15, and 16. Patients received 40 mg Dex and 1.5 mg/m 2 BZ on Days 1, 8, and 15. Cycles were repeated every 28 days in the absence of disease progression or unacceptable toxicity. Because this was a single-arm pilot study, no randomization or blinding was performed, and no power analysis was necessary.

Objectives and assessments

The primary objectives were to determine the safety, tolerability, maximum tolerated dose (MTD), and RP2D of PELA/BZ/Dex therapy in patients with RRMM. The secondary objectives included characterizing pharmacodynamic responses and antitumor activity by evaluating the objective response rate (ORR).

The MTD was defined as the dose below the level at which 2 patients in a single cohort experienced any dose-limiting toxicities (DLT, defined in Supplementary Methods) during their first treatment cycle. If the highest tested dose (9 × 10 10 TCID 50 ) is reached without 2 patients in a single cohort experiencing a DLT, this dosing level will be used as the RP2D. Toxicity was evaluated according to the NCI Common Terminology Criteria for Adverse Events (RRID:SCR_010296), Version 4.03 (June 2010).

Blood samples were collected at baseline (pretreatment) and Day 2 of Cycle 1 (C1D2, posttreatment) before administering the second PELA dose. Bone marrow aspirates and biopsies were collected at screening (pretreatment) and posttreatment, on either Day 9 of Cycle 1 (C1D9; Patients 9 to 14) or within 7 days prior to Cycle 2 (C2D1; Patients 1, 5, and 7). Patients 2, 3, 4, 6, and 8 had no posttreatment bone marrow samples.

Efficacy parameters were evaluated on all patients receiving at least one PELA dose and at least one post-dose efficacy assessment (intention to treat population). Antitumor activity was assessed each cycle using the International Myeloma Working Group Uniform Response Criteria ( 12, 13 ) and is presented as response [ORR, defined as partial response (PR) or better] and time-to-event endpoints [duration of response, time to progression, progression-free survival (PFS), and overall survival (OS)].

T and NK-cell activation

Peripheral blood mononuclear cells (PBMC) were isolated by Ficoll separation of peripheral blood samples collected at baseline (pretreatment) and 24 hours after the first treatment (posttreatment). Cells were stained with antibodies against CD3 (Thermo Fisher Scientific, Waltham, MA; catalog no. 11–0036–42, RRID:AB_1272072), CD56 (Thermo Fisher Scientific, catalog no. 12–0567–42, RRID:AB_10598200), and CD69 (Thermo Fisher Scientific, catalog no. 17–0699–42, RRID:AB_2016681) and subjected to flow cytometry. CD69 expression (early lymphocyte activation marker) was evaluated on CD3 − CD56 + NK cells and CD3 + CD56 − T cells. See Supplementary Methods for additional details.

Cytokine analysis

Blood samples were collected at screening (pretreatment) and 24 hours after the first treatment (posttreatment). Plasma was subjected to cytokine analysis performed by Myriad Rules Based Medicine (Austin, TX).

IHC

Reovirus capsid protein antibody was provided by Dr. Matt Coffey (Oncolytics Biotech Inc, Calgary, Canada). Antibodies against PD-L1 (Abcam, Cambridge, MA; catalog no. ab213524, RRID:AB_2857903), caspase-3 (Abcam, catalog no. ab179517, RRID:AB_2893359), junctional adhesion molecule A (JAM-A; Abcam, catalog no. ab52647, RRID:AB_881206), CD8 (Abcam, catalog no. ab237709, RRID:AB_2892677), and anti-CD138 (Ventana Medical Systems, Tucson, AZ; catalog no. 4248, RRID:AB_2335948) were used. Comparisons of IHC and IMC were performed by USC Pathology using antibodies against CD3 (Leica Biosystems, Deer Park, IL; catalog no. PA0553, RRID:AB_10554601) and CD138 (Leica Biosystems, catalog no. PA0088, RRID:AB_10555989). The viral RNA in situ hybridization protocol has been previously described ( 14 ). Pre- and posttreatment bone marrow samples were collected from 7 patients. The bone marrow myeloma load was calculated as the percentage of CD138 + cells. Reoviral + multiple myeloma cell counts were calculated as the percentage of CD138 + cells containing reoviral RNA or protein. CD8 + , PD-L1 + , and caspase-3 + cell counts were calculated as the number of positive cells per 500 total cells from multiple 20× fields containing at least 20% myeloma cells. At least 3,000 cells were counted. Co-expression analyses were performed using the Nuance Multispectral Imaging System (PerkinElmer, Inc., Boston, MA; RRID:SCR_015382), as previously described ( 14 ).

TCR immunosequencing

Immunosequencing of the complementarity-determining region 3 (CDR3) on human T-cell receptor beta (TCRβ) chains was performed using the ImmunoSEQ Assay (Adaptive Biotechnologies, Seattle, WA; RRID:SCR_014709). DNA was isolated from pre- and posttreatment formalin-fixed, paraffin-embedded (FFPE) bone marrow samples ( n = 3) or bone marrow mononuclear cell pellets ( n = 4). TCRβ CDR3 was amplified by multiplex bias-controlled PCR with primers targeting the V and J genes of T cells as previously described ( 15 ). PCR products were sequenced on an Illumina NextSeq (Illumina, San Diego, CA). Primers targeting housekeeping genes were used as internal controls. Total nucleated cells in each sample were quantified, as previously described. Differential abundance was calculated as previously described ( 16 ).

IMC

IMC was performed on anonymized bone marrow samples obtained from six patients before (pretreatment) and after PELA/BZ/Dex treatment (posttreatment).

Region of interest selection

Sections were stained by hematoxylin and eosin, and areas with ≥10% plasma cells and ≥30% cellularity were used to select regions of interest (ROI) on neighboring sections stained with the IMC antibody panel.

Panel design, sample preparation, staining, and ablation

A panel of 31 metal-conjugated antibodies was designed to interrogate proteins of interest in bone marrow biopsy samples, with a focus on multiple myeloma–related and immune proteins (Supplementary Table S1). FFPE sections were prepared, stained with the designed antibody panel, and ablated, as previously described ( 17 ).

IMC image preprocessing

MCD (.mcd) files generated from the Hyperion Imaging System (RRID:SCR_023195) were visualized using HistoCAT++ (version 2.2) and MCD Viewer (version 1.0.560.6, RRID:SCR_023007) to assess staining quality. After visually inspecting each ROI, the data were processed and segmented by adapting the ImcSegmentationPipeline, as previously described ( 18 ). Pixel classification–based segmentation was performed in Ilastik (version 1.3.3; RRID:SCR_015246), as previously described ( 19, 20 ). HistoCAT (version 1.76) was used to import the segmentation mask with accompanying TIFF files for each ROI to normalize and analyze the segmented data ( 21 ).

