Abstract Introduction Gene signatures to assess prognosis of breast cancer patients are generally restricted to ER-positive/luminal breast cancers, and provide limited insight into the reason of a good or poor prognosis. Based on our computational models that determine functional activity of oncogenic signaling pathways [1], we are able to assess prognosis across all breast cancer subtypes, and explain the results in terms of tumor driving pathway. Methods Previously, we developed models to determine functional activity [1] of the ER, Wnt, Hedgehog (HH), AR, PI3K and TGFβ pathways, based on gene expression levels of the respective pathway's target genes. Here, we report results of applying these models on 1294 breast cancer samples from public data sets GSE6532, GSE9195, GSE20685, GSE21653 and E-MTAB-365 (1169 with survival data), in relation to breast cancer subtype. The pathways’ individual risk association was assessed by univariate Cox proportional hazards regression. Next, a multi-pathway score (MPS) was derived using Cox regression coefficients on 164 training samples, and its prognostic value was tested on the remaining 1005 samples. Hazard ratios on different test subsets were calculated after scaling MPS to yield a unit length 95%-confidence interval, to enable fair comparison. Results In 749 (58%) of the 1294 breast cancer samples, at least one of the six pathways was found active, and in 1026 (79%) at least one moderately active. Of the 749, 167 showed two or more active pathways. Different distributions of active pathways were observed across different subtypes. For instance, ER was active in most luminal A samples, less often in luminal B samples, and not in HER2 and basal samples. Conversely, Wnt, HH and TGFβ were more often active in HER2 and basal samples. PI3K was mostly active in luminal B and HER2 samples. ER pathway activity was associated with a better prognosis (HR = 0.42, p = 9.8e-7), while Wnt, HH, PI3K and TGFβ were associated with worse prognosis (HR = 1.46 - 3.56, p = 4e-10 - 7.1e-2); AR was not associated with prognosis and left out of the MPS. On the 1005 test samples, MPS was highly associated with prognosis (HR = 4.90, p = 7.3e-15). Not only can it distinguish good from poor prognosis cases among luminal cancers (HR = 4.11, p = 2.1e-7), but also within the luminal A and B groups (HR = 5.15 & 2.43, p = 4.7e-5 & 1.3e-2, respectively). Furthermore, MPS can identify HER2 cases with a very poor prognosis (HR = 4.81, p = 3.2e-5), and basal cases with a fairly good prognosis (HR = 3.40, p = 3.7e-3). Conclusion We have demonstrated that we can assess prognosis of breast cancer patients based on functional activity of oncogenic signaling pathways. A combined multi-pathway score (MPS) clearly distinguishes good from poor prognosis cases, even within each breast cancer subtype. Furthermore, the underlying pathway activities can give relevant information for therapy selection. [1] Verhaegh W et al. Cancer Res. 2014;74:2936-45. Citation Format: Henk van Ooijen, Márcia A. Inda, Ralf Hoffmann, Paul van de Wiel, Anja van de Stolpe, Wim Verhaegh. Prognosis within different breast cancer subtypes using functional activity of signaling pathways. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5291. doi:10.1158/1538-7445.AM2015-5291