Breast Cancer Functional Genomics Laboratory, Department of Oncology University of Cambridge, Cancer Research UK Cambridge Research Institute, Li Ka-Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.
BMC Cancer. 2010 Nov 4;10:604. doi: 10.1186/1471-2407-10-604.
Elucidating the activation pattern of molecular pathways across a given tumour type is a key challenge necessary for understanding the heterogeneity in clinical response and for developing novel more effective therapies. Gene expression signatures of molecular pathway activation derived from perturbation experiments in model systems as well as structural models of molecular interactions ("model signatures") constitute an important resource for estimating corresponding activation levels in tumours. However, relatively few strategies for estimating pathway activity from such model signatures exist and only few studies have used activation patterns of pathways to refine molecular classifications of cancer.
Here we propose a novel network-based method for estimating pathway activation in tumours from model signatures. We find that although the pathway networks inferred from cancer expression data are highly consistent with the prior information contained in the model signatures, that they also exhibit a highly modular structure and that estimation of pathway activity is dependent on this modular structure. We apply our methodology to a panel of 438 estrogen receptor negative (ER-) and 785 estrogen receptor positive (ER+) breast cancers to infer activation patterns of important cancer related molecular pathways.
We show that in ER negative basal and HER2+ breast cancer, gene expression modules reflecting T-cell helper-1 (Th1) and T-cell helper-2 (Th2) mediated immune responses play antagonistic roles as major risk factors for distant metastasis. Using Boolean interaction Cox-regression models to identify non-linear pathway combinations associated with clinical outcome, we show that simultaneous high activation of Th1 and low activation of a TGF-beta pathway module defines a subtype of particularly good prognosis and that this classification provides a better prognostic model than those based on the individual pathways. In ER+ breast cancer, we find that simultaneous high MYC and RAS activity confers significantly worse prognosis than either high MYC or high RAS activity alone. We further validate these novel prognostic classifications in independent sets of 173 ER- and 567 ER+ breast cancers.
We have proposed a novel method for pathway activity estimation in tumours and have shown that pathway modules antagonize or synergize to delineate novel prognostic subtypes. Specifically, our results suggest that simultaneous modulation of T-helper differentiation and TGF-beta pathways may improve clinical outcome of hormone insensitive breast cancers over treatments that target only one of these pathways.
阐明给定肿瘤类型中分子途径的激活模式是理解临床反应异质性和开发新型更有效的治疗方法的关键挑战。来自模型系统中扰动实验的分子途径激活的基因表达特征以及分子相互作用的结构模型（“模型特征”）构成了估计肿瘤中相应激活水平的重要资源。然而，从这些模型特征估计途径活性的策略相对较少，并且只有少数研究使用途径的激活模式来完善癌症的分子分类。
在这里，我们提出了一种从模型特征估计肿瘤中途径激活的新的网络方法。我们发现，尽管从癌症表达数据推断出的途径网络与模型特征中包含的先验信息高度一致，但它们也表现出高度模块化的结构，并且途径活性的估计依赖于这种模块化结构。我们将我们的方法应用于 438 例雌激素受体阴性（ER-）和 785 例雌激素受体阳性（ER+）乳腺癌的面板中，以推断重要癌症相关分子途径的激活模式。
我们表明，在 ER 阴性基底和 HER2+乳腺癌中，反映 T 细胞辅助 1（Th1）和 T 细胞辅助 2（Th2）介导的免疫反应的基因表达模块作为远处转移的主要危险因素起着拮抗作用。使用布尔相互作用 Cox 回归模型来识别与临床结果相关的非线性途径组合，我们表明，Th1 的同时高激活和 TGF-β途径模块的低激活定义了一种特别预后良好的亚型，并且这种分类提供了比基于个体途径更好的预后模型。在 ER+乳腺癌中，我们发现同时高 MYC 和 RAS 活性比单独高 MYC 或高 RAS 活性赋予显著更差的预后。我们进一步在 173 例 ER-和 567 例 ER+乳腺癌的独立组中验证了这些新的预后分类。
我们提出了一种用于肿瘤中途径活性估计的新方法，并表明途径模块拮抗或协同作用以描绘新的预后亚型。具体而言，我们的结果表明，同时调节 T 辅助分化和 TGF-β途径可能会改善激素不敏感乳腺癌的临床结果，而不是针对这些途径中的一种进行治疗。
