A Novel Measure of Chromosome Instability Is An Independent Prognostic Factor in Multiple Myeloma

Chromosomal abnormalities are prevalent in multiple myeloma (MM) and have been useful in delineating disease subtypes and prognosis groups. Globally, a dichotomous developmental pathway based on ploidy status seems to exist. The accumulation of chromosomal abnormalities in MM often gives rise to complex copy number profiles which are difficult to be captured due to the limitations of karyotyping in a relatively non-proliferative tumor. It is therefore unclear if genomic complexity as a reflection of chromosomal instability (CIN) is of biological and clinical relevance in MM. In this study, we introduce a novel measure of CIN, chromosome instability genome event count (CINGEC), validated its prognostic relevance in myeloma, and subsequently derived a gene expression (GEP) signature, CINGECS, investigated the gene contents of it to elucidate biological mechanisms related to CIN, and assessed its association with survival in the context of other gene expression based prognostic factors.

The rationale for CINGEC is that the more unstable is a genome, the more genome events does it harbor irrespective of the size of altered segments. Hence, we first obtain a copy number level sequence from a segmentation result of copy number profile signals from aCGH or SNP chip. We developed an algorithm to estimate the minimum number of genome events to account for the observed complex genome profiles. CINGEC is the sum of genome event counts from all autosomal chromosomes. The GEP signature, CINGECS, is derived from a public dataset that has both aCGH and GEP (GSE26849 and GSE26760, n=246) by performing differential gene expression analysis (SAM, p<0.001, q<0.001, fold-change 2) using GEP data between samples grouped into the top and bottom quartiles based on their CINGEC obtained from the analysis of aCGH data. The biological mechanisms distinctive between high and low CINGEC samples are assessed with biological pathways such as Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO).

We first show that CINGEC is associated with MM patient survival by analyzing a previously published Mayo Clinic aCGH dataset (n=64, Chng WJ et al. Leukemia 2010; 24: 833–842). Furthermore, CINGEC is a stronger prognostic factor than another aCGH-based measure of CIN derived from breast cancer, the Genome Instability Index (Chin et al. Genome Biol 2007;8; R215). The CINGECS comprise of 160 differentially expressed genes with 144 up-regulated and 16 down-regulated. KEGG pathway and GO analysis of this set of genes showed enrichment by those involved in (1) cell cycle checkpoints and progression such as cell cycle, cell division, spindle organization, mitosis, (2) DNA damage responses such as response to DNA damage stimulus, DNA repair, nucleotide-excision repair, DNA gap filling, and (3) generic cancer related processes such as DNA replication, cell proliferation. Finally, CINGECS is shown to be significantly associated with poorer overall as well as progression free survival in both newly diagnosed (UAMS TT2 dataset, n=351, GSE 2658) and relapsed patients (Millenium Apex Dataset, n=188, GSE9782). Furthermore, it is found to be a significant prognostic signature independent of diverse GEP signatures known to be associated with survival for MM patients, including the Proliferation Index, the Centrosome Index, the UAMS 70-gene High-Risk Signature, the IFM High-Risk Signature, the IL-6 signature derived from cell lines, as well as GEP-based signatures of genomic instability derived from other cancers such as the CIN70 signature (Carter et al. Nat Genet 2006; 38:1043–1048) and CINSARC67 (Chibon et al. Nat Med 2010; 16: 781–787).

We conclude that CINGEC can account for CIN with high resolution copy number profile data. Also, its corresponding GEP signature, CINGECS, potentially encompasses changes reflective of both cause and consequences of the CIN phenotype and is an independent prognostic signature in MM.

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