Cancer Research UK, London Research Institute, London, UK.
Lancet Oncol. 2010 Apr;11(4):339-49. doi: 10.1016/S1470-2045(10)70008-5. Epub 2010 Feb 22.
Microarray expression profiling classifies breast cancer into five molecular subtypes: luminal A, luminal B, basal-like, HER2, and normal breast-like. Three microarray-based single sample predictors (SSPs) have been used to define molecular classification of individual samples. We aimed to establish agreement between these SSPs for identification of breast cancer molecular subtypes.
Previously described microarray-based SSPs were applied to one in-house (n=53) and three publicly available (n=779) breast cancer datasets. Agreement was analysed between SSPs for the whole classification system and for the five molecular subtypes individually in each cohort.
Fair-to-substantial agreement between every pair of SSPs in each cohort was recorded (kappa=0.238-0.740). Of the five molecular subtypes, only basal-like cancers consistently showed almost-perfect agreement (kappa>0.812). The proportion of cases classified as basal-like in each cohort was consistent irrespective of the SSP used; however, the proportion of each remaining molecular subtype varied substantially. Assignment of individual cases to luminal A, luminal B, HER2, and normal breast-like subtypes was dependent on the SSP used. The significance of associations with outcome of each molecular subtype, other than basal-like and luminal A, varied depending on SSP used. However, different SSPs produced broadly similar survival curves.
Although every SSP identifies molecular subtypes with similar survival, they do not reliably assign the same patients to the same molecular subtypes. For molecular subtype classification to be incorporated into routine clinical practice and treatment decision making, stringent standardisation of methodologies and definitions for identification of breast cancer molecular subtypes is needed.
Breakthrough Breast Cancer, Cancer Research UK.
微阵列表达谱将乳腺癌分为五个分子亚型：luminal A、luminal B、基底样、HER2 和正常乳腺样。已经使用了三种基于微阵列的单个样本预测器（SSP）来定义个体样本的分子分类。我们旨在确定这些 SSP 之间在识别乳腺癌分子亚型方面的一致性。
应用先前描述的基于微阵列的 SSP 对一个内部（n=53）和三个公开可用的（n=779）乳腺癌数据集进行分析。在每个队列中，分析了 SSP 之间整个分类系统和五个分子亚型的一致性。
在每个队列的每个 SSP 之间都记录了公平到实质性的一致性（kappa=0.238-0.740）。在五个分子亚型中，只有基底样癌症始终表现出几乎完美的一致性（kappa>0.812）。每个队列中基底样病例的比例不变，而其余分子亚型的比例则有很大差异。个体病例的分配为 luminal A、luminal B、HER2 和正常乳腺样亚型取决于使用的 SSP。除了基底样和 luminal A 之外，每个分子亚型与结局的关联的显著性取决于使用的 SSP。然而，不同的 SSP 产生了大致相似的生存曲线。
虽然每个 SSP 都可以识别具有相似生存的分子亚型，但它们并不能可靠地将相同的患者分配到相同的分子亚型中。为了将分子亚型分类纳入常规临床实践和治疗决策，需要对乳腺癌分子亚型的鉴定方法和定义进行严格的标准化。
突破乳腺癌，英国癌症研究中心。
