Survival of Genetic Subtypes of Relapsed Myeloma May Be Modulated by Secondary Events.

Abstract

The biological heterogeneity of myeloma can be largely explained by genetic subtypes characterized by primary translocations (t(4;14), t(11;14), and t(14;16)) or trisomies of odd-numbered chromosomes (hyperdiploid myeloma). In newly diagnosed patients, it is well established that these genetic subtypes have different survival. However, the prevalence and survival of the main genetic subtypes in relapse patients is unknown. In this study, we examined the prevalence and survival according to TC classes (a gene expression-based classification system which identifies the main genetic subtypes of myeloma) in patients with relapsed myeloma entered into bortezomib trials who had gene expression profiling (GEP) performed as part of a pharmacogenomics study (Bortezomib cohort). In comparison, we analyzed a large publicly available GEP dataset of newly diagnosed patients treated with total therapy II from the University of Arkansas for Medical Science (UAMS cohort, GEP data available on Gene Expression Omnibus, Zhan et al Blood 2006 epub). GEP was performed using the U133A and B chip for the bortezomib cohort and U133plus chip for the UAMS cohorts (Affymetrix, Santa Clara, CA). For both dataset, raw gene expression were log-transformed, median centered and analyzed using Genespring 7.0 (Agilent Technologies, Palo Alto, CA). A proliferation index (PI) based on the expression of 7 genes (TYMS, TOP2A, CCNB1, KIAA0101, CKS1B, UBE2C, TRIP13) was also calculated. As shown previously (Mulligan et al ASH 2005 abstract) the prevalence of the different TC classes in relapse patients was very similar to newly diagnosed patients (Chi-square p=0.25). In the bortezomib cohort, patients with t(11;14) had the worst survival when compared to patients with t(4;14), maf translocation and hyperdiploidy who had similar survival (survival of t(11;14) versus others, log-rank p=0.036). In contrast, among newly diagnosed patients (UAMS cohort) those with t(4;14) and maf translocations had the worst prognosis, whereas t(11;14) and hyperdiploid patients had the best prognosis (log-rank p=0.02). This interesting contrast in the prognosis within these main genetic subtypes of myeloma at diagnosis and relapse suggest that at relapse, the survival of these patients may be modulated by secondary events or possibly the treatment received. Indeed, a greater proportion of relapsed t(11;14) have a high PI (Chi-square p = 0.04) than newly diagnosed t(11;14). In contrast, there is no difference between the PI of newly diagnosed and relapsed hyperdiploid MM (TC D1, p=0.19). Our observations suggest that although the composition of the various genetic subtypes at diagnosis and relapse appear similar, the prognosis of some subtypes (especially t(11;14)) can be markedly different at relapse when modulated by secondary events (e.g. increase proliferation) whereas others (such as hyperdiploid MM) appear to behave similarly at diagnosis and relapse. Our observation also has important therapeutic implications as it suggests relapsed t(11;14) should be treated aggressively and drug combinations or addition of new agents may be more appropriate. Conversely, bortezomib seemed to be an effective therapy in relapse t(4;14) patients, improving the prognosis of these patients to that of genetic subgroups with good prognosis. Our study highlights the fact that different treatment strategies may be needed for newly diagnosed and relapsed patients even within same genetic subtypes.