Potential Drug Synergy Through the ERBB2 Pathway in HER2+ Breast Tumors

HER2-positive (HER2+) breast cancer is characterized by the overexpression of the ERBB2 (HER2) gene, which promotes aggressive tumor growth and poor prognosis. Targeting the ERBB2 pathway with single-agent therapies has shown limited efficacy due to resistance mechanisms and the complexity of gene interactions within the tumor microenvironment. This study aims to explore potential drug synergies by analyzing gene-drug interactions and combination therapies that target the ERBB2 pathway in HER2+ breast tumors. Using gene co-expression network analysis, we identified 23 metabolic pathways with significant cross-linking of gene interactions, including those involving EGFR tyrosine kinase inhibitors, PI3K, mTOR, and others. We visualized these interactions using Cytoscape to generate individual and combined drug-gene networks, focusing on frequently used drugs such as Erlotinib, Gefitinib, Lapatinib, and Cetuximab. Individual networks highlighted the direct effects of these drugs on their target genes and neighboring genes within the ERBB2 pathway. Combined drug networks, such as those for Cetuximab with Lapatinib, Cetuximab with Erlotinib, and Erlotinib with Lapatinib, revealed potential synergies that could enhance therapeutic efficacy by simultaneously influencing multiple genes and pathways. Our findings suggest that a network-based approach to analyzing drug combinations provides valuable insights into the molecular mechanisms of HER2+ breast cancer and offers promising strategies for overcoming drug resistance and improving treatment outcomes.

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Publication Details

Journal
International Journal of Molecular Sciences
Published
2024-11-29
DOI
https://doi.org/10.3390/ijms252312840
Citations
2
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.72
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article

Potential Drug Synergy Through the ERBB2 Pathway in HER2+ Breast Tumors

Enrique Hernández–Lemus, Guillermo de Anda‐Jáuregui, Emiliano Gómez-Montañez, Yareli L. Rojas-Salazar et al.
2 citations
International Journal of Molecular Sciences
Computational Drug Discovery Methods
0.72
article

Potential Drug Synergy Through the ERBB2 Pathway in HER2+ Breast Tumors

Enrique Hernández–Lemus, Guillermo de Anda‐Jáuregui, Emiliano Gómez-Montañez, Yareli L. Rojas-Salazar, Jorge G. Rojas-Salazar
article en
2 citations

Abstract

HER2-positive (HER2+) breast cancer is characterized by the overexpression of the ERBB2 (HER2) gene, which promotes aggressive tumor growth and poor prognosis. Targeting the ERBB2 pathway with single-agent therapies has shown limited efficacy due to resistance mechanisms and the complexity of gene interactions within the tumor microenvironment. This study aims to explore potential drug synergies by analyzing gene-drug interactions and combination therapies that target the ERBB2 pathway in HER2+ breast tumors. Using gene co-expression network analysis, we identified 23 metabolic pathways with significant cross-linking of gene interactions, including those involving EGFR tyrosine kinase inhibitors, PI3K, mTOR, and others. We visualized these interactions using Cytoscape to generate individual and combined drug-gene networks, focusing on frequently used drugs such as Erlotinib, Gefitinib, Lapatinib, and Cetuximab. Individual networks highlighted the direct effects of these drugs on their target genes and neighboring genes within the ERBB2 pathway. Combined drug networks, such as those for Cetuximab with Lapatinib, Cetuximab with Erlotinib, and Erlotinib with Lapatinib, revealed potential synergies that could enhance therapeutic efficacy by simultaneously influencing multiple genes and pathways. Our findings suggest that a network-based approach to analyzing drug combinations provides valuable insights into the molecular mechanisms of HER2+ breast cancer and offers promising strategies for overcoming drug resistance and improving treatment outcomes.

International Journal of Molecular SciencesVol. 25(23)
National Institute of Genomic Medicine (MX), Universidad Nacional Autónoma de México (MX)
Good health and well-being
Openalex Percentile: Top 24%
Computational Drug Discovery Methods
0.72
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