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      Single-cell transcriptomics reveals multi-step adaptations to endocrine therapy

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          Abstract

          Resistant tumours are thought to arise from the action of Darwinian selection on genetically heterogenous cancer cell populations. However, simple clonal selection is inadequate to describe the late relapses often characterising luminal breast cancers treated with endocrine therapy (ET), suggesting a more complex interplay between genetic and non-genetic factors. Here, we dissect the contributions of clonal genetic diversity and transcriptional plasticity during the early and late phases of ET at single-cell resolution. Using single-cell RNA-sequencing and imaging we disentangle the transcriptional variability of plastic cells and define a rare subpopulation of pre-adapted (PA) cells which undergoes further transcriptomic reprogramming and copy number changes to acquire full resistance. We find evidence for sub-clonal expression of a PA signature in primary tumours and for dominant expression in clustered circulating tumour cells. We propose a multi-step model for ET resistance development and advocate the use of stage-specific biomarkers.

          Abstract

          The development of resistance to endocrine therapy is a significant, clinical problem in breast cancer. Here, the authors identify a rare subpopulation of cells that drive resistance following transcriptional reprogramming.

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          Melanomas acquire resistance to B-RAF(V600E) inhibition by RTK or N-RAS upregulation.

          Activating B-RAF(V600E) (also known as BRAF) kinase mutations occur in ∼7% of human malignancies and ∼60% of melanomas. Early clinical experience with a novel class I RAF-selective inhibitor, PLX4032, demonstrated an unprecedented 80% anti-tumour response rate among patients with B-RAF(V600E)-positive melanomas, but acquired drug resistance frequently develops after initial responses. Hypotheses for mechanisms of acquired resistance to B-RAF inhibition include secondary mutations in B-RAF(V600E), MAPK reactivation, and activation of alternative survival pathways. Here we show that acquired resistance to PLX4032 develops by mutually exclusive PDGFRβ (also known as PDGFRB) upregulation or N-RAS (also known as NRAS) mutations but not through secondary mutations in B-RAF(V600E). We used PLX4032-resistant sub-lines artificially derived from B-RAF(V600E)-positive melanoma cell lines and validated key findings in PLX4032-resistant tumours and tumour-matched, short-term cultures from clinical trial patients. Induction of PDGFRβ RNA, protein and tyrosine phosphorylation emerged as a dominant feature of acquired PLX4032 resistance in a subset of melanoma sub-lines, patient-derived biopsies and short-term cultures. PDGFRβ-upregulated tumour cells have low activated RAS levels and, when treated with PLX4032, do not reactivate the MAPK pathway significantly. In another subset, high levels of activated N-RAS resulting from mutations lead to significant MAPK pathway reactivation upon PLX4032 treatment. Knockdown of PDGFRβ or N-RAS reduced growth of the respective PLX4032-resistant subsets. Overexpression of PDGFRβ or N-RAS(Q61K) conferred PLX4032 resistance to PLX4032-sensitive parental cell lines. Importantly, MAPK reactivation predicts MEK inhibitor sensitivity. Thus, melanomas escape B-RAF(V600E) targeting not through secondary B-RAF(V600E) mutations but via receptor tyrosine kinase (RTK)-mediated activation of alternative survival pathway(s) or activated RAS-mediated reactivation of the MAPK pathway, suggesting additional therapeutic strategies.
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            A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

            Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.
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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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                Author and article information

                Contributors
                s.hong@imperial.ac.uk
                i.barozzi@imperial.ac.uk
                l.magnani@imperial.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                2 September 2019
                2 September 2019
                2019
                : 10
                : 3840
                Affiliations
                [1 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Department of Surgery and Cancer, , Imperial College London, ; London, UK
                [2 ]ISNI 0000 0004 0470 5454, GRID grid.15444.30, Department of Internal Medicine, , Yonsei University College of Medicine, ; Seoul, Korea
                [3 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, , Imperial College London, ; London, UK
                [4 ]ISNI 0000 0004 1757 2822, GRID grid.4708.b, Division of Pathology, , European Institute of Oncology and University of Milan, School of Medicine, ; Milan, Italy
                [5 ]ISNI 0000 0004 1755 9177, GRID grid.419563.c, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, ; Meldola, Italy
                [6 ]Present Address: Nature Communications, The Macmillan Campus, 4 Crinan Street, London, N1 9XW UK
                Author information
                http://orcid.org/0000-0002-0075-8538
                http://orcid.org/0000-0003-4613-8884
                http://orcid.org/0000-0002-9173-5664
                http://orcid.org/0000-0002-7534-0785
                Article
                11721
                10.1038/s41467-019-11721-9
                6718416
                31477698
                70650967-c34c-4c7f-bc57-cfc82cc52ef0
                © The Author(s) 2019

                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
                : 26 November 2018
                : 30 July 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000289, Cancer Research UK (CRUK);
                Award ID: A23110
                Award ID: A23110
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                mechanisms of disease,computational models,breast cancer,cancer therapeutic resistance

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