Clustering and meta-clustering

T-distributed stochastic neighbor embedding (tSNE) coordinate generation and initial phenotypic clustering were performed in HistoCAT ( 22 ). The clustered data were then imported into RStudio (version 1.3.959; RRID:SCR_000432) for further analysis. The Rphenograph package (RRID:SCR_022603) was used to identify additional, infrequent phenotypes grouped within clusters identified by the initial PhenoGraph analysis.

Spatial analysis

The neighbouRhood package, an RStudio implementation of the neighborhood analysis tool from HistoCAT ( 23 ), was used for neighborhood analysis of clustered data. Distances between the centroid of a tumor cell of interest and the centroid of each immune cell proximal to the tumor cell of interest were measured to obtain a mean and standard deviation for each tumor cell. The dataset with clustering and meta-clustering was imported into the ImaCytE visualization and spatial analysis suite ( 24 ) to generate representative images of segmented data. The spatial analysis feature was used to identify unique, significant neighborhood motifs highlighted on appropriate ROIs, as previously described ( 24 ). A z-score cutoff ≥ 1.96 was used to determine significant motifs (equivalent to P < 0.05).

Statistical analyses

Clinical trial outcomes are described using descriptive statistics. Pharmacodynamic measurements were compared by two-sided Student t test using GraphPad Prism 5 (RRID:SCR_002798) or R software (RRID:SCR_001905), and P < 0.05 was considered significant. Pre- and posttreatment cytokine levels were compared using paired Student t test. IHC data were tested for normality using the Shapiro–Wilk test. Student t test was used to assess significance in normal data (unpaired for comparisons between response types; paired for comparisons between treatment time points). Non-normal data were assessed using paired or unpaired Wilcoxon signed-rank tests, as appropriate. For TCR rearrangements, P values were calculated using a binomial test according to the distribution of counts in each sample. Benjamini–Hochberg multiple hypothesis corrections were applied to rearrangements with cumulative counts of at least five in both pre- and posttreatment samples. Tests with alpha < 0.01 were designated differentially abundant. Between-group differences in spatial relationships were determined on a per-ROI basis using two-sample Wilcoxon signed-rank tests (unpaired for comparisons between response types; paired for comparisons between treatment timepoints), followed by Benjamini–Hochberg multiple comparison adjustment. For spatial analysis, P < 0.01 was considered significant unless otherwise stated. Detailed explanation for how per-ROI values were obtained can be found in Supplementary Methods.

Ethics approval and consent to participate

The study protocol was approved by the Institutional Review Board of the University of Southern California (IRB No. HS-15–00262). The study was conducted in accordance with the Good Clinical Practice Guidelines and Declaration of Helsinki. Written informed consent was obtained from all patients.

Patient consent for publication

Not applicable.

Availability of data and material

All software and code that produced the findings of the study, including Mac OS12 (Supplementary Software 1), Windows 7 (Supplementary Software 2), and Windows 10 (Supplementary Software 3) versions of HistoCAT, are available at https://github.com/BodenmillerGroup/histoCAT . All raw data, including source data for Fig. 3 , HistoCAT sessions, and interactive graphs, that support the findings of the study are available at https://figshare.com/articles/dataset/Comprehensive_Single-Cell_Immune_Profiling_Defines_the_Patient_Multiple_Myeloma_Microenvironment_Following_Oncolytic _Virus_Therapy/24175404 (DOI: 10.6084/m9.figshare.24175404). The clinical trial protocol is available upon request, and the study is registered at ClinicalTrials.gov ( NCT02514382 ).

Results

Patient characteristics

A total of 14 patients were enrolled in this phase Ib trial. The study design is presented in Fig. 1A , and patient demographic characteristics are summarized in Table 1 and Supplementary Table S2. Most patients ( n = 10) were men with a median age of 58 years (range 33–79 years). The median number of prior therapies was 3.5 (range 1–7). All patients were previously exposed to BZ, and 9 patients were previously exposed to immunomodulatory agents and carfilzomib.

Safety and toxicity

Cohort 1 (3×10 10 TCID 50 PELA) included 5 patients, Cohort 2 (4.5×10 10 TCID 50 PELA) included 3 patients, and Cohort 3 (9×10 10 TCID 50 PELA) included 6 patients. No DLTs occurred in any cohort. Three patients failed to complete one treatment cycle (1 withdrew consent; 2 experienced clinical deterioration due to underlying disease). One patient completed only one treatment cycle. One patient each completed 3, 4, 5, 9, and 10 treatment cycles. One patient with a very good PR (VGPR) completed 46 cycles, and another patient with a PR completed 55 cycles. Discontinuation occurred due to progressive disease (PD; n = 8), withdrawal of consent ( n = 2), clinical deterioration related to the underlying disease ( n = 3), and symptomatic pulmonary hypertension in a patient with prior carfilzomib exposure ( n = 1).

The PELA/BZ/Dex regimen was well-tolerated, and most treatment-emergent toxicities were transient and easily managed with supportive care. The most common treatment-related toxicities were grade 1, transient, and consistent with viremia (flu-like symptoms, diarrhea, fatigue, headache, and nausea; Supplementary Table S3).

Antitumor activity of PELA/BZ/Dex

Of 14 patients, 3 were excluded from the efficacy analysis due to a lack of post-baseline assessments. Among the 11 response-evaluable patients, 1 had a VGPR, 1 had a PR, 2 had a minor response (MR), 4 had stable disease (SD) lasting at least 1 cycle, and 3 had PD after 1 cycle ( Table 1 ). Figure 1B shows a waterfall plot illustrating observed changes in paraprotein for each response-evaluable patient. Patients with a 25% or greater reduction in paraprotein levels were defined as responders, and all other patients were defined as nonresponders. Median OS was 42.6 months [95% confidence interval (CI), 2.0 to ≥69.2 months], and median PFS was 2.6 months (95% CI, 0.9–10.3 months; Fig. 1C ).

PELA/BZ/Dex promotes an inflammatory chemokine response associated with immune cell recruitment

Cytokines stimulate both antiviral and antitumor immune responses, and cytokine levels can predict the response to immunotherapy ( 25, 26 ). We quantified 41 cytokines and chemokines in patient serum samples obtained before and after PELA/BZ/Dex treatment. Significant posttreatment increases were observed for cytokines associated with T-cell trafficking, T-cell tumor infiltration (C–X–C motif ligand 10 [CXCL10], CXCL11), NK-cell activation (IL18), and monocyte activation [C–C motif ligand 8 (CCL8)], in addition to the cytokine modulator tissue inhibitor of metalloproteinases 1 (TIMP-1), in both responders and nonresponders ( Fig. 1D ; the 14 cytokines with the largest posttreatment shifts are shown in Supplementary Fig. S1A). Consistent with our preclinical data, the most significantly increased cytokine was CXCL10, a potential driver of anticancer activities involved in T-cell trafficking and angiogenesis inhibition ( 27 ).

