Immune population dynamics predict multiple myeloma progression while on daratumumab maintenance therapy following autologous stem cell transplant Free

Introduction: Daratumumab, a CD38-targeted monoclonal antibody, is a highly effective therapy in multiple myeloma. Beyond its anti-myeloma effects, it has broad interactions with other CD38+ immune cell populations leading to complex immunomodulatory effects (Viola, et al. Leukemia 2021). We hypothesized that the efficacy of daratumumab critically relies on direct and indirect interactions with the immune system in addition to the direct effect on CD38+ myeloma cells. To test this hypothesis, we followed multiple myeloma patients receiving maintenance daratumumab following autologous stem cell transplant (ASCT) and profiled immune cell populations in the peripheral blood (PB) using cytometry by time-of-flight (CyTOF), along with pharmacokinetic analysis of daratumumab concentration. From these measurements, we modeled the patient-specific immune state and evaluated the ability of the individual's mathematically defined immune stability to predict clinical response.

Methods: Between July 2019 and December 2022, 31 patients were enrolled in a phase 2, multi-center, trial of single-agent daratumumab maintenance post ASCT (NCT03346135). Clinical trial methods and results were previously reported (Borogovac et al., IMS 2023). PB samples were collected from 26 enrolled patients before and after cycle 1 day 1 of infusion, then at the beginning of every other cycle from cycles 2-12. An immunophenotyping analysis was performed on peripheral blood mononuclear cells (PBMCs) using a panel of surface and intracellular antibodies. Complete longitudinal data were available for 19 patients, 5 of whom experienced disease progression while on trial. The panel was designed to delineate major lymphoid and myeloid cell subsets in peripheral blood and to characterize their functional and activation states. Cells were analyzed by CyTOF, with a gating strategy tracking a wide range of immune subpopulations, including specific subsets of PBMCs, T cells (CD4/CD8 status, activation and checkpoint markers), B cells and their precursors, NK and NKT cells, and various monocyte subtypes defined by marker expression and functional state.

Within the T cell compartment, markers such as CD3, CD4, and CD8 were used to distinguish helper and cytotoxic T cells, while CD45RA, CD27, CD69, and CCR7 (CD197) identified memory, naïve, and cellular activation status. CD38 status PD-1 (CD279), LAG-3 (CD223), TIGIT, and TIM-3 exhaustion markers were also tracked. Plasma, B, NK, and NKT cell populations were also tracked.

For patients with more than two sample timepoints, daratumumab dynamics were estimated using the individual patient's dosing schedule and the pharmacokinetic half-life of the drug. Using the relative proportion of NK, NKT, and B cells; monocytes; CD8+ T cells; and CD4+ T cells over time, we estimated coefficients of a linear system of differential equations for each individual. The eigenvalues of the model coefficient matrix were calculated, and the largest real eigenvalue was used as a measure of the stability of the immune system for each patient. If any eigenvalue involved a real and positive component, the immune system was considered unstable.

Results Changes in NK, NKT, B, classical and non-classical monocytes, and CD38+ CD8+ & CD4+ T cell populations were significantly (p<0.05, Pearson) correlated with time on trial. Changes in TIM-3+ monocytes were significantly correlated with progression (p<0.05, Pearson). All 5 patients who progressed on trial demonstrated unstable immune dynamics (Sensitivity = 100%). 5/10 patients with unstable immune dynamics progressed on trial, while 0/9 patients with stable immune dynamics progressed while on trial. Using the largest real eigenvalue (least stable) as a threshold, the area under the Receiver Operator Curve was 0.87 (p<0.01, H₀: AUC=0.5) for classification of progressive disease.

ConclusionImmune cell population dynamics were followed longitudinally in 19 multiple myeloma patients receiving maintenance daratumumab post-ASCT using CyTOF. Key immune cell populations were associated with time on trial and progression. Patient-specific mathematical modeling was used to determine dynamic immune stability, which was found to predict progression on daratumumab maintenance. These results highlight immune stability modeling as a promising approach for predicting and monitoring clinical status during daratumumab therapy.