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.

Better knowledge of the biology of breast cancer has allowed the use of new targeted therapies, leading to improved outcome. High-throughput technologies allow deepening into the molecular architecture of breast cancer, integrating different levels of information, which is important if it helps in making clinical decisions. microRNA (miRNA) and protein expression profiles were obtained from 71 estrogen receptor-positive (ER þ ) and 25 triple-negative breast cancer (TNBC) samples. RNA and proteins obtained from formalin-fixed, paraffin-embedded tumors were analyzed by RT-qPCR and LC/MS-MS, respectively. We applied probabilistic graphical models representing complex biologic systems as networks, confirming that ER þ and TNBC subtypes are distinct biologic entities. The integration of miRNA and protein expression data unravels molecular processes that can be related to differences in the genesis and clinical evolution of these types of breast cancer. Our results confirm that TNBC has a unique metabolic profile that may be exploited for therapeutic intervention. Cancer Res; 75(11); 2243-53. Ó2015 AACR.

Craniosynostosis, caused by the premature fusion of one or more of the cranial sutures, can be classified into non-syndromic or syndromic and by which sutures are affected. Clinical assignment is a difficult challenge due to the high phenotypic variability observed between syndromes. During routine diagnostics, we screened 182 Spanish craniosynostosis probands, implementing a four-tiered cascade screening of FGFR2, FGFR3, FGFR1, TWIST1 and EFNB1. A total of 43 variants, eight novel, were identified in 113 (62%) patients: 104 (92%) detected in level 1; eight (7%) in level 2 and one (1%) in level 3. We subsequently screened additional genes in the probands with no detected mutation: one duplication of the IHH regulatory region was identified in a patient with craniosynostosis Philadelphia type and five variants, four novel, were identified in the recently described TCF12, in probands with coronal or multisuture affectation. In the 19 Saethre-Chotzen syndrome (SCS) individuals in whom a variant was detected, 15 (79%) carried a TWIST1 variant, whereas four (21%) had a TCF12 variant. Thus, we propose that TCF12 screening should be included for TWIST1 negative SCS patients and in patients where the coronal suture is affected. In summary, a molecular diagnosis was obtained in a total of 119/182 patients (65%), allowing the correct craniosynostosis syndrome classification, aiding genetic counselling and in some cases provided a better planning on how and when surgical intervention should take place and, subsequently the appropriate clinical follow up.

BackgroundTriple-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.MethodsWe 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.ResultsUsing 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.ConclusionsThis 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.