ORIGINAL RESEARCH published: 22 November 2021 doi: 10.3389/fimmu.2021.792609 Gene Expression Analysis of the Bone Marrow Microenvironment Reveals Distinct Immunotypes in Smoldering Multiple Myeloma Associated to Progression to Edited by: Antonio Maurizi, Symptomatic Disease University of L’Aquila, Italy Reviewed by: Ignacio Isola 1, Fara Brasó-Maristany 2, David F. Moreno 1, Mari-Pau Mena 1, Laurent Garderet, Aina Oliver-Calders 1, Laia Paré 2, Luis Gerardo Rodrı́guez-Lobato 1, Assistance Publique Hopitaux De Beatriz Martin-Antonio 1, Marı́a Teresa Cibeira 1, Joan Bladé 1, Laura Rosiñol 1, Aleix Prat 2, Paris, France Ester Lozano 1,3*† and Carlos Fernández de Larrea 1,4*† Ourania Tsitsilonis, National and Kapodistrian University of 1 Department of Hematology, Hospital Clı´nic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer Athens, Greece (IDIBAPS), University of Barcelona, Barcelona, Spain, 2 Department of Medical Oncology, Hospital Clinic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain, 3 Department of Cell Biology, *Correspondence: Physiology and Immunology, Faculty of Biology, University of Barcelona, and Institute of Biomedicine of the University of Carlos Fernández de Larrea Barcelona (IBUB), Barcelona, Spain, 4 Experimental and Clinical Hematology Program (PHEC), Josep Carreras Leukaemia cfernan1@clinic.cat Research Institute, Barcelona, Spain Ester Lozano elozano@ub.edu † These authors have contributed Background: We previously reported algorithms based on clinical parameters and equally to this work and share plasma cell characteristics to identify patients with smoldering multiple myeloma (SMM) last authorship with higher risk of progressing who could benefit from early treatment. In this work, we Specialty section: analyzed differences in the immune bone marrow (BM) microenvironment in SMM to This article was submitted to better understand the role of immune surveillance in disease progression and to identify Cancer Immunity immune biomarkers associated to higher risk of progression. and Immunotherapy, a section of the journal Methods: Gene expression analysis of BM cells from 28 patients with SMM, 22 patients Frontiers in Immunology with monoclonal gammopathy of undetermined significance (MGUS) and 22 patients with Received: 10 October 2021 Accepted: 05 November 2021 symptomatic MM was performed by using Nanostring Technology. Published: 22 November 2021 Results: BM cells in SMM compared to both MGUS and symptomatic MM showed Citation: upregulation of genes encoding for key molecules in cytotoxicity. However, some of these Isola I, Brasó-Maristany F, Moreno DF, Mena M-P, Oliver-Calders A, cytotoxic molecules positively correlated with inhibitory immune checkpoints, which may Paré L, Rodrı´guez-Lobato LG, impair the effector function of BM cytotoxic cells. Analysis of 28 patients with SMM Martin-Antonio B, Cibeira MT, Bladé J, Rosiñol L, Prat A, Lozano E and revealed 4 distinct clusters based on immune composition and activation markers. Fernández de Larrea C (2021) Gene Patients in cluster 2 showed a significant increase in expression of cytotoxic molecules Expression Analysis of the Bone but also inhibitory immune checkpoints compared to cluster 3, suggesting the presence Marrow Microenvironment Reveals Distinct Immunotypes in Smoldering of cytotoxic cells with an exhausted phenotype. Accordingly, patients in cluster 3 had a Multiple Myeloma Associated to significantly longer progression free survival. Finally, individual gene expression analysis Progression to Symptomatic Disease. Front. Immunol. 12:792609. showed that higher expression of TNF superfamily members (TNF, TNFAIP3, TNFRSF14) doi: 10.3389/fimmu.2021.792609 was associated with shorter progression free survival. Frontiers in Immunology | www.frontiersin.org 1 November 2021 | Volume 12 | Article 792609 Isola et al. Immune Microenvironment in Myeloma Progression Conclusions: Our results suggest that exhausted cytotoxic cells are associated to high- risk patients with SMM. Biomarkers overexpressed in patients with this immune gene profile in combination with clinical parameters and PC characterization may be useful to identify SMM patients with higher risk of progression. Keywords: smoldering multiple myeloma, immunotherapy, immune checkpoints, TIGIT, pronostic factors, bone marrow microenvironment INTRODUCTION symptomatic MM that allow us to evaluate the need for an early intervention (9, 10). In this study we aim to investigate Smoldering multiple myeloma (SMM) is a pre-malignant whether molecular and cellular mechanisms in the BM immune condition that precedes symptomatic MM and is defined by a