Tamoxifen is an endocrine therapy which is administered to up to 70% of all breast cancer patients with oestrogen receptor alpha (ERα) expression. Despite the initial response, most patients eventually acquire resistance to the drug. MicroRNAs (miRNAs) are a class of small non‐coding RNAs which have the ability to post‐transcriptionally regulate genes. Although the role of a few miRNAs has been described in tamoxifen resistance at the single gene/target level, little is known about how concerted actions of miRNAs targeting biological networks contribute to resistance. Here we identified the miRNA cluster, C19MC, which harbours around 50 mature miRNAs, to be up‐regulated in resistant cells, with miRNA‐519a being the most highly up‐regulated. We could demonstrate that miRNA‐519a regulates tamoxifen resistance using gain‐ and loss‐of‐function testing. By combining functional enrichment analysis and prediction algorithms, we identified three central tumour‐suppressor genes (TSGs) in PI3K signalling and the cell cycle network as direct target genes of miR‐519a. Combined expression of these target genes correlated with disease‐specific survival in a cohort of tamoxifen‐treated patients. We identified miRNA‐519a as a novel oncomir in ER+ breast cancer cells as it increased cell viability and cell cycle progression as well as resistance to tamoxifen‐induced apoptosis. Finally, we could show that elevated miRNA‐519a levels were inversely correlated with the target genes' expression and that higher expression of this miRNA correlated with poorer survival in ER+ breast cancer patients. Hence we have identified miRNA‐519a as a novel oncomir, co‐regulating a network of TSGs in breast cancer and conferring resistance to tamoxifen. Using inhibitors of such miRNAs may serve as a novel therapeutic approach to combat resistance to therapy as well as proliferation and evasion of apoptosis in breast cancer. Published by John Wiley & Sons, Ltd. © 2014 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Motivation: The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Results: Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. Availability: Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. Contact: dmachado@deb.uminho.pt Supplementary information: Supplementary data are available at Bioinformatics online.

Mass spectrometry imaging (MSI) simultaneously detects and identifies the spatial distribution of numerous molecules throughout tissues. Currently, MSI is limited to providing a static and ex vivo snapshot of highly dynamic systems in which molecules are constantly synthesized and consumed. Herein, we demonstrate an innovative MSI methodology to study dynamic molecular changes of amino acids within biological tissues by measuring the dilution and conversion of stable isotopes in a mouse model. We evaluate the method specifically on hepatocellular metabolism of the essential amino acid l‐phenylalanine, associated with liver diseases. Crucially, the method reveals the localized dynamics of l‐phenylalanine metabolism, including its in vivo hydroxylation to l‐tyrosine and co‐localization with other liver metabolites in a time course of samples from different animals. This method thus enables the dynamics of localized biochemical synthesis to be studied directly from biological tissues.

The microenvironment of solid tumors is a key determinant of therapy efficacy. The co-occurrence of oxygen and nutrient deprivation is a common phenomenon of the tumor microenvironment and associated with treatment resistance. Cholangiocarcinoma (CCA) is characterized by a very poor prognosis and pronounced chemoresistance. A better understanding of the underlying molecular mechanisms is urgently needed to improve therapy strategies against CCA. We sought to investigate the importance of the conditionally essential amino acid glutamine, a centrally important nutrient for a variety of solid tumors, for CCA. Glutamine levels were strongly decreased in CCA samples and the growth of established human CCA cell lines was highly dependent on glutamine. Using gradual reduction of external glutamine, we generated derivatives of CCA cell lines which were able to grow without external glutamine (termed glutamine-depleted (GD)). To analyze the effects of coincident oxygen and glutamine deprivation, GD cells were treated with cisplatin or gemcitabine under normoxia and hypoxia. Strikingly, the well-established phenomenon of hypoxia-induced chemoresistance was completely reversed in GD cells. In order to better understand the underlying mechanisms, we focused on the oncogene c-Myc. The combination of cisplatin and hypoxia led to sustained c-Myc protein expression in wildtype cells. In contrast, c-Myc expression was reduced in response to the combinatorial treatment in GD cells, suggesting a functional importance of c-Myc in the process of hypoxia-induced chemoresistance. In summary, these findings indicate that the mechanisms driving adaption to tumor microenvironmental changes and their relevance for the response to therapy are more complex than expected.

Purpose Investigate in patients with metastatic and/or irresectable colorectal cancer treated with systemic treatment with capecitabine or TAS-102 whether: 1. Intestinal microbiota composition can act as a predictor for response. 2. Intestinal microbiota composition changes during systemic treatment and its relation to chemotoxicity. Background Gut microbiota and host determinants evolve in symbiotic and dependent relationships resulting in a personal ecosystem. In vitro studies showed prolonged and increased response to 5-fluorouracil, a fluoropyrimidine, in the presence of a favorable microbiota composition. Capecitabine and TAS-102 are both fluoropyrimidines used for systemic treatment in colorectal cancer patients. Methods An explorative prospective multicenter cohort study in the Maastricht University Medical Centre+ and Zuyderland Medical Centre will be performed in 66 patients. Before, during, and after three cycles of systemic treatment with capecitabine or TAS-102, fecal samples and questionnaires (concerning compliance and chemotoxicity) will be collected. The response will be measured by CT/MRI using RECIST-criteria. Fecal microbiota composition will be analyzed with 16S rRNA next-generation sequencing. The absolute bacterial abundance will be assessed with quantitative polymerase chain reaction. Multivariate analysis will be used for statistical analysis. Conclusions We aim to detect a microbiota composition that predicts if patients with metastatic and/or irresectable colorectal cancer will respond to systemic treatment and/or experience zero to limited chemotoxicity. If we are able to identify a favorable microbiota composition, fecal microbiota transplantation might be the low-burden alternative to chemotherapy switch in the future.