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Abstract Background The cyclin D1-cyclin dependent kinases (CDK)4/6 inhibitor palbociclib in combination with endocrine therapy shows remarkable efficacy in the management of estrogen receptor (ER)-positive and HER2-negative advanced breast cancer (BC). Nevertheless, resistance to palbociclib frequently arises, highlighting the need to identify new targets toward more comprehensive therapeutic strategies in BC patients. Methods BC cell lines resistant to palbociclib were generated and used as a model system. Gene silencing techniques and overexpression experiments, real-time PCR, immunoblotting and chromatin immunoprecipitation studies as well as cell viability, colony and 3D spheroid formation assays served to evaluate the involvement of the G protein-coupled estrogen receptor (GPER) in the resistance to palbociclib in BC cells. Molecular docking simulations were also performed to investigate the potential interaction of palbociclib with GPER. Furthermore, BC cells co-cultured with cancer-associated fibroblasts (CAFs) isolated from mammary carcinoma, were used to investigate whether GPER signaling may contribute to functional cell interactions within the tumor microenvironment toward palbociclib resistance. Finally, by bioinformatics analyses and k-means clustering on clinical and expression data of large cohorts of BC patients, the clinical significance of novel mediators of palbociclib resistance was explored. Results Dissecting the molecular events that characterize ER-positive BC cells resistant to palbociclib, the down-regulation of ERα along with the up-regulation of GPER were found. To evaluate the molecular events involved in the up-regulation of GPER, we determined that the epidermal growth factor receptor (EGFR) interacts with the promoter region of GPER and stimulates its expression toward BC cells resistance to palbociclib treatment. Adding further cues to these data, we ascertained that palbociclib does induce pro-inflammatory transcriptional events via GPER signaling in CAFs. Of note, by performing co-culture assays we demonstrated that GPER contributes to the reduced sensitivity to palbociclib also facilitating the functional interaction between BC cells and main components of the tumor microenvironment named CAFs. Conclusions Overall, our results provide novel insights on the molecular events through which GPER may contribute to palbociclib resistance in BC cells. Additional investigations are warranted in order to assess whether targeting the GPER-mediated interactions between BC cells and CAFs may be useful in more comprehensive therapeutic approaches of BC resistant to palbociclib.

Breast cancer causes hundreds of women’s deaths each year. The manual detection of breast cancer is time-consuming, complicated, and prone to inaccuracy. For Breast Cancer (BC) detection, several imaging methods are explored. However, sometimes misidentification leads to unnecessary treatment and diagnosis. Therefore, accurate detection of BC can save many people from unnecessary surgery and biopsy. Due to recent developments in the industry, deep learning’s (DL) performance in processing medical images has significantly improved. Deep Learning techniques successfully identify BC from ultrasound images due to their superior prediction ability. Transfer learning reuses knowledge representations from public models built on large-scale datasets. However, sometimes Transfer Learning leads to the problem of overfitting. The key idea of this research is to propose an efficient and robust deep-learning model for breast cancer detection and classification. Therefore, this paper presents a novel DeepBraestCancerNet DL model for breast cancer detection and classification. The proposed framework has 24 layers, including six convolutional layers, nine inception modules, and one fully connected layer. Also, the architecture uses the clipped ReLu activation function, the leaky ReLu activation function, batch normalization and cross-channel normalization as its two normalization operations. We observed that the proposed model reached the highest classification accuracy of 99.35%. We also compared the performance of the proposed DeepBraestCancerNet approach with several existing DL models, and the experiment results showed that the proposed model outperformed the state-of-the-art. Furthermore, we validated the proposed model using another standard, publicaly available dataset. The proposed DeepBraestCancerNet model reached the highest accuracy of 99.63%.

This article is the editorial of the “Process Mining and Emerging Applications” (https://www [...]

Accountability refers to the need of individuals or organizations to account for their activities, accept responsibility, and disclose results in a transparent manner. Nowadays, the pervasivity of digital systems is making increasingly critical security, reliability, and trustworthiness of such services. When a service is delivered by involving different (eventually conflicting) parties, accountability could be achieved by including in digital transactions a trusted third party (TTP). Blockchain decentralizes trust, thus avoiding to rely on a single TTP. However, to deal with accountability in concrete solutions, the issue of securely integrating digital identity and Blockchain should be solved. The paper describes the results of a three-year research project merging academic and industrial expertise, to design and implement a Blockchain-based platform for service accountability integrating eIDAS-compliant Public Digital Identity. The platform has been used in several real-life contexts made available by industrial project partners, which demonstrated the effectiveness and novelty of the solution.