BM aspiration samples were collected from 28 patients with SMM at diagnosis (patient characteristics are summarized in Table 1 ). In addition, for comparison purposes we also studied BM samples from 22 patients with MGUS and from 22 patients with symptomatic MM (12 refractory/relapsed MM patients and 10 newly diagnosed untreated patients, 5 of them corresponding to patients also analyzed at the SMM stage who later progressed, allowing a paired comparison).

All patients were diagnosed at the Amyloidosis and Myeloma Unit in the Department of Hematology (Hospital Clínic of Barcelona). Sample collection and clinical record review were performed after informed written consent in accordance with the Declaration of Helsinki. Study protocol was approved by the Institutional Review Board at Hospital Clínic of Barcelona. Patients were diagnosed according to standard International Myeloma Working Group criteria.

The “evolving” type was defined as a progressive increase of at least 10% in the M-protein size within the first 12 months from diagnosis when baseline M-protein was ≥30 g/L or over a period of 3 years (with a progressive increase in the M-protein size in each of the annual measurements) in patients with an initial M-protein <30 g/L (4, 11). Immunoparesis was defined as a decrease below normal levels of at least one of the uninvolved serum immunoglobulins. BM aspirates obtained at diagnosis were reviewed independently by two observers; plasma cell percentages were estimated from a 500-cell count by each examiner and the mean of the two values was considered for the analysis.

CD138-depleted BM cell fraction was isolated with anti-CD138 mAb-coated immunomagnetic beads using an AutoMacs cell sorter (Milteny Biotec, Bergisch Gladbach, Germany). Purity was assessed after isolation, only samples with <2% of CD138+ cells were included in this study. Total RNA from the CD138^neg BM cell fraction was extracted using the TRIzol reagent (Thermo Fisher Scientific, Waltham, MA) according to manufacturer’s instructions.

RNA expression was measured with the nCounter technology; 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 analysis of the NanoString PanCancer Immune Profiling Panel of 730 immune-associated genes and 40 housekeeping genes. Expression counts were then normalized using the nSolver 4.0 software and custom scripts in R 3.6.3. Unpaired significance analysis of microarrays (SAM), using False Discovery Rate [FDR], were used to identify differential gene expression across sample groups. In addition, we calculated scores for immune-related gene expression signatures according to previously published literature, the “Tumor Inflammation Signature” (TIS) reported by Ayers et al. (12) and a previously described “cytolytic score” for hematological malignancies ( Supplementary Table S1 ). The gene expression data have been deposited in the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/) under accession number GEO: GSE186537.

Differences in time to progression (TTP) between patient groups were analyzed using Kaplan-Meier survival curves with the log-rank test used to indicate significance. Statistical differences for numerical values were calculated using the Brown–Forsythe ANOVA test, Mann-Whitney U test and the Kruskal-Wallis 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 GraphPad Prism, v8.0.1 (GraphPad Software, Inc. San Diego, CA).

Patients with SMM have a higher number of BMPC than patients with MGUS, which is associated to a higher risk of myeloma progression. In order to evaluate changes in the immune microenvironment associated to the increase in progression risk, we first performed gene expression analysis of CD138-depleted BM cells of 28 patients with SMM compared to 22 patients with MGUS. Most of the patients with SMM showed a distinct gene profile by unsupervised hierarchical clustering ( Figure 1A ). Among the 127 genes differentially expressed in SMM, only 4 genes were downregulated (FLT3, ARG1, FCER1A and S100A12) ( Figure 1B and Supplementary Table 2 ). The top upregulated genes in SMM included molecules associated to myeloma (SLAMF7 (CS1), TNFSF13 (APRIL)) key molecules in cytotoxicity (GZMB, GZMA, GZMH, GNLY, HLA-A, HLA-B, PRF1, HLA-C, GZMM) and interleukins playing crucial roles in T cell functions (IFNL1, IL15, IL1A, IL32, TGFB1, IL1B) ( Figures 1B, C ). Although our results showed an upregulation of genes associated to cytotoxicity in the BM of patients with SMM compared to MGUS, we also found overexpression of inhibitory molecules such as LAG-3, TIGIT and IDO1, which may affect the anti-myeloma immune response in SMM.

