Occurrence and Prognostic Significance of Cytogenetic Evolution in Patients with Multiple Myeloma
Authors: Binder Moritz , Rajkumar S. Vincent , Ketterling Rhett P. , Dispenzieri Angela , Lacy Martha Q. , Gertz Morie A. , Buadi Francis , Hayman Suzanne R. , Hwa Yi L. , Zeldenrust Steven R. , Lust John A. , Russell Stephen J. , Leung Nelson , Kapoor Prashant , Go Ronald S. , Gonsalves Wilson I. , Kyle Robert A. , Kumar Shaji K. 
Published: October 2019

Abstract
Background: Cytogenetic evaluation, especially using fluorescence in situ hybridization (FISH), at the time of diagnosis is essential for initial risk stratification and the employment of risk-adapted treatment strategies in multiple myeloma. Little is known about the occurrence and prognostic significance of cytogenetic evolution during follow up.
Methods: We studied 433 patients who were diagnosed with multiple myeloma between January 2000 and December 2011 and had at least two FISH evaluations at Mayo Clinic Rochester, including the diagnostic specimen. Bone marrow aspirates were evaluated for deletions, monosomies, trisomies, and tetrasomies using chromosome- or centromere-specific FISH probes. IGH rearrangements were evaluated using an IGH break-apart probe and up to five potential partners (FGFR3, CCND1, CCND3, MAF, and MAFB). Cytogenetic evolution was defined as a new deletion, monosomy, trisomy, tetrasomy, or translocation during follow up. Multivariable-adjusted logistic regression models were used to assess the associations between the parameters of interest and the presence of cytogenetic evolution in follow-up specimens. Multivariable-adjusted Cox proportional hazards models were used to assess the effect of cytogenetic evolution on overall survival. All models were adjusted for sex, age, the presence of high-risk FISH abnormalities, and the number of abnormalities at the time of diagnosis. Likelihood ratio tests were used to assess the goodness of fit of nested models. The χ2 or Fisher's exact test was used to assess the distribution of cytogenetic abnormalities in subgroups.
Results: The median age at diagnosis was 60 years (32 - 82), 264 (61%) of the patients were male. The median overall survival for the entire cohort was 7.0 years (6.2 - 7.8). At the time of diagnosis, 150 (35%) and 57 (13%) of the 433 patients presented with a hyperdiploid karyotype and cytogenetic high-risk abnormalities, respectively. Independent of each other, the presence of a translocation at the time of diagnosis was associated with decreased odds of cytogenetic evolution during follow up (OR 0.39, 95% CI 0.24 - 0.63, p < 0.001) while the presence of at least one trisomy or tetrasomy at the time of diagnosis was associated with increased odds (OR 2.53, 95% CI 1.37 - 4.70, p = 0.003). A greater proportion of patients presenting with a hyperdiploid karyotype experienced cytogenetic evolution during follow up. Those patients more frequently evolved additional trisomies and tetrasomies, while translocations were more common in those presenting with a non-hyperdiploid karyotype (Table 1). The development of additional abnormalities during the three years following diagnosis (compared to no new abnormalities) was associated with increased subsequent mortality in those who survived at least three years (HR 3.22, 95% CI 1.82 - 5.68, p < 0.001). Including the time between first and last cytogenetic evaluation as a covariate did not significantly change the parameter estimates or improve model fit (p = 0.727).
Conclusions: Demographics, risk profile, and overall survival of this cohort reflect the fact that patients had to survive long enough to undergo repeated cytogenetic evaluation. Hyperdiploid and non-hyperdiploid genotypes were associated with distinct behavior regarding cytogenetic evolution during follow up. The identification of cytogenetic evolution was an adverse prognostic factor in those who survived at least three years after diagnosis. These findings emphasize the importance of the dynamics of the underlying clonal disease process for accurate risk assessment and suggest that selected subgroups of patients may benefit from risk stratification during follow up.
Table 1. Cytogenetic evolution during follow up in 433 patients with multiple myeloma stratified by karyotype at the time of diagnosis Hyperdiploid (n = 150) Non-hyperdiploid (n = 283) p New abnormality 76 (51%) 108 (38%) 0.012 New monosomy 8 (5%) 24 (8%) 0.243 New trisomy 48 (32%) 55 (19%) 0.004 New tetrasomy 37 (25%) 27 (10%) < 0.001 New deletion 17 (11%) 27 (10%) 0.557 New translocation 1 (1%) 11 (4%) 0.065 Most common new abnormality [type (percent of type in each group)] Monosomy mono(13) (75%) mono(13) (62%) Trisomy tri(11) (22%) tri(3) (22%) Tetrasomy tetra(15) (48%) tetra(15) (30%) Deletion del(17p) (79%) del(17p) (63%) Translocation t(11;14) (100%) t(11;14) (36%) Data are given as count (percent) unless denoted otherwise.

Disclosures
Binder: American Society of Hematology: Research Funding. Kumar:AbbVie: Research Funding; Onyx: Research Funding; Sanofi: Research Funding; Celgene, Millenium, Sanofi, Skyline, BMS, Onyx, Noxxon,: Other: Consultant, no compensation,; Skyline, Noxxon: Honoraria; Millenium/Takeda: Research Funding; Janssen: Research Funding; Celgene: Research Funding.