Biology subjects

Zunino, P.

Publications and source records attributed to Zunino, P..

3 recordsLinked to original sources

bioRxiv2023

Microvascular morphology affects radiotherapy outcomes: a computational study

Radiotherapy (RT) is the most common cancer treatment, and hypoxia is one of the main causes of resistance to RT. We investigate how microvascular morphology affects radiation therapy results, exploring the role of the microvasculature. Several computational models have been developed to analyze microvascular oxygen delivery. However, few of these models have been applied to study RT and the microenvironment. We generated 27 different networks, covering 9 scenarios defined by the vascular density and the network regularity. Leveraging these networks, we solved a computational mixed-dimensional model for fluid flow, red blood cell distribution, and oxygen delivery in the microenvironment. Then, we simulated a fractionated RT treatment (30 x 2GyRBE) using the Linear Quadratic model, accounting for oxygen-related (OER) modifications by two different models from the literature. First, the analysis of the hypoxic volume fraction and its distribution reveals a correlation between hypoxia and treatment outcome. The study also shows how vascular density and regularity are essential in determining the success of treatment. Indeed, in our computational dataset, an insufficient vascular density or regularity is sufficient to decrease the success probability for photon-based RT. We also applied our quantitative analysis to hadron therapy and different oxygenation states to assess the consistency of the microvasculatures role in various treatments and conditions. While proton RT provides a Tumor Control Probability similar to photons, carbon ions mark a clear difference, especially with bad vascular scenarios, i.e., where strong hypoxia is present. These data also suggest a scenario where carbon-based hadron therapy can help overcome hypoxia-mediated resistance to RT. As a final remark, we discuss the importance of these data with reference to clinical data and the possible identification of subvoxel hypoxia, given the size similarity between the computational domain and the imaging voxel. Author summaryThe study focuses on investigating the role of microvascular density and morphology in radiation therapy, with particular reference to its role in shaping hypoxia-mediated resistance to treatment. While several computational models have been developed to analyze microvascular oxygen delivery, few have been applied to study RT and the microenvironment. We generated 27 different vascular networks to fill this gap, considering three oxygenation states and three radiation sources (photons, protons, and carbon ions). These cases were analyzed using a complex computational model to describe the state of oxygenation and radiation treatment. The results highlight the importance of vascular density and regularity in determining the success of treatment by shaping hypoxia in the microenvironment. Data suggest that carbon-based hadron therapy can help overcome hypoxia-mediated radiation resistance. As a final remark, these data are also helpful in interpreting possible subvoxel hypoxia with reference to clinical data from different imaging techniques.

bioengineering↗

bioRxiv2022

Learning High-Order Interactions for Polygenic Risk Prediction

Within the framework of precision medicine, the stratification of individual genetic susceptibility based on inherited DNA variation has paramount relevance. However, one of the most relevant pitfalls of traditional Polygenic Risk Scores (PRS) approaches is their inability to model complex high-order non-linear SNP-SNP interactions and their effect on the phenotype (e.g. epistasis). Indeed, they incur in a computational challenge as the number of possible interactions grows exponentially with the number of SNPs considered, affecting the statistical reliability of the model parameters as well. In this work, we address this issue by proposing a novel PRS approach, called High-order Interactions-aware Polygenic Risk Score (hiPRS), that incorporates high-order interactions in modeling polygenic risk. The latter combines an interaction search routine based on frequent itemsets mining and a novel interaction selection algorithm based on Mutual Information, to construct a simple and interpretable weighted model of user-specified dimensionality that can predict a given binary phenotype. Compared to traditional PRSs methods, hiPRS does not rely on GWAS summary statistics nor any external information. Moreover, hiPRS differs from Machine Learning-based approaches that can include complex interactions in that it provides a readable and interpretable model and it is able to control overfitting, even on small samples. In the present work we demonstrate through a comprehensive simulation study the superior performance of hiPRS w.r.t. state of the art methods, both in terms of scoring performance and interpretability of the resulting model. We also test hiPRS against small sample size, class imbalance and the presence of noise, showcasing its robustness to extreme experimental settings. Finally, we apply hiPRS to a case study on real data from DACHS cohort, defining an interaction-aware scoring model to predict mortality of stage II-III Colon-Rectal Cancer patients treated with oxaliplatin. Author summaryIn the precision medicine era, understanding how genetic variants affect the susceptibility to complex diseases is key, and great attention has been posed to Single Nucleotide Polymorphisms (SNPs) and their role in disease risk or clinical treatments outomes. Several approaches to quantify and model this impact have been proposed, called Polygenic Risk Scores (PRSs), but they traditionally do not account for possible interactions among SNPs. This is a significant drawback, as complex high-order SNP-SNP interactions can play an important role in determining the phenotype (a phenomenon called epistasis). Nevertheless, the number of possible combinations grows exponentially with the number of SNPs considered and including them in a predictive model becomes computationally challenging and affects the statistical reliability of the model. Some Machine Learning algorithms can answer this problem, but they are hardly interpretable. Here, we tackle these and other drawbacks of existing approaches proposing our novel PRS approach, hiPRS, that provides an interpretable weighted model with a user-defined number of predictive interactions. We designed it to handle typical real-life research scenarios, like small sample sizes and class imbalance, and we demonstrate here its superiority with respect to state-of-the-art methods.

genomics↗

bioRxiv2020

Modeling the effect of COVID-19 disease on the cardiac function: a computational study

BackgroundThe effect of COVID-19 on the cardiac function and on the vascular system increases the morbidity and mortality of infected subjects with cardiovascular diseases. ObjectivesTo provide preliminary results on cardiac global outcomes (such as cardiac output, ventricular pressures) obtained by means of computational models in plausible scenarios characterized by COVID-19. MethodsWe considered a lumped parameters computational model of the cardiovascular system, which models, from the mechanical point of view, the systemic and pulmonary circulations, the four cardiac valves and the four heart chambers, through mathematical equations of the underlying physical processes. To study the effect of COVID-19, we varied the heart rate, the contractility and the pulmonary resistances in suitable ranges. ResultsOur computations on individuals with both otherwise normal and impaired cardiac functions revealed that COVID-19 worsen cardiac function, as shown by a decrease of some cardiac biomarkers values such as cardiac output and ejection fraction. In the case of existing impaired cardiac function, the presence of COVID-19 lead to values outside the normal ranges. ConclusionsComputational models revealed to be an effective tool to study the effect of COVID-19 on the cardiovascular system. Such effect could be significant for patients with impaired cardiac function. This is especially useful to perform a sensitivity analysis of the hemodynamics for different conditions. CONDENSED ABSTRACTEmerging studies address how COVID-19 infection might impact the cardiovascular system. This relates particularly to the development of myocardial injury, acute coronary syndrome, myocarditis, arrhythmia, and heart failure. Prospective treatment approach is advised for these patients. By the assessment of conventional important biomarkers obtained with new sources as a 0-dimentional computational model, we propose a new study protocol as an effective method to evaluate short-term prognosis. The clinical protocol proposed will help to rapidly identify which patients require intensive monitoring, diagnostic strategy and most adequate therapy.

pathology↗