Fall Deanna J, Stessman Holly, Patel Sagar S, Sachs Zohar, Van Ness Brian G, Baughn Linda B, Linden Michael A
1. Gillette Children's Specialty Healthcare, St. Paul, MN;
2. Department of Genomic Sciences, University of Washington, Seattle, WA;
J Cancer. 2014 Sep 21;5(9):720-7. doi: 10.7150/jca.9864. eCollection 2014.
Multiple myeloma (MM) is an incurable malignant neoplasm hallmarked by a clonal expansion of plasma cells, the presence of a monoclonal protein in the serum and/or urine (M-spike), lytic bone lesions, and end organ damage. Clinical outcomes for patients with MM have improved greatly over the last decade as a result of the re-purposing of compounds such as thalidomide derivatives, as well as the development of novel chemotherapeutic agents including first and second generation proteasome inhibitors, bortezomib (Bz) and carfilzomib. Unfortunately, despite these improvements, the majority of patients relapse following treatment. While Bz, one of the most commonly used proteasome inhibitors, has been successfully incorporated into clinical practice, some MM patients have de novo resistance to Bz, and the majority of the remainder subsequently develop drug resistance following treatment. A significant gap in clinical care is the lack of a reliable clinical test that would predict which MM patients have or will subsequently develop Bz resistance. Thus, as Bz resistance remains a significant challenge, research efforts are needed to identify novel biomarkers of early Bz resistance, particularly when an early therapeutic intervention can be initiated. Recent advances in MM research indicate that genomic data can be extracted to identify novel biomarkers that can be utilized to select more effective, personalized treatment protocols for individual patients. Computationally integrating large patient databases with data from whole transcriptome profiling and laboratory-based models can potentially revolutionize our understanding of MM disease mechanisms. This systems-wide approach can provide rational therapeutic targets and novel biomarkers of risk and treatment response. In this review, we discuss the use of high-content datasets (predominantly gene expression profiling) to identify novel biomarkers of treatment response and resistance to Bz in MM.
多发性骨髓瘤（MM）是一种无法治愈的恶性肿瘤，其特征为浆细胞的克隆性增殖、血清和/或尿液中存在单克隆蛋白（M蛋白峰）、溶骨性骨病变以及终末器官损伤。在过去十年中，由于沙利度胺衍生物等化合物的重新利用以及包括第一代和第二代蛋白酶体抑制剂硼替佐米（Bz）和卡非佐米在内的新型化疗药物的开发，MM患者的临床结局有了显著改善。不幸的是，尽管有这些改善，但大多数患者在治疗后仍会复发。虽然最常用的蛋白酶体抑制剂之一Bz已成功应用于临床实践，但一些MM患者对Bz存在原发性耐药，其余大多数患者在治疗后随后会产生耐药性。临床护理中一个重大差距是缺乏可靠的临床检测方法来预测哪些MM患者已经或随后会产生Bz耐药性。因此，由于Bz耐药性仍然是一个重大挑战，需要开展研究工作来确定早期Bz耐药的新型生物标志物，特别是在可以启动早期治疗干预时。MM研究的最新进展表明，可以提取基因组数据以识别新型生物标志物，这些生物标志物可用于为个体患者选择更有效、个性化的治疗方案。通过计算将大型患者数据库与来自全转录组分析和基于实验室模型的数据整合起来，有可能彻底改变我们对MM疾病机制的理解。这种全系统方法可以提供合理的治疗靶点以及风险和治疗反应的新型生物标志物。在本综述中，我们讨论了使用高内涵数据集（主要是基因表达谱分析）来识别MM中治疗反应和对Bz耐药的新型生物标志物。
