Triple-negative breast cancer is a heterogeneous disease characterized by a lack of hormonal receptors and HER2 overexpression. It is the only breast cancer subgroup that does not benefit from targeted therapies, and its prognosis is poor. Several studies have developed specific molecular classifications for triple-negative breast cancer. However, these molecular subtypes have had little impact in the clinical setting. Gene expression data and clinical information from 494 triple-negative breast tumors were obtained from public databases. First, a probabilistic graphical model approach to associate gene expression profiles was performed. Then, sparse k-means was used to establish a new molecular classification. Results were then verified in a second database including 153 triple-negative breast tumors treated with neoadjuvant chemotherapy. Clinical and gene expression data from 494 triple-negative breast tumors were analyzed. Tumors in the dataset were divided into four subgroups (luminal-androgen receptor expressing, basal, claudin-low and claudin-high), using the cancer stem cell hypothesis as reference. These four subgroups were defined and characterized through hierarchical clustering and probabilistic graphical models and compared with previously defined classifications. In addition, two subgroups related to immune activity were defined. This immune activity showed prognostic value in the whole cohort and in the luminal subgroup. The claudin-high subgroup showed poor response to neoadjuvant chemotherapy. Through a novel analytical approach we proved that there are at least two independent sources of biological information: cellular and immune. Thus, we developed two different and overlapping triple-negative breast cancer classifications and showed that the luminal immune-positive subgroup had better prognoses than the luminal immune-negative. Finally, this work paves the way for using the defined classifications as predictive features in the neoadjuvant scenario.

Breast cancer is a heterogeneous disease comprising a variety of entities with various genetic backgrounds. Estrogen receptor-positive, human epidermal growth factor receptor 2-negative tumors typically have a favorable outcome; however, some patients eventually relapse, which suggests some heterogeneity within this category. In the present study, we used proteomics and miRNA profiling techniques to characterize a set of 102 either estrogen receptor-positive (ER+)/progesterone receptor-positive (PR+) or triple-negative formalin-fixed, paraffin-embedded breast tumors. Protein expression-based probabilistic graphical models and flux balance analyses revealed that some ER+/PR+ samples had a protein expression profile similar to that of triple-negative samples and had a clinical outcome similar to those with triple-negative disease. This probabilistic graphical model-based classification had prognostic value in patients with luminal A breast cancer. This prognostic information was independent of that provided by standard genomic tests for breast cancer, such as MammaPrint, OncoType Dx and the 8-gene Score.

Triple-negative breast cancer (TNBC) accounts for 15–20% of all breast cancers and usually requires the administration of adjuvant chemotherapy after surgery but even with this treatment many patients still suffer from a relapse. The main objective of this study was to identify proteomics-based biomarkers that predict the response to standard adjuvant chemotherapy, so that patients at are not going to benefit from it can be offered therapeutic alternatives. We analyzed the proteome of a retrospective series of formalin-fixed, paraffin-embedded TNBC tissue applying high-throughput label-free quantitative proteomics. We identified several protein signatures with predictive value, which were validated with quantitative targeted proteomics in an independent cohort of patients and further evaluated in publicly available transcriptomics data. Using univariate Cox analysis, a panel of 18 proteins was significantly associated with distant metastasis-free survival of patients (p<0.01). A reduced 5-protein profile with prognostic value was identified and its prediction performance was assessed in an independent targeted proteomics experiment and a publicly available transcriptomics dataset. Predictor P5 including peptides from proteins RAC2, RAB6A, BIEA and IPYR was the best performance protein combination in predicting relapse after adjuvant chemotherapy in TNBC patients. This study identified a protein combination signature that complements histopathological prognostic factors in TNBC treated with adjuvant chemotherapy. The protein signature can be used in paraffin-embedded samples, and after a prospective validation in independent series, it could be used as predictive clinical test in order to recommend participation in clinical trials or a more exhaustive follow-up.

// <![CDATA[ $('.header-date').hide();$('#titleAuthors').hide();$('#abstractHeader').hide(); // ]]> Lucía Trilla-Fuertes1, 2, Angelo Gámez-Pozo1, 2, Jorge M. Arevalillo3, Mariana Díaz-Almirón4, Guillermo Prado-Vázquez1, Andrea Zapater-Moros1, Hilario Navarro3, Rosa Aras-López5, Irene Dapía6, 7, Rocío López-Vacas1, Paolo Nanni8, Sara Llorente-Armijo1, Pedro Arias6, 7, Alberto M. Borobia9, Paloma Maín10, Jaime Feliú11, 12, 13, Enrique Espinosa11, 12 and Juan Ángel Fresno Vara1, 2, 12 1Molecular Oncology and Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Madrid, Spain 2Biomedica Molecular Medicine SL, Madrid, Spain 3Operational Research and Numerical Analysis, National Distance Education University (UNED), Spain 4Biostatistics Unit, La Paz University Hospital-IdiPAZ, Madrid, Spain 5Congenital Malformations Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital, IdiPAZ, Madrid, Spain 6Pharmacogenetics Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Autonomous University of Madrid, Madrid, Spain 7Biomedical Research Networking Center on Rare Diseases-CIBERER, ISCIII, Madrid, Spain 8Functional Genomics Center Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland 9Clinical Pharmacology Department, La Paz University Hospital School of Medicine, IdiPAZ, Autonomous University of Madrid, Madrid, Spain 10Department of Statistics and Operations Research, Faculty of Mathematics, Complutense University of Madrid, Madrid, Spain 11Medical Oncology Service, La Paz University Hospital-IdiPAZ, Madrid, Spain 12Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, Madrid, Spain 13Cátedra UAM-AMGEN, Universidad Autónoma de Madrid, Madrid, Spain Correspondence to: Juan Ángel Fresno Vara, email: juanangel.fresno@salud.madrid.org Keywords: breast cancer; flux balance analysis; metabolism; perturbation experiments; proteomics Received: October 29, 2017 Accepted: January 03, 2018 Published: January 08, 2018 ABSTRACT Metabolic reprogramming is a hallmark of cancer. It has been described that breast cancer subtypes present metabolism differences and this fact enables the possibility of using metabolic inhibitors as targeted drugs in specific scenarios. In this study, breast cancer cell lines were treated with metformin and rapamycin, showing a heterogeneous response to treatment and leading to cell cycle disruption. The genetic causes and molecular effects of this differential response were characterized by means of SNP genotyping and mass spectrometry-based proteomics. Protein expression was analyzed using probabilistic graphical models, showing that treatments elicit various responses in some biological processes such as transcription. Moreover, flux balance analysis using protein expression values showed that predicted growth rates were comparable with cell viability measurements and suggesting an increase in reactive oxygen species response enzymes due to metformin treatment. In addition, a method to assess flux differences in whole pathways was proposed. Our results show that these diverse approaches provide complementary information and allow us to suggest hypotheses about the response to drugs that target metabolism and their mechanisms of action.