[Treatments for rare ovarian tumors: What's new?] / Avancées thérapeutiques dans la prise en charge des tumeurs rares malignes ovariennes.

Even if each rare ovarian tumor (ROT) has a low incidence, the sum of all these entities represents almost the half of all ovarian neoplasms. Thus, development of dedicated clinical trial emerged as a prerequisite to improve their managements. Owing to the spreading of dedicated institutional networks and (supra)national collaborations, the number of clinical trials has increased the past few years, with different types of trials; while some focused on specific molecular features, others assessed innovative molecules. Furthermore, relevant randomized clinical trials were designed as a mean to position new treatment options. Currently, innovative molecular-driven trials, based on master protocol trials are emerging and may shed light towards the improvement of personalized medicine regarding ROT.

Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning.

Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden deviations from the monotonicity of its evolution trigger a reset after which another expert subnetwork can be activated-if it has already been established before-or new categories can be recruited and associated with novel behavioral patterns.