Data driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer
Data driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer
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DOI：
10.1007/s10549-009-0416-z
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发表时间：
2010-04-01
影响因子：
3.8
通讯作者：
Rody, Achim
中科院分区：
文献类型：
--
作者：
Karn, Thomas;Metzler, Dirk;Rody, Achim
当关注像乳腺癌这样的异质性疾病时，将微阵列数据集合并似乎是增加样本量的合理方法。在文献中已经使用了不同的方法来适应数据集。我们使用来自乳腺癌样本的3，030个Affytron U133A微阵列分析了这些策略的影响。我们目前的数据与众所周知的参数和突出的关键陷阱的生化检测结果的一致性。我们进一步提出了一种方法，直接从数据中推断出截止值，而无需事先了解真实结果。该方法的临界值具有较高的特异性和敏感性。具有双峰分布的标志物如ER、PgR和HER2区分具有不同临床病程的疾病的不同生物学亚型。相比之下，显示连续分布的标志物，如增殖标志物Ki67，而是描述了肿瘤中细胞混合物的组成。
Pooling of microarray datasets seems to be a reasonable approach to increase sample size when a heterogeneous disease like breast cancer is concerned. Different methods for the adaption of datasets have been used in the literature. We have analyzed influences of these strategies using a pool of 3,030 Affymetrix U133A microarrays from breast cancer samples. We present data on the resulting concordance with biochemical assays of well known parameters and highlight critical pitfalls. We further propose a method for the inference of cutoff values directly from the data without prior knowledge of the true result. The cutoffs derived by this method displayed high specificity and sensitivity. Markers with a bimodal distribution like ER, PgR, and HER2 discriminate different biological subtypes of disease with distinct clinical courses. In contrast, markers displaying a continuous distribution like proliferation markers as Ki67 rather describe the composition of the mixture of cells in the tumor.