microenvironment may explain the heterogeneity observed in serum monoclonal immunoglobulin (M-protein) of ≥3 g/dL the clinic. Our goal is to better understand the composition and and/or an urinary monoclonal protein ≥500 mg per 24 h, and/ functional levels of BM cells surrounding malignant PC and or 10–60% clonal bone marrow plasma cells (BMPC), in the correlate this molecular data with clinical behavior to identify absence of end-organ damage (1, 2). Since only a fraction of key molecules and cell types associated to progression from SMM patients will progress to active MM, the initiation of an SMM to active myeloma. early anti-myeloma treatment is a subject of intense discussion. Based on the revised International Myeloma Working Group (IMWG) criteria, asymptomatic patients with ultra-high risk SMM are currently considered to have active MM and MATERIALS AND METHODS treatment is recommended (2, 3). We previously defined two subsets of patients with SMM: 1- the ‘evolving’ variant of SMM, Patient Cohorts characterized by a progressive increase in the M-protein size BM aspiration samples were collected from 28 patients with until symptomatic myeloma develops and a shorter time to SMM at diagnosis (patient characteristics are summarized in progression and 2- the non-evolving pattern, with a long- Table 1). In addition, for comparison purposes we also studied lasting stable M-protein and a longer time to progression (4). BM samples from 22 patients with MGUS and from 22 patients The actuarial transformation rates at 10 years of follow-up were with symptomatic MM (12 refractory/relapsed MM patients and 55 and 10% in patients with ‘evolving’ and ‘non-evolving’ 10 newly diagnosed untreated patients, 5 of them corresponding pattern, respectively (1, 4). In line with these results, we to patients also analyzed at the SMM stage who later progressed, recently evaluated progression risk factors in 206 patients with allowing a paired comparison). SMM, demonstrating that median time from recognition of All patients were diagnosed at the Amyloidosis and Myeloma evolving type to progression into symptomatic MM was 1.1 Unit in the Department of Hematology (Hospital Clı́nic of years and progression rate at 3 years was 71% (5). Therefore, Barcelona). Sample collection and clinical record review were confirmation of an evolving behavior drastically worsened the performed after informed written consent in accordance with the prognostic estimation made at diagnosis for every covariate Declaration of Helsinki. Study protocol was approved by the predictive of progression (serum M-protein size, BMPC Institutional Review Board at Hospital Clı́nic of Barcelona. infiltration, immunoparesis and Mayo Clinic risk score) (5). Patients were diagnosed according to standard International Accordingly, the revised IMWG risk stratification model for Myeloma Working Group criteria. SMM (“2/20/20”) identified three independent factors predicting The “evolving” type was defined as a progressive increase of at progression risk at 2 years: serum M-protein >2 g/dL, involved to least 10% in the M-protein size within the first 12 months from uninvolved free light-chain (FLC) ratio >20, and BMPC diagnosis when baseline M-protein was ≥30 g/L or over a period infiltration >20% (6). of 3 years (with a progressive increase in the M-protein size in One of the main challenges of assessing progression risk is to each of the annual measurements) in patients with an initial M- take into account the BM heterogeneity in molecular and cellular protein <30 g/L (4, 11). Immunoparesis was defined as a decrease patterns that leads to the different clinical behavior of patients below normal levels of at least one of the uninvolved serum included under the designation of SMM (7). Given the relevance immunoglobulins. BM aspirates obtained at diagnosis were of microenvironment for malignant PC survival and the absence reviewed independently by two observers; plasma cell of a clear molecular “second hit” between SMM and percentages were estimated from a 500-cell count by each symptomatic MM (8), it is crucial to find new immune examiner and the mean of the two values was considered for biomarkers associated to the risk of progression to the analysis. RNA Isolation Abbreviations: BM, Bone marrow; ISS, International stage system; MGUS, Monoclonal gammopathies of undetermined significance; MM, Multiple CD138-depleted BM cell fraction was isolated with anti-CD138 myeloma; NK, Natural killer; PC, Plasma cells; SMM, Smoldering MM; Th, mAb-coated immunomagnetic beads using an AutoMacs cell sorter T helper. (Milteny Biotec, Bergisch Gladbach, Germany). Purity was assessed