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      Molecular stratification within triple-negative breast cancer subtypes

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          Abstract

          Triple-negative breast cancer (TNBC) has been subdivided into six distinct subgroups: basal-like 1 (BL1), basal-like 2 (BL2), mesenchymal (M), mesenchymal stem–like (MSL), immunomodulatory (IM), and luminal androgen receptor (LAR). We recently identified a subgroup of TNBC with loss of the tumor suppressor PTEN and five specific microRNAs that exhibits exceedingly poor clinical outcome and contains TP53 mutation, RB1 loss and high MYC and WNT signalling. Here, show that these PTEN-low/miRNA-low lesions cluster with BL1 TNBC. These tumors exhibited high RhoA signalling and were significantly stratified on the basis of PTEN-low/RhoA-signalling-high with hazard ratios (HRs) of 8.2 (P = 0.0009) and 4.87 (P = 0.033) in training and test cohorts, respectively. For BL2 TNBC, we identified AKT1 copy gain/high mRNA expression as surrogate for poor prognosis (HR = 3.9; P = 0.02 and HR = 6.1; P = 0.0032). In IM, programmed cell death 1 (PD1) was elevated and predictive of poor prognosis (HR = 5.3; P = 0.01 and HR = 3.5; P < 0.004). Additional alterations, albeit without prognostic power, characterized each subtype including high E2F2 and TGFβ signalling and CXCL8 expression in BL2, high IFNα and IFNγ signalling and CTLA4 expression in IM, and high EGFR signalling in MSL, and may be targeted for therapy. This study identified PTEN-low/RhoA-signalling-high, and high AKT1 and PD1 expression as potent prognostications for BL1, BL2 and IM subtypes with survival differences of over 14, 2.75 and 10.5 years, respectively. This intrinsic heterogeneity could be exploited to prioritize patients for precision medicine.

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          Most cited references20

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          Molecular characterization of basal-like and non-basal-like triple-negative breast cancer.

          Triple-negative (TN) and basal-like (BL) breast cancer definitions have been used interchangeably to identify breast cancers that lack expression of the hormone receptors and overexpression and/or amplification of HER2. However, both classifications show substantial discordance rates when compared to each other. Here, we molecularly characterize TN tumors and BL tumors, comparing and contrasting the results in terms of common patterns and distinct patterns for each. In total, when testing 412 TN and 473 BL tumors, 21.4% and 31.5% were identified as non-BL and non-TN, respectively. TN tumors identified as luminal or HER2-enriched (HER2E) showed undistinguishable overall gene expression profiles when compared versus luminal or HER2E tumors that were not TN. Similar findings were observed within BL tumors regardless of their TN status, which suggests that molecular subtype is preserved regardless of individual marker results. Interestingly, most TN tumors identified as HER2E showed low HER2 expression and lacked HER2 amplification, despite the similar overall gene expression profiles to HER2E tumors that were clinically HER2-positive. Lastly, additional genomic classifications were examined within TN and BL cancers, most of which were highly concordant with tumor intrinsic subtype. These results suggest that future clinical trials focused on TN disease should consider stratifying patients based upon BL versus non-BL gene expression profiles, which appears to be the main biological difference seen in patients with TN breast cancer.
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            The shaping and functional consequences of the microRNA landscape in breast cancer.

            MicroRNAs (miRNAs) show differential expression across breast cancer subtypes, and have both oncogenic and tumour-suppressive roles. Here we report the miRNA expression profiles of 1,302 breast tumours with matching detailed clinical annotation, long-term follow-up and genomic and messenger RNA expression data. This provides a comprehensive overview of the quantity, distribution and variation of the miRNA population and provides information on the extent to which genomic, transcriptional and post-transcriptional events contribute to miRNA expression architecture, suggesting an important role for post-transcriptional regulation. The key clinical parameters and cellular pathways related to the miRNA landscape are characterized, revealing context-dependent interactions, for example with regards to cell adhesion and Wnt signalling. Notably, only prognostic miRNA signatures derived from breast tumours devoid of somatic copy-number aberrations (CNA-devoid) are consistently prognostic across several other subtypes and can be validated in external cohorts. We then use a data-driven approach to seek the effects of miRNAs associated with differential co-expression of mRNAs, and find that miRNAs act as modulators of mRNA-mRNA interactions rather than as on-off molecular switches. We demonstrate such an important modulatory role for miRNAs in the biology of CNA-devoid breast cancers, a common subtype in which the immune response is prominent. These findings represent a new framework for studying the biology of miRNAs in human breast cancer.
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              A pathway-based classification of human breast cancer.

              The hallmark of human cancer is heterogeneity, reflecting the complexity and variability of the vast array of somatic mutations acquired during oncogenesis. An ability to dissect this heterogeneity, to identify subgroups that represent common mechanisms of disease, will be critical to understanding the complexities of genetic alterations and to provide a framework to develop rational therapeutic strategies. Here, we describe a classification scheme for human breast cancer making use of patterns of pathway activity to build on previous subtype characterizations using intrinsic gene expression signatures, to provide a functional interpretation of the gene expression data that can be linked to therapeutic options. We show that the identified subgroups provide a robust mechanism for classifying independent samples, identifying tumors that share patterns of pathway activity and exhibit similar clinical and biological properties, including distinct patterns of chromosomal alterations that were not evident in the heterogeneous total population of tumors. We propose that this classification scheme provides a basis for understanding the complex mechanisms of oncogenesis that give rise to these tumors and to identify rational opportunities for combination therapies.
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                Author and article information

                Contributors
                eldad.zacksenhaus@utoronto.ca
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                13 December 2019
                13 December 2019
                2019
                : 9
                : 19107
                Affiliations
                [1 ]ISNI 0000 0004 0474 0428, GRID grid.231844.8, Toronto General Research Institute, , University Health Network, ; 67 College Street, Toronto, Ontario M5G 2M1 Canada
                [2 ]The Key laboratory of Chemistry for Natural Products of Guizhou Province and Chinese Academic of Sciences, Guiyang, Guizhou 550014 China
                [3 ]ISNI 0000 0000 9330 9891, GRID grid.413458.f, State Key Laboratory for Functions and Applications of Medicinal Plants, , Guizhou Medical University, ; Guiyang, 550025 China
                [4 ]ISNI 0000 0004 0626 6184, GRID grid.250674.2, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Avenue, ; Toronto, ON Canada
                [5 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Medicine, , University of Toronto, ; Toronto, Ontario Canada
                Author information
                http://orcid.org/0000-0002-5434-287X
                http://orcid.org/0000-0003-3731-5797
                Article
                55710
                10.1038/s41598-019-55710-w
                6911070
                31836816
                29382485-e337-4a07-9465-448eaad5a97d
                © The Author(s) 2019

                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
                : 7 August 2019
                : 2 December 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000015, Canadian Cancer Society Research Institute (Société Canadienne du Cancer);
                Funded by: FundRef https://doi.org/10.13039/501100002655, Terry Fox Foundation;
                Funded by: CBCF/CCS-Canada TFRI - Canada
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                breast cancer,genome informatics
                Uncategorized
                breast cancer, genome informatics

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