PELA/BZ/Dex promotes NK and T-cell activation

In addition to direct cytolytic effects, PELA enhances in vitro tumor cell killing through activation of patient-derived NK cells ( 28, 29 ). Multiple myeloma progression is associated with increased NK-cell dysfunction, driven by TiME reprogramming ( 30 ) and defects in the anti–multiple myeloma activity of T cells ( 31 ). To evaluate the effects of PELA on NK (CD3 − CD56 + ) and T-cell (CD3 + CD56 − ) activation, we used flow cytometry to first isolate NK and T-cell populations from pre- and posttreatment PBMCs and then to evaluate the proportions of CD69 + cells within the total NK and T-cell populations ( Fig. 1E ). Significant CD69 upregulation was observed in all posttreatment samples (CD69 + CD3 − CD56 + cells/all CD3 − CD56 + NK cells: 59% posttreatment versus 5% pretreatment, Fig. 1F ; CD69 + CD3 + CD56 − cells/all CD3 + CD56 − T cells: 41% posttreatment versus 7% pretreatment, Fig. 1G ). These effects appeared to be driven by PELA rather than BZ or Dex, as the ex vivo treatment of patient-derived multiple myeloma cells with PELA alone was sufficient to robustly activate NK and T cells (Supplementary Fig. S1B).

PELA/BZ/Dex increases bone marrow CD8 + T-cell infiltration and immune checkpoint expression in multiple myeloma cells

Multiple myeloma often lacks features of tumors that respond well to single-agent programmed cell death 1 (PD-1) blockade therapy, such as high baseline CD8 + T-cell infiltration and PD-L1 expression ( 7, 32, 33 ). However, oncolytic viral therapy offers the potential to phenotypically transform immunologically “cold” tumors into “hot” tumors. To assess whether PELA treatment can reprogram the bone marrow TiME, we analyzed PELA + cells in baseline and posttreatment biopsies from 8 patients. PELA RNA ( Fig. 2A ), a marker of reovirus entry, was detected in posttreatment biopsy samples. No significant difference in the percentage of RNA + cells was observed between responders and nonresponders. IHC suggested that PELA RNA was preferentially concentrated in cells with plasma cell phenotypes, as more than 90% of the total RNA signal was detected in CD138 + multiple myeloma cells ( Fig. 2B ). Consistent with preclinical findings that PELA enters multiple myeloma cells through JAM-A, PELA RNA colocalized with JAM-A ( Fig. 2C ). PD-L1 staining was membranous and largely restricted to cells with characteristic multiple myeloma cell morphology containing reoviral RNA ( Fig. 2D ). Although PELA RNA is an indicator of viral entry, the presence of RNA does not necessarily indicate viral proliferation. To assess viral protein production, we examined posttreatment samples for the presence of reoviral capsid protein, a marker of lytic replication. Unlike reoviral RNA, which was detected in similar proportions of multiple myeloma cells from both responders and nonresponders, the proportion of reovirus protein–positive multiple myeloma cells was significantly larger in responders than in nonresponders ( Fig. 2E ). PELA/BZ/Dex treatment resulted in significant increases in the proportions of PD-L1 + cells ( Fig. 2F ), and PD-L1 upregulation in CD138 + cells was confirmed by flow cytometric data (Supplementary Fig. S1C and S1D). The posttreatment percentages of CD8 + cytotoxic T cells ( Fig. 2G ) and activated caspase-3 + apoptotic cells ( Fig. 2H ) increased in both nonresponders and responders, but only responders showed significant posttreatment increases compared with pretreatment values. These findings indicate that PELA/BZ/Dex treatment elicits a strong inflammatory response with subsequent immune checkpoint induction.

PELA/BZ/Dex induces clonal T-cell expansion

Our results showed that PELA/BZ/Dex therapy induced T-cell activation and significantly increased CD8 + T cells in bone marrow ( Figs. 1E – G , and 2G ). Therefore, we examined changes in the TCR repertoire using TCR immunosequencing of pre- and posttreatment bone marrow samples from 7 patients, revealing clonal expansion in all patients (Supplementary Fig. S2A). The fraction of expanded clones showed a trend toward a positive correlation with the percentage of reovirus RNA + multiple myeloma cells (Supplementary Fig. S2B). These data suggest that clonal expansion occurs in response to viral entry but does not determine the response to treatment.

IMC allows multiplex visualization of the bone marrow TiME

To better understand the cellular dynamics in response to PELA/BZ/Dex treatment and obtain a detailed view of the spatial relationships within the multiple myeloma TiME, we performed IMC in pre- and posttreatment samples. Unlike IHC, which retains spatial information but is limited to a few markers, or flow cytometry, which can label multiple markers but requires the disruption of spatial relationships, IMC allows for the labeling of multiple markers simultaneously while retaining the spatial information of the local environment. We generated an antibody panel targeting 31 TiME-specific biomarkers ( Fig. 3A ; Supplementary Table S1), based on cytokine assays, flow cytometry data, and available literature, which we used to perform IMC in samples from six patients with adequate pre- and posttreatment tissue. A comparison of serial FFPE bone marrow sections separately subjected to IHC and IMC revealed similar CD138 and CD3 expression patterns, validating the ability of IMC to simultaneously visualize multiple markers in our samples (Supplementary Fig. S3A). Multiplex staining allowed for the simultaneous detection of multiple immune and tumor markers, highlighting cellular compositions, states, and interactions ( Fig. 3B shows a representative ROI; Supplementary Fig. S3B displays staining for all markers within a single ROI).

NKG2D + NK cells were observed adjacent to multiple myeloma cells expressing the NKG2D cognate receptor ULBP-2/5/6 ( Fig. 3B ), whereas NKG2A + cells were adjacent to human leukocyte antigen (HLA)-E + cells ( Fig. 3C ). CD8 + cells were positive for HLA-ABC ( Fig. 3D ), and CD4 + cells were positive for HLA-DR ( Fig. 3E ). PELA+ CD163 + cells were closely associated with CD4 + and CD8 + T cells indicating dendritic cell cross-priming following viral entry ( Fig. 3F ). Consistent with the IHC results, a proportion of multiple myeloma cells (CD138 + ) were apoptotic (CC3 + , Fig. 3G ), and PD-L1 + multiple myeloma cells were adjacent to PD-1 + CD3 + cells ( Fig. 3H ). Distinct expression patterns were observed for other immune regulators, such as CD16 + NK cells positive for negative regulator lymphocyte-activation gene 3 (LAG-3; Fig. 3I ), CD68 + macrophages positive for indoleamine 2,3-dioxygenase 1 (IDO1; Fig. 3J ), and CD3 + T cells positive for T-cell immunoglobin and mucin domain–containing protein 3 (TIM3; Fig. 3K ). The proliferation marker Ki-67 ( Fig. 3L ) was detected in both T cells (CD3 + ) and multiple myeloma cells (CD138 + ), whereas the transcription factor MUM1/IRF4 ( Fig. 3M ) was observed only in multiple myeloma cells. Our comprehensive immune cell panel also detected other immune cell types, including B cells, granulocytes, and macrophages ( Fig. 3N ).