Frontiers in Immunology | www.frontiersin.org 2 November 2021 | Volume 12 | Article 792609 Isola et al. Immune Microenvironment in Myeloma Progression TABLE 1 | Patient characteristics. MGUS SMM MM Number of patients 22 28 22 Age, median years (range) 70 (40–88) 69 (38–84) 68 (49–80) Gender, male/female 12/10 11/17 11/11 Isotype (%) • IgG 15 (68) 15 (54) 12 (55) • IgA 7 (32) 11 (39) 10 (45) • Biclonal 1 (3,5) • Light chain 1 (3,5) Serum M-protein g/L* 15.3 (13.1-20.6) 20.2 (13.4-32.1) 25.8 (11.6-40.8) Serum FLCr* 2.2 (0.1-8.8) 1.5 (0.7-11.3) 33.4 (2.7-423) BMPC (%)* 5.5 (3–8) 19 (12.5 -24.7) 34 (15.5-47) Abnormal BMPC (%)*Ɨ 71.5 (33.2-85.7) 98 (96.7-100) 100 (99–100) ISS stage (%) — — I 5 (26) II 7 (37) III 7 (37) Risk Stage (%)‡ — Low 2 (9) 11 (41) Intermediate 17 (77) 8 (29.5) High 3 (14) 8 (29.5) SMM, smoldering multiple myeloma; MGUS, monoclonal gammopathy of undetermined significance; MM, symptomatic multiple myeloma; FLCr, serum free light chain ratio (kappa/ lambda); BMPC, bone marrow plasma cell count. ISS, International staging system for multiple myeloma. *Measurements are median (interquartile range). Ɨ Percentage of bone marrow plasma cells with abnormal phenotype by flow cytometry. ‡ The International Myeloma Working Group (IMWG) SMM revised risk model includes serum M-protein >2 g/dL, involved to uninvolved free light-chain ratio >20 and bone marrow plasma cell infiltration >20%. The Mayo Clinic MGUS revised risk model includes serum FLCr 1.65, non-IgG MGUS and M protein >15 g/L. after isolation, only samples with <2% of CD138+ cells were rank test used to indicate significance. Statistical differences for included in this study. Total RNA from the CD138neg BM cell numerical values were calculated using the Brown–Forsythe fraction was extracted using the TRIzol reagent (Thermo Fisher ANOVA test, Mann-Whitney U test and the Kruskal-Wallis Scientific, Waltham, MA) according to manufacturer’s instructions. test. Spearman r was used to measure markers correlations. Differences were considered statistically significant at P values less than 0.05. All statistical analyses were performed using Gene Expression Analysis GraphPad Prism, v8.0.1 (GraphPad Software, Inc. San RNA expression was measured with the nCounter technology; Diego, CA). preparation and analyses were performed according to the manufacturer’s protocol (NanoString Technologies, Inc. Seattle, WA). A minimum of 100 ng of total RNA per sample was loaded and run on the HuV1_Cancer Immu_v1_1_Nanostring for RESULTS analysis of the NanoString PanCancer Immune Profiling Panel of 730 immune-associated genes and 40 housekeeping genes. Upregulation of Gene Sets Associated Expression counts were then normalized using the nSolver 4.0 With Cytotoxicity and T Cell Functions in software and custom scripts in R 3.6.3. Unpaired significance Patients With SMM Compared to MGUS analysis of microarrays (SAM), using False Discovery Rate [FDR], Patients with SMM have a higher number of BMPC than patients were used to identify differential gene expression across sample with MGUS, which is associated to a higher risk of myeloma groups. In addition, we calculated scores for immune-related gene progression. In order to evaluate changes in the immune expression signatures according to previously published literature, microenvironment associated to the increase in progression risk, the “Tumor Inflammation Signature” (TIS) reported by Ayers we first performed gene expression analysis of CD138-depleted et al. (12) and a previously described “cytolytic score” for BM cells of 28 patients with SMM compared to 22 patients with hematological malignancies (Supplementary Table S1). The MGUS. Most of the patients with SMM showed a distinct gene gene expression data have been deposited in the Gene profile by unsupervised hierarchical clustering (Figure 1A). Expression Omnibus database (http://www.ncbi.nlm.nih.gov/ Among the 127 genes differentially expressed in SMM, only 4 geo/) under accession number GEO: GSE186537. genes were downregulated (FLT3, ARG1, FCER1A and S100A12) (Figure 1B and Supplementary Table 2). The top upregulated Statistical Analysis genes in SMM included molecules associated to myeloma Differences in time to progression (TTP) between patient groups (SLAMF7 (CS1), TNFSF13 (APRIL)) key molecules in were analyzed using Kaplan-Meier survival curves with the log- cytotoxicity (GZMB, GZMA, GZMH, GNLY, HLA-A, HLA-B, Frontiers in Immunology | www.frontiersin.org 3 November 2021 | Volume 12 | Article 792609 Isola et al. Immune Microenvironment in Myeloma Progression A B C FIGURE 1 | Genes associated with cytotoxicity were significantly upregulated