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      PLK1 inhibition exhibits strong anti-tumoral activity in CCND1-driven breast cancer metastases with acquired palbociclib resistance

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      1 , 2 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 3 , 3 , 3 , 4 , 2 , 2 , 2 , 2 , 2 , 5 , 5 , 5 , 1 , 1 , 1 , 6 , 7 , 7 , 3 , 1 , 8 , 1 , 8 , 9 , 10 , 3 , 8 , 3 , 3 , 2 , 1 ,
      Nature Communications
      Nature Publishing Group UK
      Cancer models, Targeted therapies, Breast cancer

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          Abstract

          A significant proportion of patients with oestrogen receptor (ER) positive breast cancers (BC) develop resistance to endocrine treatments (ET) and relapse with metastatic disease. Here we perform whole exome sequencing and gene expression analysis of matched primary breast tumours and bone metastasis-derived patient-derived xenografts (PDX). Transcriptomic analyses reveal enrichment of the G2/M checkpoint and up-regulation of Polo-like kinase 1 (PLK1) in PDX. PLK1 inhibition results in tumour shrinkage in highly proliferating CCND1-driven PDX, including different RB-positive PDX with acquired palbociclib resistance. Mechanistic studies in endocrine resistant cell lines, suggest an ER-independent function of PLK1 in regulating cell proliferation. Finally, in two independent clinical cohorts of ER positive BC, we find a strong association between high expression of PLK1 and a shorter metastases-free survival and poor response to anastrozole. In conclusion, our findings support clinical development of PLK1 inhibitors in patients with advanced CCND1-driven BC, including patients progressing on palbociclib treatment.

          Abstract

          Identifying novel therapies for the treatment of CDK4/6 inhibitor-resistant patients is of great importance. Here, the authors demonstrate that PLK1 inhibition is a potential therapeutic target in CCND1-driven and in RB-positive Palbociclib-resistant breast cancers.

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          Most cited references40

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            Integration of biological networks and gene expression data using Cytoscape.

            Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.
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              Cell cycle proteins as promising targets in cancer therapy

              Cancer is characterized by uncontrolled tumour cell proliferation resulting from aberrant activity of various cell cycle proteins. Therefore, cell cycle regulators are considered attractive targets in cancer therapy. Intriguingly, animal models demonstrate that some of these proteins are not essential for proliferation of non-transformed cells
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                Author and article information

                Contributors
                Elisabetta.marangoni@curie.fr
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 August 2020
                13 August 2020
                2020
                : 11
                : 4053
                Affiliations
                [1 ]GRID grid.418596.7, ISNI 0000 0004 0639 6384, Translational Research Department, Institut Curie, ; 26 Rue d’Ulm, 75005 Paris, France
                [2 ]GRID grid.18886.3f, ISNI 0000 0001 1271 4623, Institute of Cancer Research, ; 123 Old Brompton Road, SW7 3RP London, UK
                [3 ]GRID grid.418596.7, ISNI 0000 0004 0639 6384, Department of Genetics, Institut Curie, ; Paris, France
                [4 ]Alfort Veterinary School, 7 Av. Du Général-de-Gaulle, 94000 Maisons-Alfort, France
                [5 ]GRID grid.418596.7, ISNI 0000 0004 0639 6384, Department of Pathology, Institut Curie, ; Paris, France
                [6 ]INRA, APEX-PAnTher, Oniris, Rue De La Géraudière Cedex 3, 44322 Nantes, France
                [7 ]Genomics of Excellence (ICGex) Platform, Institut Curie Research Center, Paris, France
                [8 ]GRID grid.418596.7, ISNI 0000 0004 0639 6384, Department of Medical Oncology, Institut Curie, ; Paris, France
                [9 ]GRID grid.462282.8, ISNI 0000 0004 0384 0005, Inserm U1052, Centre de Recherche en Cancérologie de Lyon, ; 28 Rue Laennec, 69000 Lyon, France
                [10 ]GRID grid.418596.7, ISNI 0000 0004 0639 6384, Department of Surgery, Institut Curie, ; Paris, France
                Author information
                http://orcid.org/0000-0001-8879-7592
                http://orcid.org/0000-0002-0880-1588
                http://orcid.org/0000-0002-0396-3190
                http://orcid.org/0000-0003-3104-1684
                http://orcid.org/0000-0001-6434-3932
                http://orcid.org/0000-0002-3337-6448
                Article
                17697
                10.1038/s41467-020-17697-1
                7426966
                32792481
                0451b50c-0b29-4831-adbe-9b347e9f4506
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 December 2019
                : 16 July 2020
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                © The Author(s) 2020

                Uncategorized
                cancer models,targeted therapies,breast cancer
                Uncategorized
                cancer models, targeted therapies, breast cancer

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