Genomic Evolution and Clinical Correlates

Multiple myeloma (MM) is an incurable plasma cell malignancy with a complex and incompletely understood molecular pathogenesis. It is classically subdivided into a subtype with rearrangements involving the IGH locus and the hyperdiploid subtype, which harbors multiple trisomies involving odd-numbered chromosomes. These chromosomal abnormalities are nevertheless insufficient for malignant transformation because they are also observed in monoclonal gammopathy of unknown significance, a pre-malignant syndrome that precedes MM. Malignant progression is associated with additional events such as somatic mutations, aberrant methylation, and chromosomal copy-number changes. This implies that the genetic landscape of MM changes over time, driving its symptomatic emergence and progression. Furthermore, evidence suggests that the molecular events necessary for myeloma progression are not attained in a linear fashion but rather through branching, nonlinear pathways. Through whole exome and genome sequencing, copy number profiling and cytogenetics, it has been possible to identify new candidate genes, define diverse mutational processes contributing to the genomic landscape, and study patterns of disease evolution. In particular, the overwhelming majority of MM patients have at least one cluster of subclonal variants, including subclonal driver mutations, implying ongoing tumor evolution. Serial samples reveal diverse patterns of clonal evolution, including linear evolution, differential clonal response and branching evolution. These new findings reveal the myeloma genome to be heterogeneous across patients and within individual patients and that it can exhibit diversity in clonal admixture and dynamics in response to therapy. As modern genomics technologies will increasingly become available in routine diagnostic laboratories, it will be critical to develop an understanding of myeloma as a restless and dynamic disease, with multiple subclones exploring the genomic landscape seeking fitter states. This understanding should, in turn, be used to shape our therapeutic approach to the disease.