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      HER2-Low Breast Cancer: Molecular Characteristics and Prognosis

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          The dichotomous classification of HER2 status is experiencing a paradigm change, leading to the identification of the so-called “HER2-low” category. The aim of our retrospective, observational study was to characterize intrinsic PAM50 subtypes within HER2-low primary breast tumors extracted from The Cancer Genome Atlas (TCGA) dataset and to describe the prognostic impact of HER2-low on survival outcomes. We evaluated 804 breast cancers and we identified 410 HER2-low tumors (336 with positive hormonal receptor status (HR+) and 74 with negative HR status (HR−)). HER2-enriched tumors were more frequent in HER2-low/HR− and HER2-low/HR+ subtypes, compared to HER2-negative/HR− and HER2-negative/HR+ subtypes, respectively (13.7% versus 1.6% and 1.2% versus 0.5%, respectively). We observed no significant differences in prognosis between HER2-low subtypes and each non-HER2-low subtype when paired by HR status. Our characterization of PAM50 intrinsic subtypes within HER2-low breast cancer ultimately supports further investigation of new treatment strategies in the HER2-low category, with new promising drugs being tested in the context.

          Abstract

          Background: We aimed to determine the distribution of intrinsic subtypes within HER2-low breast cancer (BC), and to describe the prognostic impact of HER2-low status on survival outcomes. Methods: This is a retrospective, observational study of primary BC extracted from The Cancer Genome Atlas dataset. We described the distribution of PAM50 intrinsic subtypes within HER2-low BC subtype according to hormonal receptor status (positive (HR+) and negative (HR−)). Secondly, we assessed the impact of HER2-low on survival outcomes (progression-free interval (PFI), disease-free interval (DFI), and overall survival (OS)). Results: We analyzed 804 primary BCs, including 410 (51%) HER2-low BCs (336 HR+ and 74 HR−). The proportion of HER2-enriched tumors was higher in the HER2-low/HR− group compared to HER2-low/HR+ (13.7% versus 1.2%, respectively). HER2-enriched tumors were more frequent in HER2-low/HR− and HER2-low/HR+ subtypes, compared to HER2-negative/HR− and HER2-negative/HR+ subtypes, respectively (13.7% versus 1.6% and 1.2% versus 0.5%, respectively). We observed no significant differences in PFI, DFI, and OS between HER2-low subtypes and each non-HER2-low subtype paired by HR status. Conclusions: Our characterization of PAM50 intrinsic subtypes within HER2-low breast cancer may explain the different clinical behaviors and responses to treatment, and ultimately support further investigation of new treatment strategies in the HER2-low category. Moreover, it highlights the importance of considering HR status in the HER2-low category.

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          Comprehensive molecular portraits of human breast tumors

          Summary We analyzed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, mRNA arrays, microRNA sequencing and reverse phase protein arrays. Our ability to integrate information across platforms provided key insights into previously-defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at > 10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the Luminal A subtype. We identified two novel protein expression-defined subgroups, possibly contributed by stromal/microenvironmental elements, and integrated analyses identified specific signaling pathways dominant in each molecular subtype including a HER2/p-HER2/HER1/p-HER1 signature within the HER2-Enriched expression subtype. Comparison of Basal-like breast tumors with high-grade Serous Ovarian tumors showed many molecular commonalities, suggesting a related etiology and similar therapeutic opportunities. The biologic finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biologic subtypes of breast cancer.
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            TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data

            The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.
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              An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

              SUMMARY For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Cancers (Basel)
                Cancers (Basel)
                cancers
                Cancers
                MDPI
                2072-6694
                05 June 2021
                June 2021
                : 13
                : 11
                : 2824
                Affiliations
                [1 ]Academic Trials Promoting Team, Institut Jules Bordet, l’Université Libre de Bruxelles (U.L.B), 1000 Brussels, Belgium; veronique.debien@ 123456bordet.be (V.D.); evandro.azambuja@ 123456bordet.be (E.d.A.)
                [2 ]Humanitas Clinical and Research Center—IRCCS, Humanitas Cancer Center, Via Manzoni 56, 20089 Milan, Italy
                [3 ]Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Milan, Italy
                [4 ]Breast Cancer Translational Research Laboratory J.-C. Heuson, Institut Jules Bordet, Université Libre de Bruxelles, 1000 Brussels, Belgium; mattia.rediti@ 123456bordet.be (M.R.); danai.fimereli@ 123456bordet.be (D.F.); christos.sotiriou@ 123456bordet.be (C.S.)
                [5 ]Medical Oncology Department, Institut Jules Bordet, 1000 Brussels, Belgium; martine.piccart@ 123456bordet.be
                [6 ]Clinical Trials Conduct Unit, Institut Jules Bordet—Université Libre de Bruxelles, 1000 Brussels, Belgium; philippe.aftimos@ 123456bordet.be
                Author notes
                [* ]Correspondence: elisa.agostinetto@ 123456bordet.be ; Tel.: +32-(0)49-292-18-17
                [†]

                These authors contributed equally.

                Author information
                https://orcid.org/0000-0003-0295-7511
                https://orcid.org/0000-0002-6713-2599
                https://orcid.org/0000-0001-9501-4509
                Article
                cancers-13-02824
                10.3390/cancers13112824
                8201345
                34198891
                91309bef-1293-4b73-8f46-9a9be776a297
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 23 April 2021
                : 03 June 2021
                Categories
                Article

                her2-low,breast cancer,pam50,tcga,prognosis
                her2-low, breast cancer, pam50, tcga, prognosis

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