Drug repurposing for Basal breast cancer subpopulations using modular network signatures

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,...

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Bibliographic Details
Published in:Computational biology and chemistry 2023-08, Vol.105, p.107902-107902, Article 107902
Main Authors: Coria-Rodríguez, Hiram, Ochoa, Soledad, de Anda-Jáuregui, Guillermo, Hernández-Lemus, Enrique
Format: Article
Language:English
Subjects:
Basal breast cancer
Drug Repositioning
Drug repurposing
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Humans
Modular network signatures
MultiOmic regulation
Neoplasms
Perturbation assays
Transcriptome
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Summary: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. [Display omitted]
ISSN:1476-9271
1476-928X
DOI:10.1016/j.compbiolchem.2023.107902