SummaryBreast cancers (BCs) typically express estrogen receptors (ERs) but frequently exhibit de novo or acquired resistance to hormonal therapies. Here, we show that short-term treatment with the anti-estrogens tamoxifen or fulvestrant decrease cell proliferation but increase BC stem cell (BCSC) activity through JAG1-NOTCH4 receptor activation both in patient-derived samples and xenograft (PDX) tumors. In support of this mechanism, we demonstrate that high ALDH1 predicts resistance in women treated with tamoxifen and that a NOTCH4/HES/HEY gene signature predicts for a poor response/prognosis in 2 ER+ patient cohorts. Targeting of NOTCH4 reverses the increase in Notch and BCSC activity induced by anti-estrogens. Importantly, in PDX tumors with acquired tamoxifen resistance, NOTCH4 inhibition reduced BCSC activity. Thus, we establish that BCSC and NOTCH4 activities predict both de novo and acquired tamoxifen resistance and that combining endocrine therapy with targeting JAG1-NOTCH4 overcomes resistance in human breast cancers.
Basigin, which has four isoforms, plays an important role in invasion of hepatocellular carcinoma (HCC).Detailed transcriptional regulation and functions of the basigin isoforms have not been reported except in the case of the predominant isoform basigin-2, which act as inducer of matrix metalloproteinases (MMPs). Here we determined that basigin-2, basigin-3, and basigin-4 were the most abundant transcript variants in human cell lines. GeneRacer PCR and luciferase reporter assays showed that basigin-3 and basigin-4 were initiated from an alternative promoter. Basigin-3 and basigin-4 were widely expressed in various normal human tissues at the mRNA level and were upregulated in HCC tissues compared to in normal tissues. Western blotting and confocal imaging showed that glycosylated basigin-3 and basigin-4 were expressed and localized to the plasma membrane. However, in cultured cell lines, only native basigin-3, and not basigin-4, was detected at protein level. Overexpression of basigin-3 inhibited HCC cell proliferation, MMP induction, and cell invasion in vitro and in vivo. Bimolecular fluorescence complementation assays and nuclear magnetic resonance (NMR) analysis indicated that basigin-3 interacted with basigin-2 to form hetero-oligomers. In conclusion, we systematically investigated the alternative splicing of basigin and found that basigin-3 could inhibit HCC proliferation and invasion, probably through interaction with basigin-2 as an endogenous inhibitor via hetero-oligomerization.
Future smart grid (SG) has been considered a complex and advanced power system, where energy consumers are connected not only to the traditional energy retailers (e.g., the utility companies) but also to some local energy networks for bidirectional energy trading opportunities. This paper aims to investigate a hybrid energy trading market that is comprised of an external utility company and a local trading market managed by a local trading center (LTC). The existence of local energy market provides new opportunities for the energy consumers and the distributed energy sellers to perform the local energy trading in a cooperative manner such that they all can benefit. This paper first quantifies the respective benefits of the energy consumers and the sellers from the local trading and then investigates how they can optimize their benefits by controlling their energy scheduling, in response to the LTC's pricing. Two different types of the LTC are considered, i.e., i) the nonprofit-oriented LTC which solely aims at benefiting the energy consumers and the sellers, and ii) the profit-oriented LTC which aims at maximizing its own profit while guaranteeing the required benefit for each consumer and seller. For each type of the LTC, the optimal trading problem is formulated and the associated algorithm is further proposed to efficiently find the LTC's optimal price as well as the optimal energy scheduling for each consumer and seller. Numerical results are provided to validate the benefits of the hybrid energy trading market and the performance of the proposed algorithms.
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