Abstract
Background The immune system is altered in multiple myeloma (MM) and contributes to therapy resistance. The availability of novel immunotherapies necessitates understanding the influence of the immune microenvironment on disease progression which may inform sensitivity to therapy. The objective of this study is to fully characterize the immune microenvironment in MM precursor diseases and MM and identify any immune contribution to progression. To accomplish this we used high-dimensional mass cytometry (CyTOF) to investigate immune alterations associated with progression in pre-malignant and malignant stages of MM.
Methods Cryopreserved bone marrow mononuclear cells (BMMCs) from healthy donors (HD, n=13), MGUS (n=21), SMM (n=19), newly diagnosed MM (NDMM, n=17), and ~3 months post- first autologous stem cell transplant (ASCT, n=21) were assessed using a panel of 35 cell surface and 3 intracellular antibodies that includes cell lineage markers for identification of immune populations and functional markers indicative of positive or negative immune regulation. BMMCs were thawed, stained with antibodies, and analyzed on a Helios mass cytometer. Data were normalized using bead normalization, transformed using the inverse hyperbolic sine function with a cofactor of 5 and gated for 45+ live, intact, singlets for global analysis by gating in FCS express and clustering by viSNE for visualization. Differences in population abundance were identified in an unbiased manner by FlowSOM and in marker intensity by CITRUS. Marker intensity analysis was performed using the multiple testing permutation procedure (SAM), with an FDR of 1% and minimum population size of 0.5%.
Results To identify changes in the immune microenvironment associated with progression we compared immune population abundance and marker intensity indicative of immune status including activation, exhaustion, or senescence. MGUS was distinguished from HD by increased abundance of CD4 central memory (CM, p<0.001), effector memory (EM, p<0.001) and plasmacytoid and monocyte-derived dendritic cells (DC, p< 0.01). In MGUS, TIM3 and CD57 were elevated on NK cells and NKT cells, respectively, compared to HD suggesting reduced activity. In SMM increased abundance of B regulatory cells (3.0 vs 5.9 %, p<0.01) but reduced inhibitory markers on T cells including PD1, CTLA4 CD55, FOXP3 and TIGIT was observed compared to MGUS. NDMM was distinguished from SMM by reduced abundance of CD4 EM (p<0.01), CD8 early EM (p< 0.001), and B regulatory cells (p<0.01) and increased abundance of active Tregs (CD38+, P<0.01) and total NK cells (p<0.01) which had increased CD55, a complement inhibitory protein. Post-ASCT changes in immune abundance include increased total CD8 and CD8 terminal effectors (CD57 +, p< 0.0001), B regulatory cells (p<0.0001), and reduced total CD4 and CD4 CM (p<0.0001), compared to NDMM. CD4 T cells post-ASCT were characterized by reduced CD127 and CCR7 and increased CD28, CTLA4, FOXP3 and TIGIT and CD8 T cells had reduced CD28, CD127 and CCR7 and increased CD57 and TIGIT compared to NDMM. Interestingly, significant difference in NK cells were not observed but post-ASCT NK cells may be active as suggested by reduced CD59 and TIM3 compared to NDMM. To determine whether the immune microenvironment had normalized by 3 months post-ASCT we compared population abundance to HD, MGUS, and SMM cases. Immune abundance post-ASCT revealed a significantly lower percentage of CD4 CM, 4 -8 - T cells, normal PCs, and post-switch B cells (25+) and elevated CD8 terminal effector (57+) and B regulatory cells than all 3 other groups. Overall major differences in abundance of total T and B cells and their subsets were observed with differences in NK cells between stages primarily reflected in marker expression (e.g. CD161+ subset) rather than abundance.
Conclusions Early changes in the immune microenvironment observed in MGUS/SMM lead to immune suppression and eventually immune evasion allowing MM to emerge. In this study the immune ME did not appear to normalize 3 months post-therapy indicated by an increase in B regulatory cells and markers of inactive effector cells. Profiling of the immune microenvironment throughout MM treatment may allow us to identify novel therapeutic targets and optimal timing of administration of novel immunotherapies and patients that would most benefit from these therapies.
Disclosures
Walker: Sanofi: Speakers Bureau; Bristol Myers Squibb: Research Funding. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees.
Deep Profiling of the Immune Microenvironment throughout Myeloma Disease Stages