For breast cancer, clinically important subtypes are well characterized at the molecular level in terms of gene expression profiles. In addition, signaling pathways in breast cancer have been extensively studied as therapeutic targets due to their roles in tumor growth and metastasis. However, it is challenging to put signaling pathways and gene expression profiles together to characterize biological mechanisms of breast cancer subtypes since many signaling events result from post-translational modifications, rather than gene expression differences. We designed a logic-based computational framework to explain the differences in gene expression profiles among breast cancer subtypes using Pathway Logic and transcriptional network information. Pathway Logic is a rewriting-logic-based formal system for modeling biological pathways including post-translational modifications. Our method demonstrated its utility by constructing subtype-specific path from key receptors (TNFR, TGFBR1 and EGFR) to key transcription factor (TF) regulators (RELA, ATF2, SMAD3 and ELK1) and identifying potential association between pathways via TFs in basal-specific paths, which could provide a novel insight on aggressive breast cancer subtypes. Codes and results are available at http://epigenomics.snu.ac.kr/PL/.

中文翻译：

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基于逻辑的基因表达数据分析预测 TNF、TGFB1 和 EGF 通路在基底样乳腺癌中的关联

对于乳腺癌，临床上重要的亚型在基因表达谱方面在分子水平上得到了很好的表征。此外，乳腺癌中的信号通路因其在肿瘤生长和转移中的作用而被广泛研究为治疗靶点。然而，将信号通路和基因表达谱放在一起来表征乳腺癌亚型的生物学机制具有挑战性，因为许多信号事件是由翻译后修饰引起的，而不是基因表达差异。我们设计了一个基于逻辑的计算框架，以使用通路逻辑和转录网络信息来解释乳腺癌亚型之间基因表达谱的差异。Pathway Logic 是一种基于重写逻辑的形式系统，用于对包括翻译后修饰在内的生物途径进行建模。我们的方法通过构建从关键受体（TNFR、TGFBR1 和 EGFR）到关键转录因子 (TF) 调节器（RELA、ATF2、SMAD3 和 ELK1）的亚型特异性路径并通过基础特异性中的 TF 确定通路之间的潜在关联，证明了其效用。路径，这可以为侵袭性乳腺癌亚型提供新的见解。代码和结果可在 http://epigenomics.snu.ac.kr/PL/ 获得。这可以为侵袭性乳腺癌亚型提供新的见解。代码和结果可在 http://epigenomics.snu.ac.kr/PL/ 获得。这可以为侵袭性乳腺癌亚型提供新的见解。代码和结果可在 http://epigenomics.snu.ac.kr/PL/ 获得。