bioRxivCancer Biology
Targeting the methyltransferase SETD8 impairs tumor cell survival and overcomes drug resistance independently of p53 status in multiple myeloma
L. Herviou, F. IZARD, O. Karmous-Gadacha, C. Gourzones, C. Bellanger, E. Desmedt, A. Ma, L. Vincent, G. Cartron, J. Jin, E. De Bruyne, C. Grimaud, E. JULIEN, J. Moreaux
Abstract
Multiple myeloma (MM) is a malignancy of plasma cells that largely remains incurable. The search for new therapeutic targets is therefore essential. Here we show that a higher expression of the lysine methyltransferase SETD8, which is responsible for histone H4K20 mono-methylation, is an adverse prognosis factor associated with a poor outcome in two cohorts of newly diagnosed patients. Remarkably, primary malignant plasma cells are particularly addicted to SETD8 activity. Indeed, pharmacological inhibition of this enzyme by the chemical compound UNC0379 demonstrated a significantly higher toxicity in MM cells compared to normal cells from the bone marrow microenvironment. Moreover, RNA sequencing and functional studies revealed that SETD8 inhibition induces a mature non-proliferating plasma cell signature and an activation of the p53 canonical pathway, which together leads to an impairment of myeloma cell proliferation and survival. However, UNC0379 treatment triggers a deadly level of replicative stress in p53 deficient MM cells, indicating that the cytotoxicity associated with SETD8 inhibition is independent of the p53 status. Consistent with this, the combination of UNC0379 with the conventional cytotoxic agent melphalan strongly enhances DNA damage and overcomes drug resistance in myeloma cells. Thus, targeting SETD8 could be of therapeutic interest to improve MM treatment in high-risk patients independently of the p53 status.
- organism: human
- assay: bulk RNA-seq
- research focus: cancer & tumor microenvironment
- research focus: differential expression
- research focus: drug response / drug discovery
- research focus: perturbation response prediction
- approach: experimental assay
- approach: statistical model
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