Sommaluan Suchada, Rerkamnuaychoke Budsaba, Pauwilai Teeraya, Chancharunee Suporn, Onsod Preeyaporn, Pornsarayuth Pitichai, Chareonsirisuthigul Takol, Tammachote Rachaneekorn, Siriboonpiputtana Teerapong
Department of Pathology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand. Email:
Asian Pac J Cancer Prev. 2017 Nov 26;18(11):3135-3142. doi: 10.22034/APJCP.2017.18.11.3135.
Multiple myeloma (MM) is a hematological malignancy characterized by abnormal accumulation of clonal plasma cells in the bone marrow. Recently, multiplex ligation-dependent probe amplification (MLPA) has emerged as an effective and robust method for detection of common genetic alterations in MM patients. Here, we aimed to confirm MLPA utility for this purpose and furthermore to test the feasibility of a combination of karyotyping, interphase fluorescence in situ hybridization (iFISH) and MLPA methods for diagnosis, prognostic assessment and risk stratification of MM. Thirty-five genomic DNA samples isolated from CD138-enriched plasma cells from bone marrow of MM patients were analyzed using the MLPA method. We found that amp (1q) was the most frequent genetic alteration (48.6%) in the tested samples, followed by del (1p) and del (13q) (34.3%). Moreover, concordant results between sensitivity and specificity of iFISH and MLPA for the detection of del (13q) (p-value >0.05) and del (17p) (p-value >0.05) were obtained. In summary, we could provide evidence of MLPA assay utility for the detection of common genetic alterations in MM. The combination of karyotyping, iFISH, and MLPA proved very helpful for clinical risk stratification.
多发性骨髓瘤(MM)是一种血液系统恶性肿瘤,其特征是骨髓中克隆性浆细胞异常积聚。最近,多重连接依赖探针扩增(MLPA)已成为检测MM患者常见基因改变的一种有效且可靠的方法。在此,我们旨在证实MLPA在此方面的实用性,并进一步测试将核型分析、间期荧光原位杂交(iFISH)和MLPA方法联合用于MM诊断、预后评估和风险分层的可行性。使用MLPA方法分析了从MM患者骨髓中富集CD138的浆细胞中分离出的35份基因组DNA样本。我们发现,在所检测的样本中,1q扩增是最常见的基因改变(48.6%),其次是1p缺失和13q缺失(34.3%)。此外,在检测13q缺失(p值>0.05)和17p缺失(p值>0.05)时,iFISH和MLPA的敏感性和特异性结果具有一致性。总之,我们能够提供证据证明MLPA检测在MM常见基因改变检测中的实用性。核型分析、iFISH和MLPA的联合应用对临床风险分层非常有帮助。