Spatial proteomics reveals enhanced tumor–immune interactions in patients with multiple myeloma that respond to PELA/BZ/Dex

Up to three 1-mm 2 ROIs containing multiple myeloma and immune cells were segmented into single cells, yielding an average of 2,278 cells per ROI (70,633 cells total), as previously described ( 19, 20, 23 ). Lineage marker expression, cell sizes, and cell shapes were hierarchically clustered using PhenoGraph ( Fig. 4A ; ref. 34 ), identifying distinct immune phenotype clusters: macrophages (CD163 + or CD68 + ), B cells (CD20 + ), cytotoxic T cells (CD3 + CD8 + ), NK cells (NKG2D + CD3 − or NKp46 + Granzyme B + CD3 − or NKG2A + CD3 − ), monocytes (CD14 + ), granulocytes (CD11b + CD15 + ), T helper cells (CD3 + CD4 + ), and endothelial cells or megakaryocytes (both CD31 + ). The application of PhenoGraph to the initial multiple myeloma phenotype identified a CD31 + multiple myeloma cluster and a unique IP meta-cluster (CD138 + IDO1 + HLA-ABC High ). To better visualize this meta-cluster, we imported custom phenotypes into ImaCytE (representative image in Fig. 4B ; ref. 24 ) and applied tSNE analysis, which revealed each cluster as distinct ( Fig. 4C ). Although frequency analysis revealed heterogeneous cluster compositions across patients ( Fig. 4D ), the proportion of cytotoxic T cells was significantly higher in responders than in nonresponders in both pre- ( P = 0.00117) and posttreatment ( P = 0.00175) samples. This finding suggests that cytotoxic T-cell infiltration may be a determinant of the clinical response to PELA/BZ/Dex treatment. Granzyme B + NK and T cells also increased significantly in posttreatment samples compared with pretreatment samples, indicating increased immune cell activation (Supplementary Fig. S4A).

Visual analysis of multiplexed images revealed fewer tumor-infiltrating CD8 + T cells in nonresponder samples ( Fig. 5A , top) than in responder samples ( Fig. 5A , bottom). Neighborhood analysis using ImaCytE revealed significant posttreatment differences between nonresponders and responders. Representative images of the most commonly identified posttreatment neighborhood compositions revealed that in nonresponders, the most abundant posttreatment neighborhood consisted almost entirely of tumor cells ( Fig. 5B , top), whereas, in responders, the most abundant neighborhood consisted of tumor cells located near immune cells ( Fig. 5B , bottom), including cytotoxic T cells and macrophages, consistent with increased CD8 + T-cell infiltration. We then examined the distances between tumor cells and various immune cells, including cytotoxic T cells, CD68 + macrophages, NKG2A + NK cells, and T helper cells, and found that tumor cells were located significantly closer to cytotoxic T cells in responders than in nonresponders, after treatment ( Fig. 5C ; distances for each patient can be found in Supplementary Table S4). Notably, in posttreatment samples, cytotoxic T cells were 3-fold closer to multiple myeloma cells in responders than in nonresponders. A representative image from a responder (Patient 10) showing that areas with high multiple myeloma populations have high cytotoxic T-cell presence, whereas areas with low multiple myeloma populations have low numbers of cytotoxic T cells, can be found in Supplementary Fig. S4B. These findings suggest that the quantification of distances between tumor and cytotoxic T cells may allow for the assessment of posttreatment responses and the pretreatment prediction of which patients are likely to respond to therapy.

We next conducted a focused evaluation of the IMC data obtained for Patient 10, who had a 90% reduction in paraprotein sustained over 46 cycles and displayed significantly increased posttreatment levels of IP multiple myeloma cells ( Fig. 4D ), suggesting that the presence and expansion of IP multiple myeloma cells may serve as a marker of treatment response. Assessment of the most abundant neighborhood motifs associated with different multiple myeloma types revealed that non-IP multiple myeloma phenotypes (5,514 cells) were most commonly located in neighborhoods with few or no immune cells ( Fig. 5D , left), whereas the IP phenotype (2,771 cells) was most commonly found in neighborhoods containing various immune cells, including cytotoxic T cells, NK cells, T helper cells, and macrophages ( Fig. 5D , right; Supplementary Fig. S4C shows both non-IP multiple myeloma and IP multiple myeloma cells within the same ROI). Distance analysis revealed that IP multiple myeloma cells were significantly closer to cytotoxic T cells than non-IP multiple myeloma cells following PELA/BZ/Dex treatment ( Fig. 5E ; distances for each patient can be found in Supplementary Table S5). Our findings suggest that the spatial dynamics between IP multiple myeloma cells and cytotoxic T cells may also determine the patient response.

Discussion

Our study demonstrates that intravenous PELA administration (at a dose as high as 9×10 10 TCID 50 ) combined with BZ and Dex therapy is well-tolerated in patients with RRMM. Most of the reported therapy-related toxicities were transient flu-like symptoms that were easily managed with acetaminophen, anti-emetics, and anti-diarrheal medications and significantly diminished after completion of the first treatment cycle. As no DLTs occurred, the MTD was not technically reached. A 3×10 10 TCID 50 PELA dose administered for 5 consecutive days in 21- and 28-day treatment cycles (as monotherapy and in combination with chemotherapy) has been used in multiple studies to date ( 35 ). Our study integrated PELA into the BZ/Dex regimen established for patients with RRMM using a 5-day PELA treatment–free interval between two consecutive rounds of daily PELA doses. This study represents the first clinical trial to evaluate the 9×10 10 TCID 50 PELA dose for any indication.

No objective responses were observed in a previous study of PELA monotherapy conducted among a similar population of patients with RRMM ( 36 ). However, preclinical and clinical trial data indicate that PELA is more effective when combined with other anticancer agents ( 6 ). Although all patients in the current study had prior BZ exposure, 6 of 11 response-evaluable patients (55%) showed a decrease in paraprotein following treatment. Preclinical studies have shown that proteasome inhibitors may enhance PELA activity through several mechanisms, including enhanced PELA-induced oncolysis and synergistic endoplasmic reticulum stress induction ( 6, 37 ), and multiple mechanisms may have contributed to the patient responses observed in this trial.

Compared with nonresponders, responders to PELA/BZ/Dex therapy exhibited a larger proportion of multiple myeloma cells containing PELA protein, indicating active viral proliferation. The mechanism underlying the lack of viral proliferation in nonresponders is not immediately clear. Reoviral proliferation depends on a number of different factors, including an active transcriptional environment and a limited antiviral response ( 38 ), and differences in any of these elements could inhibit proliferation. These differences are being actively studied in an ongoing clinical trial of reovirus, BZ, and pembrolizumab ( NCT05514990 ).