To assess changes in immune microenvironment of patients with SMM compared to symptomatic MM, we analyzed gene expression data from the 28 patients with SMM compared to 22 patients with MM. Unsupervised hierarchical clustering did not show a distinct gene profiling discriminating patients with SMM versus MM underlying the wide heterogeneity found in SMM ( Figure 2A ). Among the 136 genes differentially expressed in SMM, 30 genes were downregulated and 106 upregulated 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, KLRD1, IRF1, KLRC1) ( Figure 2C ). Upregulation of inhibitory receptors such as KLRB1 (CD161), LILRB1 (CD85j), and KLRD1 (CD94) may indicate impairment in NK cell cytotoxicity. Accordingly, we also observed an increase in inhibitory checkpoints that can be expressed in both NK and T cells such as LAG-3 and suppressors of anti-tumor immunity such as IDO1 ( Figure 2C and Supplementary Table 2 ). In patients with symptomatic MM, we found higher levels of genes previously associated with MM (NCAM1, ATM, CD163, IL32) and genes 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 during follow up, 5 were available for gene expression analysis after progression. Paired gene expression analysis before and after progression showed a lower number of differentially expressed genes. Interestingly, the 54 upregulated genes in the SMM stage included several members of the TNF family (TNFRSF9 (CD137), TNFSF8, TNFRSF14, TNFRSF1A) but also the ITIM-bearing inhibitory receptors PVR and LILRB3 ( Supplementary Figure S1 ).

Taken together, our results could be indicative of an enrichment in gene sets associated to cytotoxic immune response in patients with SMM. However, the upregulation of inhibitory receptors may also suggest an exhausted phenotype in the cytotoxic cell compartment.

When comparing the tumor microenvironment in SMM with MGUS and MM, we found that in both cases the most relevant cell type was cytotoxic CD8⁺ T cells ( Figure 3A ). Indeed, key molecules in cytotoxicity such as Tbet (TBX21), perforin (PRF1), granzyme b (GZMB) and granulysin (GNLY) were significantly increased in SMM compared to MM ( Figure 3B ). Although these molecules are also important in NK cytotoxicity, we observed an increase in molecules playing crucial roles in T cell functions such as CD3zeta (CD247), co-stimulatory receptor CD28 and CD6, consistent with the relevance of cytotoxic CD8⁺ T cells in SMM. Furthermore, expression of transcription factors Tbet and Eomes strongly correlate with key molecules in cytotoxicity such as perforin (PRF1), granzyme b (GZMB) and granulysin (GNLY) in patients with SMM ( Figures 3C, D ). Both transcription factors also correlate with genes important for T cell function such as IFNG, IL7, IL7R, IL2RG, CD247 and CD28. Of note, the natural cytotoxicity triggering receptor 1 NCR1 (also known as NKp46, LY94) also correlated with these key genes suggesting that NK cells could also play a role in anti-myeloma cytotoxicity in SMM. As expected, cytotoxic molecules did not correlate with genes involved in Treg function such as NRP1, ENTPD1(CD39) and CXCL12 ( Figure 3D ). As shown in Figure 3B , patients with SMM showed a wide range of expression levels, suggesting the presence of subgroups with differences in the composition and activation levels of the immune cells in the BM microenvironment.

