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Breast cancer (BC) is the second cause of cancer-related deceases in the worldwide female population. Despite the successful treatment advances, 25% of BC develops resistance to current therapeutic regimens, thereby remaining a major hurdle for patient management. Current therapies, targeting the molecular events underpinning the adaptive resistance, still require effort to improve BC treatment. Using BC sphere cells (BCSphCs) as a model, here we showed that BC stem-like cells express high levels of Myc, which requires the presence of the multifunctional DNA/RNA binding protein Sam68 for the DNA-damage repair. Analysis of a cohort of BC patients displayed that Sam68 is an independent negative factor correlated with the progression of the disease. Genetic inhibition of Sam68 caused a defect in PARP-induced PAR chain synthesis upon DNA-damaging insults, resulting in cell death of TNBC cells. In contrast, BC stem-like cells were able to survive due to an upregulation of Rad51. Importantly, the inhibition of Rad51 showed synthetic lethal effect with the silencing of Sam68, hampering the cell viability of patient-derived BCSphCs and stabilizing the growth of tumor xenografts, including those TNBC carrying BRCA mutation. Moreover, the analysis of Myc, Sam68 and Rad51 expression demarcated a signature of a poor outcome in a large cohort of BC patients. Thus, our findings suggest the importance of targeting Sam68-PARP1 axis and Rad51 as potential therapeutic candidates to counteract the expansion of BC cells with an aggressive phenotype.

Despite advances in the curative approach, the survival rate of advanced colorectal cancer (CRC) patients is still poor, which is likely due to the emergence of cancer cell clones resistant to the available therapeutic options. We have already shown that CD44v6-positive CRC stem cells (CR-CSCs) are refractory toward standard anti-tumor therapeutic agents due to the activation of the PI3K pathway together with high HER2 expression levels. Tumor microenvironmental cytokines confer resistance to CR-CSCs against HER2/PI3K targeting by enhancing activation of the MAPK pathway. Here, we show that the CSC compartment, spared by BRAF inhibitor-based targeted therapy, is associated with increased expression levels of CD44v6 and Myc and retains boosted clonogenic activity along with residual tumorigenic potential. Inhibition of Myc transcription, downstream of the MAPK cascade components, and PI3K pathway activity was able to overcome the protective effects of microenvironmental cytokines, affecting the survival and the clonogenic activity of CR-CSCs, regardless of their mutational background. Likewise, the double targeting induced stabilization of mouse tumor avatars. Altogether, these data outline the rationale for dual kinase targeting of CR-CSCs to prevent their adaptive response, which would lead to disease progression.

Obesity is a strong risk factor for cancer progression, posing obesity-related cancer as one of the leading causes of death. Nevertheless, the molecular mechanisms that endow cancer cells with metastatic properties in patients affected by obesity remain unexplored. Here, we show that IL-6 and HGF, secreted by tumor neighboring visceral adipose stromal cells (V-ASCs), expand the metastatic colorectal (CR) cancer cell compartment (CD44v6 +), which in turn secretes neurotrophins such as NGF and NT-3, and recruits adipose stem cells within tumor mass. Visceral adipose-derived factors promote vasculogenesis and the onset of metastatic dissemination by activation of STAT3, which inhibits miR-200a and enhances ZEB2 expression, effectively reprogramming CRC cells into a highly metastatic phenotype. Notably, obesity-associated tumor microenvironment provokes a transition in the transcriptomic expression profile of cells derived from the epithelial consensus molecular subtype (CMS2) CRC patients towards a mesenchymal subtype (CMS4). STAT3 pathway inhibition reduces ZEB2 expression and abrogates the metastatic growth sustained by adipose-released proteins. Together, our data suggest that targeting adipose factors in colorectal cancer patients with obesity may represent a therapeutic strategy for preventing metastatic disease.

