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      A combination of extracellular matrix‐ and interferon‐associated signatures identifies high‐grade breast cancers with poor prognosis

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          Abstract

          Breast cancer (BC) is a heterogeneous disease in which the tumor microenvironment (TME) seems to impact the clinical outcome. Here, we investigated whether a combination of gene expression signatures relating to both the structural and immune TME aspects can help predict prognosis in women with high‐grade BC (HGBC). Thus, we focused on a combined molecular biomarker variable that involved extracellular matrix (ECM)‐associated gene expression (ECM3 signature) and interferon (IFN)‐associated metagene (IFN metagene) expression. In 97 chemo‐naive HGBCs from the METABRIC dataset, the dichotomous ECM3/IFN (dECIF) variable identified a group of high‐risk patients (ECM3 +/IFN vs other; hazard ratio = 3.2, 95% confidence interval: 1.5–6.7). Notably, ECM3 +/IFN tumors showed low tumor‐infiltrating lymphocytes, high levels of CD33‐positive cells, absence of PD‐1 expression, or low expression of PD‐L1, as suggested by immune profiles and immune‐histochemical analysis on an independent cohort of 131 HGBCs. To make our results transferable to clinical use, we refined the dECIF biomarker using reduced ECM3 and IFN signatures; notably, the prognostic value of this reduced dECIF was comparable to that of the original dECIF. After validation in a new BC cohort, reduced dECIF was translated into a robust qPCR classifier for real‐world clinical use.

          Abstract

          This study investigates the combination of two relevant molecular signatures of the tumor microenvironment (TME) in identifying aggressive high‐grade breast cancers (HGBCs). The novel molecular biomarker described here highlighted patients with worst HGBC prognosis and a peculiar TME. Finally, to improve the potential of the identified biomarker in the clinic, we reduced the two original signatures and technically validated the biomarker's expression through a qPCR‐based assay.

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          Robust enumeration of cell subsets from tissue expression profiles

          We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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            The measurement of observer agreement for categorical data.

            This paper presents a general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies. The procedure essentially involves the construction of functions of the observed proportions which are directed at the extent to which the observers agree among themselves and the construction of test statistics for hypotheses involving these functions. Tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interobserver agreement are developed as generalized kappa-type statistics. These procedures are illustrated with a clinical diagnosis example from the epidemiological literature.
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              The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups

              The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ~40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA–RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the ‘CNA-devoid’ subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.
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                Author and article information

                Contributors
                massimo.dinicola@istitutotumori.mi.it
                Journal
                Mol Oncol
                Mol Oncol
                10.1002/(ISSN)1878-0261
                MOL2
                Molecular Oncology
                John Wiley and Sons Inc. (Hoboken )
                1574-7891
                1878-0261
                19 February 2021
                May 2021
                : 15
                : 5 ( doiID: 10.1002/mol2.v15.5 )
                : 1345-1357
                Affiliations
                [ 1 ] Bioinformatics and Biostatistics Unit Department of Applied Research and Technological Development Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy
                [ 2 ] Biomarker Unit Department of Applied Research and Technological Development Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy
                [ 3 ] Unit of Immunotherapy and Anticancer Innovative Therapeutics Department of Medical Oncology and Hematology Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy
                [ 4 ] Pathology A Unit Department of Pathology Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy
                [ 5 ] Molecular Immunology Unit Department of Research Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy
                [ 6 ] Molecular Targeting Unit Department of Research Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy
                [ 7 ] Medical Oncology Department Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy
                Author notes
                [*] [* ] Correspondence

                M. Di Nicola, Immunotherapy and Innovative Therapeutics Unit, Fondazione IRCCS Istituto Nazionale Tumori, Via Venezian, 1 20133 Milan, Italy

                Tel: +39 02 2390 2506

                E‐mail: massimo.dinicola@ 123456istitutotumori.mi.it

                Author information
                https://orcid.org/0000-0001-5877-0639
                Article
                MOL212912
                10.1002/1878-0261.12912
                8096783
                33523584
                849aebf4-fefb-429e-b376-2431ab0f201e
                © 2021 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.

                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
                : 14 December 2020
                : 23 September 2020
                : 27 January 2021
                Page count
                Figures: 6, Tables: 4, Pages: 13, Words: 7111
                Funding
                Funded by: Ministero della Salute , open-funder-registry 10.13039/501100003196;
                Funded by: Associazione Italiana per la Ricerca sul Cancro , open-funder-registry 10.13039/501100005010;
                Award ID: IG4915
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                May 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.2 mode:remove_FC converted:04.05.2021

                Oncology & Radiotherapy
                gene signature,high‐grade breast cancer,prognostic marker,tumor microenvironment

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