An APOBEC/Inflammation-based classifier improves the stratification of multiple myeloma patients and identifies novel risk subgroups

Abstract Background: Recent insights into the pathogenesis of multiple myeloma (MM) have highlighted inflammation and genome editing, e.g. by APOBEC enzymes, as major drivers of disease onset and progression. We hypothesized that inclusion of molecular features corresponding to these two mechanisms can be utilized to define novel MM risk groups at initial diagnosis. Methods: Using two independent patient cohorts (MMRF and IFM/DFCI 2009), we developed and validated an easy-to-calculate novel risk-score that is based on mRNA expression levels of APOBEC2 and APOBEC3B, as well as inflammatory cytokines (IL11, TGFB1 and TGFB3) and serum levels of ß2-microglobulin and LDH. Results: Performance of the Editor- and Inflammation-based score (EI-score) was superior to current cytogenetics-based risk classifiers. Moreover, the EI-score was able to identify previously unrecognized MM patients who experience favourable outcomes despite carrying adverse risk cytogenetics. Conclusions: Through accurate risk stratification we can identify patients who are currently over-or undertreated. The EI-score is a contemporary and superior prognostic score, calculated based on transcript levels at diagnosis, allowing the identification of unrecognized MM risk subgroups potentially leading to adjustment of clinical treatment and improvement of patient outcomes.