Genetics of relapse and treatment resistance in multiple myeloma

Abstract

Better understanding of the mechanisms of relapse and treatment resistance in myeloma could improve clinical outcomes. Copy number aberrations (CNA) are a major feature of multiple myeloma, however their evolution over time in the context of modern biological therapy is not well characterised. To investigate acquisition of CNA and their prognostic relevance in the context of first-line therapy, I have profiled paired tumour samples at diagnosis-relapse from NCRI Myeloma XI (ISCRTN4907852) trial patients using digital multiplex ligation-dependent probe amplification. To facilitate identification of driver genes and molecular pathways associated with CNA evolution at relapse, sequential gene expression profiles were also analysed using the affymetrix HGU133 plus 2.0 array. CNA profiles acquired at relapse differed substantially between myeloma subtypes: hyperdiploid (HRD) tumors evolved predominantly in branching pattern vs linear pattern in t(4;14) vs stable pattern in t(11;14). CNA acquisition also differed between subtypes based on cyclin D expression, with a marked enrichment of acquired del(17p) in CCND2 over CCND1 tumors. Acquired CNA were not influenced by high-dose melphalan or lenalidomide maintenance randomisation. A branching evolution pattern was associated with a significantly inferior overall survival. Acquisition of gain(1q) at relapse was the most common new CNA to evolve at relapse and associated with significantly shorter OS, independent of other risk factors or time of relapse. High risk signatures increased at relapse in the majority of tumours. Gene set enrichment analysis highlighted mechanisms of tumour proliferation and leading edge analysis highlighted the role of XPO1 inhibition in treating relapsed myeloma. No significant gene set enrichment at relapse in relation to high-dose melphalan or lenalidomide maintenance randomization was identified. There is an increasing need for rational therapy sequencing in myeloma. This data supports the value of repeat molecular profiling to characterise disease evolution and inform management of relapsing myeloma.