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      Combined Targeting of Estrogen Receptor Alpha and XPO1 Prevent Akt Activation, Remodel Metabolic Pathways and Induce Autophagy to Overcome Tamoxifen Resistance

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          Abstract

          A majority of breast cancer specific deaths in women with ERα (+) tumors occur due to metastases that are resistant to endocrine therapy. There is a critical need for novel therapeutic approaches to resensitize recurrent ERα (+) tumors to endocrine therapies. The objective of this study was to elucidate mechanisms of improved effectiveness of combined targeting of ERα and the nuclear transport protein XPO1 in overcoming endocrine resistance. Selinexor (SEL), an XPO1 antagonist, has been evaluated in multiple late stage clinical trials in patients with relapsed and/or refractory hematological and solid tumor malignancies. Our transcriptomics analysis showed that 4-Hydroxytamoxifen (4-OHT), SEL alone or their combination induced differential Akt signaling- and metabolism-associated gene expression profiles. Western blot analysis in endocrine resistant cell lines and xenograft models validated differential Akt phosphorylation. Using the Seahorse metabolic profiler, we showed that ERα-XPO1 targeting changed the metabolic phenotype of TAM-resistant breast cancer cells from an energetic to a quiescent profile. This finding demonstrated that combined targeting of XPO1 and ERα rewired the metabolic pathways and shut down both glycolytic and mitochondrial pathways that would eventually lead to autophagy. Remodeling metabolic pathways to regenerate new vulnerabilities in endocrine resistant breast tumors is novel, and given the need for better strategies to improve therapy response in relapsed ERα (+) tumors, our findings show great promise for uncovering the role that ERα-XPO1 crosstalk plays in reducing cancer recurrences.

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          Most cited references46

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          Choosing the right cell line for breast cancer research

          Breast cancer is a complex and heterogeneous disease. Gene expression profiling has contributed significantly to our understanding of this heterogeneity at a molecular level, refining taxonomy based on simple measures such as histological type, tumour grade, lymph node status and the presence of predictive markers like oestrogen receptor and human epidermal growth factor receptor 2 (HER2) to a more sophisticated classification comprising luminal A, luminal B, basal-like, HER2-positive and normal subgroups. In the laboratory, breast cancer is often modelled using established cell lines. In the present review we discuss some of the issues surrounding the use of breast cancer cell lines as experimental models, in light of these revised clinical classifications, and put forward suggestions for improving their use in translational breast cancer research.
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            20-Year Risks of Breast-Cancer Recurrence after Stopping Endocrine Therapy at 5 Years.

            The administration of endocrine therapy for 5 years substantially reduces recurrence rates during and after treatment in women with early-stage, estrogen-receptor (ER)-positive breast cancer. Extending such therapy beyond 5 years offers further protection but has additional side effects. Obtaining data on the absolute risk of subsequent distant recurrence if therapy stops at 5 years could help determine whether to extend treatment.
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              Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression

              One of the most common analysis tasks in genomic research is to identify genes that are differentially expressed (DE) between experimental conditions. Empirical Bayes (EB) statistical tests using moderated genewise variances have been very effective for this purpose, especially when the number of biological replicate samples is small. The EB procedures can however be heavily influenced by a small number of genes with very large or very small variances. This article improves the differential expression tests by robustifying the hyperparameter estimation procedure. The robust procedure has the effect of decreasing the informativeness of the prior distribution for outlier genes while increasing its informativeness for other genes. This effect has the double benefit of reducing the chance that hypervariable genes will be spuriously identified as DE while increasing statistical power for the main body of genes. The robust EB algorithm is fast and numerically stable. The procedure allows exact small-sample null distributions for the test statistics and reduces exactly to the original EB procedure when no outlier genes are present. Simulations show that the robustified tests have similar performance to the original tests in the absence of outlier genes but have greater power and robustness when outliers are present. The article includes case studies for which the robust method correctly identifies and downweights genes associated with hidden covariates and detects more genes likely to be scientifically relevant to the experimental conditions. The new procedure is implemented in the limma software package freely available from the Bioconductor repository.
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                Author and article information

                Journal
                Cancers (Basel)
                Cancers (Basel)
                cancers
                Cancers
                MDPI
                2072-6694
                04 April 2019
                April 2019
                : 11
                : 4
                : 479
                Affiliations
                [1 ]Department of Food Science and Human Nutrition, University of Illinois, Urbana-Champaign, Urbana, IL 61801, USA; kulkoyl2@ 123456illinois.edu (E.K.-C.); brandis2@ 123456illinois.edu (B.P.S.); kinga.wrobel@ 123456hotmail.com (K.W.); yiruster@ 123456gmail.com (Y.C.Z.); kadriye@ 123456hieronymi.de (K.H.)
                [2 ]Illinois Informatics Institute, University of Illinois, Urbana-Champaign, Champaign, IL 61820, USA
                [3 ]Division of Nutritional Sciences, University of Illinois, Urbana-Champaign, Urbana, IL 61801, USA; kchen9282@ 123456gmail.com
                [4 ]School of Molecular and Cellular Biology, University of Illinois, Urbana-Champaign, Urbana, IL 61801, USA; berk.imir@ 123456gmail.com (O.B.I.); kduong6@ 123456illinois.edu (K.D.)
                [5 ]Cancer Center at Illinois, University of Illinois, Urbana-Champaign, Urbana, IL 61801, USA; caitlinocallaghan22@ 123456gmail.com (C.O.); aditirmehta00@ 123456gmail.com (A.M.)
                [6 ]Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
                [7 ]UTSW Medical Center, Dallas, TX 75390, USA; sunati.sahoo@ 123456utsouthwestern.edu (S.S.); barbara.haley@ 123456utsouthwestern.edu (B.H.)
                [8 ]Karyopharm Therapeutics, Newton, MA 02459, USA; hchang@ 123456karyopharm.com (H.C.); ylandesman@ 123456karyopharm.com (Y.L.)
                [9 ]National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Urbana, IL 61801, USA
                [10 ]Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana-Champaign, Urbana, IL 61801, USA
                Author notes
                [* ]Correspondence: zmadake2@ 123456illinois.edu
                Author information
                https://orcid.org/0000-0001-7686-5105
                https://orcid.org/0000-0003-2607-1643
                Article
                cancers-11-00479
                10.3390/cancers11040479
                6520695
                30987380
                eb228295-93de-44fa-a619-79eebdaf0d7b
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 27 February 2019
                : 29 March 2019
                Categories
                Article

                breast cancer,endocrine resistance,nuclear transport pathways,xpo1,erα,metabolic rewiring

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