Department of Hematology, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan.
Int J Hematol. 2012 Oct;96(4):492-500. doi: 10.1007/s12185-012-1171-1. Epub 2012 Sep 13.
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.
单核苷酸多态性基因分型微阵列(SNP 微阵列)分析提供了关于染色体拷贝数异常的详细信息。为了获得与多发性骨髓瘤(MM)发病机制或预后相关的基因组异常的详细信息,我们对 39 例 MM 患者和 11 个细胞系进行了 250K SNP 微阵列分析。我们确定了 22q12.1 处的缺失和单亲二体的积累。在超二倍体 MM 病例中,该部位的染色体不平衡与预后不良相关。在测序中,我们还在 MM 细胞系 NOP1 中位于 ch.22q12.1 的与癫痫相关的 6 同源物(小鼠)样(SEZ6L)基因中发现了一个突变。我们进一步发现了 17 个基因的孤立缺失,其中 5 个是已知的肿瘤抑制基因。其中,在三个样本中发现了蛋白酪氨酸磷酸酶,受体型 D(PTPRD)缺失,包括两名患者。与先前的报道一致,非超二倍体 MM、del13q(del13q)缺失和非超二倍体 MM 中的 1q 增益预测预后不良(p=0.039、p=0.049 和 p=0.013)。然而,我们的分析表明,除非伴有 1q 增益,否则非超二倍体 MM 的预后与超二倍体 MM 一样好。因此,SNP 微阵列分析提供了对理解 MM 的发病机制和预后有重要意义的信息。