In responders, viral proliferation was associated with increased apoptosis, indicated by the detection of increased abundance of cleaved caspase-3. Although BZ/Dex therapy may also induce apoptosis and nonresponders showed some increase in caspase-3 + cells, only responders showed both PELA protein expression and a significant increase in caspase-3 + cells after treatment. Therefore, PELA viral proliferation likely had direct apoptotic effects. In addition, the 2 patients with the best responses and who received the most treatment cycles (Patient 10, VGPR, 46 cycles and Patient 11, PR, 55 cycles) were on BZ maintenance prior to this trial. Patient 10 spent 12 months on BZ maintenance, whereas Patient 11 had progressed while on month 4 of BZ maintenance and had high-risk cytogenetic features (17p deletion and 1q amplification). The response to PELA/BZ/Dex treatment in these patients lasted far longer than would be expected for BZ retreatment alone (Patient 11: PFS of 45 months; Patient 10: PFS 52 months for PELA/BZ/Dex vs. 6.4 months expected for BZ retreatment; ref. 39 ). Patient 7 (PFS 314 days), who received 10 cycles of study treatment with a MR, experienced PD on another proteasome inhibitor, carfilzomib, suggesting that proteasome inhibition alone was not responsible for the observed outcome.

A preclinical study of chronic lymphocytic leukemia reported that PELA phenotypically and functionally activated patient NK cells resulting in enhanced NK cell killing ( 28 ). Consistently, our data showed that oncolytic viral therapy induced the production of chemokines involved in T cell, NK cell, and monocyte homing (CXCL9, CXCL10, CXCL11, CCL20, IL18, CCL2, and CCL8) and activation (CD69), which may have contributed to TiME remodeling ( 40 ). The observed upregulation in cytokines in both responders and nonresponders may have occurred in response to the presence of reovirus but may not require active viral replication. However, the presence of reovirus protein may be required for an anti-myeloma effect because the reoviral protein levels were higher in responders than in nonresponders.

Our IHC analysis revealed similar proportions of cytotoxic T cells in pre- and posttreatment bone marrow samples regardless of response status. However, in our IMC analysis, responders demonstrated a significant increase in cytotoxic T-cell proportions following treatment, which was not observed in nonresponders. Although IHC provides a broad overview of protein expression in the tumor tissue, IMC offers a close spatial analysis of the TiME immediately surrounding multiple myeloma cells. IMC revealed that multiple myeloma cells were more likely to be located in neighborhoods containing various immune cells, including cytotoxic T cells, NK cells, and macrophages, in responders than in nonresponders, and that the proportions of cytotoxic T cells in the immediate area surrounding multiple myeloma cells were significantly higher in responders than in nonresponders at posttreatment time points. These findings suggest that cytotoxic T cells may be more likely to cluster around multiple myeloma cells in responders and highlight the benefits of using IMC to better understand the spatial dynamics among cell types in the TiME. The relationship between spatial dynamics and clinical response warrants further investigation not only for PELA-based regimens but also for other immunomodulatory therapies. Validation of this technique and immunophenotype as a potential early predictor of response is of particular interest.

Other approaches are also being developed to predict response to therapy. A 12-chemokine signature associated with increased survival in patients with melanoma ( 26 ) is being developed to predict the response to immune checkpoint inhibitor therapy. Eight of these 12 chemokines were measured in this study (CXCL9, CXCL10, CXCL11, CCL2, CCL3, CCL4, CCL8, and CCL9), and all were significantly increased posttreatment. Similarly, to predict response to pembrolizumab, a 6-gene panel has been developed, including CXCL9 and CXCL10, which were both increased in this study ( 25 ).

Although the development of immunotherapy approaches is changing the cancer treatment paradigm, tumor cells proliferate by evading antitumor immune responses. Some tumor types have a T cell–inflamed phenotype characterized by high concentrations of infiltrating T cells and chemokine secretion, indicative of innate immune activation ( 41 ). Because these tumors survive through an immune checkpoint-dependent mechanism, they respond well to single-agent immune checkpoint inhibitors. However, not all tumor types respond to single-agent approaches, and cancer types that lack the T cell–inflamed phenotype, including multiple myeloma, resist immune attack through immune system exclusion ( 7 ). Consistently, no objective responses were reported in a clinical trial of single-agent pembrolizumab in multiple myeloma ( 42 ). Alternative strategies are necessary to expand immunotherapy approaches to intrinsically immunologically “cold” cancer types.

The strategy of combining priming agents with immune checkpoint inhibitors in patients with RRMM was tested in a study examining pembrolizumab combined with lenalidomide, a potent inducer of the innate immune response ( 43, 44 ). However, phase III studies of pembrolizumab + lenalidomide were halted due to significant toxicity. Oncolytic viruses represent attractive priming agents due to general tolerability, and several oncolytic viruses are being examined in multiple myeloma in ongoing clinical trials, including PELA, measles virus ( NCT00450814 and NCT02192775 ), and vesicular stomatitis virus ( NCT00450814 ; ref. 45 ). Although oncolytic viruses display anticancer activity when used alone or in combination with conventional chemotherapy, their greatest potential may be their ability to sensitize cancers to immune checkpoint inhibitor therapies ( 46, 47 ). For example, a recent phase I study showed that the oncolytic herpes virus, talimogene laherparepvec, enhanced the response to immune checkpoint blockade by increasing CD8 + T-cell infiltration and PD-L1 protein expression ( 47 ). Talimogene laherparepvec is now FDA approved as an oncolytic viral therapy for melanoma ( 48 ). We previously demonstrated that PELA treatment selectively induces significantly increased PD-L1 expression in primary multiple myeloma cells and multiple myeloma cell lines ( 7 ). Our current study demonstrates that PELA has PD-L1–related immune-priming effects, supporting its potential to sensitize multiple myeloma cells for improved responses to immune checkpoint inhibitor therapy.

On the basis of the results of this phase Ib, we designed a follow-up phase I/II clinical trial of PELA, BZ, and pembrolizumab ( NCT05514990 ) to further investigate and validate PELA's immune reprogramming effects in patients with multiple myeloma. This trial is now actively accruing patients. PELA is also being evaluated in combination with other therapies [PELA, carfilzomib, and nivolumab ( NCT03605719 )], including monoclonal antibodies targeting markers that were enriched in the IP multiple myeloma TiME in this study: NKG2A (monalizumab; refs. 49, 50 ) and CD47 (magrolimab; ref. 51 ).

One limitation of this study, which we hope will be better addressed in future studies, was the overrepresentation of male patients among the study cohort. This is not surprising given that multiple myeloma is known to disproportionately affect men (1.53 men are affected for every 1 woman). In a random population of 15 patients with multiple myeloma, we would expect to encounter 9 men and 6 women. This is similar to the proportions observed in our study (10 men and 4 women), indicating that our study population is representative of the overall multiple myeloma population. We used a non-random sampling approach, enrolling consecutive patients who met our study criteria and were referred to our treatment center without consideration for demographic factors. The underrepresentation of women in clinical trials of multiple myeloma is a known issue in the field, and future studies should take this into consideration when designing eligibility and enrollment criteria to strive for better male:female ratios.