in patients with SMM compared to MGUS. (A) Heatmap showing the unsupervised hierarchical clustering of patients with MGUS (n=22) and with SMM (n=28) based on the NanoString PanCancer Immune panel. (B) Volcano plot showing differentially expressed genes in SMM compared to MGUS. (C) Ranking of gene set functions according to the Global Significance Score quantified by NanoString software nSolver v.4.0. PRF1, HLA-C, GZMM) and interleukins playing crucial roles in T expression data from the 28 patients with SMM compared to 22 cell functions (IFNL1, IL15, IL1A, IL32, TGFB1, IL1B) patients with MM. Unsupervised hierarchical clustering did not (Figures 1B, C). Although our results showed an upregulation show a distinct gene profiling discriminating patients with SMM of genes associated to cytotoxicity in the BM of patients with SMM versus MM underlying the wide heterogeneity found in SMM compared to MGUS, we also found overexpression of inhibitory (Figure 2A). Among the 136 genes differentially expressed in molecules such as LAG-3, TIGIT and IDO1, which may affect the SMM, 30 genes were downregulated and 106 upregulated in SMM anti-myeloma immune response in SMM. compared to MM (Figure 2B and Supplementary Table 2). Global significance score showed that gene sets associated to NK functions were overrepresented in SMM (KLRB1, LILRB1, Genes Associated With NK and T Cell KLRD1, IRF1, KLRC1) (Figure 2C). Upregulation of inhibitory Functions Were Differentially Expressed in receptors such as KLRB1 (CD161), LILRB1 (CD85j), and KLRD1 Patients With SMM Compared to (CD94) may indicate impairment in NK cell cytotoxicity. Symptomatic MM Accordingly, we also observed an increase in inhibitory To assess changes in immune microenvironment of patients with checkpoints that can be expressed in both NK and T cells such SMM compared to symptomatic MM, we analyzed gene as LAG-3 and suppressors of anti-tumor immunity such as IDO1 Frontiers in Immunology | www.frontiersin.org 4 November 2021 | Volume 12 | Article 792609 Isola et al. Immune Microenvironment in Myeloma Progression A B C FIGURE 2 | Genes associated with NK and T cell functions were differentially expressed in patients with SMM compared to symptomatic MM (A) Heatmap showing the unsupervised hierarchical clustering of patients with MM (n=22) and with SMM (n=28) based on the NanoString PanCancer Immune panel. (B) Volcano plot showing differentially expressed genes in SMM compared to MM. (C) Ranking of gene set functions according to the Global Significance Score quantified by NanoString software nSolver v.4.0. (Figure 2C and Supplementary Table 2). In patients with response in patients with SMM. However, the upregulation of symptomatic MM, we found higher levels of genes previously inhibitory receptors may also suggest an exhausted phenotype in associated with MM (NCAM1, ATM, CD163, IL32) and genes the cytotoxic cell compartment. highly expressed in regulatory T cells (Tregs) such as MFGE8, NT5E (CD73) and TIGIT. Out of the 10 patients with SMM which progressed to MM Highly Expressed Genes Associated during follow up, 5 were available for gene expression analysis With Cytotoxic T Cell Function after progression. Paired gene expression analysis before and Correlated With Transcription Factors after progression showed a lower number of differentially Tbet and Eomes in SMM expressed genes. Interestingly, the 54 upregulated genes in the SMM stage included several members of the TNF family When comparing the tumor microenvironment in SMM with (TNFRSF9 (CD137), TNFSF8, TNFRSF14, TNFRSF1A) but also MGUS and MM, we found that in both cases the most relevant the ITIM-bearing inhibitory receptors PVR and LILRB3 cell type was cytotoxic CD8+ T cells (Figure 3A). Indeed, key (Supplementary Figure S1). molecules in cytotoxicity such as Tbet (TBX21), perforin (PRF1), Taken together, our results could be indicative of an granzyme b (GZMB) and granulysin (GNLY) were significantly enrichment in gene sets associated to cytotoxic immune increased in SMM compared to MM (Figure 3B). Although Frontiers in Immunology | www.frontiersin.org 5 November 2021 | Volume 12 | Article 792609 Isola et al. Immune Microenvironment in Myeloma Progression A B C D FIGURE 3 | Highly expressed genes associated with cytotoxic T cell function correlated with transcription factors Tbet and Eomes in SMM. (A) Statistical analysis of cell types involved in SMM compared to MM. (B) Gene expression of genes significantly upregulated in patients with SMM. Kruskal Wallis test *p<0.05, **p<0.01, ***p<0.001. (C) Positive correlation between transcription factor Tbet (TBX21) and both perforin (PRF1) and