Colorectal cancer (CRC) mortality is mainly caused by patient refractoriness to common anti-cancer therapies and consequent metastasis formation. Besides, the notorious toxic side effects of chemotherapy are a concurrent obstacle to be tackled. Thus, new treatment approaches are needed to effectively improve patient outcomes. Compelling evidence demonstrated that cancer stem cells (CSCs) are responsible for treatment failure and relapse. New natural treatment approaches showed capabilities to selectively target the CSC subpopulation by rendering them targetable by standard cytotoxic compounds. Herein we show the anti-cancer properties of the polymethoxyflavones and prenylflavonoids extracted from Citrus sinensis and Humulus lupulus, respectively. The natural biofunctional fractions, singularly and in combination, reduced the cell viability of CRC stem cells (CR-CSCs) and synergized with 5-fluorouracil and oxaliplatin (FOX) chemotherapy. These phenomena were accompanied by a reduced S and G2/M phase of the cell cycle and upregulation of cell death-related genes. Notably, both phytoextracts in combination with FOX thwarted stemness features in CR-CSCs as demonstrated by the impaired clonogenic potential and decreased Wnt pathway activation. Extracts lowered the expression of CD44v6 and affected the expansion of metastatic CR-CSCs in patients refractory to chemotherapy. Together, this study highlights the importance of polymethoxyflavones and prenylflavonoids as natural remedies to aid oncological therapies.

Limited therapeutic options are available for advanced colorectal cancer (CRC). Herein, we report that exposure to a neo-synthetic bis(indolyl)thiazole alkaloid analog, nortopsentin 234 (NORA234), leads to an initial reduction of proliferative and clonogenic potential of CRC sphere cells (CR-CSphCs), followed by an adaptive response selecting the CR-CSphC-resistant compartment. Cells spared by the treatment with NORA234 express high levels of CD44v6, associated with a constitutive activation of Wnt pathway. In CR-CSphC-based organoids, NORA234 causes a genotoxic stress paralleled by G2-M cell cycle arrest and activation of CHK1, driving the DNA damage repair of CR-CSphCs, regardless of the mutational background, microsatellite stability, and consensus molecular subtype. Synergistic combination of NORA234 and CHK1 (rabusertib) targeting is synthetic lethal inducing death of both CD44v6-negative and CD44v6-positive CRC stem cell fractions, aside from Wnt pathway activity. These data could provide a rational basis to develop an effective strategy for the treatment of patients with CRC.

Different paradigms have been used to model the behaviour of pedestrians in a crowd. While some approaches analyze the modelization of crowd motion from a physical perspective, other techniques prefer to focus on similar abstractions following classic paradigms, such as mobile agents or cellular automata, or even from the point of view of social forces interaction. Particular interest has emerged around the topic of crowd behaviour under condition of stress caused by a dramatic event, such as an earthquake, a terrorist attack or a fire. This paper presents a novel approach including a GSPN to describe the behaviour of an individual, which is subsequently translated into a set of Markovian Agents. The model under study reproduces a scenario where a fire, developed in a closed environment, triggers erratic crowd behaviour. It is possible to perceive that a panic situation can be mitigated by enforcing certain measures involving human leaders or small embedded systems (such as Internet-of-things enabled devices), reducing the level of risk related to safety.

Cloud Computing has made possible flexible resources provisioning from an almost unlimited pool. This has created the opportunity to broaden the horizon of data that can be analyzed, allowing to support the so called Big Data Analytics applications. New programming paradigms, such as NoSQL queries and Map-Reduce applications, have emerged within frameworks such as Microsoft Azure, Hadoop and Apache Spark. In many cases, applications execute jobs that are split into stages, each one composed of tasks that can be run in parallel on many computational nodes. Directed acyclic graphs describe the precedence between stages, defining the execution rules and controlling the degree of parallelism. This work presents a Process Algebra dialect aimed at describing both jobs and execution environments. The proposed framework is then used to model and study standard parallel programming benchmarks, to demonstrate its applicability.

The faithful reproduction and accurate prediction of the phe-notypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because of a variety of reasons. For instance, many quantitative data (e.g., kinetic rates) are usually not available, a problem that hinders the execution of simulation algorithms as long as some parameter estimation methods are used. Though, even with a candidate parameterization, the simulation of mechanistic models could be challenging due to the extreme computational effort required. In this context, model reduction techniques and High-Performance Computing infrastructures could be leveraged to mitigate these issues. In addition, as cellular processes are characterized by multiple scales of temporal and spatial organization, novel hybrid simulators able to harmonize different modeling approaches (e.g., logic-based, constraint-based, continuous deterministic, discrete stochastic, spatial) should be designed. This chapter describes a putative unified approach to tackle these challenging tasks, hopefully paving the way to the definition of large-scale comprehensive models that aim at the comprehension of the cell behavior by means of computational tools.