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Breast cancer stem cells (BCSC) are presumed to be responsible for treatment resistance, tumor recurrence and metastasis of breast tumors. However, development of BCSC-targeting therapies has been held back by their heterogeneity and the lack of BCSC-selective molecular targets. Here, we demonstrate that Rac1b, the only known alternatively spliced variant of the small GTPase Rac1, is expressed in a subset of BCSCs in vivo and its function is required for the BCSC maintenance and the chemoresistance of breast tumor cells. In human breast cancer cell line MCF7, RAC1B is required for BCSC plasticity and chemoresistance in vitro and for tumor-initiating abilities in vivo. Unlike Rac1, Rac1b function is dispensable for normal mammary gland development and mammary epithelial stem cell (MaSC) activity. In contrast, loss of Rac1b function in a mouse model of breast cancer hampers BCSC activity in vivo and increases the chemosensitivity of primary tumor cells to doxorubicin. Collectively, our data suggest that RAC1B is a clinically relevant molecular target for the development of BCSC-targeting therapies that will improve the effectiveness of currently available chemotherapy modalities.

Background Colorectal cancer (CRC) is a heterogeneous and biologically poorly understood disease. To tailor CRC treatment, it is essential to first model this heterogeneity by defining subtypes of patients with homogeneous biological and clinical characteristics and second match these subtypes to cell lines for which extensive pharmacological data is available, thus linking targeted therapies to patients most likely to respond to treatment. Methods We applied a new unsupervised, iterative approach to stratify CRC tumor samples into subtypes based on genome-wide mRNA expression data. By applying this stratification to several CRC cell line panels and integrating pharmacological response data, we generated hypotheses regarding the targeted treatment of different subtypes. Results In agreement with earlier studies, the two dominant CRC subtypes are highly correlated with a gene expression signature of epithelial-mesenchymal-transition (EMT). Notably, further dividing these two subtypes using iNMF (iterative Non-negative Matrix Factorization) revealed five subtypes that exhibit activation of specific signaling pathways, and show significant differences in clinical and molecular characteristics. Importantly, we were able to validate the stratification on independent, published datasets comprising over 1600 samples. Application of this stratification to four CRC cell line panels comprising 74 different cell lines, showed that the tumor subtypes are well represented in available CRC cell line panels. Pharmacological response data for targeted inhibitors of SRC, WNT, GSK3b, aurora kinase, PI3 kinase, and mTOR, showed significant differences in sensitivity across cell lines assigned to different subtypes. Importantly, some of these differences in sensitivity were in concordance with high expression of the targets or activation of the corresponding pathways in primary tumor samples of the same subtype. Conclusions The stratification presented here is robust, captures important features of CRC, and offers valuable insight into functional differences between CRC subtypes. By matching the identified subtypes to cell line panels that have been pharmacologically characterized, it opens up new possibilities for the development and application of targeted therapies for defined CRC patient sub-populations.

482 Background: Colorectal cancer (CRC) is generally stratified based on genetic and epigenetic features, such as KRAS mutation and microsatellite instability status. In order to facilitate the development of new targeted drugs and treatment regimens, it is important to redefine CRC at the molecular level by identifying subtypes that are relevant for response to targeted therapy. Methods: We applied a new unsupervised approach for iteratively stratifying tumor samples using genome-wide mRNA expression data. The resulting gene expression signatures were used to subtype CRC cell line panels and publicly available CRC tumor datasets. We employed pharmacological data on the cell line panels to link the subtypes to therapy response. Results: Starting from a gene expression dataset of 63 CRC tumor samples, we employed non-negative matrix factorization (NMF) and identified two dominant CRC subtypes. In agreement with previously published results, one of the types showed a mesenchymal and the other an epithelial-like gene expression pattern. In a second step, we applied NMF on these two dominant subtypes and further stratified them into two and three subtypes, respectively. The resulting five CRC subtypes show many differences, most notably activation of specific signaling pathways. Importantly, we recovered these five subtypes in several independent, publicly available CRC datasets. This strongly suggests that the signatures capture disease-relevant features of CRC. Furthermore, we found that the different subtypes corresponded to different cell lines in a panel of CRC cell lines. The clustered CRC cell lines displayed differential responses to a number of targeted compounds, indicating that the new CRC clusters may represent disease subtypes that of differential drug sensitivity. Conclusions: The CRC subtypes discovered using our new method offer new insights into the functional and molecular processes driving CRC. Furthermore, the evidence suggests that these subtypes may differ in activated pathway status and the response to some targeted inhibitors, indicating that targeting pathways conserved in these subtypes may provide new drug discovery opportunities.