Developing an integrated approach to Identifying and validating candidate therapeuticcompounds for triple negative breast cancer

Connectivity mapping (Lamb et al 2006) is an advanced bioinformatics technique that establishes
connections among different biological states via their gene expression profiles/signatures. An important
application of connectivity mapping is the identification of small molecule compounds capable of inhibiting a
disease state. Our research team has undertaken pioneering research in this area by developing a robust
framework of connectivity mapping and releasing tested software for high throughput connectivity mapping
tasks (Zhang & Gant 2008, 2009, McArt & Zhang2011, McArt et al 2013, Wen et al 2015). Connectivity
mapping has been used to successfully identify medications with anti-cancer properties. For instance,
cimetidine has been identified as a potential treatment for lung cancer and pre-clinically validated using
mouse models (Sirota et al 2011) and rapamycin has been shown to overcome dexamethasone resistance
in acute lymphoblastic leukaemia (ALL) (Lamb et al 2006). Recently, our research team has used the
connectivity map approach to predict and subsequently validate, in a mouse model, entinostat as a
potential inhibitor of acute myeloid leukaemia (AML) (Ramsey et al 2013).

This project aims to develop an integrated approach to the identification and subsequent validation of
candidate drugs with potential anti-cancer properties. Triple negative breast cancer will be the primary
disease to test out this novel approach. Once developed, the process can be similarly applied to other
diseases.

For more information please refer to the following site: http://www.science.ulster.ac.uk/gradschool/files/2016/01/B-Developing-an-integrated-approach-to-Identifying-and-validating-candidate-therapeutic-compounds-for-triple-negative-breast-cancer.pdf