Breast cancer is one of the most common cancer types. Due to the complexity of this disease, it is important to face its study with an integrated and multilevel approach, from genes, transcripts and proteins to molecular networks, cell populations and tissues. According to the systems biology perspective, the biological functions arise from complex networks: in this context, concepts like molecular pathways, protein-protein interactions (PPIs), mathematical models and ontologies play an important role for dissecting such complexity.

Clonal dominance characterizes hematopoiesis during aging and increases susceptibility to blood cancers and common nonmalignant disorders. VEXAS syndrome is a recently discovered, adult-onset, autoinflammatory disease burdened by a high mortality rate and caused by dominant hematopoietic clones bearing somatic mutations in the UBA1 gene. However, pathogenic mechanisms driving clonal dominance are unknown. Moreover, the lack of disease models hampers the development of disease-modifying therapies. In the present study, we performed immunophenotype characterization of hematopoiesis and single-cell transcriptomics in a cohort of nine male patients with VEXAS syndrome, revealing pervasive inflammation across all lineages. Hematopoietic stem and progenitor cells (HSPCs) in patients are skewed toward myelopoiesis and acquire senescence-like programs. Humanized models of VEXAS syndrome, generated by inserting the causative mutation in healthy HSPCs through base editing, recapitulated proteostatic defects, cytological alterations and senescence signatures of patients’ cells, as well as hematological and inflammatory disease hallmarks. Competitive transplantations of human UBA1-mutant and wild-type HSPCs showed that, although mutant cells are more resilient to the inflammatory milieu, probably through the acquisition of the senescence-like state, wild-type ones are progressively exhausted and overwhelmed by VEXAS clones, overall impairing functional hematopoiesis and leading to bone marrow failure. Our study unveils the mechanism of clonal dominance and provides models for preclinical studies and preliminary insights that could inform therapeutic strategies.

Background: The cell cycle is a complex process that allows eukaryotic cells to replicate chromosomal DNA and partition it into two daughter cells. A relevant regulatory step is in the G₀/G₁ phase, a point called the restriction (R) point where intracellular and extracellular signals are monitored and integrated. Subcellular localization of cell cycle proteins is increasingly recognized as a major factor that regulates cell cycle transitions. Nevertheless, current mathematical models of the G₁/S networks of mammalian cells do not consider this aspect. Hence, there is a need for a computational model that incorporates this regulatory aspect that has a relevant role in cancer, since altered localization of key cell cycle players, notably of inhibitors of cyclin-dependent kinases, has been reported to occur in neoplastic cells and to be linked to cancer aggressiveness. Results: The network of the model components involved in the G₁ to S transition process was identified through a literature and web-based data mining and the corresponding wiring diagram of the G₁ to S transition drawn with Cell Designer notation. The model has been implemented in Mathematica using Ordinary Differential Equations. Time-courses of level and of sub-cellular localization of key cell cycle players in mouse fibroblasts re-entering the cell cycle after serum starvation/re-feeding have been used to constrain network design and parameter determination. The model allows to recapitulate events from growth factor stimulation to the onset of S phase. The R point estimated by simulation is consistent with the R point experimentally determined. Conclusion: The major element of novelty of our model of the G₁ to S transition is the explicit modeling of cytoplasmic/nuclear shuttling of cyclins, cyclin-dependent kinases, their inhibitor and complexes. Sensitivity analysis of the network performance newly reveals that the biological effect brought about by Cki overexpression is strictly dependent on whether the Cki is promoting nuclear translocation of cyclin/Cdk containing complexes.

Signal transduction and gene regulation determine a major reorganization of metabolic activities in order to support cell proliferation. Protein Kinase B (PKB), also known as Akt, participates in the PI3K/Akt/mTOR pathway, a master regulator of aerobic glycolysis and cellular biosynthesis, two activities shown by both normal and cancer proliferating cells. Not surprisingly considering its relevance for cellular metabolism, Akt/PKB is often found hyperactive in cancer cells. In the last decade, many efforts have been made to improve the understanding of the control of glucose metabolism and the identification of a therapeutic window between proliferating cancer cells and proliferating normal cells. In this context, we have modeled the link between the PI3K/Akt/mTOR pathway, glycolysis, lactic acid production, and nucleotide biosynthesis. We used a computational model to compare two metabolic states generated by two different levels of signaling through the PI3K/Akt/mTOR pathway: one of the two states represents the metabolism of a growing cancer cell characterized by aerobic glycolysis and cellular biosynthesis, while the other state represents the same metabolic network with a reduced glycolytic rate and a higher mitochondrial pyruvate metabolism. Biochemical reactions that link glycolysis and pentose phosphate pathway revealed their importance for controlling the dynamics of cancer glucose metabolism.

The cell cycle is one of the biological processes most frequently investigated in systems biology studies and it involves the knowledge of a large number of genes and networks of protein interactions. A deep knowledge of the molecular aspect of this biological process can contribute to making cancer research more accurate and innovative. In this context the mathematical modelling of the cell cycle has a relevant role to quantify the behaviour of each component of the systems. The mathematical modelling of a biological process such as the cell cycle allows a systemic description that helps to highlight some features such as emergent properties which could be hidden when the analysis is performed only from a reductionism point of view. Moreover, in modelling complex systems, a complete annotation of all the components is equally important to understand the interaction mechanism inside the network: for this reason data integration of the model components has high relevance in systems biology studies.