Modeling hypoxia-induced radiation resistance and the impact of radiation sources.

Hypoxia contributes significantly to resistance in radiotherapy. Our research rigorously examines the influence of microvascular morphology on radiotherapy outcome, specifically focusing on how microvasculature shapes hypoxia within the microenvironment and affects resistance to a standard treatment regimen (30×2GyRBE). Our computational modeling extends to the effects of different radiation sources. For photons and protons, our analysis establishes a clear correlation between hypoxic volume distribution and treatment effectiveness, with vascular density and regularity playing a crucial role in treatment success. On the contrary, carbon ions exhibit distinct effectiveness, even in areas of intense hypoxia and poor vascularization. This finding points to the potential of carbon-based hadron therapy in overcoming hypoxia-induced resistance to RT. Considering that the spatial scale analyzed in this study is closely aligned with that of imaging data voxels, we also address the implications of these findings in a clinical context envisioning the possibility of detecting subvoxel hypoxia.

Sensitivity analysis of a multi-physics model for the vascular microenvironment.

The vascular microenvironment is the scale at which microvascular transport, interstitial tissue properties and cell metabolism interact. The vascular microenvironment has been widely studied by means of quantitative approaches, including multi-physics mathematical models as it is a central system for the pathophysiology of many diseases, such as cancer. The microvascular architecture is a key factor for fluid balance and mass transfer in the vascular microenvironment, together with the physical parameters characterizing the vascular wall and the interstitial tissue. The scientific literature of this field has witnessed a long debate about which factor of this multifaceted system is the most relevant. The purpose of this work is to combine the interpretative power of an advanced multi-physics model of the vascular microenvironment with state of the art and robust sensitivity analysis methods, in order to determine the factors that most significantly impact quantities of interest, related in particular to cancer treatment. We are particularly interested in comparing the factors related to the microvascular architecture with the ones affecting the physics of microvascular transport. Ultimately, this work will provide further insight into how the vascular microenvironment affects cancer therapies, such as chemotherapy, radiotherapy or immunotherapy.

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.

A Mesoscale Computational Model for Microvascular Oxygen Transfer.

We address a mathematical model for oxygen transfer in the microcirculation. The model includes blood flow and hematocrit transport coupled with the interstitial flow, oxygen transport in the blood and the tissue, including capillary-tissue exchange effects. Moreover, the model is suited to handle arbitrarily complex vascular geometries. The purpose of this study is the validation of the model with respect to classical solutions and the further demonstration of its adequacy to describe the heterogeneity of oxygenation in the tissue microenvironment. Finally, we discuss the importance of these effects in the treatment of cancer using radiotherapy.

Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity.

AIM: To identify the effect of single nucleotide polymorphism (SNP) interactions on the risk of toxicity following radiotherapy (RT) for prostate cancer (PCa) and propose a new method for polygenic risk score incorporating SNP-SNP interactions (PRSi). MATERIALS AND METHODS: Analysis included the REQUITE PCa cohort that received external beam RT and was followed for 2 years. Late toxicity endpoints were: rectal bleeding, urinary frequency, haematuria, nocturia, decreased urinary stream. Among 43 literature-identified SNPs, the 30% most strongly associated with each toxicity were tested. SNP-SNP combinations (named SNP-allele sets) seen in ≥10% of the cohort were condensed into risk (RS) and protection (PS) scores, respectively indicating increased or decreased toxicity risk. Performance of RS and PS was evaluated by logistic regression. RS and PS were then combined into a single PRSi evaluated by area under the receiver operating characteristic curve (AUC). RESULTS: Among 1,387 analysed patients, toxicity rates were 11.7% (rectal bleeding), 4.0% (urinary frequency), 5.5% (haematuria), 7.8% (nocturia) and 17.1% (decreased urinary stream). RS and PS combined 8 to 15 different SNP-allele sets, depending on the toxicity endpoint. Distributions of PRSi differed significantly in patients with/without toxicity with AUCs ranging from 0.61 to 0.78. PRSi was better than the classical summed PRS, particularly for the urinary frequency, haematuria and decreased urinary stream endpoints. CONCLUSIONS: Our method incorporates SNP-SNP interactions when calculating PRS for radiotherapy toxicity. Our approach is better than classical summation in discriminating patients with toxicity and should enable incorporating genetic information to improve normal tissue complication probability models.

