Breast cancer is the most common cancer in women worldwide. Predictive and prognostic factors are not yet sufficient to allow for individualised therapies and optimal treatment for this very heterogenous disease. For several years, gene expression analysis has been studied as a useful method for routine diagnosis. It is considered to be a promising complement to established routine parameters in breast cancer characterisation for the prognosis of disease outcome and therapy response.
In this study primary mamma carcinomas from patients treated with neoadjuvant therapy were analysed. Correlations between gene expression profiles and clinical parameters (tumour- and patient-associated) were detected using a breast-cancer-related microarray, which was specifically designed within the project. The primary focus of the work was to analyse if, (i) different breast tumours could be distinguished based on their expression profiles, (ii) there are any correlations between tumour categories and clinical characteristics, and (iii) single genes could be identified that show significant expression characteristics in the analysed tumours.
On a functional level the expression of steroid hormone and growth factor pathways was identified as a significant difference between the analysed tumours. Both pathways are closely related and affect tumour progression and therapy response in breast cancer. Furthermore, the determined gene expression profiles were used for tumour clustering, resulting in five tumour clusters associated with distinct clinical characteristics. The cluster analysis further indicated that tumour samples from one patient are more closely related to each other than tumour samples from different patients, independent of the method or time (before or after treatment) of the tissue sampling.
The dominant role of estrogen and steroid biosynthesis in breast cancer is one conclusion of the present study. The complex mechanisms influencing the disease require further studies concerning additional molecular levels (genome, proteome, metabolome) and outcomes (genes/gene products, therapies, survival/disease-free survival) for the long-term goal of individualised breast cancer treatment.