Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer
Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer
复制标题
DOI：
10.1186/s13073-020-00745-2
复制
发表时间：
2020-05-29
期刊：
影响因子：
12.3
通讯作者：
Przytycka, Teresa M.
中科院分区：
文献类型：
--
作者：
Kim, Yoo-Ah;Wojtowicz, Damian;Przytycka, Teresa M.
癌症突变的研究通常集中在确定赋予癌细胞生长优势的癌症驱动突变。然而，癌症基因组积累了大量由各种内源性和外源性原因引起的体细胞突变，包括正常的DNA损伤和修复过程或与癌症相关的DNA维持机制畸变，以及致癌暴露引发的突变。不同的诱变过程通常会产生称为突变特征的特征突变模式。确定形成癌症基因组的突变特征的致突变过程是理解肿瘤发生的重要一步。方法为了研究与突变特征相关的遗传畸变，我们采用了基于网络的方法，将突变特征视为癌症表型。具体来说，我们的分析旨在回答以下两个互补的问题：(i)哪些功能途径的基因表达活动与突变特征的强度相关，以及（ii）是否存在遗传改变可能导致特定突变特征的途径？为了识别突变路径，我们采用了一种基于整数线性规划的优化方法。通过分析乳腺癌数据集，我们确定了与表达和突变水平上的突变特征相关的途径。我们的分析捕获了apobecc相关特征和两个时钟样特征病因学上的重要差异。特别是，它揭示了聚集和分散的APOBEC突变可能是由不同的诱变过程引起的。此外，我们的分析阐明了两种与年龄相关的特征之间的差异——其中一种特征与细胞周期基因的表达相关，而另一种特征没有这种相关性，但显示出与暴露于环境/外部过程一致的模式。这项工作首次研究了突变特征和失调通路的网络水平关联。已确定的途径和子网络为癌症基因组可能经历的诱变过程提供了新的见解，并为开发个性化药物治疗提供了重要线索。
Background Studies of cancer mutations have typically focused on identifying cancer driving mutations that confer growth advantage to cancer cells. However, cancer genomes accumulate a large number of passenger somatic mutations resulting from various endogenous and exogenous causes, including normal DNA damage and repair processes or cancer-related aberrations of DNA maintenance machinery as well as mutations triggered by carcinogenic exposures. Different mutagenic processes often produce characteristic mutational patterns called mutational signatures. Identifying mutagenic processes underlying mutational signatures shaping a cancer genome is an important step towards understanding tumorigenesis. Methods To investigate the genetic aberrations associated with mutational signatures, we took a network-based approach considering mutational signatures as cancer phenotypes. Specifically, our analysis aims to answer the following two complementary questions: (i) what are functional pathways whose gene expression activities correlate with the strengths of mutational signatures, and (ii) are there pathways whose genetic alterations might have led to specific mutational signatures? To identify mutated pathways, we adopted a recently developed optimization method based on integer linear programming. Results Analyzing a breast cancer dataset, we identified pathways associated with mutational signatures on both expression and mutation levels. Our analysis captured important differences in the etiology of the APOBEC-related signatures and the two clock-like signatures. In particular, it revealed that clustered and dispersed APOBEC mutations may be caused by different mutagenic processes. In addition, our analysis elucidated differences between two age-related signatures-one of the signatures is correlated with the expression of cell cycle genes while the other has no such correlation but shows patterns consistent with the exposure to environmental/external processes. Conclusions This work investigated, for the first time, a network-level association of mutational signatures and dysregulated pathways. The identified pathways and subnetworks provide novel insights into mutagenic processes that the cancer genomes might have undergone and important clues for developing personalized drug therapies.