Genomic and immune determinants of resistance to daratumumab-based therapy in relapsed refractory multiple myeloma

Abstract

Targeted immunotherapy combinations, including the anti-CD38 monoclonal antibody (MoAb) daratumumab, have shown promising results in patients with relapsed/refractory multiple myeloma (RRMM), leading to a considerable increase in progression-free survival. However, a large fraction of patients inevitably relapse. To understand this, we investigated 32 relapsed MM patients treated with daratumumab, lenalidomide, and dexamethasone (Dara-Rd; NCT03848676). We conducted an integrated analysis using whole-genome sequencing (WGS) and flow cytometry in patients with RRMM. WGS before and after treatment pinpointed genomic drivers associated with early progression, including RPL5 loss, APOBEC mutagenesis, and gain of function structural variants involving MYC and chromothripsis. Flow cytometry on 202 blood samples, collected every 3 months until progression for 31 patients, revealed distinct immune changes significantly impacting clinical outcomes. Progressing patients exhibited significant depletion of CD38-positive NK cells, persistence of T-cell exhaustion, and reduced depletion of regulatory T cells over time. These findings underscore the influence of immune composition and daratumumab-induced immune changes in promoting MM resistance. Integrating genomics and flow cytometry unveiled associations between adverse genomic features and immune patterns. Overall, this study sheds light on the intricate interplay between genomic complexity and the immune microenvironment driving resistance to Dara-Rd in patients with RRMM.

Fig. 1. Study design and outcome in RRMM treated with anti-CD38 MoAb.

A Study design. B Swimmer plot summarizing the clinical outcome of 32 patients with RRMM enrolled in this study. Arrows indicate patients that were still alive at the last follow-up; the no progressed patients (i.e., durable responders) are in light blue, while the progressors in orange. C Even free survival (EFS) Kaplan–Meier curve for the entire cohort. D EFS Kaplan–Meier curve comparing patients with and without the high-risk cytogenetic alterations [HRCA; t(4;14), t(14;16), del17p]; P values for (C, D) was generated with log-rank test. HY hyperdiploid.

Fig. 2. Impact of genomic alterations on clinical outcome in RRMM treated with Dara-Rd.

A A heatmap showing all the genomic alterations associated with progression after Dara-Rd among patients with available WGS data (N = 28); at the top, the bar plot with the relative contribution per each SBS mutational signature. Minimal residual disease (MRD) was tested using Euroflow [17]. WT wild type, HDM-ASCT high-dose melphalan and autologous stem cell transplant, ROS radical oxygen stress. B–I Kaplan–Meier curves showing the impact of APOBEC (B), SBS18 (C), deletion 1p22.1 (RPL5); D), deletion 10p15.3 (E), deletion 16p13.3 (CREBBP); F), deletion 17p13.1 (TP53); G), structural variants on MYC (MYC, PTV1, and NSMCE2); H) and presence of chromothripsis (I) on event-free survival (EFS); P value is calculated with log-rank test.

Fig. 3. Clonal evolution of patients with RRMM treated with daratumumab-based treatment.

Top of each panel: the phylogenetic tree with the trunk in violet and the branches in salmon, light blue, and light green. The y axis is the number of single nucleotide variants. At the bottom left side, the cancer cell fraction (CCF) scatter-plot per each cluster at baseline (BAS) and progression (PD). In the bottom right side, the bar plot with the mutational signature relative contribution for each cluster. The mutational signature color legend is the same of the Fig. 2. HY hyperdiploid.

Fig. 4. Daratumumab immunomodulation on natural killer cells.

A Boxplot showing the percentage of CD38 pos among NK cells (CD56 pos). Light blue indicates durable responders, orange the progressors. P value was calculated with Wilcoxon test two-sided. B Heatmap showing CD38 pos in CD56 pos NK cells z-scores per each patient at seven different time points. Dots and black lines indicate that the sample was not collected at that time point, as the patient has previously progressed; one patient (AIRC124) was excluded because several samples were not collected at different time points. C Boxplots displaying the percentage of CD38 pos among CD56 pos NK cells over time. The red line connects the median value at each time point. D Boxplots showing the absolute number of cytotoxic NK cells over time. E Smooth plot presenting different NK cells patterns over time. F Boxplots showing the number of proliferative NK cells over time. C–F The P values were estimated using Wilcoxon test two-sided. The full list of all P values is reported in Supplemental Tables 11 and 12.

Fig. 5. Regulatory and exhausted T cells change over time.

A Boxplot showing the percentage of FOXP3 T-regs among all T-CD4 (top) and the percentage of CD38 pos among FOXP3 T-regs (bottom). The red line connects the median value at each time point. P values were estimated using Wilcoxon test two-sided. B Radar plots for each time point reporting the contribution of each cell types. The gray network indicates the z-scores values (from −3 to +3), each corner represents an immune cell type; the values represented are the median values of the z-scores for each cell type; the light blue hexagons refer to the values of durable responders, while the orange ones refer to the progressors. Bold and colored cell type names indicate that the specific cell type was significantly enriched at that time point. z-scores were computed using the relative values. T_reg = FOXP3 + T-reg. All P values were estimated using Wilcoxon test two-sided. C Boxplot displaying the percentage of LAG3 helper T cells among all T-CD4 over time. The red line connects the median value at each time point. P values were estimated using Wilcoxon test two-sided. The full list of all P values is in the Supplemental Table 11.

Fig. 6. Association between intrinsic-tumor drivers and microenvironment cell populations.

A Heatmap showing the BM immune cell composition at baseline that were significantly associated with genomic drivers and outcomes. One patient (AIRC123) was excluded because without baseline BM sample. B–F Boxplots showing the distribution of the immune cells according to the presence of absence of distinct genomic drivers linked to shorter event-free survival (EFS). The color legend is the same of (A). All P values were computed using Wilcoxon test two-sided.