Novel Monte Carlo approach quantifies data assemblage utility and reveals power of integrating molecular and clinical information for cancer prognosis.
Novel Monte Carlo approach quantifies data assemblage utility and reveals power of integrating molecular and clinical information for cancer prognosis.
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DOI：
10.1038/srep15563
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发表时间：
2015-10-27
影响因子：
4.6
通讯作者：
Smith VA
中科院分区：
文献类型：
--
作者：
Verleyen W;Langdon SP;Faratian D;Harrison DJ;Smith VA
目前癌症的临床实践基于肿瘤组织学对患者进行分层以确定预后。分子分析被誉为个性化护理的途径，但分子数据通常仍独立于已知的临床信息进行分析。如果使用常规临床和组织病理学数据，则仅添加以改进分子预测，从而对单独提供信息的分子数据施加高负担。在这里，我们开发了一种新的蒙特卡罗分析来评估数据集合的有用性。我们将我们的分析应用于卵巢癌数据集中不同的临床数据和分子数据组合，评估它们区分一年无进展生存期（PFS）和三年总生存期（OS）的能力。我们发现，基于这两种数据类型的考克斯比例风险回归模型比单独使用任何一种模型提供了更大的区分能力。特别是，我们发现，蛋白质组学数据组合，单独是没有信息（p = 0.245的PFS，p = 0.526的OS）成为信息时，结合临床信息（p = 0.022的PFS，p = 0.048的OS）。因此，临床和分子数据的同时分析使得能够利用可能无法从这些数据类型的独立分析中获得的与疾病相关的信息。
Current clinical practice in cancer stratifies patients based on tumour histology to determine prognosis. Molecular profiling has been hailed as the path towards personalised care, but molecular data are still typically analysed independently of known clinical information. Conventional clinical and histopathological data, if used, are added only to improve a molecular prediction, placing a high burden upon molecular data to be informative in isolation. Here, we develop a novel Monte Carlo analysis to evaluate the usefulness of data assemblages. We applied our analysis to varying assemblages of clinical data and molecular data in an ovarian cancer dataset, evaluating their ability to discriminate one-year progression-free survival (PFS) and three-year overall survival (OS). We found that Cox proportional hazard regression models based on both data types together provided greater discriminative ability than either alone. In particular, we show that proteomics data assemblages that alone were uninformative (p = 0.245 for PFS, p = 0.526 for OS) became informative when combined with clinical information (p = 0.022 for PFS, p = 0.048 for OS). Thus, concurrent analysis of clinical and molecular data enables exploitation of prognosis-relevant information that may not be accessible from independent analysis of these data types.