Gene Expression Analysis of the Bone Marrow Microenvironment Reveals Distinct Immunotypes in Smoldering Multiple Myeloma Associated to Progression to Symptomatic Disease

Abstract

Background: We previously reported algorithms based on clinical parameters and plasma cell characteristics to identify patients with smoldering multiple myeloma (SMM) with higher risk of progressing who could benefit from early treatment. In this work, we analyzed differences in the immune bone marrow (BM) microenvironment in SMM to better understand the role of immune surveillance in disease progression and to identify immune biomarkers associated to higher risk of progression.

Methods: Gene expression analysis of BM cells from 28 patients with SMM, 22 patients with monoclonal gammopathy of undetermined significance (MGUS) and 22 patients with symptomatic MM was performed by using Nanostring Technology.

Results: BM cells in SMM compared to both MGUS and symptomatic MM showed upregulation of genes encoding for key molecules in cytotoxicity. However, some of these cytotoxic molecules positively correlated with inhibitory immune checkpoints, which may impair the effector function of BM cytotoxic cells. Analysis of 28 patients with SMM revealed 4 distinct clusters based on immune composition and activation markers. Patients in cluster 2 showed a significant increase in expression of cytotoxic molecules but also inhibitory immune checkpoints compared to cluster 3, suggesting the presence of cytotoxic cells with an exhausted phenotype. Accordingly, patients in cluster 3 had a significantly longer progression free survival. Finally, individual gene expression analysis showed that higher expression of TNF superfamily members (TNF, TNFAIP3, TNFRSF14) was associated with shorter progression free survival.

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: TIGIT; bone marrow microenvironment; immune checkpoints; immunotherapy; pronostic factors; smoldering multiple myeloma.

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.

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 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.

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.

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.