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      An In Vivo CRISPR Screen Identifies Stepwise Genetic Dependencies of Metastatic Progression

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          Abstract

          A loss-of-function CRISPR screen in human CTC-derived xenografts identifies genes critical for individual steps of the metastatic cascade, suggesting novel drivers and treatment opportunities for metastatic breast cancers.

          Abstract

          Blood-borne metastasis of breast cancer involves a series of tightly regulated sequential steps, including the growth of a primary tumor lesion, intravasation of circulating tumor cells (CTC), and adaptation in various distant metastatic sites. The genes orchestrating each of these steps are poorly understood in physiologically relevant contexts, owing to the rarity of experimental models that faithfully recapitulate the biology, growth kinetics, and tropism of human breast cancer. Here, we conducted an in vivo loss-of-function CRISPR screen in newly derived CTC xenografts, unique in their ability to spontaneously mirror the human disease, and identified specific genetic dependencies for each step of the metastatic process. Validation experiments revealed sensitivities to inhibitors that are already available, such as PLK1 inhibitors, to prevent CTC intravasation. Together, these findings present a new tool to reclassify driver genes involved in the spread of human cancer, providing insights into the biology of metastasis and paving the way to test targeted treatment approaches.

          Significance:

          A loss-of-function CRISPR screen in human CTC-derived xenografts identifies genes critical for individual steps of the metastatic cascade, suggesting novel drivers and treatment opportunities for metastatic breast cancers.

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          Most cited references41

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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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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Journal
                Cancer Res
                Cancer Res
                Cancer Research
                American Association for Cancer Research
                0008-5472
                1538-7445
                15 February 2022
                16 December 2021
                : 82
                : 4
                : 681-694
                Affiliations
                [1 ]Department of Biomedicine, Cancer Metastasis Laboratory, University of Basel and University Hospital Basel, Basel, Switzerland.
                [2 ]Department of Biology, Molecular Oncology Laboratory, Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
                [3 ]Swiss Institute of Bioinformatics, Lausanne, Switzerland.
                [4 ]Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland.
                [5 ]Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
                [6 ]Department of Medical Oncology, University Hospital Basel, Basel, Switzerland.
                [7 ]Department of Biomedicine, Embryology and Stem Cell Biology Laboratory, University of Basel and University Hospital Basel, Basel, Switzerland.
                Author notes
                [* ] Corresponding Author: Nicola Aceto, Swiss Federal Institute of Technology (ETH) Zurich, Institute of Molecular Health Sciences, Otto-Stern-Weg 7, CH-8093 Zurich, Switzerland. Phone: 41-44-633-4023; E-mail: nicola.aceto@ 123456biol.ethz.ch
                Author information
                https://orcid.org/0000-0001-6111-0754
                https://orcid.org/0000-0002-8332-0899
                https://orcid.org/0000-0002-2914-7052
                Article
                CAN-21-3908
                10.1158/0008-5472.CAN-21-3908
                7612409
                34916221
                c3e8e86a-980b-4c97-ad88-b063522c7ba2
                ©2021 The Authors; Published by the American Association for Cancer Research

                This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

                History
                : 14 November 2021
                : 30 November 2021
                : 13 December 2021
                Page count
                Pages: 14
                Funding
                Funded by: Swiss Cancer League, DOI ;
                Award ID: KLS-4518-08-2018
                Funded by: European Research Council, DOI https://doi.org/10.13039/501100000781;
                Award ID: #101001652
                Funded by: European Union, DOI ;
                Award ID: 801159-B2B
                Funded by: Swiss National Science Foundation, DOI ;
                Award ID: PP00P3_190077
                Funded by: Swiss Cancer League, DOI ;
                Award ID: KLS-4834-08-2019
                Funded by: Basel Cancer League, DOI ;
                Award ID: KLbB-4763-02-2019
                Funded by: ETH Zürich, DOI ;
                Award ID: PMB-01-16
                Funded by: University of Basel and the ETH Zurich, DOI ;
                Award ID: #101028567
                Categories
                Tumor Biology and Immunology

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