Lymphoid gene expression as a predictor of risk of secondary brain tumors
Lymphoid gene expression as a predictor of risk of secondary brain tumors
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DOI：
10.1002/gcc.20121
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发表时间：
2005-02-01
影响因子：
3.7
通讯作者：
Relling, MV
中科院分区：
文献类型：
--
作者：
Edick, MJ;Cheng, C;Relling, MV
基因表达谱是组织特异性的，但也可以反映跨组织类型的种系驱动的表达模式。在此之前，我们使用靶向药理学方法，确定了与急性淋巴细胞白血病（ALL）儿童放疗和化疗诱导的继发性脑肿瘤风险相关的单个基因（巯基嘌呤甲基转移酶）的生殖系多态性。为了确定其他候选遗传风险因素，在接受相同治疗的患者中，我们比较了那些发生放射相关脑肿瘤的患者（n = 9）与那些没有发生放射相关脑肿瘤的患者（n = 33）的诊断性ALL原始细胞的基因表达谱。加权秩回归用于识别与脑肿瘤的时间依赖性发展相关的33个探针集; k均值聚类（k = 2）识别出脑肿瘤累积发病率显著不同的2组（P = 0.012）。使用排列分析来估计偶然获得2个这样的簇的概率（P = 0.18）。使用线性判别分析（结果的时间无关分类）来鉴定70个探针集，其表达在2组患者之间区分。使用排列分析（n = 1，000）来估计偶然选择这些探针组的概率（P = 0.055）。五个探针组之间的时间无关性和时间依赖性的方法是共同的。区别基因涉及神经生长（FGFR 1）和核运输（HNRPL、KPNB 1）。这些数据表明，从可访问的组织的基因表达谱可能会确定涉及治疗相关的恶性肿瘤在不相关的组织的目标。(C)2004 Wiley-Liss，Inc.
Gene expression profiles are tissue-specific but may also reflect germ-line-driven expression patterns across tissue types. Previously, using a targeted pharmacologic approach, we identified germ-line polymorphisms in a single gene (thiopurine methyltransferase) associated with the risk of irradiation- and chemotherapy-induced secondary brain tumors in children with acute lymphoblastic leukemia (ALL). To identify additional candidate genetic risk factors, in identically treated patients, we compared the gene expression profiles of diagnostic ALL blasts of those who did develop irradiation-associated brain tumors (n = 9) with the profiles from those who did not (n = 33). Weighted rank regression was used to identify 33 probe sets associated with the time-dependent development of brain tumors; k-means clustering (k = 2) identified 2 groups that differed significantly in cumulative incidence of brain tumors (P = 0.012). Permutation analysis was used to estimate the probability (P = 0.18) of obtaining 2 such clusters by chance. Linear discriminant analysis (time-independent categorization of outcome) was used to identify 70 probe sets whose expression differentiated between the 2 groups of patients. Permutation analyses (n = 1,000) was used to estimate the probability of selecting these probe sets by chance (P = 0.055). Five probe sets were in common between the time-independent and time-dependent methods. The distinguishing genes are involved in neural growth (FGFR 1) and in nuclear trafficking (HNRPL, KPNB1). These data suggest that gene expression profiling from accessible tissues may identify targets involved in therapy-related malignancies in unrelated tissues. (C) 2004 Wiley-Liss, Inc.