In silico strategies to improve insight in breast cancer

In clinical practice, breast cancer is currently divided into subtypes based on immunohistochemical expression of the estrogen receptor and human epidermal growth factor receptor 2. These subtypes are crucial for treatment choice and outcome. However, even within these subgroups there is great variability in tumor behavior. This variability within breast cancer subtypes should presumably have clinical implications for treatment decision-making and the potential of novel therapeutic targets. However, conducting trials to investigate treatment efficacy and validate diagnostics are costly, labor-intensive and time-consuming. Therefore, it takes a long time to translate the knowledge regarding tumor variability within breast cancer subtypes into clinical implications for patients. To speed up this translation, using low-cost tools for hypothesis-generation could be very convenient.

In this thesis, we used a large database of publicly available gene expression profiles as a low-cost tool to gain insight into how to improve patient selection for systemic therapy and to explore potential new therapeutic targets for difficult to treat subtypes of breast cancer. This has led to the generation of multiple hypotheses which require further study in sets of tumors from patients participating in larger prospective, preferably randomized trials. Ultimately, these findings could contribute to the further improvement of patient outcome in early-stage breast cancer.