PurposeAnthracyclines and taxanes are considered the standard for neoadjuvant chemotherapy of breast cancer, although they are often associated with serious side effects and wide variability of individual response. In this study, we analyzed the value of topoisomerase II alpha (TOP2A) and transducin-like enhancer of split 3 (TLE3) as predictive markers of response to therapy with anthracyclines and taxanes.Materials and methodsTOP2A and TLE3 protein expressions were evaluated using immunohistochemistry on 28 samples, obtained by core needle biopsy in patients with locally advanced breast carcinoma, subsequently subjected to epirubicin- and paclitaxel-based neoadjuvant chemotherapy. The immunohistochemical staining was correlated with the clinical response measured by the tumor size reduction evaluated by breast magnetic resonance imaging, prior and after chemotherapy, and by pathologic evaluation of the surgical specimen.ResultsNeoadjuvant chemotherapy achieved a size reduction in 26/28 tumors (92.9%), with an average percentage decrease of 45.6%. A downstaging was achieved in 71.4% of the cases of locally advanced carcinoma. TOP2A positivity was correlated with a greater reduction in tumor diameter (P=0.06); negative staining for TLE3 was predictive of a better response to neoadjuvant chemotherapy (P=0.07). A higher reduction in tumor diameter (P=0.03) was also found for tumors that were concurrently TLE3-negative and TOP2A-positive.ConclusionTOP2A and TLE3 showed a correlation with response to neoadjuvant chemotherapy. While TOP2A is a well-known marker of response to anthracyclines-based chemotherapy, TLE3 is a new putative predictor of response to taxanes. Data from the current study suggest that TOP2A and TLE3 warrant further investigation in a larger series as predictors of response to neoadjuvant chemotherapy for locally advanced breast carcinoma.
Native language identification (NLI) is the task of training (via supervised machine learning) a classifier that guesses the native language of the author of a text. This task has been extensively researched in the last decade, and the performance of NLI systems has steadily improved over the years. We focus on a different facet of the NLI task, i.e., that of analysing the internals of an NLI classifier trained by an explainable machine learning algorithm, in order to obtain explanations of its classification decisions, with the ultimate goal of gaining insight into which linguistic phenomena "give a speaker's native language away". We use this perspective in order to tackle both NLI and a (much less researched) companion task, i.e., guessing whether a text has been written by a native or a non-native speaker. Using three datasets of different provenance (two datasets of English learners' essays and a dataset of social media posts), we investigate which kind of linguistic traits (lexical, morphological, syntactic, and statistical) are most effective for solving our two tasks, namely, are most indicative of a speaker's L1. We also present two case studies, one on Spanish and one on Italian learners of English, in which we analyse individual linguistic traits that the classifiers have singled out as most important for spotting these L1s. Overall, our study shows that the use of explainable machine learning can be a valuable tool for the scholar who investigates interlanguage facts and language transfer.
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