Computational Genomics Division, National Institute of Genomic Medicine, Periferico Sur 4809, Mexico City, 14610, Mexico.
Computational Genomics Division, National Institute of Genomic Medicine, Periferico Sur 4809, Mexico City, 14610, Mexico; Center for Complexity Sciences, Universidad Nacional Autonoma de Mexico, Circuito Exterior, Mexico City, 04510, Mexico; Catedras Conacyt, National Council on Science and Technology, Insurgentes Sur, Mexico City, 03940, Mexico.
Comput Biol Chem. 2023 Aug;105:107902. doi: 10.1016/j.compbiolchem.2023.107902. Epub 2023 Jun 16.
Breast cancer is characterized as being a heterogeneous pathology with a broad phenotype variability. Breast cancer subtypes have been developed in order to capture some of this heterogeneity. Each of these breast cancer subtypes, in turns retains varied characteristic features impacting diagnostic, prognostic and therapeutics. Basal breast tumors, in particular have been challenging in these regards. Basal breast cancer is often more aggressive, of rapid evolution and no tailor-made targeted therapies are available yet to treat it. Arguably, epigenetic variability is behind some of these intricacies. It is possible to further classify basal breast tumor in groups based on their non-coding transcriptome and methylome profiles. It is expected that these groups will have differences in survival as well as in sensitivity to certain classes of drugs. With this in mind, we implemented a computational learning approach to infer different subpopulations of basal breast cancer (from TCGA multi-omic data) based on their epigenetic signatures. Such epigenomic signatures were associated with different survival profiles; we then identified their associated gene co-expression network structure, extracted a signature based on modules within these networks, and use these signatures to find and prioritize drugs (in the LINCS dataset) that may be used to target these types of cancer. In this way we are introducing the analytical workflow for an epigenomic signature-based drug repurposing structure.
乳腺癌的特点是具有广泛的表型变异性的异质病理学。已经开发了乳腺癌亚型，以捕捉其中的一些异质性。这些乳腺癌亚型中的每一个，反过来又保留了不同的特征，影响诊断、预后和治疗。基底乳腺肿瘤在这些方面尤其具有挑战性。基底乳腺癌通常更具侵袭性，演变迅速，目前还没有针对它的定制靶向治疗方法。可以说，表观遗传变异性是这些复杂性的背后原因之一。根据非编码转录组和甲基组谱，可以进一步将基底乳腺肿瘤分为不同的组。预计这些组在生存和对某些类药物的敏感性方面会有差异。考虑到这一点，我们基于表观遗传特征，采用计算学习方法来推断基底乳腺癌（来自 TCGA 多组学数据）的不同亚群。这些表观遗传特征与不同的生存曲线相关联；然后我们确定了它们相关的基因共表达网络结构，从这些网络中的模块中提取特征，并使用这些特征在 LINCS 数据集）中找到并优先考虑可能用于靶向这些类型癌症的药物。通过这种方式，我们引入了基于表观基因组签名的药物再利用结构的分析工作流程。
