Stem cells have the remarkable ability to undergo proliferative symmetric divisions and self-renewing asymmetric divisions. Balancing of the two modes of division sustains tissue morphogenesis and homeostasis. Asymmetric divisions of Drosophila neuroblasts (NBs) and sensory organ precursor (SOP) cells served as prototypes to learn what we consider now principles of asymmetric mitoses. They also provide initial evidence supporting the notion that aberrant symmetric divisions of stem cells could correlate with malignancy. However, transferring the molecular knowledge of circuits underlying asymmetry from flies to mammals has proven more challenging than expected. Several experimental approaches have been used to define asymmetry in mammalian systems, based on daughter cell fate, unequal partitioning of determinants and niche contacts, or proliferative potential. In this review, we aim to provide a critical evaluation of the assays used to establish the stem cell mode of division, with a particular focus on the mammary gland system. In this context, we will discuss the genetic alterations that impinge on the modality of stem cell division and their role in breast cancer development.Keywords asymmetric cell divisions; asymmetric cell division assays; breast cancer; mammary stem cells

SummaryLoss of p53 function is invariably associated with cancer. Its role in tumor growth was recently linked to its effects on cancer stem cells (CSCs), although the underlying molecular mechanisms remain unknown. Here, we show that c-myc is a transcriptional target of p53 in mammary stem cells (MaSCs) and is activated in breast tumors as a consequence of p53 loss. Constitutive Myc expression in normal mammary cells leads to increased frequency of MaSC symmetric divisions, extended MaSC replicative-potential, and MaSC-reprogramming of progenitors, whereas Myc activation in breast cancer is necessary and sufficient to maintain the expanding pool of CSCs. Concomitant p53 loss and Myc activation trigger the expression of 189 mitotic genes, which identify patients at high risk of mortality and relapse, independently of other risk factors. Altogether, deregulation of the p53:Myc axis in mammary tumors increases CSC content and plasticity and is a critical determinant of tumor growth and clinical aggressiveness.

Motivation Acute myeloid leukemia (AML) is one of the most common hematological malignancies, characterized by high relapse and mortality rates. The inherent intra-tumor heterogeneity in AML is thought to play an important role in disease recurrence and resistance to chemotherapy. Although experimental protocols for cell proliferation studies are well established and widespread, they are not easily applicable to in vivo contexts, and the analysis of related time-series data is often complex to achieve. To overcome these limitations, model-driven approaches can be exploited to investigate different aspects of cell population dynamics. Results In this work, we present ProCell, a novel modeling and simulation framework to investigate cell proliferation dynamics that, differently from other approaches, takes into account the inherent stochasticity of cell division events. We apply ProCell to compare different models of cell proliferation in AML, notably leveraging experimental data derived from human xenografts in mice. ProCell is coupled with Fuzzy Self-Tuning Particle Swarm Optimization, a swarm-intelligence settings-free algorithm used to automatically infer the models parameterizations. Our results provide new insights on the intricate organization of AML cells with highly heterogeneous proliferative potential, highlighting the important role played by quiescent cells and proliferating cells characterized by different rates of division in the progression and evolution of the disease, thus hinting at the necessity to further characterize tumor cell subpopulations. Availability and implementation The source code of ProCell and the experimental data used in this work are available under the GPL 2.0 license on GITHUB at the following URL: https://github.com/aresio/ProCell. Supplementary information Supplementary data are available at Bioinformatics online.

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The investigation of cell proliferation can provide useful insights for the comprehension of cancer progression, resistance to chemotherapy and relapse. To this aim, computational methods and experimental measurements based on in vivo label-retaining assays can be coupled to explore the dynamic behavior of tumoral cells. Pro-Cell is a software that exploits flow cytometry data to model and simulate the kinetics of fluorescence loss that is due to stochastic events of cell division. Since the rate of cell division is not known, ProCell embeds a calibration process that might require thousands of stochastic simulations to properly infer the parameterization of cell proliferation models. To mitigate the high computational costs, in this paper we introduce a parallel implementation of ProCell's simulation algorithm, named cuProCell, which leverages Graphics Processing Units (GPUs). Dynamic Parallelism was used to Manuscript