BACKGROUND: Longitudinal single-cell sequencing experiments of patient-derived models are increasingly employed to investigate cancer evolution. In this context, robust computational methods are needed to properly exploit the mutational profiles of single cells generated via variant calling, in order to reconstruct the evolutionary history of a tumor and characterize the impact of therapeutic strategies, such as the administration of drugs. To this end, we have recently developed the LACE framework for the Longitudinal Analysis of Cancer Evolution. RESULTS: The LACE 2.0 release aimed at inferring longitudinal clonal trees enhances the original framework with new key functionalities: an improved data management for preprocessing of standard variant calling data, a reworked inference engine, and direct connection to public databases. CONCLUSIONS: All of this is accessible through a new and interactive Shiny R graphical interface offering the possibility to apply filters helpful in discriminating relevant or potential driver mutations, set up inferential parameters, and visualize the results. The software is available at: github.com/BIMIB-DISCo/LACE.

A current challenge in cancer research is the development of therapeutic strategies aimed at reducing the toxicity of treatments, since Adverse Events (AEs) typically cause substantial problems and long-term damages to the patients. A possible solution to this issue lies in the personalization of therapy dosages according to demographic factors and in the employment of optimized data-driven drug administration protocols. Control theory can be exploited to this end, as its application in pharmacology allows to define optimized dosages and schedules, aimed at minimizing AEs and maximizing the therapy efficacy. However, an effective application of control theory approaches to this issue is constrained by our ability in inferring the parameters of the mathematical models from currently available data.We here present a closed-loop optimization framework of patient-specific pharmacokinetics (PK) and pharmacodynamics (PD) models, combined with a mathematical model of a liquid tumor, which aims at overcoming such limitations. The most relevant feature of our framework is the ability to learn the value of patient-specific parameters via a Bayesian update, by exploiting a feedback signal obtained monitoring the tumor burden dynamics of the patient. Our framework employs CasADi, an open-source tool for nonlinear optimization, and guarantees a good and robust numerical estimation of the optimized schedule and a parsimonious use of computational time.As a case study, we present the application of our framework to Tyrosine Kinase Inhibitor administration in Chronic Myeloid Leukemia (CML), in which we show that our optimized protocols result in a faster decay of CSCs and in a reduction of the overall toxicity.

The metabolic processes related to the synthesis of the molecules needed for a new round of cell division underlie the complex behaviour of cell populations in multi-cellular systems, such as tissues and organs, whereas their deregulation can lead to pathological states, such as cancer. Even within genetically homogeneous populations, complex dynamics, such as population oscillations or the emergence of specific metabolic and/or proliferative patterns, may arise, and this aspect is highly amplified in systems characterized by extreme heterogeneity. To investigate the conditions and mechanisms that link metabolic processes to cell population dynamics, we here employ a previously introduced multi-scale model of multi-cellular system, named FBCA (Flux Balance Analysis with Cellular Automata), which couples biomass accumulation, simulated via Flux Balance Analysis of a metabolic network, with the simulation of population and spatial dynamics via Cellular Potts Models. In this work, we investigate the influence that different modes of nutrients diffusion within the system may have on the emerging behaviour of cell populations. In our model, metabolic communication among cells is allowed by letting secreted metabolites to diffuse over the lattice, in addition to diffusion of nutrients from given sources. The inclusion of the diffusion processes in the model proved its effectiveness in characterizing plausible biological scenarios.

Metabolic reprogramming is a general feature of cancer cells.Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways.By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality.These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance.To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes.We show that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites.The scFBA suite of MATLAB functions is available at https://github.com/ BIMIB-DISCo/scFBA, as well as the case study datasets. Author summaryCytotoxicity of chemotherapeutic agents and resistance to targeted treatments are the main reasons why cancer is still one of the top causes of death.As tumor cells are intrinsically resistant to therapies that target signaling pathways, targeting the metabolic hallmarks of cancer holds promise for more incisive treatments.Regrettably, the

MOTIVATION: Intratumour heterogeneity poses many challenges to the treatment of cancer. Unfortunately, the transcriptional and metabolic information retrieved by currently available computational and experimental techniques portrays the average behaviour of intermixed and heterogeneous cell subpopulations within a given tumour. Emerging single-cell genomic analyses are nonetheless unable to characterize the interactions among cancer subpopulations. In this study, we propose popFBA , an extension to classic Flux Balance Analysis, to explore how metabolic heterogeneity and cooperation phenomena affect the overall growth of cancer cell populations. RESULTS: We show how clones of a metabolic network of human central carbon metabolism, sharing the same stoichiometry and capacity constraints, may follow several different metabolic paths and cooperate to maximize the growth of the total population. We also introduce a method to explore the space of possible interactions, given some constraints on plasma supply of nutrients. We illustrate how alternative nutrients in plasma supply and/or a dishomogeneous distribution of oxygen provision may affect the landscape of heterogeneous phenotypes. We finally provide a technique to identify the most proliferative cells within the heterogeneous population. AVAILABILITY AND IMPLEMENTATION: the popFBA MATLAB function and the SBML model are available at https://github.com/BIMIB-DISCo/popFBA . CONTACT: [email protected].