Literature DB >> 33574293

Characterization of four subtypes in morphologically normal tissue excised proximal and distal to breast cancer.

Louise J Jones1, Claude Chelala2,3, Emanuela Gadaleta4, Pauline Fourgoux4,5, Stefano Pirró4, Graeme J Thorn4, Rachel Nelan1, Alastair Ironside1, Vinothini Rajeeve6, Pedro R Cutillas6, Anna E Lobley4, Jun Wang6, Esteban Gea5,7, Helen Ross-Adams4, Conrad Bessant5,7, Nicholas R Lemoine8.   

Abstract

Widespread mammographic screening programs and improved self-monitoring allow for breast cancer to be detected earlier than ever before. Breast-conserving surgery is a successful treatment for select women. However, up to 40% of women develop local recurrence after surgery despite apparently tumor-free margins. This suggests that morphologically normal breast may harbor early alterations that contribute to increased risk of cancer recurrence. We conducted a comprehensive transcriptomic and proteomic analysis to characterize 57 fresh-frozen tissues from breast cancers and matched histologically normal tissues resected proximal to (<2 cm) and distant from (5-10 cm) the primary tumor, using tissues from cosmetic reduction mammoplasties as baseline. Four distinct transcriptomic subtypes are identified within matched normal tissues: metabolic; immune; matrisome/epithelial-mesenchymal transition, and non-coding enriched. Key components of the subtypes are supported by proteomic and tissue composition analyses. We find that the metabolic subtype is associated with poor prognosis (p < 0.001, HR6.1). Examination of genes representing the metabolic signature identifies several genes able to prognosticate outcome from histologically normal tissues. A subset of these have been reported for their predictive ability in cancer but, to the best of our knowledge, these have not been reported altered in matched normal tissues. This study takes an important first step toward characterizing matched normal tissues resected at pre-defined margins from the primary tumor. Unlocking the predictive potential of unexcised tissue could prove key to driving the realization of personalized medicine for breast cancer patients, allowing for more biologically-driven analyses of tissue margins than morphology alone.

Year:  2020        PMID: 33574293     DOI: 10.1038/s41523-020-00182-9

Source DB:  PubMed          Journal:  NPJ Breast Cancer        ISSN: 2374-4677


  42 in total

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Authors:  Jean-Philippe Brunet; Pablo Tamayo; Todd R Golub; Jill P Mesirov
Journal:  Proc Natl Acad Sci U S A       Date:  2004-03-11       Impact factor: 11.205

2.  Metabolic shifts in residual breast cancer drive tumor recurrence.

Authors:  Kristina M Havas; Vladislava Milchevskaya; Ksenija Radic; Ashna Alladin; Eleni Kafkia; Marta Garcia; Jens Stolte; Bernd Klaus; Nicole Rotmensz; Toby J Gibson; Barbara Burwinkel; Andreas Schneeweiss; Giancarlo Pruneri; Kiran R Patil; Rocio Sotillo; Martin Jechlinger
Journal:  J Clin Invest       Date:  2017-05-15       Impact factor: 14.808

3.  Breast conservation versus mastectomy for patients with T3 primary tumors (>5 cm): A review of 5685 medicare patients.

Authors:  Richard J Bleicher; Karen Ruth; Elin R Sigurdson; John M Daly; Marcia Boraas; Penny R Anderson; Brian L Egleston
Journal:  Cancer       Date:  2015-10-19       Impact factor: 6.860

4.  Long-term follow-up of a randomised trial designed to determine the need for irradiation following conservative surgery for the treatment of invasive breast cancer.

Authors:  H T Ford; R C Coombes; J-C Gazet; R Gray; C C McConkey; R Sutcliffe; J Quilliam; S Lowndes
Journal:  Ann Oncol       Date:  2005-12-05       Impact factor: 32.976

5.  Antineoplastic effects of an Aurora B kinase inhibitor in breast cancer.

Authors:  Christopher P Gully; Fanmao Zhang; Jian Chen; James A Yeung; Guermarie Velazquez-Torres; Edward Wang; Sai-Ching Jim Yeung; Mong-Hong Lee
Journal:  Mol Cancer       Date:  2010-02-22       Impact factor: 27.401

6.  The Molecular Signatures Database (MSigDB) hallmark gene set collection.

Authors:  Arthur Liberzon; Chet Birger; Helga Thorvaldsdóttir; Mahmoud Ghandi; Jill P Mesirov; Pablo Tamayo
Journal:  Cell Syst       Date:  2015-12-23       Impact factor: 10.304

7.  Supervised risk predictor of breast cancer based on intrinsic subtypes.

Authors:  Joel S Parker; Michael Mullins; Maggie C U Cheang; Samuel Leung; David Voduc; Tammi Vickery; Sherri Davies; Christiane Fauron; Xiaping He; Zhiyuan Hu; John F Quackenbush; Inge J Stijleman; Juan Palazzo; J S Marron; Andrew B Nobel; Elaine Mardis; Torsten O Nielsen; Matthew J Ellis; Charles M Perou; Philip S Bernard
Journal:  J Clin Oncol       Date:  2009-02-09       Impact factor: 44.544

8.  Kinase-substrate enrichment analysis provides insights into the heterogeneity of signaling pathway activation in leukemia cells.

Authors:  Pedro Casado; Juan-Carlos Rodriguez-Prados; Sabina C Cosulich; Sylvie Guichard; Bart Vanhaesebroeck; Simon Joel; Pedro R Cutillas
Journal:  Sci Signal       Date:  2013-03-26       Impact factor: 8.192

9.  Transcriptional Profiles from Paired Normal Samples Offer Complementary Information on Cancer Patient Survival--Evidence from TCGA Pan-Cancer Data.

Authors:  Xiu Huang; David F Stern; Hongyu Zhao
Journal:  Sci Rep       Date:  2016-02-03       Impact factor: 4.379

10.  Comprehensive analysis of normal adjacent to tumor transcriptomes.

Authors:  Dvir Aran; Roman Camarda; Justin Odegaard; Hyojung Paik; Boris Oskotsky; Gregor Krings; Andrei Goga; Marina Sirota; Atul J Butte
Journal:  Nat Commun       Date:  2017-10-20       Impact factor: 14.919

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