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      Genomic profile of metastatic breast cancer patient-derived xenografts established using percutaneous biopsy

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          Abstract

          Background

          Metastatic breast cancer (mBC) is a complex and life-threatening disease and although it is difficult to cure, patients can benefit from sequential anticancer treatment, including endocrine therapy, targeted therapy and cytotoxic chemotherapy. The patient-derived xenograft (PDX) model is suggested as a practical tool to predict the clinical outcome of this disease as well as to screen novel drugs. This study aimed to establish PDX models in Korean patients and analyze their genomic profiles and utility for translational research.

          Methods

          Percutaneous core needle biopsy or punch biopsy samples were used for xenotransplantation. Whole exome sequencing and transcriptome analysis were performed to assess the genomic and RNA expression profiles, respectively. Copy number variation and mutational burden were analyzed and compared with other metastatic breast cancer genomic results. Mutational signatures were also analyzed. The antitumor effect of an ATR inhibitor was tested in the relevant PDX model.

          Results

          Of the 151 cases studied, 40 (26%) PDX models were established. Notably, the take rate of all subtypes, including the hormone receptor-positive (HR +) subtype, exceeded 20%. The PDX model had genomic fidelity and copy number variation that represented the pattern of its donor sample. TP53, PIK3CA, ESR1, and GATA3 mutations were frequently found in our samples, with TP53 being the most frequently mutated, and the somatic mutations in these genes strengthened their frequency in the PDX model. The ESR1 mutation, CCND1 amplification, and the APOBEC signature were significant features in our HR + HER2- PDX model. Fulvestrant in combination with palbociclib showed a partial response to the relevant patient’s tumor harboring the ESR1 mutation, and CCND1 amplification was found in the PDX model. AZD6738, an ATR inhibitor, delayed tumor growth in a relevant PDX model.

          Conclusions

          Our PDX model was established using core needle biopsy samples from primary and metastatic tissues. Genomic profiles of the samples reflected their original tissue characteristics and could be used for the interpretation of clinical outcomes.

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          Most cited references46

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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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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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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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                Author and article information

                Contributors
                moisa@snu.ac.kr
                jongil@snu.ac.kr
                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central (London )
                1479-5876
                6 January 2021
                6 January 2021
                2021
                : 19
                : 7
                Affiliations
                [1 ]GRID grid.31501.36, ISNI 0000 0004 0470 5905, Cancer Research Institute, , Seoul National University, ; Seoul, Korea
                [2 ]GRID grid.31501.36, ISNI 0000 0004 0470 5905, Department of Biomedical Sciences, , Seoul National University College of Medicine, ; Seoul, Korea
                [3 ]GRID grid.412484.f, ISNI 0000 0001 0302 820X, Biomedical Research Institute, , Seoul National University Hospital, ; Seoul, Korea
                [4 ]GRID grid.31501.36, ISNI 0000 0004 0470 5905, Medical Research Center, Genomic Medicine Institute (GMI), , Seoul National University, ; 101 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
                [5 ]GRID grid.411076.5, Ewha Institute of Convergence Medicine, , Ewha Womans University Mokdong Hospital, ; Seoul, Korea
                [6 ]GRID grid.412484.f, ISNI 0000 0001 0302 820X, Department of General Surgery, , Seoul National University Hospital, ; Seoul, Korea
                [7 ]GRID grid.412484.f, ISNI 0000 0001 0302 820X, Department of Pathology, , Seoul National University Hospital, ; Seoul, Korea
                [8 ]GRID grid.31501.36, ISNI 0000 0004 0470 5905, Translational Medicine, , Seoul National University College of Medicine, ; Seoul, Korea
                [9 ]GRID grid.411725.4, ISNI 0000 0004 1794 4809, Department of Internal Medicine, , Chungbuk University Hospital, ; Cheong-Ju, Korea
                [10 ]Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
                [11 ]GRID grid.255649.9, ISNI 0000 0001 2171 7754, Department of Life Science, , Ewha Womans University, ; Seoul, Korea
                [12 ]GRID grid.249880.f, ISNI 0000 0004 0374 0039, The Jackson Laboratory for Genomic Medicine, ; Farmington, Connecticut USA
                [13 ]GRID grid.452438.c, Precision Medicine Center, , The First Affiliated Hospital of Xi’an Jiaotong University, ; Xi’an, China
                Author information
                http://orcid.org/0000-0002-5396-6533
                Article
                2607
                10.1186/s12967-020-02607-2
                7789010
                33407601
                5560a2eb-3e14-442b-8b84-2cb5edab2d64
                © The Author(s) 2020

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 4 May 2020
                : 5 November 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003710, Korea Health Industry Development Institute;
                Award ID: 800-20200226
                Award Recipient :
                Funded by: National Research Foundation of Korea
                Award ID: 0411-20200059
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004332, Seoul National University Hospital;
                Award ID: 04-2016-0630
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                Medicine
                metastatic breast cancer,patient-derived xenograft,whole-exome sequencing
                Medicine
                metastatic breast cancer, patient-derived xenograft, whole-exome sequencing

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