This is the first study to use targeted proteomics via IMC to comprehensively characterize changes in the TiME of patients with RRMM following oncolytic virotherapy. Although prior studies have attempted to perform single-cell analyses to assess changes in gene expression correlated with disease stages, many have focused solely on the immune component (CD138 − ), and those that have examined multiple myeloma cells have used bone marrow aspirates or utilized filtration methods that disrupted the spatial relationships between immune cells and multiple myeloma cells ( 52–54 ). Our application of IMC allowed us to evaluate the multifaceted immune response to oncolytic viral therapy through the simultaneous analysis of protein expression and spatial information in both immune cells and multiple myeloma cells, resulting in high-dimensional marker detection and the detection of a novel IP multiple myeloma phenotype (CD138 + IDO1 + HLA-ABC High ). Our results indicate that virotherapy was associated with a shift from the typically “cold” multiple myeloma TiME to a “hot” phenotype in a subset of responding patients, favoring an anti–multiple myeloma immune response. Our findings suggest that IMC-based analyses of biopsy tissues may be able to validate the immune-priming effects of oncolytic viral therapy and identify potential “good” responders prior to initiating treatment. Further investigations of the application of IMC in response prediction are warranted. Collectively, our findings not only establish the safety and immunomodulatory effects of PELA in multiple myeloma combination therapy but also provide rationale for developing new combination approaches able to exploit virotherapy-induced changes in the TiME, particularly using immune checkpoint inhibitors. Our ongoing phase I/II clinical trial of PELA, BZ, and pembrolizumab ( NCT05514990 ) will rigorously test this strategy, and our planned correlative analyses will yield deeper mechanistic insights into the relationship between PELA's architectural reorganization of the TiME and patient outcomes.

Supplementary Material

Supplementary Figure 1. Induced expression of cytokines, CD69 in lymphocytes, and PD-L1 in MM cells with no changes in T cell clonality

Click here for additional data file.

Supplementary Figure 2. TCR analysis

Click here for additional data file.

Supplementary Figure 3. Imaging mass cytometry (IMC) staining overview

Click here for additional data file.

Supplementary Figure 4. Increased immune cell activity observed by imaging mass cytometry (IMC)

Click here for additional data file.

Supplementary Methods

Click here for additional data file.

Supplementary Table 1. Detailed IMC panel

Click here for additional data file.

Supplementary Table 2: Representativeness of Study Participants

Click here for additional data file.

Supplementary Table 3. Toxicity summary excluding toxicities defined as unrelated to either bortezomib or pelareorep

Click here for additional data file.

Supplementary Table 4. Distance from tumor cell to immune cell for each patient (Figure 5C)

Click here for additional data file.

Supplementary Table 5. Distance from tumor cell to immune cell for each patient (Figure 5E)

Click here for additional data file.

Acknowledgments

This clinical trial was funded by a grant from the Whittier Foundation (to K.R. Kelly) and by Oncolytics Biotech Inc (to K.R. Kelly). The correlative studies were funded by NIH/NCI grant R01CA190789 (to S.T. Nawrocki) and by Oncolytics Biotech Inc. Imaging mass cytometry was supported by funding from the Ming Hsieh Institute at the University of Southern California (to K.R. Kelly, A. Merchant, and P. Kuhn). Medical editing support was funded by Oncolytics Biotech Inc. Statistical analysis support was provided by the Norris Comprehensive Cancer Center Data Science Core, supported in part by award number P30CA014089 from the NIH/NCI.

The authors would like to acknowledge all of the patients who participated in this study. The authors thank Lisa Giles, PhD, at BioScience Writers, LLC, for medical editing support.

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

Footnotes

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

Authors' Disclosures

S.T. Nawrocki reports other support from Majestic Therapeutics, LLC, outside the submitted work. J. Olea reports Grant P30 CA014089 from NIH/National Cancer Center Core Grants during the conduct of the study; and personal fees from Standard Biotools outside the submitted work. H. Dadrastoussi reports Grant P30 CA014089 from NIH/National Cancer Center Core Grants during the conduct of the study. K. Wu reports grants from NIH during the conduct of the study. A.R. Colombo reports that the science was performed by Dr. Kevin Kelly's lab and his academic research team, which includes A.R. Colombo; that the reolysin drug was provided with a supportive relationship from a pharmaceutical company interested in reolysin for multiple myeloma in a collaboration and supportive relationship; and that the pharmaceutical company was not involved with the science of the writing of the manuscript but there was, to A.R. Colombo's knowledge, support of the drug through the company in a collaborative relationship with a company. M. Coffey reports personal fees and other support from Oncolytics Biotech Inc. during the conduct of the study; personal fees from Oncolytics Biotech Inc. outside the submitted work; in addition, M. Coffey has a patent for US-20180344853-A1 issued to Oncolytics Biotech Inc. J.S. Carew reports other support from Majestic Therapeutics, LLC, outside the submitted work. P. Fields reports personal fees from Adaptive Biotechnologies outside the submitted work. A. Merchant reports personal fees from Oncovalent and from Novartis outside the submitted work. K.R. Kelly reports grants from Oncolytics Biotech Inc., National Cancer Center Core Grants, Ming Hsieh Institute at the University of Southern California, Merck, Whittier Initiative at the University of Southern California–Translational Research during the conduct of the study, and Takeda outside the submitted work; and personal fees from Janssen, Ipsen Biopharmaceuticals, AstraZeneca, Karyopharm Therapeutics, Servier Pharmaceuticals, Gilead Sciences Inc., Seagen Inc., Eli Lilly and Company, Bristol-Myers Squibb, GSK, AbbVie, ACI Clinical, Denovo Biopharma, Incyte Pharmaceuticals, TG Therapeutics, Inc, Epizyme, and Sanofi. No disclosures were reported by the other authors.

Disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NCI or NIH.

Authors' Contributions

S.T. Nawrocki: Conceptualization, data curation, formal analysis, funding acquisition, validation, visualization, methodology, writing–original draft, writing–review and editing. J. Olea: Data curation, software, formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. C. Villa Celi: Investigation, visualization. H. Dadrastoussi: Data curation, formal analysis, validation, investigation, visualization. K. Wu: Formal analysis, funding acquisition, validation, investigation, visualization, methodology. D. Tsao-Wei: Formal analysis, funding acquisition, validation, investigation, visualization, methodology. A. Colombo: Formal analysis, funding acquisition, validation, investigation, visualization, methodology, project administration, writing–review and editing. M. Coffey: Formal analysis, funding acquisition, validation, investigation, visualization, methodology, project administration, writing–review and editing. E. Fernandez Hernandez: Formal analysis, validation, investigation, visualization, methodology, project administration, writing–review and editing. X. Chen: Conceptualization, resources, formal analysis, funding acquisition, validation, investigation, visualization, methodology, project administration, writing–review and editing. G.J. Nuovo: Conceptualization, resources, formal analysis, funding acquisition, validation, investigation, visualization, methodology, writing–review and editing. J.S. Carew: Conceptualization, resources, formal analysis, funding acquisition, validation, investigation, visualization, methodology, writing–review and editing. A.F. Mohrbacher: Conceptualization, resources, formal analysis, funding acquisition, validation, investigation, visualization, methodology, writing–review and editing. P. Fields: Conceptualization, resources, formal analysis, funding acquisition, validation, investigation, visualization, methodology, writing–review and editing. P. Kuhn: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. I. Siddiqi: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. A. Merchant: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. K.R. Kelly: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing.

