Myeloperoxidase creates a permissive microenvironmental niche for the progression of multiple myeloma.

Expression of myeloperoxidase (MPO), a key inflammatory enzyme restricted to myeloid cells, is negatively associated with the development of solid tumours. Activated myeloid cell populations are increased in multiple myeloma (MM); however, the functional consequences of myeloid-derived MPO within the myeloma microenvironment are unknown. Here, the role of MPO in MM pathogenesis was investigated, and the capacity for pharmacological inhibition of MPO to impede MM progression was evaluated. In the 5TGM1-KaLwRij mouse model of myeloma, the early stages of tumour development were associated with an increase in CD11b+ myeloid cell populations and an increase in Mpo expression within the bone marrow (BM). Interestingly, MM tumour cell homing was increased towards sites of elevated myeloid cell numbers and MPO activity within the BM. Mechanistically, MPO induced the expression of key MM growth factors, resulting in tumour cell proliferation and suppressed cytotoxic T-cell activity. Notably, tumour growth studies in mice treated with a small-molecule irreversible inhibitor of MPO (4-ABAH) demonstrated a significant reduction in overall MM tumour burden. Taken together, our data demonstrate that MPO contributes to MM tumour growth, and that MPO-specific inhibitors may provide a new therapeutic strategy to limit MM disease progression.

CKLF and IL1B transcript levels at diagnosis are predictive of relapse in children with pre-B-cell acute lymphoblastic leukaemia.

Disease relapse is the greatest cause of treatment failure in paediatric B-cell acute lymphoblastic leukaemia (B-ALL). Current risk stratifications fail to capture all patients at risk of relapse. Herein, we used a machine-learning approach to identify B-ALL blast-secreted factors that are associated with poor survival outcomes. Using this approach, we identified a two-gene expression signature (CKLF and IL1B) that allowed identification of high-risk patients at diagnosis. This two-gene expression signature enhances the predictive value of current at diagnosis or end-of-induction risk stratification suggesting the model can be applied continuously to help guide implementation of risk-adapted therapies.