A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort.

Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors. Methods: The study involved REQUITE prostate cancer patients treated with external beam radiotherapy who had complete 2-year follow-up. We used five separate late toxicity endpoints: ≥grade 1 late rectal bleeding, ≥grade 2 urinary frequency, ≥grade 1 haematuria, ≥ grade 2 nocturia, ≥ grade 1 decreased urinary stream. Forty-three single nucleotide polymorphisms (SNPs) already reported in the literature to be associated with the toxicity endpoints were included in the analysis. No SNP had been studied before in the REQUITE cohort. Deep Sparse AutoEncoders (DSAE) were trained to recognize features (SNPs) identifying patients with no toxicity and tested on a different independent mixed population including patients without and with toxicity. Results: One thousand, four hundred and one patients were included, and toxicity rates were: rectal bleeding 11.7%, urinary frequency 4%, haematuria 5.5%, nocturia 7.8%, decreased urinary stream 17.1%. Twenty-four of the 43 SNPs that were associated with the toxicity endpoints were validated as identifying patients with toxicity. Twenty of the 24 SNPs were associated with the same toxicity endpoint as reported in the literature: 9 SNPs for urinary symptoms and 11 SNPs for overall toxicity. The other 4 SNPs were associated with a different endpoint. Conclusion: Deep learning algorithms can validate SNPs associated with toxicity after radiotherapy for prostate cancer. The method should be studied further to identify polygenic SNP risk signatures for radiotherapy toxicity. The signatures could then be included in integrated normal tissue complication probability models and tested for their ability to personalize radiotherapy treatment planning.

In silico model of the early effects of radiation therapy on the microcirculation and the surrounding tissues.

BACKGROUND: Radiation-induced organ dysfunction are frequently described by Normal Tissue Complication Probability models. The approximations of this radiobiological approach do not allow to consider the important role played by the microvasculature not only in the dose-response of the blood vessels but also of the organs where it is located. To this purpose, we presented a computational model that describes the fluid dynamics of microcirculation when the parameters of the network and the surrounding tissues are affected by radio-induced changes. MATERIALS AND METHODS: The effects of the ionizing radiation on the capillary bed are mediated by the inflammatory response. We derived from a literature search the possible morphological and functional variations of the network due to the process of the acute inflammation. Specifically, we considered vasodilation, increased membrane permeability with consequent fluid extravasation and increased wall elasticity. These perturbations to the system were included in a computational model, already able to describe the physics of the microcirculation and its exchanges with the surrounding tissues. RESULTS: Two computational descriptions were considered. In the first one, we changed a set of 4 parameters associated with the increased fluid exchange from the health scenario at the baseline to a seriously compromised scenario with the edema formation. The second study investigated the effect of a perturbation to the vessel wall elasticity. CONCLUSIONS: These simulations represent a first step towards the challenging objective of understanding and describing in a mechanistic way the effects of radiation on the vascular microenvironment.

A computational study of cancer hyperthermia based on vascular magnetic nanoconstructs.

The application of hyperthermia to cancer treatment is studied using a novel model arising from the fundamental principles of flow, mass and heat transport in biological tissues. The model is defined at the scale of the tumour microenvironment and an advanced computational scheme called the embedded multiscale method is adopted to solve the governing equations. More precisely, this approach involves modelling capillaries as one-dimensional channels carrying flow, and special mathematical operators are used to model their interaction with the surrounding tissue. The proposed computational scheme is used to analyse hyperthermic treatment of cancer based on systemically injected vascular magnetic nanoconstructs carrying super-paramagnetic iron oxide nanoparticles. An alternating magnetic field is used to excite the nanoconstructs and generate localized heat within the tissue. The proposed model is particularly adequate for this application, since it has a unique capability of incorporating microvasculature configurations based on physiological data combined with coupled capillary flow, interstitial filtration and heat transfer. A virtual tumour model is initialized and the spatio-temporal distribution of nanoconstructs in the vascular network is analysed. In particular, for a reference iron oxide concentration, temperature maps of several different hypothesized treatments are generated in the virtual tumour model. The observations of the current study might in future guide the design of more efficient treatments for cancer hyperthermia.

