Identification of unbalanced genome copy number abnormalities in patients with multiple myeloma by single-nucleotide polymorphism genotyping microarray analysis View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-10

AUTHORS

Yuhei Kamada, Mamiko Sakata-Yanagimoto, Masashi Sanada, Aiko Sato-Otsubo, Terukazu Enami, Kazumi Suzukawa, Naoki Kurita, Hidekazu Nishikii, Yasuhisa Yokoyama, Yasushi Okoshi, Yuichi Hasegawa, Seishi Ogawa, Shigeru Chiba

ABSTRACT

Single-nucleotide polymorphism genotyping microarray (SNP array) analysis provides detailed information on chromosomal copy number aberrations. To obtain detailed information on genomic abnormalities related to pathogenesis or prognosis of multiple myeloma (MM), we performed 250K SNP array analysis in 39 MM patients and 11 cell lines. We identified an accumulation of deletions and uniparental disomies at 22q12.1. Among the hyperdiploid MM cases, chromosomal imbalance at this locus was associated with poor prognosis. On sequencing, we also found a mutation in the seizure-related 6 homolog (mouse)-like (SEZ6L) gene located at ch.22q12.1 in an MM cell line, NOP1. We further found isolated deletions in 17 genes, five of which are known tumor suppressor genes. Of these, deletion of protein tyrosine phosphatase, receptor type D (PTPRD) was found in three samples, including two patients. Consistent with previous reports, non-hyperdiploid MM, deletion of 13q (del13q) and gain of 1q in non-hyperdiploid MMs were predictive of poor prognosis (p = 0.039, p = 0.049, and p = 0.013, respectively). However, our analysis revealed that unless accompanied by gain of 1q, the prognosis of non-hyperdiploid MM was as good as that of hyperdiploid MM. Thus, SNP array analysis provides significant information useful to understanding the pathogenesis and prognosis of MM. More... »

PAGES

492-500

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12185-012-1171-1

DOI

http://dx.doi.org/10.1007/s12185-012-1171-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1006072988

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22972171


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