Male breast cancer: correlation between immunohistochemical subtyping and PAM50 intrinsic subtypes, and the subsequent clinical outcomes.

Male breast cancer is a rare disease that is still poorly understood. It is mainly classified by immunohistochemistry as a luminal disease. In this study, we assess for the first time the correlation between molecular subtypes based on a validated six-marker immunohistochemical panel and PAM50 signature in male breast cancer, and the subsequent clinical outcome of these different subtypes. We collected 67 surgical specimens of invasive male breast cancer from four different Spanish pathology laboratories. Immunohistochemical staining for the six-marker panel was performed on tissue microarrays. PAM50 subtypes were determined in a research-use-only nCounter Analysis System. We explored the association of immunohistochemical and PAM50 subtypes. Overall survival and disease-free survival were analyzed in the different subtypes of each classification. The distribution of tumor molecular subtypes according PAM50 was: 60% luminal B, 30% luminal A and 10% human epidermal growth factor receptor 2 (Her2) enriched. Only one Her2-enriched tumor was also positive by immunohistochemistry and was treated with trastuzumab. None of the tumors were basal-like. Using immunohistochemical surrogates, 51% of the tumors were luminal B, 44% luminal A, 4% triple-negative and 1% Her2-positive. The clinicopathological characteristics did not differ significantly between immunohistochemical and PAM50 subtypes. We found a significant worse overall survival in Her2-enriched compared with luminal tumors. Male breast cancer seems to be mainly a genomic luminal disease with a predominance of the luminal B subtype. In addition, we found a proportion of patients with Her2-negative by immunohistochemistry but Her2-enriched profile by PAM50 tumors with a worse outcome compared with luminal subtypes that may benefit from anti-Her2 therapies.

Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science.

One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.