4.5 Article

Molecular characterization of triple negative breast cancer formaldehyde-fixed paraffin-embedded samples by data-independent acquisition proteomics

Journal

PROTEOMICS
Volume 22, Issue 3, Pages -

Publisher

WILEY
DOI: 10.1002/pmic.202100110

Keywords

molecular characterization; personalized medicine; proteomics; triple negative breast cancer

Funding

  1. Instituto de Salud Carlos III, Spanish Economy and Competitiveness Ministry, Spain [PI12/00444]
  2. PE I+D+i 2013-2016 from the Instituto de Salud Carlos III (ISCIII) [PT17/0019]
  3. ERDF
  4. Spanish Ministry of Science, Innovation and Universities, Centro de Excelencia Severo Ochoa 2013-2017 [SEV-2012-0208]
  5. Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya [2017SGR595]
  6. European Union [823839]
  7. Spanish Economy and Competitiveness Ministry [DI-15-07614, PTQ2018-009760]
  8. Consejeria de Educacion e Investigacion de la Comunidad de Madrid [IND2018/BMD-9262]

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The study analyzed the proteomes of 125 patients with triple negative breast cancer and identified two molecular groups with differences in biological processes, which may help in designing new therapeutic strategies in the future.
Triple negative breast cancer accounts for 15%-20% of all breast carcinomas and is clinically characterized by an aggressive phenotype and poor prognosis. Triple negative tumors do not benefit from targeted therapies, so further characterization is needed to define subgroups with potential therapeutic value. In this work, the proteomes of 125 formalin-fixed paraffin-embedded samples from patients diagnosed with non-metastatic triple negative breast cancer were analyzed using data-independent acquisition + in a LTQ-Orbitrap Fusion Lumos mass spectrometer coupled to an EASY-nLC 1000. 1206 proteins were identified in at least 66% of the samples. Hierarchical clustering, probabilistic graphical models and Significance Analysis of Microarrays were combined to characterize proteomics-based molecular groups. Two molecular groups were defined with differences in biological processes such as glycolysis, translation and immune response. These two molecular groups showed also several differentially expressed proteins. This clinically homogenous dataset may serve to design new therapeutic strategies in the future.

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