Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver General Hospital, Vancouver, BC, Canada.
University College London Cancer Institute, Department of Pathology, UK.
J Pathol. 2015 Aug;236(4):397-402. doi: 10.1002/path.4542. Epub 2015 May 22.
Cancer management decisions are currently informed by cancer type and clinical stage, as well as age, health condition, and individual patient needs. Cancer is a genetic disease and recent genomic studies have revealed the genomic landscape of multiple tumour types. This has led to readily available catalogues of genomic features for many cancers and efforts to incorporate such information into treatment decisions. From this has evolved the concept that mutation-based taxonomies may supersede the current cell of origin-based categorization of neoplasia. Unfortunately, genomic features as clinically actionable information may not be directly transferable between tumour types, due to the importance of cellular and genomic context. However, we believe that high-level views of different genomic landscapes could broadly inform research study design and treatment strategies. Herein, we use ovarian and bone cancer as examples to propose a genomic complexity-based categorization for cancer. In addition to informing clinical study design, we describe how this categorization scheme could impact (i) improvement of accuracy of histological diagnoses, (ii) stratification of patients for targeted therapies, (iii) research study design, and (iv) personalized treatment strategies.
癌症的治疗决策目前取决于癌症的类型和临床分期，以及患者的年龄、健康状况和个体需求。癌症是一种遗传性疾病，最近的基因组研究揭示了多种肿瘤类型的基因组图谱。这导致了许多癌症的基因组特征的目录，并努力将这些信息纳入治疗决策。由此产生了这样一种概念，即基于突变的分类可能会取代目前基于肿瘤起源细胞的肿瘤分类。不幸的是，由于细胞和基因组背景的重要性，作为临床可操作信息的基因组特征可能不能直接在肿瘤类型之间转移。然而，我们相信，不同基因组图谱的高级视图可以广泛地为研究设计和治疗策略提供信息。在这里，我们以卵巢癌和骨癌为例，提出了一种基于基因组复杂性的癌症分类方法。除了为临床研究设计提供信息外，我们还描述了这种分类方案如何影响（i）提高组织学诊断的准确性，（ii）针对靶向治疗的患者分层，（iii）研究设计，以及（iv）个性化治疗策略。
