Cambridge Research Institute, Cancer Research UK, Li Ka Shing Centre, Cambridge CB2 0RE, UK.
Bioinformatics. 2011 Oct 1;27(19):2679-85. doi: 10.1093/bioinformatics/btr450. Epub 2011 Jul 30.
Copy number alterations (CNAs) associated with cancer are known to contribute to genomic instability and gene deregulation. Integrating CNAs with gene expression helps to elucidate the mechanisms by which CNAs act and to identify the transcriptional downstream targets of CNAs. Such analyses can help to sort functional driver events from the many accompanying passenger alterations. However, the way CNAs affect gene expression can vary in different cellular contexts, for example between different subtypes of the same cancer. Thus, it is important to develop computational approaches capable of inferring differential connectivity of regulatory networks in different cellular contexts.
We propose a statistical deregulation model that integrates copy number and expression data of different disease subtypes to jointly model common and differential regulatory relationships. Our model not only identifies CNAs driving gene expression changes, but at the same time also predicts differences in regulation that distinguish one cancer subtype from the other. We implement our model in a penalized regression framework and demonstrate in a simulation study the feasibility and accuracy of our approach. Subsequently, we show that this model can identify both known and novel aspects of cross-talk between the ER and NOTCH pathways in ER-negative-specific deregulations, when compared with ER-positive breast cancer. This flexible model can be applied on other modalities such as methylation or microRNA and expression to disentangle cancer signaling pathways.
The Bioconductor-compliant R package DANCE is available from www.markowetzlab.org/software/
与癌症相关的拷贝数改变（CNAs）已知会导致基因组不稳定性和基因失调。将 CNA 与基因表达相结合有助于阐明 CNA 作用的机制，并确定 CNA 的转录下游靶标。这种分析可以帮助从许多伴随的乘客改变中区分功能驱动事件。然而，CNA 影响基因表达的方式在不同的细胞环境中可能有所不同，例如在同一癌症的不同亚型之间。因此，开发能够推断不同细胞环境中调节网络差异连接的计算方法非常重要。
我们提出了一种统计失调模型，该模型整合了不同疾病亚型的拷贝数和表达数据，以共同建模常见和差异调节关系。我们的模型不仅可以识别导致基因表达变化的 CNA，而且还可以预测区分一种癌症亚型与另一种亚型的调节差异。我们在惩罚回归框架中实现了我们的模型，并在模拟研究中证明了我们方法的可行性和准确性。随后，我们表明，与 ER 阳性乳腺癌相比，该模型可以识别 ER 阴性特异性失调中 ER 和 NOTCH 途径之间交叉对话的已知和新方面。这种灵活的模型可以应用于其他模态，如甲基化或 microRNA 和表达，以解析癌症信号通路。
符合 Bioconductor 标准的 R 包 DANCE 可从 www.markowetzlab.org/software/ 获取。
