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      Aberrant FGFR signaling mediates resistance to CDK4/6 inhibitors in ER+ breast cancer

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          Abstract

          Using an ORF kinome screen in MCF-7 cells treated with the CDK4/6 inhibitor ribociclib plus fulvestrant, we identified FGFR1 as a mechanism of drug resistance. FGFR1-amplified/ER+ breast cancer cells and MCF-7 cells transduced with FGFR1 were resistant to fulvestrant ± ribociclib or palbociclib. This resistance was abrogated by treatment with the FGFR tyrosine kinase inhibitor (TKI) lucitanib. Addition of the FGFR TKI erdafitinib to palbociclib/fulvestrant induced complete responses of FGFR1-amplified/ER+ patient-derived-xenografts. Next generation sequencing of circulating tumor DNA (ctDNA) in 34 patients after progression on CDK4/6 inhibitors identified FGFR1/2 amplification or activating mutations in 14/34 (41%) post-progression specimens. Finally, ctDNA from patients enrolled in MONALEESA-2, the registration trial of ribociclib, showed that patients with FGFR1 amplification exhibited a shorter progression-free survival compared to patients with wild type FGFR1. Thus, we propose breast cancers with FGFR pathway alterations should be considered for trials using combinations of ER, CDK4/6 and FGFR antagonists.

          Abstract

          Era+ breast cancer patients often develop resistance to endocrine therapy. Here, the authors show that FGFR1 amplification is a resistance mechanism to CDK4/6 inhibitor and endocrine therapy and that combined treatment with FGFR, CDK4/6, and anti-estrogens is a potential therapeutic strategy in Era+ breast cancer tumors.

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

            The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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              The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

              Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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                Author and article information

                Contributors
                carlos.arteaga@utsouthwestern.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                26 March 2019
                26 March 2019
                2019
                : 10
                : 1373
                Affiliations
                [1 ]ISNI 0000 0004 1936 9916, GRID grid.412807.8, Departments of Medicine, , Vanderbilt University Medical Center, ; Nashville, 37232-6307 TN USA
                [2 ]ISNI 0000 0000 9482 7121, GRID grid.267313.2, UTSW Simmons Cancer Center, ; Dallas, TX 75230 USA
                [3 ]ISNI 0000 0004 1936 9916, GRID grid.412807.8, Breast Cancer Program, Vanderbilt-Ingram Cancer Center, , Vanderbilt University Medical Center, ; Nashville, 37232-6307 TN USA
                [4 ]ISNI 0000 0004 1936 9916, GRID grid.412807.8, Departments of Biochemistry, , Vanderbilt University Medical Center, ; Nashville, 37232-6307 TN USA
                [5 ]ISNI 0000 0001 2264 7217, GRID grid.152326.1, Vanderbilt Center for Quantitative Sciences, , Vanderbilt University School of Medicine, ; Nashville, 37232-6307 TN USA
                [6 ]ISNI 0000 0004 1936 9916, GRID grid.412807.8, Departments of Pathology, Microbiology & Immunology, , Vanderbilt University Medical Center, ; Nashville, 37232-6307 TN USA
                [7 ]ISNI 0000 0001 2299 3507, GRID grid.16753.36, Robert H Lurie Comprehensive Cancer Center, ; Chicago, 60611 IL USA
                [8 ]ISNI 000000041936754X, GRID grid.38142.3c, Massachusetts General Hospital Cancer Center, , Harvard Medical School, ; Boston, 02114 MA USA
                [9 ]ISNI 0000 0004 0412 5468, GRID grid.420754.0, Baylor University Medical Center, Texas Oncology, , , US Oncology, ; Dallas, 75246 TX USA
                [10 ]Guardant Health, Redwood City, 94063 CA USA
                [11 ]ISNI 0000 0004 0439 2056, GRID grid.418424.f, Novartis Institutes for Biomedical Research, ; Cambridge, 02139 MA USA
                [12 ]ISNI 0000 0004 0439 2056, GRID grid.418424.f, Novartis Pharmaceuticals Corporation, ; East Hanover, 07936 NJ USA
                Author information
                http://orcid.org/0000-0002-8655-8341
                http://orcid.org/0000-0003-0318-9120
                http://orcid.org/0000-0002-6292-6963
                http://orcid.org/0000-0001-8122-4329
                http://orcid.org/0000-0002-4263-5974
                Article
                9068
                10.1038/s41467-019-09068-2
                6435685
                30914635
                2c6aadad-7c33-4e61-962a-e485da29836d
                © 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
                : 2 July 2018
                : 14 February 2019
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