Unsupervised hierarchical clustering after gene expression analysis with the PanCancer Immune panel classified the 28 SMM patients in four clusters ( Figure 4A ). Among the top upregulated genes in cluster 1 (n=7) we found cytokines and chemokines (CCL11, CCL13, CCL16, CCL17, CCL21, CCL24, CXCL13, CX3CL1, XCR1, IFNL1, IFNL2, IL13, IL17A, IL25, LTB, TGFB2, TNFSF13) and cytokine receptors (TNFRSF12A, IL17RB). In contrast, cluster 2 (n=8) showed an expression profile notably enriched for T and NK cell markers and signaling (PRF1, GZMA/B/H/K, GNLY, CD2, CD3D/E, KLRD1, KLRF1, KLRB1, KLRK1, CD274 (PD-L1), LAG3) and interferon signaling (IFNG, MX1, ISG15, STAT1/2/4) ( Figure 4A and Supplementary Table 2 ). Indeed, the analysis of the expression of gene expression signatures (GES) associated with specific immune cell-types showed that cluster 2 included a distinct immune signature characterized by higher levels of transcripts associated to cytotoxic cells ( Figure 4B ). We also evaluated the tumor inflammation signature (TIS), which contains IFN-γ responsive genes associated with T cell activation, which has been shown to predict response to Programmed Cell Death Protein 1 (PD-1) blockade across 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 expression analysis of cluster 2 compared to cluster 3 (n=10) showed a significant increase in genes associated to NK and T cell function in cluster 2 ( Figure 4C and Supplementary Table 2 ). Indeed, most of the genes upregulated in SMM compared to MGUS were upregulated in cluster 2 compared to cluster 3 suggesting that the differences between SMM and MGUS were due to cluster 2 of patients with SMM while cluster 3 of SMM patients were more similar to a MGUS immune profile ( Figure 4D ). Therefore, although patients from cluster 2 showed an immune signature associated to a strong cytotoxic response, they also upregulated the expression of inhibitory checkpoints (CD96, LAG3, BTLA, KLRB1) which raised the question of whether this immune signature has an impact in progression to myeloma.

We next analyzed the association between clinical parameters used to assess risk to progression in patients with SMM and their gene immune signatures ( Figure 5A ). SMM patients with an evolving pattern of the M-protein showed an increase in genes associated to myeloma (SLAMF7, CD79A, CD79B) and AXL ( Figure 5B and Supplementary Table 2 ). Some of these molecules are expressed in a variety of immune cell types but also may be found in PCs, thus we cannot rule out that this high-sensitivity gene expression analysis may detect presence of remaining PCs. However, our experimental approach can still capture major differences in key immune genes of interest. In high-risk SMM patients, 155 upregulated genes included molecules associated to MM (TNFRSF17, NCAM1, IRF4, CD79B), cytotoxic molecules (KLRC1, GZMA) and AXL ( Figure 5C ). The median follow up of the patients with SMM was 5.3 years. Ten patients with SMM progressed to symptomatic MM, with a median time to progression (TTP) of 1.9 years. SMM patients that progressed to MM showed 76 differentially expressed genes when compared to SMM patients who showed no progression during follow up, with an increase in 63 including molecules involved in immune response activation such as TNF, IL-1B and granzyme M ( Figure 5D ). Most of the genes upregulated in high-risk patients were included in the list of upregulated genes in cluster 2 ( Figure 5E ). We next wanted to assess whether distinct clusters based in immune signatures were associated to differences in progression free survival (PFS). Four patients in cluster 2 were excluded from this analysis since they started treatment in a clinical trial for high-risk SMM patients (GEM-CESAR). As shown in Figure 5F , log-rank test showed that patients in cluster 3 with lower expression of cytotoxic genes and an immune signature more similar to MGUS patients had a significantly longer PFS compared to the other of clusters (p=0.04).

Some of the studied genes were independently associated with shorter PFS such as TNF, TNFAIP3, GZMM and TNFRSF14 (also known as HVEM) ( Supplementary Figure 2A ). Conversely, high expression of transcripts for MAPK14, LTF, SMAD3, FOS, PSEN1 and THBS1 were associated with increased progression free survival ( Supplementary Figure 2B ). Taken together, our results could indicate that genes associated to an exhausted phenotype in cytotoxic T cells are upregulated in high-risk SMM patients while some members of the TNF superfamily are significantly associated to myeloma progression.