Gene Expression Profiles of Tumor Biology Provide a Novel Approach to Prognosis and May Guide the Selection of Therapeutic Targets in Multiple Myeloma
Gene Expression Profiles of Tumor Biology Provide a Novel Approach to Prognosis and May Guide the Selection of Therapeutic Targets in Multiple Myeloma
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DOI：
10.1200/jco.2008.19.1916
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发表时间：
2009-09-01
影响因子：
45.3
通讯作者：
Potti, Anil
中科院分区：
文献类型：
--
作者：
Anguiano, Ariel;Tuchman, Sascha A.;Potti, Anil
目的意义不明的单克隆丙种球蛋白病（MGUS）和多发性骨髓瘤（MM）是一种异质性疾病，分子缺陷不完全清楚，临床特征多变。我们用微阵列数据进行基因表达谱分析（GEP），以更好地剖析分子表型、对特定化疗剂的敏感性以及这些疾病的复发。我们应用了反映致癌途径和肿瘤微环境失调的基因表达特征，以突出NPC发生的分子变化，转移到MM，创建高风险MGUS基因签名，并将国际分期系统（ISS）分期分组为更准确的患者群。最后，我们使用基因签名来预测的敏感性，以传统的细胞毒性化疗确定群集patients.ResultsMyc上调和增加染色体不稳定性（CIN）的特点是从NPC的演变RMM（P < .0001）。MGUS的研究表明，一些样本与RMM共享生物学特征，这构成了高风险MGUS签名的基础。关于MM，我们根据肿瘤生物学的共同特征将ISS分期细分为聚类。这些集群分化的预测预后和化疗敏感性的基础上（例如，在ISS阶段I，一个集群的特点是增加CIN，环磷酰胺耐药，预后不良）。GEP还可以完善MGUS和MM当前的预后和治疗模型。
PurposeMonoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma (MM) comprise heterogeneous disorders with incompletely understood molecular defects and variable clinical features. We performed gene expression profiling (GEP) with microarray data to better dissect the molecular phenotypes, sensitivity to particular chemotherapeutic agents, and prognoses of these diseases.MethodsUsing gene expression and clinical data from 877 patients ranging from normal plasma cells (NPC) to relapsed MM (RMM), we applied gene expression signatures reflecting deregulation of oncogenic pathways and tumor microenvironment to highlight molecular changes that occur as NPCs transition to MM, create a high-risk MGUS gene signature, and subgroup International Staging System (ISS) stages into more prognostically accurate clusters of patients. Lastly, we used gene signatures to predict sensitivity to conventional cytotoxic chemotherapies among identified clusters of patients.ResultsMyc upregulation and increasing chromosomal instability (CIN) characterized the evolution from NPC to RMM (P < .0001 for both). Studies of MGUS revealed that some samples shared biologic features with RMM, which comprised the basis for a high-risk MGUS signature. Regarding MM, we subclassified ISS stages into clusters based on shared features of tumor biology. These clusters differentiated themselves based on predictions for prognosis and chemotherapy sensitivity (eg, in ISS stage I, one cluster was characterized by increased CIN, cyclophosphamide resistance, and a poor prognosis).ConclusionGEP provides insight into the molecular defects underlying plasma cell dyscrasias that may explain their clinical heterogeneity. GEP also may also refine current prognostic and therapeutic models for MGUS and MM.