Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer.
Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer.
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DOI：
10.1186/1471-2105-12-s2-s3
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发表时间：
2011-03-29
影响因子：
3
通讯作者：
Kim S
中科院分区：
文献类型：
--
作者：
Nasser S;Cunliffe HE;Black MA;Kim S
乳腺癌在分子改变和细胞组成方面是高度异质性的疾病，使得治疗和临床结果不可预测。这种多样性在开发临床上可靠的预后预测肿瘤分类方面产生了重大挑战。本文描述了一种无监督的上下文分析，用于从从公开可用的基因表达数据中获得的1，614个样本中推断上下文特定的基因调控网络，这是之前发表的方法的扩展。我们使用上下文特异性基因调控网络将肿瘤分类为临床相关亚组，并为先前已知的内源性肿瘤的更精细亚组提供候选者，重点是基底样肿瘤。我们在关键背景下对途径富集的分析提供了对所识别的乳腺癌亚型的生物学机制的深入了解。使用背景特定的基因调控网络从异质性乳腺癌数据集中识别生物学背景，能够识别之前报道的内在亚型内亚组的基因组驱动因素。这些亚组（背景）支持内在亚型的临床相关特征，与内在亚型相比，与生存差异增加相关。我们相信，我们的计算方法导致了新的合理化假设的产生，以解释乳腺癌子背景下的疾病进展机制，一旦验证，就可以在治疗上加以利用。
Breast cancer is a highly heterogeneous disease with respect to molecular alterations and cellular composition making therapeutic and clinical outcome unpredictable. This diversity creates a significant challenge in developing tumor classifications that are clinically reliable with respect to prognosis prediction. This paper describes an unsupervised context analysis to infer context-specific gene regulatory networks from 1,614 samples obtained from publicly available gene expression data, an extension of a previously published methodology. We use the context-specific gene regulatory networks to classify the tumors into clinically relevant subgroups, and provide candidates for a finer sub-grouping of the previously known intrinsic tumors with a focus on Basal-like tumors. Our analysis of pathway enrichment in the key contexts provides an insight into the biological mechanism underlying the identified subtypes of breast cancer. The use of context-specific gene regulatory networks to identify biological contexts from heterogenous breast cancer data set was able to identify genomic drivers for subgroups within the previously reported intrinsic subtypes. These subgroups (contexts) uphold the clinical relevant features for the intrinsic subtypes and were associated with increased survival differences compared to the intrinsic subtypes. We believe our computational approach led to the generation of novel rationalized hypotheses to explain mechanisms of disease progression within sub-contexts of breast cancer that could be therapeutically exploited once validated.