BACKGROUND: Loss of immune surveillance is critical in the pathogenesis of multiple myeloma (MM) and the progression from smoldering to symptomatic MM. To date, no clear efficacy signal has been observed with programmed-death 1 and programmed death ligand-1 inhibitors in patients with MM. General immune dysfunction in MM is well documented, but the evolving immune landscape in relapsed/refractory MM (RRMM) vs newly diagnosed MM (NDMM) is less well characterized. This study aimed to characterize immune profiles in peripheral blood and bone marrow from patients with NDMM and RRMM.
METHODS: Peripheral blood samples were collected from 35 NDMM and 146 RRMM patients and 36 age-matched healthy volunteers (HVs). Cell surface and intracellular antigen staining using fluorochrome labeled antibodies was performed on a BD FACSCanto II flow cytometer. Bone marrow aspirates were collected from 26 NDMM and 73 RRMM patients, and the transcriptome was assessed by mRNA-Seq.
RESULTS: In peripheral blood, T-cell populations differed between HVs and NDMM and RRMM patients. Absolute numbers of lymphocytes were higher in HVs than in NDMM and RRMM, regardless of the MM disease state. Absolute numbers of total CD4+ T cells and naïve CD4+ T cells were lower in RRMM patients, whereas CD4+ effector memory T cells as a proportion of total CD4+ T cells were increased in RRMM patients. Blood from RRMM patients also contained increased levels of proliferating CD4+ T cells, as evidenced by Ki67, ICOS, and HLA-DR, compared with blood from NDMM patients; HVs had values much closer to those from NDMM than from RRMM patients, suggesting a trend influenced by disease state or therapeutic intervention.
In bone marrow, immunologic gene expression signatures were elevated in NDMM vs RRMM patients; the differences were similar to those in peripheral blood. Using limma to model the differential expression of all measured genes between NDMM and RRMM, we identified 367 genes that were elevated in NDMM patients vs 52 in RRMM patients. Gene set analyses using Molecular Signatures Database immunologic signatures (C7) applied to those 367 genes showed that naïve T-cell genes were increased in the bone marrow of NDMM vs RRMM patients. Gene set enrichment analysis with limma, using 489 gene sets from xCell representing 64 cell types and controlling for differences in tumor burden, indicated that macrophage, monocyte, and neutrophil genes were upregulated and T cells, particularly naïve CD4+ T cells, were downregulated in RRMM patients. Immunohistochemistry results from bone marrow biopsies showed increased programmed death-ligand 1 expression on tumor and infiltrating immune cells and increased CD8 infiltration into bone marrow in RRMM vs NDMM patients. Multiparameter immunofluorescence is underway to confirm these findings and further understand the tumor immune microenvironment in patient subsets.
As expected, baseline RRMM immune cell populations depended on prior lines of therapy. Daratumumab-exposed RRMM patients had elevated total CD8+ T cells in peripheral blood but decreased CD38+, CD4+, and CD8+ T cells, as well as decreased total natural killer cells, compared with the daratumumab-naïve patients. Transcriptome analyses of bone marrow from daratumumab-exposed RRMM patients revealed increased T-cell gene expression signatures relative to marrow from daratumumab-naïve patients. Additionally, pomalidomide-exposed RRMM patients had increased activated CD4+ and CD8+ T cells vs pomalidomide-naïve patients.
CONCLUSIONS: These data indicate that RRMM patients have peripheral blood and bone marrow environments with highly differentiated T-cell populations, whereas NDMM patients show elevated T-cell levels with proliferative capacity. Furthermore, the bone marrow of RRMM patients is enriched with neutrophils and macrophages; investigation is ongoing to determine if these cell types contribute to an immunosuppressive tumor microenvironment. Understanding immune system function based on disease progression, patient segments, and prior lines of therapy is imperative as treatment of MM improves, and it may inform the administration and sequence of next generation immunotherapeutics and identify predictive biomarkers for optimal treatment selection.
Pietz:Celgene Corporation: Employment. Tometsko:Celgene Corporation: Employment, Equity Ownership. Copeland:Celgene Corporation: Employment, Equity Ownership. Whalen:Celgene Corporation: Employment, Equity Ownership. Schmitz:Celgene Corporation: Employment, Equity Ownership. Thompson:Celgene Corporation: Employment, Equity Ownership. Agarwal:Celgene Corporation: Employment, Equity Ownership. Foy:Celgene Corporation: Employment, Equity Ownership. Buchholz:Celgene Corporation: Employment. Komashko:Celgene Corporation: Employment. Dell'Aringa:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment.
Asterisk with author names denotes non-ASH members.