Anna Clara Monti

The cHER2+ breast cancer subtype is characterized by the overexpression of the HER2 oncoprotein based on immunohistochemistry (IHC)/or by ERBB2 gene amplification using in situ hybridization (ISH) techniques. Targeted therapies are significantly changing cancer treatment outcomes. However, not all patients benefit from it due to misclassification or intrinsic mechanisms of resistance. Identifying predictive factors of response to therapy is thus crucial for optimizing treatment protocol. In addition, with the development of effective antibody–drug conjugates for targeting HER2-low subtypes, enhancing the HER2 molecular classification is crucial. In this study, a comprehensive analysis of publicly available datasets (TCGA, METABRIC, I-SPY, NOAH and CHER-LOB trials) has been considered. We present a metagene expression score (HER2MtGx 31-gene assay) based on the most informative genes associated with each molecular profile. HER2MtGx scores represent three linear subspaces associated with the HER2, Luminal and Basal-like profiles (STAT). In the METABRIC cohort, the scores are useful to discriminate against the HER2-enriched phenotype and this classification is significantly associated with long-term survival in cHER2+ patients (HR = 1.76; 95%CI = 1.09–2.86). In terms of response to neoadjuvant chemo/target therapy including I-SPY, NOAH, and CHER-LOB trials, the metagene scores are associated with the pathological response to therapy (OR = 2.26; 95%CI = 1.74–2.98). The HER2MtGx assay is a reliable tool for selecting patients for HER2-targeted therapy.

This paper studies the relationship between risk propensity, education and financial literacy. Theresults of the empirical investigation confirm the importance of the key explanatory variables ofeducation and financial competence. Since they are both included in the model, the differentroles of each are singled out. In particular, while education turns out to be a factor contributingto raising risk tolerance, financial literacy tends to reduce risk propensity. Risk attitude is evaluated by self-reported assessment and modeled through cumulative logit models. In order to handle anomalous data, M estimators with a bounded influence function are considered.