High-Parameter Mass Cytometry Evaluation of Relapsed/Refractory Multiple Myeloma Patients Treated with Daratumumab Demonstrates Immune Modulation as a Novel Mechanism of Action

Abstract

Daratumumab is a CD38-targeted human monoclonal antibody with direct anti-myeloma cell mechanisms of action. Flow cytometry in relapsed and/or refractory multiple myeloma (RRMM) patients treated with daratumumab revealed cytotoxic T-cell expansion and reduction of immune-suppressive populations, suggesting immune modulation as an additional mechanism of action. Here, we performed an in-depth analysis of the effects of daratumumab on immune-cell subpopulations using high-dimensional mass cytometry. Whole-blood and bone-marrow baseline and on-treatment samples from RRMM patients who participated in daratumumab monotherapy studies (SIRIUS and GEN501) were evaluated with high-throughput immunophenotyping. In daratumumab-treated patients, the intensity of CD38 marker expression decreased on many immune cells in SIRIUS whole-blood samples. Natural killer (NK) cells were depleted with daratumumab, with remaining NK cells showing increased CD69 and CD127, decreased CD45RA, and trends for increased CD25, CD27, and CD137 and decreased granzyme B. Immune-suppressive population depletion paralleled previous findings, and a newly observed reduction in CD38⁺ basophils was seen in patients who received monotherapy. After 2 months of daratumumab, the T-cell population in whole-blood samples from responders shifted to a CD8 prevalence with higher granzyme B positivity (P = 0.017), suggesting increased killing capacity and supporting monotherapy-induced CD8⁺ T-cell activation. High-throughput cytometry immune profiling confirms and builds upon previous flow cytometry data, including comparable CD38 marker intensity on plasma cells, NK cells, monocytes, and B/T cells. Interestingly, a shift toward cytolytic granzyme B⁺ T cells was also observed and supports adaptive responses in patients that may contribute to depth of response. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Trial registration: ClinicalTrials.gov NCT01985126 NCT00574288.

Keywords: CyTOF; cytometry by time of flight; immune profiling.

Figure 1

Quality control assessment using whole‐blood controls (displayed in blue) by (A) EMD analysis (ordinal embedding [43; top] and hierarchical clustering [UPGMA; bottom]) and (B) MEM revealed no evidence of technical sources of variation that required batch correction. EMD, earth mover's distance; UPGMA, unweighted pair group method with arithmetic means; MEM, marker enrichment modeling; C, cycle; D, day.

Figure 2

Hierarchy of CD38 expression across immune subtypes in bone‐marrow samples of 3 RRMM GEN501 patients as assessed by CyTOF®. Exported patient‐specific SPADE tree bubbles fcs files for corresponding immune‐cell populations were concatenated and subsequently visualized using Cytobank® software. CD38 marker intensity in NK, monocyte, and B‐ and T‐cell compartments. NK, natural killer; RRMM, relapsed/refractory multiple myeloma; NK, natural killer.

Figure 3

Pre‐ versus on‐ and post‐daratumumab treatment samples revealed (A) CD38 expression changes in SIRIUS whole‐blood samples across different immune cells and NK‐cell depletion in (B) SIRIUS whole‐blood and (C) GEN501 bone‐marrow samples. Data is visualized using the SPADE‐blend tree method, which involved the projection of the differential testing results (here, contrasting pre‐ versus on‐ and post‐treatment sample data) as colored highlighting of the SPADE tree clusters to maximize exploration of the statistical analysis results, simultaneously, in the samples under investigation. SPADE, spanning‐tree progression analysis of density‐normalized events; NK, natural killer; C, cycle; D, day; MMI, mean marker intensity; ProInflamm Monos, pro‐inflammatory monocytes; MDSC, myeloid‐derived suppressor cell; PBMC, peripheral blood mononuclear cell; mDC, myeloid dendritic cell; pDC, plasmacytoid dendritic cell; NKT, natural killer T cell; T_cm, central memory T cell; T_em, effector memory T cell; T_emra, effector memory RA⁺ T cell; T_reg, regulatory T cell. Nodes are colored by decrease (cyan; green if significant [raw P value]) or increase (magenta; red if significant [raw P value]).

Figure 4

Depletion of the immune‐suppressive populations (A) CD38⁺ T_regs and (B) CD38⁺ basophils in whole‐blood samples from patients at baseline and after 2 months of daratumumab monotherapy treatment (SIRIUS). Analysis of changes in distribution of signal intensity shows a decrease in the fraction of T_regs expressing high levels of CD38, suggesting a change in the T_reg population (C). For basophils (D) a shift of cell fractions from high intensity to low intensity is observed, suggesting a change in CD38 expression. T_reg, regulatory T‐cell; IRR, infusion‐related reaction C, cycle; D, day; MMI, mean marker instensity. Lines indicate paired samples.

Figure 5

High‐dimensional algorithm–based analysis result visualization of whole blood T‐cell phenotypic changes in responders and non‐responders from SIRIUS and GEN501 after 2 months of daratumumab monotherapy. (A) Radviz density projections of T cells from SIRIUS and GEN501 studies. (B) Fan plots and scatterplots of T‐cell data from SIRIUS patients. Radviz plots of SPADE tree‐defined T‐cell populations reveal a shift to CD8 prevalence with granzyme B positivity in responders (red circles). Arrows on fan plots highlight the contribution of granzyme B to the phenotype of T cells at a given time point for patients. The analysis was based on whole‐blood samples from 32 SIRIUS patients (17 non‐responders; 15 responders) and 4 GEN501 patients (2 non‐responders; 2 responders). SPADE, spanning‐tree progression analysis of density‐normalized events; WB, whole‐blood; C, cycle; D, day; Inf, infusion.

Figure 6

CyTOF® manual gating performed on SIRIUS whole‐blood samples, showing changes in CD8⁺ T‐cell (A) activation and (B) exhaustion marker changes following daratumumab treatment in responders and non‐responders. Activation marker analysis revealed a significant increase (P = 0.017) in granzyme B production among responders. Modest changes were observed in markers associated with exhaustion. Dotted lines indicate paired samples.