Multiple myeloma (MM) is characterized by marked genomic heterogeneity. Beyond structural rearrangements, a relevant role in its biology is represented by allelic imbalances leading to significant variations in ploidy status. To elucidate better the genomic complexity of MM, we analyzed a panel of 45 patients using combined FISH and microarray approaches. We firstly generated genome-wide profiles of 41 MMs and four plasma cell leukemias, using a self-developed procedure to infer exact local copy numbers (CNs) for each sample. Our analysis allowed the identification of a significant fraction of patients showing near-tetraploidy. Furthermore, a conventional hierarchical clustering analysis showed that near-tetraploidy, 1q gain, hyperdiploidy, and recursive deletions at 1p and chromosomes 13, 14, and 22 were the main aberrations driving samples grouping. Moreover, mapping information was integrated with gene expression profiles of the tumor samples. A multiclass analysis of transcriptional profiles characterizing the different clusters showed marked gene-dosage effects, particularly concerning 1q transcripts; this finding was also confirmed by a nonparametric analysis between normalized gene expression levels and local CN variations (1027 highly-significant correlated genes). Finally, we identified several loci in which gene expression correlated with the occurrence of loss of heterozygosity. Our results provide insights into the composite network linking genome structure and transcriptional features in MM.
ASJC Scopus subject areas
- Cancer Research