Detecting Cancer Gene Networks Characterized by Recurrent Genomic Alterations in a Population

High resolution, system-wide characterizations have demonstrated the capacity to identify genomic regions that undergo genomic aberrations. Such research efforts often aim at associating these regions with disease etiology and outcome. Identifying the corresponding biologic processes that are responsible for disease and its outcome remains challenging. Using novel analytic methods that utilize the structure of biologic networks, we are able to identify the specific networks that are highly significantly, nonrandomly altered by regions of copy number amplification observed in a systems-wide analysis. We demonstrate this method in breast cancer, where the state of a subset of the pathways identified through these regions is shown to be highly associated with disease survival and recurrence.

高分辨率全系统表征（system-wide characterization）技术已证实可精准识别存在基因组畸变（genomic aberration）的基因组区域。此类研究多致力于将上述基因组区域与疾病病因学（disease etiology）及疾病转归（disease outcome）建立关联，但明确驱动疾病发生与转归的对应生物学过程仍极具挑战。本研究借助依托生物网络（biologic network）结构的新型分析方法，通过全系统分析，成功识别出受拷贝数扩增（copy number amplification）区域以非随机方式发生显著改变的特异性生物网络。我们以乳腺癌（breast cancer）为研究模型验证了该方法：通过上述基因组区域所识别的部分通路（pathway）的状态，被证实与患者的疾病生存及复发情况存在显著关联。