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      Response and resistance to BET bromodomain inhibitors in triple negative breast cancer

      1 , 2 , 1 , 2 , 1 , 2 , 3 , 4 , 5 , 1 , 2 , 1 , 1 , 1 , 2 , 1 , 2 , 4 , 1 , 2 , 1 , 6 , 7 , 3 , 1 , 2 , 1 , 2 , 1 , 2 , 7 , 1 , 2 , 1 , 8 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 9 , 9 , 10 , 10 , 11 , 11 , 1 , 2 , 1 , 2 , 2 , 3 , 7 , 9 , 1 , 2 , 9 , 3 , 9 , 12 , 1 , 2 , 3 , 1 , 2 , 12 , 1 , 2 , 9 , 12


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          Triple negative breast cancer (TNBC) is a heterogeneous and clinically aggressive disease for which there is no targeted therapy 1- 3 . BET bromodomain inhibitors, which have shown efficacy in several models of cancer 4- 6 , have not been evaluated in TNBC. These inhibitors displace BET bromodomain proteins such as BRD4 from chromatin by competing with their acetyllysine recognition modules, leading to inhibition of oncogenic transcriptional programs 7- 9 . Here we report the preferential sensitivity of TNBCs to BET bromodomain inhibition in vitro and in vivo, establishing a rationale for clinical investigation and further motivation to understand mechanisms of resistance. In paired cell lines selected for acquired resistance to BET inhibition from previously sensitive TNBCs, we failed to identify gatekeeper mutations, new driver events or drug pump activation. BET-resistant TNBC cells remain dependent on wild-type BRD4, which supports transcription and cell proliferation in a bromodomain-independent manner. Proteomic studies of resistant TNBC identify strong association with MED1 and hyper-phosphorylation of BRD4 attributable to decreased activity of PP2A, identified here as a principal BRD4 serine phosphatase. Together, these studies provide a rationale for BET inhibition in TNBC and present mechanism-based combination strategies to anticipate clinical drug resistance.

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          Most cited references 17

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          Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors.

           P Talalay,  T C Chou (1984)
          A generalized method for analyzing the effects of multiple drugs and for determining summation, synergism and antagonism has been proposed. The derived, generalized equations are based on kinetic principles. The method is relatively simple and is not limited by whether the dose-effect relationships are hyperbolic or sigmoidal, whether the effects of the drugs are mutually exclusive or nonexclusive, whether the ligand interactions are competitive, noncompetitive or uncompetitive, whether the drugs are agonists or antagonists, or the number of drugs involved. The equations for the two most widely used methods for analyzing synergism, antagonism and summation of effects of multiple drugs, the isobologram and fractional product concepts, have been derived and been shown to have limitations in their applications. These two methods cannot be used indiscriminately. The equations underlying these two methods can be derived from a more generalized equation previously developed by us (59). It can be shown that the isobologram is valid only for drugs whose effects are mutually exclusive, whereas the fractional product method is valid only for mutually nonexclusive drugs which have hyperbolic dose-effect curves. Furthermore, in the isobol method, it is laborious to find proper combinations of drugs that would produce an iso-effective curve, and the fractional product method tends to give indication of synergism, since it underestimates the summation of the effect of mutually nonexclusive drugs that have sigmoidal dose-effect curves. The method described herein is devoid of these deficiencies and limitations. The simplified experimental design proposed for multiple drug-effect analysis has the following advantages: It provides a simple diagnostic plot (i.e., the median-effect plot) for evaluating the applicability of the data, and provides parameters that can be directly used to obtain a general equation for the dose-effect relation; the analysis which involves logarithmic conversion and linear regression can be readily carried out with a simple programmable electronic calculator and does not require special graph paper or tables; and the simplicity of the equation allows flexibility of application and the use of a minimum number of data points. This method has been used to analyze experimental data obtained from enzymatic, cellular and animal systems.
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            The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data

            Affinity purification coupled with mass spectrometry (AP-MS) is now a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (e.g. proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. While the standard approach is to identify nonspecific interactions using one or more negative controls, most small-scale AP-MS studies do not capture a complete, accurate background protein set. Fortunately, negative controls are largely bait-independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the Contaminant Repository for Affinity Purification (the CRAPome) and describe the use of this resource to score protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely available online at www.crapome.org.
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              The JAK2/STAT3 signaling pathway is required for growth of CD44⁺CD24⁻ stem cell-like breast cancer cells in human tumors.

              Intratumor heterogeneity is a major clinical problem because tumor cell subtypes display variable sensitivity to therapeutics and may play different roles in progression. We previously characterized 2 cell populations in human breast tumors with distinct properties: CD44+CD24- cells that have stem cell-like characteristics, and CD44-CD24+ cells that resemble more differentiated breast cancer cells. Here we identified 15 genes required for cell growth or proliferation in CD44+CD24- human breast cancer cells in a large-scale loss-of-function screen and found that inhibition of several of these (IL6, PTGIS, HAS1, CXCL3, and PFKFB3) reduced Stat3 activation. We found that the IL-6/JAK2/Stat3 pathway was preferentially active in CD44+CD24- breast cancer cells compared with other tumor cell types, and inhibition of JAK2 decreased their number and blocked growth of xenografts. Our results highlight the differences between distinct breast cancer cell types and identify targets such as JAK2 and Stat3 that may lead to more specific and effective breast cancer therapies.

                Author and article information

                8 December 2015
                06 January 2016
                21 January 2016
                06 July 2016
                : 529
                : 7586
                : 413-417
                [1 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
                [2 ]Department of Medicine, Brigham and Women's Hospital, and Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.
                [3 ]Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts USA.
                [4 ]Princess Margaret Cancer Center/University Health Network, Toronto, Ontario, M5G1L7, Canada.
                [5 ]Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5G2M9, Canada.
                [6 ]Harvard University, Cambridge, Massachusetts, USA.
                [7 ]Cancer Research UK, Cambridge Institute, University of Cambridge, UK, CB2 0RE.
                [8 ]Department of Pathology, Brigham and Women's Hospital, and Department of Pathology, Harvard Medical School, Boston, Massachusetts, USA.
                [9 ]Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
                [10 ]Simmons Comprehensive Cancer Center, Departments of Biochemistry and Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
                [11 ]Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, USA.
                [12 ]Broad Institute, Cambridge, Massachusetts, USA.
                Author notes
                Correspondence should be addressed to K.P. ( kornelia_polyak@ 123456dfci.harvard.edu ) or J.B. ( james_bradner@ 123456dfci.harvard.edu )

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