Marlene and Stewart Greenebaum Cancer Center, University of Maryland School of Medicine and Veterans Administration Medical Center, Baltimore, MD 21201, USA.
Clin Cancer Res. 2012 Sep 1;18(17):4538-48. doi: 10.1158/1078-0432.CCR-12-0217. Epub 2012 Jun 27.
Currently, two major classification systems segregate diffuse large B-cell lymphoma (DLBCL) into subtypes based on gene expression profiles and provide great insights about the oncogenic mechanisms that may be crucial for lymphomagenesis as well as prognostic information regarding response to current therapies. However, these current classification systems primarily look at expression and not dependency and are thus limited to inductive or probabilistic reasoning when evaluating alternative therapeutic options. The development of a deductive classification system that identifies subtypes in which all patients with a given phenotype require the same oncogenic drivers, and would therefore have a similar response to a rational therapy targeting the essential drivers, would significantly advance the treatment of DLBCL. This review highlights the putative drivers identified as well as the work done to identify potentially dependent populations. These studies integrated genomic analysis and functional screens to provide a rationale for targeted therapies within defined populations. Personalizing treatments by identifying patients with oncogenic dependencies via genotyping and specifically targeting the responsible drivers may constitute a novel approach for the treatment of DLBCL.
目前,有两种主要的分类系统根据基因表达谱将弥漫性大 B 细胞淋巴瘤 (DLBCL) 分为亚型,为肿瘤发生的致癌机制以及当前治疗反应的预后信息提供了重要的见解。然而,这些当前的分类系统主要关注表达而不是依赖性,因此在评估替代治疗方案时,仅限于归纳或概率推理。开发一种演绎分类系统,该系统可以识别出所有具有给定表型的患者都需要相同致癌驱动因素的亚型,因此对针对关键驱动因素的合理治疗具有相似的反应,这将极大地推动 DLBCL 的治疗。这篇综述强调了已确定的假定驱动因素以及为确定潜在依赖性人群所做的工作。这些研究通过整合基因组分析和功能筛选,为在特定人群中进行靶向治疗提供了依据。通过基因分型识别具有致癌依赖性的患者,并特异性靶向负责的驱动因素,从而实现治疗的个体化,可能是治疗 DLBCL 的一种新方法。