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      Efficacy of futibatinib, an irreversible fibroblast growth factor receptor inhibitor, in FGFR-altered breast cancer

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          Abstract

          Several alterations in fibroblast growth factor receptor (FGFR) genes have been found in breast cancer; however, they have not been well characterized as therapeutic targets. Futibatinib (TAS-120; Taiho) is a novel, selective, pan-FGFR inhibitor that inhibits FGFR1-4 at nanomolar concentrations. We sought to determine futibatinib’s efficacy in breast cancer models. Nine breast cancer patient–derived xenografts (PDXs) with various FGFR1-4 alterations and expression levels were treated with futibatinib. Antitumor efficacy was evaluated by change in tumor volume and time to tumor doubling. Alterations indicating sensitization to futibatinib in vivo were further characterized in vitro . FGFR gene expression between patient tumors and matching PDXs was significantly correlated; however, overall PDXs had higher FGFR3-4 expression. Futibatinib inhibited tumor growth in 3 of 9 PDXs, with tumor stabilization in an FGFR2-amplified model and prolonged regression (> 110 days) in an FGFR2 Y375C mutant/amplified model. FGFR2 overexpression and, to a greater extent, FGFR2 Y375C expression in MCF10A cells enhanced cell growth and sensitivity to futibatinib. Per institutional and public databases, FGFR2 mutations and amplifications had a population frequency of 1.1%–2.6% and 1.5%–2.5%, respectively, in breast cancer patients. FGFR2 alterations in breast cancer may represent infrequent but highly promising targets for futibatinib.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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                Author and article information

                Contributors
                fmeric@mdanderson.org
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 November 2023
                18 November 2023
                2023
                : 13
                : 20223
                Affiliations
                [1 ]Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, ( https://ror.org/04twxam07) 1400 Holcombe Boulevard, Unit 455, Houston, TX 77030 USA
                [2 ]Department of Basic Oncology, Graduate School of Health Sciences, Hacettepe University, ( https://ror.org/04kwvgz42) Ankara, 06100 Turkey
                [3 ]Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, ( https://ror.org/04twxam07) Houston, TX 77030 USA
                [4 ]GRID grid.249880.f, ISNI 0000 0004 0374 0039, The Jackson Laboratory for Genomic Medicine, ; Farmington, CT 06032 USA
                [5 ]Department of Pediatrics, University of Connecticut Health Center, ( https://ror.org/02kzs4y22) Farmington, CT 06030 USA
                [6 ]Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, ( https://ror.org/04twxam07) Houston, TX 77030 USA
                [7 ]Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University, ( https://ror.org/009avj582) Portland, OR 97239 USA
                [8 ]Precision Oncology, Knight Cancer Institute, Oregon Health and Science University, ( https://ror.org/009avj582) Portland, OR 97239 USA
                [9 ]Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, ( https://ror.org/04twxam07) Houston, TX 77030 USA
                [10 ]Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, ( https://ror.org/04twxam07) Houston, TX 77030 USA
                [11 ]The Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, ( https://ror.org/04twxam07) Houston, TX 77030 USA
                [12 ]Present Address: Department of Biostatistics, Graduate School of Public Health, Yonsei University, ( https://ror.org/01wjejq96) Seoul, Republic of Korea
                Article
                46586
                10.1038/s41598-023-46586-y
                10657448
                37980453
                d83fc707-e5d5-48ab-86a3-538acdf17ee2
                © The Author(s) 2023

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 June 2023
                : 2 November 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100001006, Breast Cancer Research Foundation;
                Award ID: BCRF-21-110
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100009634, Susan G. Komen;
                Award ID: SAC110052
                Award Recipient :
                Funded by: Taiho Pharmaceuticals
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: U54 CA224065
                Award Recipient :
                Funded by: Nellie B. Connally Breast Cancer Research Endowment
                Funded by: The University of Texas MD Anderson Cancer Center Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer
                Funded by: The University of Texas MD Anderson Breast Cancer Moonshot Program
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: UL1TR003167
                Award Recipient :
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                © Springer Nature Limited 2023

                Uncategorized
                breast cancer,cancer therapy,cancer genomics
                Uncategorized
                breast cancer, cancer therapy, cancer genomics

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