granzyme b (GZMB). Spearman r and p values are indicated. (D) Summary of correlation analyses in gene expression in patients with SMM. these molecules are also important in NK cytotoxicity, we Gene Profiling of Bone Marrow Cells observed an increase in molecules playing crucial roles in T Identified Distinct Clusters in Patients With cell functions such as CD3zeta (CD247), co-stimulatory receptor SMM Based on Immune Cell Composition CD28 and CD6, consistent with the relevance of cytotoxic CD8+ and Activation Markers T cells in SMM. Furthermore, expression of transcription factors Unsupervised hierarchical clustering after gene expression Tbet and Eomes strongly correlate with key molecules in analysis with the PanCancer Immune panel classified the 28 cytotoxicity such as perforin (PRF1), granzyme b (GZMB) and SMM patients in four clusters (Figure 4A). Among the top granulysin (GNLY) in patients with SMM (Figures 3C, D). Both upregulated genes in cluster 1 (n=7) we found cytokines and transcription factors also correlate with genes important for T chemokines (CCL11, CCL13, CCL16, CCL17, CCL21, CCL24, cell function such as IFNG, IL7, IL7R, IL2RG, CD247 and CD28. CXCL13, CX3CL1, XCR1, IFNL1, IFNL2, IL13, IL17A, IL25, LTB, Of note, the natural cytotoxicity triggering receptor 1 NCR1 (also TGFB2, TNFSF13) and cytokine receptors (TNFRSF12A, known as NKp46, LY94) also correlated with these key genes IL17RB). In contrast, cluster 2 (n=8) showed an expression suggesting that NK cells could also play a role in anti-myeloma profile notably enriched for T and NK cell markers and cytotoxicity in SMM. As expected, cytotoxic molecules did not signaling (PRF1, GZMA/B/H/K, GNLY, CD2, CD3D/E, KLRD1, correlate with genes involved in Treg function such as NRP1, KLRF1, KLRB1, KLRK1, CD274 (PD-L1), LAG3) and interferon ENTPD1(CD39) and CXCL12 (Figure 3D). As shown in signaling (IFNG, MX1, ISG15, STAT1/2/4) (Figure 4A and Figure 3B, patients with SMM showed a wide range of Supplementary Table 2). Indeed, the analysis of the expression levels, suggesting the presence of subgroups with expression of gene expression signatures (GES) associated with differences in the composition and activation levels of the specific immune cell-types showed that cluster 2 included a immune cells in the BM microenvironment. distinct immune signature characterized by higher levels of Frontiers in Immunology | www.frontiersin.org 6 November 2021 | Volume 12 | Article 792609 Isola et al. Immune Microenvironment in Myeloma Progression A B D C FIGURE 4 | Gene profiling of bone marrow cells identified distinct clusters in patients with SMM based on immune cell composition and activation markers. (A) Heatmap showing the unsupervised hierarchical clustering of patients with SMM (n=28) based on the NanoString PanCancer Immune panel. (B) Statistical analysis of gene set associated to cytotoxic cells and the tumor inflammation signature (TIS) in the 4 distinct clusters of patients with SMM. (C) Heatmaps of genes associated to NK and T cell functions comparing cluster 2 versus 3. (D) Volcano plot showing genes differentially expressed in cluster 2 versus cluster 3. transcripts associated to cytotoxic cells (Figure 4B). We also showed an immune signature associated to a strong cytotoxic evaluated the tumor inflammation signature (TIS), which response, they also upregulated the expression of inhibitory contains IFN-g responsive genes associated with T cell checkpoints (CD96, LAG3, BTLA, KLRB1) which raised the activation, which has been shown to predict response to question of whether this immune signature has an impact in Programmed Cell Death Protein 1 (PD-1) blockade across progression to myeloma. multiple solid tumors (13). The TIS score correlated with the CD8+ T cell and cytotoxic cell signatures and was significantly higher in patients from cluster 2 (Figure 4B). Furthermore, gene Patients With SMM in Cluster 3 With expression analysis of cluster 2 compared to cluster 3 (n=10) Lower Expression of Cytotoxic Associated showed a significant increase in genes associated to NK and T cell Molecules Showed Significantly Longer function in cluster 2 (Figure 4C and Supplementary Table 2). Progression Free Survival Indeed, most of the genes upregulated in SMM compared to MGUS were upregulated in cluster 2 compared to cluster 3 We next analyzed the association between clinical parameters used suggesting that the differences between SMM and MGUS were to assess risk to progression in patients with SMM and their gene due to cluster 2 of patients with SMM while cluster 3 of SMM immune signatures (Figure 5A). SMM patients with an evolving patients were more