References

1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin 2017;67:7–30.

2. Kyle RA, Gertz MA, Witzig TE, Lust JA, Lacy MQ, Dispenzieri A, et al. Review of 1027 patients with newly diagnosed multiple myeloma. Mayo Clin Proc 2003;78:21–33.

3. Laubach J, Garderet L, Mahindra A, Gahrton G, Caers J, Sezer O, et al. Management of relapsed multiple myeloma: recommendations of the International Myeloma Working Group. Leukemia 2016;30:1005–17.

4. Bobin A, Leleu X. Recent advances in the treatment of multiple myeloma: a brief review. Fac Rev 2022;11:28.

5. Kelly KR, Espitia CM, Zhao W, Wendlandt E, Tricot G, Zhan F, et al. Junctional adhesion molecule-A is overexpressed in advanced multiple myeloma and determines response to oncolytic reovirus. Oncotarget 2015;6:41275–89.

6. Kelly KR, Espitia CM, Mahalingam D, Oyajobi BO, Coffey M, Giles FJ, et al. Reovirus therapy stimulates endoplasmic reticular stress, NOXA induction, and augments bortezomib-mediated apoptosis in multiple myeloma. Oncogene 2012;31:3023–38.

7. Kelly KR, Espitia CM, Zhao W, Wu K, Visconte V, Anwer F, et al. Oncolytic reovirus sensitizes multiple myeloma cells to anti–PD-L1 therapy. Leukemia 2018;32:230–3.

8. Errington F, Steele L, Prestwich R, Harrington KJ, Pandha HS, Vidal L, et al. Reovirus activates human dendritic cells to promote innate antitumor immunity. J Immunol 2008;180:6018–26.

9. Keren L, Bosse M, Marquez D, Angoshtari R, Jain S, Varma S, et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 2018;174:1373–87.

10. Phillips D, Schurch CM, Khodadoust MS, Kim YH, Nolan GP, Jiang S. Highly multiplexed phenotyping of immunoregulatory proteins in the tumor microenvironment by CODEX tissue imaging. Front Immunol 2021;12:687673.

11. Nerurkar SN, Goh D, Cheung CCL, Nga PQY, Lim JCT, Yeong JPS. Transcriptional spatial profiling of cancer tissues in the era of immunotherapy: the potential and promise. Cancers 2020;12:2572.

12. Kumar S, Paiva B, Anderson KC, Durie B, Landgren O, Moreau P, et al. International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol 2016;17:e328–e46.

13. Durie BG, Harousseau JL, Miguel JS, Blade J, Barlogie B, Anderson K, et al. International uniform response criteria for multiple myeloma. Leukemia 2006;20:1467–73.

14. Nuovo GJ. In situ detection of microRNAs in paraffin-embedded, formalin-fixed tissues and the co-localization of their putative targets. Methods 2010;52:307–15.

15. Carlson CS, Emerson RO, Sherwood AM, Desmarais C, Chung MW, Parsons JM, et al. Using synthetic templates to design an unbiased multiplex PCR assay. Nat Commun 2013;4:2680.

16. DeWitt WS, Emerson RO, Lindau P, Vignali M, Snyder TM, Desmarais C, et al. Dynamics of the cytotoxic T-cell response to a model of acute viral infection. J Virol 2015;89:4517–26.

17. Giesen C, Wang HA, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods 2014;11:417–22.

18. Riccardo VZT, Bodenmiller B. ImcSegmentationPipeline: a pixelclassification based multiplexed image segmentation pipeline. 2022.

19. Berg S, Kutra D, Kroeger T, Straehle CN, Kausler BX, Haubold C, et al. ilastik: interactive machine learning for (bio)image analysis. Nat Methods 2019;16:1226–32.

20. McQuin C, Goodman A, Chernyshev V, Kamentsky L, Cimini BA, Karhohs KW, et al. CellProfiler 3.0: Next-generation image processing for biology. PLoS Biol 2018;16:e2005970.

21. Jackson HW, Fischer JR, Zanotelli VRT, Ali HR, Mechera R, Soysal SD, et al. The single-cell pathology landscape of breast cancer. Nature 2020;578:615–20.

22. Levine JH, Simonds EF, Bendall SC, Davis KL, Amir el AD, Tadmor MD, et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 2015;162:184–97.

23. Schapiro D, Jackson HW, Raghuraman S, Fischer JR, Zanotelli VRT, Schulz D, et al. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat Methods 2017;14:873–6.

24. Somarakis A, Van Unen V, Koning F, Lelieveldt B, Hollt T. ImaCytE: visual exploration of cellular micro-environments for imaging mass cytometry data. IEEE Trans Vis Comput Graph 2021;27:98–110.

25. Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, et al. IFNγ-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest 2017;127:2930–40.

26. Messina JL, Fenstermacher DA, Eschrich S, Qu X, Berglund AE, Lloyd MC, et al. 12-Chemokine gene signature identifies lymph node-like structures in melanoma: potential for patient selection for immunotherapy? Sci Rep 2012;2:765.

27. Carew JS, Espitia CM, Zhao W, Mita MM, Mita AC, Nawrocki ST. Oncolytic reovirus inhibits angiogenesis through induction of CXCL10/IP-10 and abrogation of HIF activity in soft-tissue sarcomas. Oncotarget 2017;8:86769–83.

28. Parrish C, Scott GB, Migneco G, Scott KJ, Steele LP, Ilett E, et al. Oncolytic reovirus enhances rituximab-mediated antibody-dependent cellular cytotoxicity against chronic lymphocytic leukaemia. Leukemia 2015;29:1799–810.

29. Adair RA, Scott KJ, Fraser S, Errington-Mais F, Pandha H, Coffey M, et al. Cytotoxic and immune-mediated killing of human colorectal cancer by reovirus-loaded blood and liver mononuclear cells. Int J Cancer 2013;132:2327–38.

30. Pittari G, Vago L, Festuccia M, Bonini C, Mudawi D, Giaccone L, et al. Restoring natural killer cell immunity against multiple myeloma in the era of new drugs. Front Immunol 2017;8:1444.

31. Joshua D, Suen H, Brown R, Bryant C, Ho PJ, Hart D, et al. The T cell in myeloma. Clin Lymphoma Myeloma Leuk 2016;16:537–42.

32. Herbst RS, Soria JC, Kowanetz M, Fine GD, Hamid O, Gordon MS, et al. Predictive correlates of response to the anti–PD-L1 antibody MPDL3280A in cancer patients. Nature 2014;515:563–7.

33. Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJ, Robert L, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 2014;515:568–71.

