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      Intraductal xenografts show lobular carcinoma cells rely on their own extracellular matrix and LOXL1

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          Abstract

          Invasive lobular carcinoma (ILC) is the most frequent special histological subtype of breast cancer, typically characterized by loss of E‐cadherin. It has clinical features distinct from other estrogen receptor‐positive (ER +) breast cancers but the molecular mechanisms underlying its characteristic biology are poorly understood because we lack experimental models to study them. Here, we recapitulate the human disease, including its metastatic pattern, by grafting ILC‐derived breast cancer cell lines, SUM‐44 PE and MDA‐MB‐134‐VI cells, into the mouse milk ducts. Using patient‐derived intraductal xenografts from lobular and non‐lobular ER + HER2 tumors to compare global gene expression, we identify extracellular matrix modulation as a lobular carcinoma cell‐intrinsic trait. Analysis of TCGA patient datasets shows matrisome signature is enriched in lobular carcinomas with overexpression of elastin, collagens, and the collagen modifying enzyme LOXL1. Treatment with the pan LOX inhibitor BAPN and silencing of LOXL1 expression decrease tumor growth, invasion, and metastasis by disrupting ECM structure resulting in decreased ER signaling. We conclude that LOXL1 inhibition is a promising therapeutic strategy for ILC.

          Abstract

          Intraductal xenografts of invasive lobular carcinoma (ILC) cells faithfully model this breast cancer subtype, and reveal tumor cell intrinsic ECM remodeling as a critical feature of disease progression that can be exploited therapeutically by targeting LOXL1.

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          Most cited references74

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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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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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                cathrin.brisken@epfl.ch
                Journal
                EMBO Mol Med
                EMBO Mol Med
                10.1002/(ISSN)1757-4684
                EMMM
                embomm
                EMBO Molecular Medicine
                John Wiley and Sons Inc. (Hoboken )
                1757-4676
                1757-4684
                22 February 2021
                05 March 2021
                : 13
                : 3 ( doiID: 10.1002/emmm.v13.3 )
                : e13180
                Affiliations
                [ 1 ] ISREC ‐ Swiss Institute for Experimental Cancer Research School of Life Sciences Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland
                [ 2 ] Lausanne University Hospital Lausanne Switzerland
                [ 3 ] Réseau Lausannois du Sein (RLS) Lausanne Switzerland
                [ 4 ] International Cancer Prevention Institute Epalinges Switzerland
                Author notes
                [*] [* ] Corresponding author. Tel: +41 (0)21 693 07 81/sec: +41 (0)21 693 07 62; Fax: +41 (0)21 693 07 40; E‐mail: cathrin.brisken@ 123456epfl.ch

                Author information
                https://orcid.org/0000-0003-2972-0549
                https://orcid.org/0000-0002-9370-6736
                https://orcid.org/0000-0001-6568-2151
                https://orcid.org/0000-0001-7167-9223
                https://orcid.org/0000-0002-2797-7087
                https://orcid.org/0000-0002-9608-985X
                https://orcid.org/0000-0003-2496-0025
                https://orcid.org/0000-0003-2694-4491
                https://orcid.org/0000-0003-1294-6541
                https://orcid.org/0000-0002-6857-3230
                Article
                EMMM202013180
                10.15252/emmm.202013180
                7933935
                33616307
                41a9a1d0-73c1-4864-9827-f882449ca440
                © 2021 The Authors. Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 July 2020
                : 23 December 2020
                : 05 January 2021
                Page count
                Figures: 9, Tables: 0, Pages: 19, Words: 12713
                Funding
                Funded by: Biltema and ISREC Foundation
                Funded by: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (SNF) , open-funder-registry 10.13039/501100001711;
                Funded by: Swiss Cancer Research Foundation (Swiss Cancer Research)
                Categories
                Article
                Articles
                Custom metadata
                2.0
                05 March 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.9 mode:remove_FC converted:05.03.2021

                Molecular medicine
                extracellular matrix,lobular carcinoma,loxl1,preclinical models,xenografts,cancer
                Molecular medicine
                extracellular matrix, lobular carcinoma, loxl1, preclinical models, xenografts, cancer

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