Annexin A1 (AnxA1) is a candidate regulator of the epithelial-to mesenchymal (EMT)-like phenotypic switch, a pivotal event in breast cancer progression. We show here that AnxA1 expression is associated with a highly invasive basal-like breast cancer subtype both in a panel of human breast cancer cell lines as in breast cancer patients and that AnxA1 is functionally related to breast cancer progression. AnxA1 knockdown in invasive basal-like breast cancer cells reduced the number of spontaneous lung metastasis, whereas additional expression of AnxA1 enhanced metastatic spread. AnxA1 promotes metastasis formation by enhancing TGFβ/Smad signaling and actin reorganization, which facilitates an EMT-like switch, thereby allowing efficient cell migration and invasion of metastatic breast cancer cells.

We describe two water‐soluble ruthenium complexes, [1]Cl2 and [2]Cl2, that photodissociate to release a cytotoxic nicotinamide phosphoribosyltransferase (NAMPT) inhibitor with a low dose (21 J cm−2) of red light in an oxygen‐independent manner. Using a specific NAMPT activity assay, up to an 18‐fold increase in inhibition potency was measured upon red‐light activation of [2]Cl2, while [1]Cl2 was thermally unstable. For the first time, the dark and red‐light‐induced cytotoxicity of these photocaged compounds could be tested under hypoxia (1 % O2). In skin (A431) and lung (A549) cancer cells, a 3‐ to 4‐fold increase in cytotoxicity was found upon red‐light irradiation for [2]Cl2, whether the cells were cultured and irradiated with 1 % or 21 % O2. These results demonstrate the potential of photoactivated chemotherapy for hypoxic cancer cells, in which classical photodynamic therapy, which relies on oxygen activation, is poorly efficient.

Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for future personalized metabolic modeling applications, enhancing the search for novel selective anticancer therapies.DOI: http://dx.doi.org/10.7554/eLife.03641.001

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