similar to a MGUS immune profile pattern of the M-protein showed an increase in genes associated to (Figure 4D). Therefore, although patients from cluster 2 myeloma (SLAMF7, CD79A, CD79B) and AXL (Figure 5B and Frontiers in Immunology | www.frontiersin.org 7 November 2021 | Volume 12 | Article 792609 Isola et al. Immune Microenvironment in Myeloma Progression A B C D E F FIGURE 5 | Patients with SMM with cytotoxic immune signature showed high-risk characteristics. (A) Clinical characteristics in patients with SMM divided into 4 clusters according to the results of the unsupervised hierarchical clustering using the PanCancer immune panel. Immunoparesis was defined qualitatively as one or more of uninvolved immunoglobulins below the normal levels. The International Myeloma Working Group (IMWG) SMM revised risk model includes serum M-protein >2 g/dL, involved to uninvolved free light-chain ratio >20 and BMPC infiltration >20%. *Patients enrolled in clinical trials were unavailable for determination of the M-protein behavior or progression to symptomatic disease. (B) Volcano plot showing genes differentially expressed in patients with evolving pattern of M-protein. (C) Volcano plot showing genes differentially expressed in patients with high progression risk according to IMWG. (D) Volcano plot showing genes differentially expressed in patients that progress to asymptomatic MM. (E) Venn diagram to assess common upregulated genes in cluster 2 and in patients with higher risk of progression. (F) Kaplan-Maier plot showing progression free survival (PFS) of patients in 4 clusters. Long-rank test. Supplementary Table 2). Some of these molecules are expressed in based in immune signatures were associated to differences in a variety of immune cell types but also may be found in PCs, thus progression free survival (PFS). Four patients in cluster 2 were we cannot rule out that this high-sensitivity gene expression analysis excluded from this analysis since they started treatment in a clinical may detect presence of remaining PCs. However, our experimental trial for high-risk SMM patients (GEM-CESAR). As shown in approach can still capture major differences in key immune genes of Figure 5F, log-rank test showed that patients in cluster 3 with interest. In high-risk SMM patients, 155 upregulated genes included lower expression of cytotoxic genes and an immune signature more molecules associated to MM (TNFRSF17, NCAM1, IRF4, CD79B), similar to MGUS patients had a significantly longer PFS compared cytotoxic molecules (KLRC1, GZMA) and AXL (Figure 5C). The to the other of clusters (p=0.04). median follow up of the patients with SMM was 5.3 years. Ten Some of the studied genes were independently associated with patients with SMM progressed to symptomatic MM, with a median shorter PFS such as TNF, TNFAIP3, GZMM and TNFRSF14 (also time to progression (TTP) of 1.9 years. SMM patients that known as HVEM) (Supplementary Figure 2A). Conversely, progressed to MM showed 76 differentially expressed genes when high expression of transcripts for MAPK14, LTF, SMAD3, compared to SMM patients who showed no progression during FOS, PSEN1 and THBS1 were associated with increased follow up, with an increase in 63 including molecules involved in progression free survival (Supplementary Figure 2B). Taken immune response activation such as TNF, IL-1B and granzyme M together, our results could indicate that genes associated to an (Figure 5D). Most of the genes upregulated in high-risk patients exhausted phenotype in cytotoxic T cells are upregulated in high- were included in the list of upregulated genes in cluster 2 risk SMM patients while some members of the TNF superfamily (Figure 5E). We next wanted to assess whether distinct clusters are significantly associated to myeloma progression. Frontiers in Immunology | www.frontiersin.org 8 November 2021 | Volume 12 | Article 792609 Isola et al. Immune Microenvironment in Myeloma Progression DISCUSSION that TIGIT blockade can be a useful therapeutic strategy in patients with SMM and active MM with Nectin-2 expressing PC One of the most important challenges in the fight against MM is the (29). Thus, the role of TIGIT-Nectin-2 interaction in myeloma assessment of progression risk in patients with asymptomatic progression and its relevance as therapeutic target in patients conditions that precede this malignancy. Several algorithms have with SMM remains to be elucidated. been developed based on clinical and laboratory parameters to Finally, paired gene expression analysis showed several identify high-risk patients (5, 6). To better understand the basic members of the TNF family (TNFRSF9 (CD137), TNFSF8, mechanisms of myeloma progression in patients with SMM, most of TNFRSF14 (HVEM), TNFRSF1A) upregulated genes in the the genetic studies have focused on investigating CD138+ PC SMM stage compared to active MM. In this regard, we also characteristics such as chromosomal aberrations (14), gene found an association of high expression of TNF superfamily expression profiling (15), whole-exome sequencing of clonal PC (16– members (TNF, TNFAIP3, TNFRSF14 (HVEM)) with a 18). However, PC extrinsic factors in the BM microenvironment may significantly shorter PFS in patients with SMM suggesting that also play a crucial role in myeloma progression (8). In this regard, the presence of a pro-inflammatory microenvironment may several studies have reported impairment of immune cell functions in contribute to myeloma progression instead of generating an symptomatic MM compared to MGUS (19, 20). In addition, immune efficient anti-myeloma response. Thus, chronic inflammation cells from peripheral blood of high-risk SMM patients have shown an and exhaustion of cytotoxic T cells may partly explain a impaired immune system that could be reactivated by therapeutic defective control of malignant PC growth in SMM. Our findings immunomodulation to delay the progression to MM (21). Here, we may contribute to a better understanding of the immune wanted to investigate the immune cell compartment in the BM of dysfunction mechanisms underlying multiple myeloma patients with SMM to dissect the immune evasion strategies involved in progression (30). Since there is no specific treatment for patients malignant PC survival, with important clinical implications for patient with SMM, high-risk patients are treated with agents approved for risk stratification and early treatment. symptomatic MM. In MM, several immunotherapies have been In this study, we investigated changes in the BM immune approved against CD38 (31), signaling lymphocytic activation microenvironment in SMM that could affect the efficacy of the molecule F7 (SLAMF7) and B cell maturation antigen (BCMA) immune response against malignant PC and, consequently, the (32, 33). In this study, we found several inhibitory immune time to symptomatic MM progression. Our results showed an checkpoints (TIGIT, CD96, BTLA, LAG3, KLRC1) upregulated upregulation of genes associated to cytotoxicity in SMM in high-risk SMM patients, some of them being currently tested in compared to both MGUS and symptomatic MM. However, we clinical trials for patients with relapsed refractory MM, such as also found overexpression of inhibitory molecules such as LAG- dual blockade of TIGIT and LAG-3 (NCT04150965). Further 3, TIGIT and IDO1, which may affect the anti-myeloma immune research is needed to evaluate the relevance of these molecules as response in SMM. Patients with SMM showed a wide potential therapeutic targets to avoid myeloma progression heterogeneity in their immune compartment (22), which let us in SMM. to identify 4 clusters based on their gene expression profiles. Importantly, patients in cluster 2 had higher expression of cytotoxic molecules (GZMB, PRF, GNLY, IFNG) but also CONCLUSIONS upregulation of some inhibitory molecules (LAG3, KLRC1, In summary, our results provide insight into the composition CD96, BTLA), suggesting the presence of exhausted T cells. and activation levels of the BM immune cells from distinct Consistently, features of exhaustion in both CD4+ and CD8+ T clusters of patients with SMM which may have an impact in cells, such as a significant increase in PD-1 and CTLA-4 progression to symptomatic disease. Gene expression profiling of compared to healthy donors, have been reported in the BM of BM cells surrounding malignant PC revealed changes in genes patients with symptomatic MM (23). Interestingly, a recent associated to exhausted cytotoxic T cells that can be relevant as analysis based on single-cell RNA sequencing of BM cells from biomarkers to better characterize the progression risk of 8 patients with SMM compared to 4 healthy controls also showed asymptomatic patients with SMM. Furthermore, our findings an increase in negative immune checkpoints such as LAG-3 and could be useful to guide the implementation of current approved TIGIT (24). Our study, including a larger cohort of patients with treatments and to develop new targeted immunotherapies. SMM and comparing them with asymptomatic MGUS and active MM, revealed distinct clusters in SMM based on their immune signature associated to risk of progression. However, DATA AVAILABILITY STATEMENT not all inhibitory immune checkpoints were overexpressed in SMM. For instance, PD-1 was not