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      Mutual exclusivity of ESR1 and TP53 mutations in endocrine resistant metastatic breast cancer

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          Abstract

          Both TP53 and ESR1 mutations occur frequently in estrogen receptor positive (ER+) metastatic breast cancers (MBC) and their distinct roles in breast cancer tumorigenesis and progression are well appreciated. Recent clinical studies discovered mutual exclusivity between TP53 and ESR1 mutations in metastatic breast cancers; however, mechanisms underlying this intriguing clinical observation remain largely understudied and unknown. Here, we explored the interplay between TP53 and ESR1 mutations using publicly available clinical and experimental data sets. We first confirmed the robust mutational exclusivity using six independent cohorts with 1,056 ER+ MBC samples and found that the exclusivity broadly applies to all ER+ breast tumors regardless of their clinical and distinct mutational features. ESR1 mutant tumors do not exhibit differential p53 pathway activity, whereas we identified attenuated ER activity and expression in TP53 mutant tumors, driven by a p53-associated E2 response gene signature. Further, 81% of these p53-associated E2 response genes are either direct targets of wild-type (WT) p53-regulated transactivation or are mutant p53-associated microRNAs, representing bimodal mechanisms of ER suppression. Lastly, we analyzed the very rare cases with co-occurrences of TP53 and ESR1 mutations and found that their simultaneous presence was also associated with reduced ER activity. In addition, tumors with dual mutations showed higher levels of total and PD-L1 positive macrophages. In summary, our study utilized multiple publicly available sources to explore the mechanism underlying the mutual exclusivity between ESR1 and TP53 mutations, providing further insights and testable hypotheses of the molecular interplay between these two pivotal genes in ER+ MBC.

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          Cancer Statistics, 2021

          Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2017) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2018) were collected by the National Center for Health Statistics. In 2021, 1,898,160 new cancer cases and 608,570 cancer deaths are projected to occur in the United States. After increasing for most of the 20th century, the cancer death rate has fallen continuously from its peak in 1991 through 2018, for a total decline of 31%, because of reductions in smoking and improvements in early detection and treatment. This translates to 3.2 million fewer cancer deaths than would have occurred if peak rates had persisted. Long-term declines in mortality for the 4 leading cancers have halted for prostate cancer and slowed for breast and colorectal cancers, but accelerated for lung cancer, which accounted for almost one-half of the total mortality decline from 2014 to 2018. The pace of the annual decline in lung cancer mortality doubled from 3.1% during 2009 through 2013 to 5.5% during 2014 through 2018 in men, from 1.8% to 4.4% in women, and from 2.4% to 5% overall. This trend coincides with steady declines in incidence (2.2%-2.3%) but rapid gains in survival specifically for nonsmall cell lung cancer (NSCLC). For example, NSCLC 2-year relative survival increased from 34% for persons diagnosed during 2009 through 2010 to 42% during 2015 through 2016, including absolute increases of 5% to 6% for every stage of diagnosis; survival for small cell lung cancer remained at 14% to 15%. Improved treatment accelerated progress against lung cancer and drove a record drop in overall cancer mortality, despite slowing momentum for other common cancers.
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            Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

            The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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              GSVA: gene set variation analysis for microarray and RNA-Seq data

              Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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                Author and article information

                Contributors
                oesterreichs@upmc.edu
                Journal
                NPJ Breast Cancer
                NPJ Breast Cancer
                NPJ Breast Cancer
                Nature Publishing Group UK (London )
                2374-4677
                10 May 2022
                10 May 2022
                2022
                : 8
                : 62
                Affiliations
                [1 ]GRID grid.21925.3d, ISNI 0000 0004 1936 9000, Department of Pharmacology and Chemical Biology, , University of Pittsburgh, ; Pittsburgh, PA USA
                [2 ]GRID grid.460217.6, ISNI 0000 0004 0387 4432, Women’s Cancer Research Center, Magee Women’s Research Institute, UPMC Hillman Cancer Center, ; Pittsburgh, PA USA
                [3 ]GRID grid.430503.1, ISNI 0000 0001 0703 675X, Department of Pathology, , University of Colorado Anschutz Medical Campus, ; Aurora, CO USA
                [4 ]GRID grid.430503.1, ISNI 0000 0001 0703 675X, School of Medicine, Division of Oncology, University of Colorado Anschutz Medical Campus, ; Aurora, CO USA
                Author information
                http://orcid.org/0000-0003-2915-7442
                http://orcid.org/0000-0002-9960-0991
                http://orcid.org/0000-0001-9917-514X
                http://orcid.org/0000-0002-2537-6923
                Article
                426
                10.1038/s41523-022-00426-w
                9090919
                35538119
                983c8f04-8c96-4374-9fc1-9a628d91ecc7
                © The Author(s) 2022

                Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 November 2021
                : 31 March 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100001006, Breast Cancer Research Foundation (BCRF);
                Funded by: FundRef https://doi.org/10.13039/100009634, Susan G. Komen (Susan G. Komen Breast Cancer Foundation);
                Award ID: SAC160073
                Award ID: SAC110021
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000054, U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI);
                Award ID: R01CA221303
                Award Recipient :
                Funded by: John S. Lazo Cancer Pharmacology Fellowship
                Funded by: FundRef https://doi.org/10.13039/100000005, U.S. Department of Defense (United States Department of Defense);
                Award ID: W81XWH-13-1-0090/91
                Award ID: W81XWH-13-1-0090/91
                Award Recipient :
                Categories
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                Custom metadata
                © The Author(s) 2022

                breast cancer,epistasis,transcription
                breast cancer, epistasis, transcription

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