So-called “targeted therapeutics”, agents with tumour-selective action, promise to revolutionise treatment of specific malignancies; however, realisation of the therapeutic promise of such drugs requires new methods of rapidly distinguishing patients who will derive treatment benefit. Tyrosine Kinase Inhibitors (TKIs), a group of small molecule therapeutics, inhibit specific aspects of the phosphorylation-mediated intracellular signalling pathways commonly altered in cancer. Overexpression of one such pathway, initiated by the HER2 growth factor receptor, occurs commonly in breast cancer. Lapatinib, a HER2 TKI, has been used in combinations with other cancer drugs for treating HER2 overexpressing breast cancer. The aim of this study was to evaluate gene expression changes in response to these targeted therapies to examine their ability to predict treatment response. In this thesis, microarray data from lapatinib-treated drug sensitive breast cancer cell lines was interrogated using an emerging bioinformatic technique, Co-inertia analysis (CIA). Using this technique, 512 genes were found to be altered in a specific response to lapatinib treatment in the cell lines. 27 gene targets were chosen for more detailed analysis using Taqman RT-PCR, of which five showed predictive response in a broader panel of breast cancer cell lines treated with lapatinib. Expression of the five genes was further examined in response to other HER2 targeted therapies and the analysis indicated that the gene expression changes remained consistent with these other treatments, demonstrating a more broadly representative anti-HER2 response pattern. An in vivo study sought to evaluate these gene expression responses in a more in vivo-relevant scenario and found that they were also conserved in this model. Our research indicates that there are commonalities among the gene expression response to HER2-targeting therapeutics in responsive cells which may extrapolate to HER2-amplified patient tumours and more broadly suggests that characterisation of gene changes shortly after treatment may provide a valuable rapid predictor of inhibitor response, potentially guiding a more specific use of such agents by identifying patients that will benefit from these therapies.