Whole-genome analysis identifies novel drivers and high-risk double-hit events in relapsed/refractory myeloma.
Ansari-Pour N., Samur MK., Flynt E., Gooding S., Towfic F., Stong N., Ortiz Estevez M., Mavrommatis K., Walker BA., Morgan GJ., Munshi NC., Avet Loiseau H., Thakurta A.
Large-scale analyses of genomic data from newly-diagnosed multiple myeloma patients (ndMM) have been undertaken, however, large-scale analysis of relapsed/refractory multiple myeloma (rrMM) has not been performed. We hypothesize that somatic variants chronicle the therapeutic exposures and clonal structure of myeloma from ndMM to rrMM stages. We generated whole genome sequencing (WGS) data from 418 tumors (386 patients) derived from six rrMM clinical trials and compared them with WGS from 198 unrelated ndMM patients in a population-based case-control fashion. We identified significantly enriched events at the rrMM stage, including drivers (DUOX2, EZH2, TP53), biallelic inactivation (TP53), non-coding mutations in bona fide drivers (TP53BP1, BLM), copy number aberrations (CNA; 1qGain, 17pLOH) and double-hit events (Amp1q-ISS3, 1qGain-17pLOH). Mutational signature analysis identified a subclonal defective mismatch repair signature enriched in rrMM and highly active in high mutation burden tumors, a likely feature of therapy-associated expanding subclones. Further analysis focused on the association of genomic aberrations enriched at different stages of resistance to immunomodulatory agent (IMiDsÒ)-based therapy. This analysis revealed that TP53, DUOX2, 1qGain and 17pLOH increased in prevalence from ndMM to lenalidomide (LEN)- to pomalidomide (POM)-resistant stages while enrichment of MAML3 along with IGL and MYC translocations distinguished POM from the LEN subgroup. Genomic drivers associated with rrMM are those that confer clonal selective advantage under therapeutic pressure. Their role in therapy evasion should be evaluated further in longitudinal patient samples, to confirm these associations with the evolution of clinical resistance and to identify molecular subsets of rrMM for the development of targeted therapies.