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Molecular characterization of triple negative breast cancer formaldehyde-fixed paraffin-embedded samples by data-independent acquisition proteomics |
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| Authors: | Silvia García-Adrián Lucía Trilla-Fuertes Angelo Gámez-Pozo Cristina Chiva Rocío López-Vacas Elena López-Camacho Andrea Zapater-Moros María I. Lumbreras-Herrera David Hardisson Laura Yébenes Pilar Zamora Eduard Sabidó Juan Ángel Fresno Vara Enrique Espinosa |
| Affiliation: | 1. Medical Oncology Service, Hospital Universitario de Móstoles, Madrid, Spain;2. Biomedica Molecular Medicine SL, Madrid, Spain;3. Molecular Oncology and Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Madrid, Spain;4. Proteomics Unit, Center for Genomics Regulation, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain Proteomics Unit, Universitat Pompeu Fabra, Barcelona, Spain;5. Molecular Pathology and Therapeutic Targets Group, La Paz University Hospital (IdiPAZ), Madrid, Spain;6. Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, Madrid, Spain Medical Oncology Service, La Paz University Hospital-IdiPAZ, Madrid, Spain Cátedra UAM-Amgen, Universidad Autónoma de Madrid, Madrid, Spain;7. Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, Madrid, Spain |
| Abstract: | 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. |
| Keywords: | molecular characterization personalized medicine proteomics triple negative breast cancer |