Breast cancer is an important cause of disease-burden and death in women. To prevent over- and undertreatment of breast cancer patients, new prognostic and predictive factors are needed. We aimed to study associations between prognosis and body constitution, genetic factors, and tumor-specific protein levels related to metabolic pathways, and to examine the prognostic impact of statin use and body size changes in breast cancer patients. In paper I, we studied the prognostic impact of tumor-specific HMG-CoA reductase (HMGCR), the rate-limiting enzyme of the mevalonate pathway and the target of statin treatment, in breast cancer. Moderate/strong HMGCR levels were associated with older age and favorable tumor characteristics but not with breast cancer-free interval. In paper II, we studied the interplay between tumor-specific HMGCR levels, preoperative statin use, and the multi-drug resistance gene (MDR1/ABCB1) in breast cancer. Significant interactions were found between ABCB1 C3435T genotype and statin use on clinical outcomes. Preoperative statin use was not associated with prognosis. In paper III, we studied cytoplasmic and nuclear levels of the master regulator aryl hydrocarbon receptor (AhR) and their associations with intratumoral aromastase, patients’ characteristics, clinicopathological factors, and prognosis in adjuvant-treated breast cancer patients. High cytoplasmic levels of AhR conferred good prognosis, particularly if tamoxifen was used as the only endocrine therapy. Intratumoral aromatase had little prognostic impact. In paper IV, we studied the prognostic impact of body size changes during the first postoperative year. Landmark survival analyses revealed that weight gain conferred poor prognosis in patients <50 years while weight loss was associated with poor prognosis in patients ≥70 years. Changes between WHR categories were associated with differential recurrence risk depending on tumor ER status.In conclusion, our results suggest that patient factors could, when taken into account, yield additional prognostic information when combined with clinically established tumor characteristics for personalized breast cancer treatment
