Department of Molecular and Experimental Medicine, Avera Cancer Institute, Sioux Falls, SD, USA; Department of Internal Medicine, SSOM, University of South Dakota, SD, USA.
Department of Molecular and Experimental Medicine, Avera Cancer Institute, Sioux Falls, SD, USA.
Cancer Treat Rev. 2017 Apr;55:136-149. doi: 10.1016/j.ctrv.2017.03.002. Epub 2017 Mar 16.
As a genetic disease [1] cancer dysregulates key oncogenic pathways that influence cell growth, proliferation, survival, angiogenesis, and metastasis. Among the major determinants that enable cancer cells to acquire malignant traits are genomic diversity and instability. In the post human genome project era, cancer-specific genomic maps are redesigning tumor taxonomy. The treatment modalities, as well as the overall management of cancer as a disease in today's clinic, have started depending heavily on the molecular pathology of the individual tumor(s) in addition to the fundamental classification of cancers by histopathology. The enrichment tumor taxonomy by genomic morphology has also opened up the possibilities for genomics-driven drug development. The success of a cancer drug today is fundamentally based on the success in identifying target genes that control tumorigenic pathways. One primary goal of precision cancer medicine is to make clinical decisions based on genomic/proteomic data, which can identify a target or targets for therapy, and subsequent inevitable development of therapeutic resistance to the drug. The ability to exploit tumor genetic information for its full clinical potential has only recently become evident. Over the last decade, the convergence of discovery, technology, and therapeutic development has created an unparalleled opportunity to test the hypothesis that systematic knowledge of genomic and proteomic information from individual tumor(s) may significantly improve clinical outcomes for many patients with unmanageable tumor burden. This review presents the signaling logic behind the ground rules for the rational approach to the genomics-driven precision medicine.
作为一种遗传性疾病,癌症会使关键的致癌途径失调,从而影响细胞的生长、增殖、存活、血管生成和转移。使癌细胞获得恶性特征的主要决定因素之一是基因组的多样性和不稳定性。在后人类基因组计划时代,癌症特异性基因组图谱正在重新设计肿瘤分类学。在当今的临床实践中,除了通过组织病理学对癌症进行基本分类外,癌症的治疗方式以及作为一种疾病的整体管理,已经开始严重依赖于个体肿瘤的分子病理学。通过基因组形态学丰富肿瘤分类学也为基于基因组学的药物开发开辟了可能性。癌症药物的成功今天在很大程度上是基于成功识别控制肿瘤发生途径的靶基因。精准癌症医学的一个主要目标是基于基因组/蛋白质组数据做出临床决策,这些数据可以识别治疗的靶点,以及随后不可避免的药物治疗耐药性。最近才明显意识到利用肿瘤遗传信息的全部临床潜力的能力。在过去的十年中,发现、技术和治疗开发的融合为测试以下假设创造了前所未有的机会:即系统地了解个体肿瘤的基因组和蛋白质组信息可能会显著改善许多肿瘤负担不可管理的患者的临床结局。这篇综述介绍了基因组驱动的精准医学合理方法背后的信号逻辑。