New steps on an old path: Novel estrogen receptor inhibitors in breast cancer.

Estrogen receptor (ER) signaling represents the main driver of tumor growth and survival in hormone receptor positive (HR+) breast cancer (BC). Thus, endocrine therapy (ET) alone or in combination with targeted agents constitutes the mainstay of the treatment for this BC subtype. Despite its efficacy, intrinsic or acquired resistance to ET occurs in a large proportion of cases, mainly due to aberrant activation of ER signaling (i.e. through ligand-independent ER activation, in the presence of estrogen receptor 1 (ESR1) gene aberration or ER protein phosphorylation) and/or the upregulation of escape pathways, such as the PI3K/AKT/mTOR pathway. Therefore, the development of new ER pathway targeting agents remains essential to delay and overcome ET resistance, enhance treatment efficacy and tolerability, and ultimately prolong patient survival and improve their quality of life. Several novel ER targeting agents are currently under investigation. Among these, the oral selective ER degraders (SERDs) represent the pharmacological class at the most advanced stage of development and promise to enrich the therapeutic armamentarium of HR+ BC in the next few years, as they showed promising results in several clinical trials, either as single ET agents or in combination with targeted therapies. In this manuscript, we aim to provide a comprehensive overview on the clinical development of novel ER targeting agents, reporting the most up-to-date evidence on oral SERDs and other compounds, including new selective ER modulators (SERMs), ER proteolysis targeting chimera (PROTACs), selective ER covalent antagonists (SERCAs), complete ER antagonists (CERANs), selective human ER partial agonists (ShERPAs). Furthermore, we discuss the potential implications of introducing these novel treatment strategies in the evolving and complex therapeutic scenario of HR+ BC.

A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results.

The mortality associated to breast cancer is in many cases related to metastasization and recurrence. Personalized treatment strategies are critical for the outcomes improvement of BC patients and the Clinical Decision Support Systems can have an important role in medical practice. In this paper, we present the preliminary results of a prediction model of the Breast Cancer Recurrence (BCR) within five and ten years after diagnosis. The main breast cancer-related and treatment-related features of 256 patients referred to Istituto Tumori "Giovanni Paolo II" of Bari (Italy) were used to train machine learning algorithms at the-state-of-the-art. Firstly, we implemented several feature importance techniques and then we evaluated the prediction performances of BCR within 5 and 10 years after the first diagnosis by means different classifiers. By using a small number of features, the models reached highly performing results both with reference to the BCR within 5 years and within 10 years with an accuracy of 77.50% and 80.39% and a sensitivity of 92.31% and 95.83% respectively, in the hold-out sample test. Despite validation studies are needed on larger samples, our results are promising for the development of a reliable prognostic supporting tool for clinicians in the definition of personalized treatment plans.

Decision support systems for the prediction of lymph node involvement in early breast cancer.

The prediction of lymph node involvement represents an important task which could reduce unnecessary surgery and improve the definition of oncological therapies. An artificial intelligence model able to predict it in pre-operative phase requires the interface among multiple data structures. The trade-off among time consuming, expensive and invasive methodologies is emerging in experimental setups exploited for the analysis of sentinel lymph nodes, where machine learning algorithms represent a key ingredient in recorded data elaboration. The accuracy required for clinical applications is obtainable matching different kind of data. Statistical associations of prognostic factors with symptoms and predictive models implemented also through on-line softwares represent useful diagnostic support tools which translate into patients quality of life improvement and costs reduction.