Ferritin Metabolism Reflects Multiple Myeloma Microenvironment and Predicts Patient Outcome

Abstract

Multiple myeloma (MM) is a hematologic malignancy with a multistep evolutionary pattern, in which the pro-inflammatory and immunosuppressive microenvironment and genomic instability drive tumor evolution. MM microenvironment is rich in iron, released by pro-inflammatory cells from ferritin macromolecules, which contributes to ROS production and cellular damage. In this study, we showed that ferritin increases from indolent to active gammopathies and that patients with low serum ferritin had longer first line PFS (42.6 vs. 20.7 months and, p = 0.047, respectively) and OS (NR vs. 75.1 months and p = 0.029, respectively). Moreover, ferritin levels correlated with systemic inflammation markers and with the presence of a specific bone marrow cell microenvironment (including increased MM cell infiltration). Finally, we verified by bioinformatic approaches in large transcriptomic and single cell datasets that a gene expression signature associated with ferritin biosynthesis correlated with worse outcome, MM cell proliferation, and specific immune cell profiles. Overall, we provide evidence of the role of ferritin as a predictive/prognostic factor in MM, setting the stage for future translational studies investigating ferritin and iron chelation as new targets for improving MM patient outcome.

Keywords: bone marrow microenvironment; ferritin; monoclonal gammopathy of undetermined significance; multiple myeloma; smoldering myeloma.

Figure 1

Survival probability and multivariate analysis. (A) Ferritin values in MGUS, SMM and MM patients. (B) Progression free survival (PFS) and overall survival (OS) in patients with high ferritin and those with low ferritin. (C) Analysis of direct correlations between biochemical variables and network analysis of relationships. (D) Difference in percentage incidence of bone disease (BD) between subjects with high ferritin levels and those with low levels.

Figure 2

FlowCT analysis of BD OneFlow™ PCD and PCST tubes. (A) Uniform manifold approximation and projection (UMAP) of macropopulations (eosinophils, erythroblasts, granulocytes, B and T/NK lymphocytes, monocytes and plasma cells) identified in the PCD tube. (B) Boxplots and UMAPs show the different distribution of these populations between low ferritin (LF) and high ferritin levels group (HF) (p value < 0.05). (C) Subclustering focusing on lymphocyte subsets. (D) UMAP of macropopulations (eosinophils, erythroblasts, granulocytes, B and T lymphocytes, immature B lymphocytes, monocytes, NK cells, and plasma cells) identified in the PCST tube. (E) Subclustering of NK cells, B and T lymphocytes. (F) Boxplots and UMAPs showing the different distribution of NK cells and B/T lymphocytes between LF and HF groups (p value ≤ 0.05).

Figure 3

Ferritin related clustering on PCs derived from MM patients at single cell resolution. (A) Workflow of the whole analysis. Below UMAP, reporting patients clustering and main genes expressed in each cluster. (B) GSEA on high ferritin versus low ferritin group show a significant enrichment score (ES) for the high ferritin cluster. (C) Kaplan–Meier curves of ferritin-enriched clusters versus non-enriched clusters based on ASCT eligibility reporting overall survival (OS) and progression free survival (FPS).

Figure 4

Single cell analysis of MM and immune cells. (A) Workflow of the integrative analysis. Below UMAPs, reporting plasma cells clustering according to disease status (left) and immune cells clustering including cell annotations (right). (B) Boxplot reporting main differences in cell populations distribution confirming what was observed through flow cytometry and laboratory data.