Revealing Putative Drug Targets for Basal-Like Breast Cancer

The development of targeted drugs has revolutionised the treatment strategy for breast cancer, improving patients’ clinical outcome and quality of life. However, the lack of targetable proteins has limited treatment options for patients with Basal-like breast cancer to non-selective cytotoxic and cytostatic drugs. This research aimed to reveal putative drug targets for Basal-like breast cancer.

The advances of sequencing technologies and computational methods have benefited drug target identification research, which is a critical early step in the drug discovery process. The method developed in this research integrated omic data and modelling approaches to uncover the transcriptional characteristics underlying Basal-like breast cancer. This molecular characterisation revealed potential candidates for further pharmaceutical intervention.

To reveal putative drug targets for Basal-like breast cancer, an unsupervised clustering approach was first performed to study the heterogeneity in breast cancer. The clustering analysis revealed several differently expressed transcriptional factors in Basal-like breast cancer. Using a network modelling approach, these transcriptional factors were then prioritised according to their topological features. Assuming gene expression is the first proxy of protein expression, the expression correlations between critical candidate genes were explored at the protein level. Using bioinformatics platforms and databases, several putative protein-protein interactions that are associate with candidate genes were identified. This was followed by a specific target protein selection. Furthermore, a molecular docking analysis was performed to determine the structural characteristics and molecular recognition features of these interactions. Finally, a laboratory experiment was developed to evaluate the protein interactions in breast cancer cell lines.

This research has not only identified putative drug targets for Basal-like, but also developed an integrative target identification approach that can be adapted easily to study other diseases.