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.

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-A new method for three-dimensional quantitative evaluation of target volume delineation is presented. It is composed by a new 3D reconstruction method called Origami, based on the combination of two bidimensional analysis of the volume of each organ instead of one 3D analysis, and a 3D description of the error distribution in the space. The Origami method avoid external errors introduced by a 3D rendering and has shown to work correctly in both convex and convex-concave volumes, accurately fitting the contours of the planning study. Its performance has been evaluated comparing it with the Convex Hull algorithm using Delaunay triangulation, resulting in a much more adjusted evaluation. Moreover, Origami computes thousands of control points in less than one second in a common PC.