Alessandro Vici

Fenretinide [N-(4-hydroxyphenyl) retinamide, 4-HPR] is a synthetic retinoid known for decades for its antitumor activity and favorable toxicological profile. In vitro and in vivo studies have demonstrated that fenretinide has broad-spectrum anticancer potential, eliciting multiple biological effects and acting as a multi-target compound that influences both tumor cells and the tumor microenvironment. However, the clinical application of fenretinide has been hampered by unfavorable physicochemical properties, such as poor solubility and low bioavailability, leading to highly variable outcomes in clinical studies. The development of novel fenretinide nanoformulations based on drug encapsulation in nanomicelles has helped overcome bioavailability limitations and improve anticancer efficacy in preclinical studies, thereby paving the way for new clinical opportunities. Moreover, recent studies have shown that fenretinide restrains the cancer stem cell compartment and promotes tumor cell dormancy, suggesting its potential use in dormancy-inducing strategies aimed at preventing and/or controlling metastatic disease. Here, we provide an overview of fenretinide’s mechanisms of action in cancer cells, summarize the available clinical evidence and discuss advances achieved with novel formulations. Finally, we discuss the potential integration of fenretinide at different stages of clinical management, with particular emphasis on its prospective use as a chemopreventive strategy or as a therapeutic option for disease control.

In many biological investigations, the relevant information does not coincide with the most powerful signals (most elevated eigenvalues, dominant frequencies, most populated clusters...), but very often hides in minor features that are difficult to discriminate from random noise. Here we propose an algorithm that, by the combined use of a non-linear cluster analysis procedure and a strategy to discriminate minor signal components from noise, allows singling out biologically relevant hidden information. We tested the algorithm on a sparse data set corresponding to single-cell RNA-Seq measures, being able to identify a very small population of cells in charge of the immune response toward cancer tissue.

The differentiation/maturation trajectories of different blood cell types stemming from a CD34⁺ common ancestor takes place in different biologically relevant multidimensional spaces. Here, we generated microRNA and cytokine profiles from highly purified populations of hematopoietic progenitors/precursors derived from cord blood hematopoietic stem/progenitor cells. MicroRNA and cytokine landscapes were then analyzed to find their mutual relationships under the hypothesis that the highly variable miRNome corresponds to the ‘force field’ driving the goal of a stable phenotype (here corresponding to the cytokine abundance pattern) typical of each cell kind. The high dimensionality and lack of linearity of the hematopoietic process pushed us to adopt a distance–geometry approach to compare different trajectories, while a complex network analysis was instrumental in revealing the fine structure of microRNA–cytokine relations. Importantly, the approach enabled us to identify a limited number of factors (represented either by microRNAs or cytokines) corresponding to crucial nodes responsible for connecting distinct interaction modules. Subtle changes in ‘master nodes’, keeping the connections between different regulatory networks, may therefore be crucial in influencing hematopoietic differentiation. These findings highlight the extremely interconnected network structures underlying hematopoiesis regulation and identify key factors in the microRNA/cytokine landscape that may be potentially crucial for influencing network stability.