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

Authors
  • Gadaleta, Emanuela1
  • Fourgoux, Pauline1, 1
  • Pirró, Stefano1
  • Thorn, Graeme J.1
  • Nelan, Rachel1
  • Ironside, Alastair1
  • Rajeeve, Vinothini1
  • Cutillas, Pedro R.1
  • Lobley, Anna E.1
  • Wang, Jun1
  • Gea, Esteban1, 2
  • Ross-Adams, Helen1
  • Bessant, Conrad1, 2
  • Lemoine, Nicholas R.1
  • Jones, Louise J.1
  • Chelala, Claude1, 1
  • 1 Queen Mary University of London, London, EC1M 6BQ, UK , London (United Kingdom)
  • 2 Queen Mary University of London, London, E1 4NS, UK , London (United Kingdom)
Type
Published Article
Journal
npj Breast Cancer
Publisher
Nature Publishing Group UK
Publication Date
Aug 21, 2020
Volume
6
Issue
1
Identifiers
DOI: 10.1038/s41523-020-00182-9
Source
Springer Nature
License
Green

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.

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