Kryukov Fedor, Nemec Pavel, Radova Lenka, Kryukova Elena, Okubote Samuel, Minarik Jiri, Stefanikova Zdena, Pour Ludek, Hajek Roman
Department of Haematooncology, Faculty of Medicine, University of Ostrava, Dvořákova 7, 702 00, Ostrava, Czech Republic.
Department of Haematooncology, University Hospital Ostrava, 17.listopadu 1790, 708 52, Ostrava-Poruba, Czech Republic.
J Transl Med. 2016 May 28;14(1):150. doi: 10.1186/s12967-016-0906-9.
The genome of multiple myeloma (MM) cells is extremely unstable, characterized by a complex combination of structure and numerical abnormalities. It seems that there are several "myeloma subgroups" which differ in expression profile, clinical manifestations, prognoses and treatment response. In our previous work, the list of 35 candidate genes with a known role in carcinogenesis and associated with centrosome structure/function was used as a display of molecular heterogeneity with an impact in myeloma pathogenesis. The current study was devoted to establish a risk stratification model based on the aforementioned candidate genes.
A total of 151 patients were included in this study. CD138+ cells were separated by magnetic-activated cell sorting (MACS). Gene expression profiling (GEP) and Interphase FISH with cytoplasmic immunoglobulin light chain staining (cIg FISH) were performed on plasma cells (PCs). All statistical analyses were performed using freeware R and its additional packages. Training and validation cohort includes 73 and 78 patients, respectively.
We have finally established a model that includes 12 selected genes (centrosome associated gene pattern, CAGP) which appears to be an independent prognostic factor for MM stratification. We have shown that the new CAGP model can sub-stratify prognosis in patients without TP53 loss as well as in IMWG high risk patients' group.
We assume that newly established risk stratification model complements the current prognostic panel used in multiple myeloma and refines the classification of patients in relation to the disease risks. This approach can be used independently as well as in combination with other factors.
多发性骨髓瘤(MM)细胞的基因组极其不稳定,其特征是结构和数量异常的复杂组合。似乎存在几个“骨髓瘤亚组”,它们在表达谱、临床表现、预后和治疗反应方面存在差异。在我们之前的工作中,一份包含35个在致癌过程中具有已知作用且与中心体结构/功能相关的候选基因列表,被用作显示对骨髓瘤发病机制有影响的分子异质性的指标。当前的研究致力于基于上述候选基因建立一个风险分层模型。
本研究共纳入151例患者。通过磁珠分选法(MACS)分离CD138+细胞。对浆细胞(PCs)进行基因表达谱分析(GEP)和间期荧光原位杂交(FISH)并结合细胞质免疫球蛋白轻链染色(cIg FISH)。所有统计分析均使用免费软件R及其附加包进行。训练队列和验证队列分别包括73例和78例患者。
我们最终建立了一个包含12个选定基因的模型(中心体相关基因模式,CAGP),该模型似乎是MM分层的一个独立预后因素。我们已经表明,新的CAGP模型可以对无TP53缺失的患者以及国际骨髓瘤工作组(IMWG)高危患者组的预后进行亚分层。
我们认为新建立的风险分层模型补充了目前用于多发性骨髓瘤的预后指标,并根据疾病风险完善了患者分类。这种方法可以单独使用,也可以与其他因素结合使用。