34. Colombo A, Hav M, Singh M, Xu A, Gamboa A, Lemos T, et al. Single-cell spatial analysis of tumor immune architecture in diffuse large B-cell lymphoma. Blood Adv 2022;6:4675–90.

35. Karapanagiotou EM, Roulstone V, Twigger K, Ball M, Tanay M, Nutting C, et al. Phase I/II trial of carboplatin and paclitaxel chemotherapy in combination with intravenous oncolytic reovirus in patients with advanced malignancies. Clin Cancer Res 2012;18:2080–9.

36. Sborov DW, Nuovo GJ, Stiff A, Mace TA, Lesinski GB, Benson DM Jr., et al. A phase I trial of single agent reolysin in patients with relapsed multiple myeloma. Clin Cancer Res 2014;20:5946–55.

37. Manning ML, Mason-Osann E, Onda M, Pastan I. Bortezomib reduces pre-existing antibodies to recombinant immunotoxins in mice. J Immunol 2015;194:1695–701.

38. Eledge MR, Zita MD, Boehme KW. Reovirus: friend and foe. Curr Clin Microbiol Rep 2019;6:132–8.

39. Hulin C, de la Rubia J, Dimopoulos MA, Terpos E, Katodritou E, Hungria V, et al. Bortezomib retreatment for relapsed and refractory multiple myeloma in real-world clinical practice. Health Sci Rep 2019;2:e104.

40. Ravirala D, Pei G, Zhao Z, Zhang X. Comprehensive characterization of tumor immune landscape following oncolytic virotherapy by single-cell RNA sequencing. Cancer Immunol Immunother 2022;71:1479–95.

41. Gajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol 2013;14:1014–22.

42. Ribrag V, Avigan DE, Green DJ, Wise-Draper T, Posada JG, Vij R, et al. Phase 1b trial of pembrolizumab monotherapy for relapsed/refractory multiple myeloma: KEYNOTE-013. Br J Haematol 2019;186:e41–e44.

43. Usmani SZ, Schjesvold F, Oriol A, Karlin L, Cavo M, Rifkin RM, et al. Pembrolizumab plus lenalidomide and dexamethasone for patients with treatment-naive multiple myeloma (KEYNOTE-185): a randomized, open-label, phase III trial. Lancet Haematol 2019;6:e448–e58.

44. Mateos M-V, Blacklock H, Schjesvold F, Oriol A, Simpson D, George A, et al. Pembrolizumab plus pomalidomide and dexamethasone for patients with relapsed or refractory multiple myeloma (KEYNOTE-183): a randomized, open-label, phase III trial. Lancet Haematol 2019;6:e459–e69.

45. Calton CM, Kelly KR, Anwer F, Carew JS, Nawrocki ST. Oncolytic viruses for multiple myeloma therapy. Cancers 2018;10:198.

46. Zhang Y, Li Y, Chen K, Qian L, Wang P. Oncolytic virotherapy reverses the immunosuppressive tumor microenvironment and its potential in combination with immunotherapy. Cancer Cell Int 2021;21:262.

47. Ribas A, Dummer R, Puzanov I, VanderWalde A, Andtbacka RHI, Michielin O, et al. Oncolytic virotherapy promotes intratumoral T-cell infiltration and improves anti–PD-1 immunotherapy. Cell 2017;170:1109–19.

48. Andtbacka RH, Kaufman HL, Collichio F, Amatruda T, Senzer N, Chesney J, et al. Talimogene laherparepvec improves durable response rate in patients with advanced melanoma. J Clin Oncol 2015;33:2780–8.

49. van Hall T, Andre P, Horowitz A, Ruan DF, Borst L, Zerbib R, et al. Monalizumab: inhibiting the novel immune checkpoint NKG2A. J Immunother Cancer 2019;7:263.

50. Andre P, Denis C, Soulas C, Bourbon-Caillet C, Lopez J, Arnoux T, et al. Anti-NKG2A mAb is a checkpoint inhibitor that promotes antitumor immunity by unleashing both T and NK cells. Cell 2018;175:1731–43.

51. Chao MP, Takimoto CH, Feng DD, McKenna K, Gip P, Liu J, et al. Therapeutic targeting of the macrophage immune checkpoint CD47 in myeloid malignancies. Front Oncol 2019;9:1380.

52. Zavidij O, Haradhvala NJ, Mouhieddine TH, Sklavenitis-Pistofidis R, Cai S, Reidy M, et al. Single-cell RNA sequencing reveals compromised immune microenvironment in precursor stages of multiple myeloma. Nat Cancer 2020;1:493–506.

53. Yao L, Jayasinghe RG, Lee BH, Bhasin SS, Pilcher W, Doxie DB, et al. Comprehensive characterization of the multiple myeloma immune microenvironment using integrated scRNA-seq, CyTOF, and CITE-seq analysis. Cancer Res Commun 2022;2:1255–65.

54. Lightbody ED, Firer DT, Sklavenitis-Pistofidis R, Tsuji J, Agius MP, Dutta AK, et al. Single-cell RNA sequencing of rare circulating tumor cells in precursor myeloma patients reveals molecular underpinnings of tumor cell circulation. Blood 2022;140:602–3.

Associated Data

Supplementary Materials

Supplementary Figure 1. Induced expression of cytokines, CD69 in lymphocytes, and PD-L1 in MM cells with no changes in T cell clonality

Click here for additional data file.

Supplementary Figure 2. TCR analysis

Click here for additional data file.

Supplementary Figure 3. Imaging mass cytometry (IMC) staining overview

Click here for additional data file.

Supplementary Figure 4. Increased immune cell activity observed by imaging mass cytometry (IMC)

Click here for additional data file.

Supplementary Methods

Click here for additional data file.

Supplementary Table 1. Detailed IMC panel

Click here for additional data file.

Supplementary Table 2: Representativeness of Study Participants

Click here for additional data file.

Supplementary Table 3. Toxicity summary excluding toxicities defined as unrelated to either bortezomib or pelareorep

Click here for additional data file.

Supplementary Table 4. Distance from tumor cell to immune cell for each patient (Figure 5C)

Click here for additional data file.

Supplementary Table 5. Distance from tumor cell to immune cell for each patient (Figure 5E)

Click here for additional data file.

Data Availability Statement

All software and code that produced the findings of the study, including Mac OS12 (Supplementary Software 1), Windows 7 (Supplementary Software 2), and Windows 10 (Supplementary Software 3) versions of HistoCAT, are available at https://github.com/BodenmillerGroup/histoCAT . All raw data, including source data for Fig. 3 , HistoCAT sessions, and interactive graphs, that support the findings of the study are available at https://figshare.com/articles/dataset/Comprehensive_Single-Cell_Immune_Profiling_Defines_the_Patient_Multiple_Myeloma_Microenvironment_Following_Oncolytic _Virus_Therapy/24175404 (DOI: 10.6084/m9.figshare.24175404). The clinical trial protocol is available upon request, and the study is registered at ClinicalTrials.gov ( NCT02514382 ).