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      Pan‐Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival

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          Abstract

          Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours have been extensively characterised, the measurements of protein activities have been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours and 77 cell lines that we used to estimate activity changes in 218 kinases and 292 TFs. Co‐regulation of kinase and TF activities reflects previously known regulatory relationships and allows us to dissect genetic drivers of signalling changes in cancer. We find that loss‐of‐function mutations are not often associated with the dysregulation of downstream targets, suggesting frequent compensatory mechanisms. Finally, we identified the activities most differentially regulated in cancer subtypes and showed how these can be linked to differences in patient survival. Our results provide broad insights into the dysregulation of protein activities in cancer and their contribution to disease severity.

          Abstract

          Estimation of activity changes for over 500 kinases and transcription factors in more than 1,000 cancer samples and cell lines identifies signalling regulators often dysregulated in cancer associated with patient survival.

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          Hallmarks of Cancer: The Next Generation

          The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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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
                pbeltrao@ethz.ch
                Journal
                Mol Syst Biol
                Mol Syst Biol
                10.1002/(ISSN)1744-4292
                MSB
                msb
                Molecular Systems Biology
                John Wiley and Sons Inc. (Hoboken )
                1744-4292
                23 January 2023
                March 2023
                : 19
                : 3 ( doiID: 10.1002/msb.v19.3 )
                : e10631
                Affiliations
                [ 1 ] European Molecular Biology Laboratory European Bioinformatics Institute Cambridge UK
                [ 2 ] Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3s) Porto Portugal
                [ 3 ] Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP) Porto Portugal
                [ 4 ] Graduate Program in Areas of Basic and Applied Biology (GABBA) Abel Salazar Biomedical Sciences Institute, University of Porto Porto Portugal
                [ 5 ] Faculty of Medicine, and Heidelberg University Hospital Institute for Computational Biomedicine, Heidelberg University Heidelberg Germany
                [ 6 ] Faculty of Medicine Institute of Experimental Medicine and Systems Biology, RWTH Aachen University Aachen Germany
                [ 7 ] Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland
                Author notes
                [*] [* ]Corresponding author. Tel: +41 44 633 36 88; E‐mail: pbeltrao@ 123456ethz.ch
                Author information
                https://orcid.org/0000-0002-8294-2995
                https://orcid.org/0000-0002-8552-8976
                https://orcid.org/0000-0002-2724-7703
                Article
                MSB202110631
                10.15252/msb.202110631
                9996241
                36688815
                df414181-5ed3-4eec-bfd9-d4429f22ac36
                © 2023 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
                : 06 January 2023
                : 06 August 2021
                : 09 January 2023
                Page count
                Figures: 6, Tables: 0, Pages: 18, Words: 17446
                Funding
                Funded by: Bundesministerium für Bildung und Forschung (BMBF) , doi 10.13039/501100002347;
                Award ID: 031L0212A
                Funded by: European Molecular Biology Laboratory (EMBL) , doi 10.13039/100013060;
                Funded by: Foundation for science and Technology Portugal
                Award ID: PD/BD/128007/2016
                Categories
                Article
                Articles
                Custom metadata
                2.0
                9 March 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.6 mode:remove_FC converted:09.03.2023

                Quantitative & Systems biology
                adaptation,cancer genomics,cell signalling,phosphoproteomics,protein activities,cancer,computational biology,signal transduction

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