significantly expressed in The datasets presented in this study can be found in online SMM microenvironment, suggesting that PD-1 may not play a repositories. The names of the repository/repositories and crucial role in myeloma progression. This finding would be accession number(s) can be found below: GEO, GSE186537. consistent with the negative results obtained in clinical trials blocking PD-1 signaling in MM (25) and in SMM (26). On the contrary, patients with SMM in cluster 2 showed a significant ETHICS STATEMENT increase in the ITIM-bearing inhibitory receptor TIGIT (27, 28) The studies involving human participants were reviewed and which is consistent with our previous findings demonstrating approved by Institutional Review Board at Hospital Clı́nic of Frontiers in Immunology | www.frontiersin.org 9 November 2021 | Volume 12 | Article 792609 Isola et al. Immune Microenvironment in Myeloma Progression Barcelona (HCB/2019/0382). The patients/participants provided ACKNOWLEDGMENTS their written informed consent to participate in this study. The authors want to thank all the members of the department of Hematology (Hospital Clı́nic of Barcelona) and all the patients AUTHOR CONTRIBUTIONS included in this study for their altruistic help. II performed experiments, analyzed data and wrote the manuscript. FB-M analyzed gene expression results. DM, AO-C, LR-L, MC, JB, and LR recruited patients and collect SUPPLEMENTARY MATERIAL clinical data. M-PM, BM-A, LP, and EL performed experiments The Supplementary Material for this article can be found online and data analysis. AP provided reagents and helped with at: https://www.frontiersin.org/articles/10.3389/fimmu.2021. Nanostring technology. EL and CF designed research, analyzed 792609/full#supplementary-material data and wrote the manuscript. All authors contributed to the article and approved the submitted version. Supplementary Figure 1 | Genes upregulated in 5 patients with SMM compared to their gene profile after progression to symptomatic MM (A) Heatmap showing the unsupervised hierarchical clustering of 5 patients before and after progression. (B) Expression of ITIM-bearing receptors, poliovirus receptor (PVR) and leukocyte FUNDING immunoglobulin like receptor B3 (LILRB3) before and after progression. This work was supported in part by Grants PI16/00423, PI19/ Supplementary Figure 2 | Analysis of progression free survival versus genes 00669 and PI20/00436 from Instituto de Salud Carlos III differentially expressed in patients with SMM. (A) Kaplan-Maier plot showing (Ministerio de Economı́a y Competitividad, co-funded by progression free survival (PFS) of patients (n=28) versus indicated genes. Upregulation of TNF superfamily members were associated to shorter PFS. Long- Fondo Europeo de Desarrollo Regional (FEDER)-Una manera rank test p values are indicated. 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Pembrolizumab Combined With Lenalidomide and Low-Dose and do not necessarily represent those of their affiliated organizations, or those of Dexamethasone for Relapsed or Refractory Multiple Myeloma: Phase I the publisher, the editors and the reviewers. Any product that may be evaluated in KEYNOTE-023 Study. Br J Haematol (2019) 186(5):e117–21. doi: 10.1111/ this article, or claim that may be made by its manufacturer, is not guaranteed or bjh.15946 endorsed by the publisher. 26. Manasanch EE, Han G, Mathur R, Qing Y, Zhang Z, Lee H, et al. A Pilot Study of Pembrolizumab in Smoldering Myeloma: Report of the Clinical, Immune, and Copyright © 2021 Isola, Braso-́ Maristany, Moreno, Mena, Oliver-Calders, Pare,́ Genomic Analysis. Blood Adv (2019) 3(15):2400–8. doi: 10.1182/ ́ Rodrıguez-Lobato, Martin-Antonio, Cibeira, Blade,́ Rosiñol, Prat, Lozano and bloodadvances.2019000300 Fernández de Larrea. This is an open-access article distributed under the terms of 27. Chauvin JM, Zarour HM. TIGIT in Cancer Immunotherapy. J Immuno Ther the Creative Commons Attribution License (CC BY). The use, distribution or Cancer (2020) 8:e000957. doi: 10.1136/jitc-2020-000957 reproduction in other forums is permitted, provided the original author(s) and the 28. Blake SJ, Dougall WC, Miles JJ, Teng MWL, Smyth MJ. Molecular Pathways: copyright owner(s) are credited and that the original publication in this journal is Targeting CD96 and TIGIT for Cancer Immunotherapy. Clin Cancer Res cited, in accordance with accepted academic practice. No use, distribution or (2016) 22(21):5183–8. doi: 10.1158/1078-0432.CCR-16-0933 reproduction is permitted which does not comply with these terms. Frontiers in Immunology | www.frontiersin.org 11 November 2021 | Volume 12 | Article 792609