Shaughnessy John D, Zhan Fenghuang, Burington Bart E, Huang Yongsheng, Colla Simona, Hanamura Ichiro, Stewart James P, Kordsmeier Bob, Randolph Christopher, Williams David R, Xiao Yan, Xu Hongwei, Epstein Joshua, Anaissie Elias, Krishna Somashekar G, Cottler-Fox Michele, Hollmig Klaus, Mohiuddin Abid, Pineda-Roman Mauricio, Tricot Guido, van Rhee Frits, Sawyer Jeffrey, Alsayed Yazan, Walker Ronald, Zangari Maurizio, Crowley John, Barlogie Bart
Donna D. and Donald M. Lambert Laboratory of Myeloma Genetics at the Myeloma Institute for Research and Therapy, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
Blood. 2007 Mar 15;109(6):2276-84. doi: 10.1182/blood-2006-07-038430. Epub 2006 Nov 14.
To molecularly define high-risk disease, we performed microarray analysis on tumor cells from 532 newly diagnosed patients with multiple myeloma (MM) treated on 2 separate protocols. Using log-rank tests of expression quartiles, 70 genes, 30% mapping to chromosome 1 (P < .001), were linked to early disease-related death. Importantly, most up-regulated genes mapped to chromosome 1q, and down-regulated genes mapped to chromosome 1p. The ratio of mean expression levels of up-regulated to down-regulated genes defined a high-risk score present in 13% of patients with shorter durations of complete remission, event-free survival, and overall survival (training set: hazard ratio [HR], 5.16; P < .001; test cohort: HR, 4.75; P < .001). The high-risk score also was an independent predictor of outcome endpoints in multivariate analysis (P < .001) that included the International Staging System and high-risk translocations. In a comparison of paired baseline and relapse samples, the high-risk score frequency rose to 76% at relapse and predicted short postrelapse survival (P < .05). Multivariate discriminant analysis revealed that a 17-gene subset could predict outcome as well as the 70-gene model. Our data suggest that altered transcriptional regulation of genes mapping to chromosome 1 may contribute to disease progression, and that expression profiling can be used to identify high-risk disease and guide therapeutic interventions.
为了从分子层面定义高危疾病,我们对532例新诊断的多发性骨髓瘤(MM)患者的肿瘤细胞进行了微阵列分析,这些患者按照2种不同方案接受治疗。通过对表达四分位数进行对数秩检验,70个基因(30%定位于1号染色体,P <.001)与早期疾病相关死亡有关。重要的是,大多数上调基因定位于1q染色体,而下调基因定位于1p染色体。上调基因与下调基因的平均表达水平之比定义了一个高危评分,13%的患者存在该评分,这些患者的完全缓解期、无事件生存期和总生存期较短(训练集:风险比[HR],5.16;P <.001;测试队列:HR,4.75;P <.001)。在多变量分析(P <.001)中,高危评分也是包括国际分期系统和高危易位在内的结局终点的独立预测因子。在配对的基线和复发样本比较中,高危评分频率在复发时升至76%,并预测复发后生存期较短(P <.05)。多变量判别分析显示,一个17基因子集与70基因模型一样能够预测结局。我们的数据表明,定位于1号染色体的基因转录调控改变可能导致疾病进展,并且表达谱分析可用于识别高危疾病并指导治疗干预。