Integrated molecular analysis of endometrial carcinoma: translation of biomarker profiles into the clinical practice

The Cancer Genome Atlas (TCGA) collaborative project identified four distinct prognostic groups of endometrial carcinoma (EC) based on molecular alterations: (i) the ultramutated subtype that encompassed POLE mutated (POLE) cases; (ii) the hypermutated subtype, characterized by MisMatch Repair deficiency (MMRd); (iii) the copy-number high subtype, with p53 abnormal/mutated features (p53abn); (iv) the copy-number low subtype, known as No Specific Molecular Profile (NSMP). Although the prognostic value of TCGA molecular classification, NSMP tumors present a wide variability in molecular alterations and biological aggressiveness. This study aims to investigate the impact of ARID1A and CTNNB1/β-catenin alterations by targeted Next-generation sequencing (NGS) and immunohistochemistry (IHC) in a consecutive series of 125 molecularly classified ECs. NGS and IHC were used to assign surrogate TCGA groups and to identify molecular alterations of multiple target genes including POLE, PTEN, ARID1A, CTNNB1, TP53. Associations with clinicopathologic parameters, molecular subtypes, and outcomes identified NSMP category as the most heterogeneous group in terms of clinicopathologic features and outcome. Integration of surrogate TCGA molecular classification with ARID1A and β-catenin analysis showed NSMP cases with ARID1A mutation characterized by the worst outcome with early recurrence, while NSMP tumors with ARID1A wild-type and β-catenin alteration had indolent clinicopathologic features and no recurrence. This study indicates how the identification of ARID1A and β-catenin alterations in EC represents a simple and effective way to characterize NSMP tumor aggressiveness and metastatic potential.

A ROS-based software architecture for a versatile collaborative dual-armed autonomous mobile robot for the manufacturing industry

The industrial context is changing rapidly due to advancements in technology fueled by the Internet and Information Technology. The fourth industrial revolution counts integration, flexibility, and optimization as its fundamental pillars, and, in this context, Human-Robot Collaboration has become a crucial factor for manufacturing sustainability in Europe. Collaborative robots are appealing to many companies due to their low installation and running costs and high degree of flexibility, making them ideal for reshoring production facilities with a short return on investment. The ROSSINI European project aims to implement a true Human-Robot Collaboration by designing, developing, and demonstrating a modular and scalable platform for integrating human-centred robotic technologies in industrial production environments. The project focuses on safety concerns related to introducing a cobot in a shared working area and aims to lay the groundwork for a new working paradigm at the industrial level. The need for a software architecture suitable to the robotic platform employed in one of three use cases selected to deploy and test the new technology was the main trigger of this Thesis. The chosen application consists of the automatic loading and unloading of raw-material reels to an automatic packaging machine through an Autonomous Mobile Robot composed of an Autonomous Guided Vehicle, two collaborative manipulators, and an eye-on-hand vision system for performing tasks in a partially unstructured environment. The results obtained during the ROSSINI use case development were later used in the SENECA project, which addresses the need for robot-driven automatic cleaning of pharmaceutical bins in a very specific industrial context. The inherent versatility of mobile collaborative robots is evident from their deployment in the two projects with few hardware and software adjustments. The positive impact of Human-Robot Collaboration on diverse production lines is a motivation for future investments in research on this increasingly popular field by the industry.