Comprehensive High-Resolution Studies Differentiate and Characterize Hypodiploid Multiple Myeloma
Authors: Wier Scott Van , Braggio Esteban , Fonseca Rafael 
Published: October 2019

Abstract 2869

Background:

Chromosome abnormalities are universal in multiple myeloma (MM) and will ultimately categorize patients into hyperdiploid and non-hyperdiploid MM. Among non-hyperdiploid patients those that exhibit hypodiploidy have the most aggressive clinical phenotype. What genetic features are unique to hypodiploidy are not fully described. Therefore, we performed a comprehensive high-resolution analysis to differentiate and characterize hypodiploid MM.

Materials and methods:

MM patients were analyzed using a combination of array-based comparative genomic hybridization (aCGH) (n=275) and gene expression profiling (GEP) (n=239). Agilent 244K and Affymetrix U133A Plus 2.0 arrays were used in the aCGH and GEP experiments, respectively. Hypodiploid MM was differentiated using pseudokaryotyping based on aCGH findings. Samples estimated to have less than or equal to 44 chromosomes were designated hypodiploid, 45–47 chromosomes were nonhyperdiploid and greater than or equal to 48 and less than 74 chromosomes were considered hyperdiploid. Using GEP the main gene indices and signatures associated with outcome were determined including the translocation and cyclin D (TC) classification, UAMS 70-gene index, proliferation index, centrosome signature and NF-kB index. Differentially expressed genes were also investigated.

Results:

A total of 53 (19%) MM patients were classified into the hypodiploid group, mainly characterized by monosomies of chromosomes 13 (83%), 14 (42%), 22 (23%) and × (50%) (females) with p and/or q-arm aberrations including gains of 1q (51%) and 8q (25%) and losses of 1p (49%), 4p (21%), 4q (23%), 6q (38%), 8p (34%), 12p (25%), 12q (26%), 14q (32%), 16p (25%), 16q (51%) and 17p (25%). Patients with loss of 1p were associated with 4p- (p <0.029), 4q- (p<0.0001), 12p- (p<0.007), 12q- (p<0.0002), any 14 (p<0.022), 16p- (p<0.005), 16q- (p<0.001), and monosomy 22 (p<0.024). Patients with loss of 17p were associated with 12p- (p<0.025), 12q- (p<0.039) and 16q- (p<0.031). The main gene indices and signatures in MM showed that nearly one half of the hypodiploid patients having high-risk disease, ranging from 45% with a high 70-gene index, 47% with high centrosome signature and 51% with a high proliferation index. In addition, hypodiploid patients also displayed a translocation type signature in the TC classification defined by 11q13 (24%), 4p16 (24%) and maf (12%). Overall, 253 genes have >2 fold expression change comparing hypodiploid vs. hyperdiploid including a five fold decrease in the heparin-degrading endosulfatase gene SULF2, a decrease of genes in the TGF-b signaling pathway (MYC, ID3, SMAD1, LTBP1) and those involved in Wnt signaling (DKK1, FRZB). Up regulated genes included those from the p53 signaling pathway and cell cycle (CCND2, CDKN1C, RPRM), cell adhesion molecules (ITGB8, CD28) and tight junction pathway (RRAS2, RRAS, CSDA).

Conclusion:

This represents the most comprehensive genomic characterization of hypodiploid MM to date. These cases exhibit a high propensity for high-risk gene expression profiles and have a high prevalence of −13, −14, 1q gain and 1p loss as predicted. Given our findings it is likely that hypodiploid is not a separate category but rather the genetic “phenotype” of a more advanced clone. Today, using these two platforms together in a routine setting would provide the most comprehensive genetic analysis, important for individualized therapeutics.

Disclosures:

Fonseca: Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.