Trends in biomedical engineering: focus on Smart Bio-Materials and Drug Delivery.

The present article reviews on different research lines, namely: drug and gene delivery, surface modification/modeling, design of advanced materials (shape memory polymers and biodegradable stents), presently developed at Politecnico di Milano, Italy. For gene delivery, non-viral polycationic-branched polyethylenimine (b-PEI) polyplexes are coated with pectin, an anionic polysaccharide, to enhance the polyplex stability and decrease b-PEI cytotoxicity. Perfluorinated materials, specifically perfluoroether, and perfluoro-polyether fluids are proposed as ultrasound contrast agents and smart agents for drug delivery. Non-fouling, self-assembled PEG-based monolayers are developed on titanium surfaces with the aim of drastically reducing cariogenic bacteria adhesion on dental implants. Femtosecond laser microfabrication is used for selectively and spatially tuning the wettability of polymeric biomaterials and the effects of femtosecond laser ablation on the surface properties of polymethylmethacrylate are studied. Innovative functionally graded Alumina-Ti coatings for wear resistant articulating surfaces are deposited with PLD and characterized by means of a combined experimental and computational approach. Protein adsorption on biomaterials surfaces with an unlike wettability and surface-modification induced by pre-adsorbed proteins are studied by atomistic computer simulations. A study was performed on the fabrication of porous Shape Memory Polymeric structures and on the assessment of their potential application in minimally invasive surgical procedures. A model of magnesium (alloys) degradation, in a finite element framework analysis, and a bottom-up multiscale analysis for modeling the degradation mechanism of PLA matrices was developed, with the aim of providing valuable tools for the design of bioresorbable stents.

Trends in biomedical engineering: focus on Patient Specific Modeling and Life Support Systems.

Over the last twenty years major advancements have taken place in the design of medical devices and personalized therapies. They have paralleled the impressive evolution of three-dimensional, non invasive, medical imaging techniques and have been continuously fuelled by increasing computing power and the emergence of novel and sophisticated software tools. This paper aims to showcase a number of major contributions to the advancements of modeling of surgical and interventional procedures and to the design of life support systems. The selected examples will span from pediatric cardiac surgery procedures to valve and ventricle repair techniques, from stent design and endovascular procedures to life support systems and innovative ventilation techniques.

The non-circular shape of FloWatch-PAB prevents the need for pulmonary artery reconstruction after banding. Computational fluid dynamics and clinical correlations.

OBJECTIVE: To evaluate the differences between non-circular shape of FloWatch-PAB and conventional pulmonary artery (PA) banding. METHODS: Geometrical analysis. Conventional banding and FloWatch-PAB perimeters were plotted against cross-sections. Computational fluid dynamics (CFD) model. CFD compared non-circular FloWatch-PAB cross-sections with conventional banding regarding pressure gradients. Clinical data. Seven children, median age 2 months (7 days to 3 years), median weight 4.2 kg (3.2-9.8 kg), with complex congenital heart defects underwent PA banding with FloWatch-PAB implantation. RESULTS: Geometrical analysis. Conventional banding: progressive reduction of cross-sections was accompanied by progressive reduction of PA perimeters. FloWatch-PAB: with equal reduction of cross-sections the PA perimeter remained constant. CFD model. Non-circular and circular banding provided same trans-banding pressure gradients for same cross-sections at any given flow. Clinical data. Mean PA internal diameter at banding was 13.3+/-4.5 mm. After a mean interval of 5.9+/-3.7 months, all children underwent intra-cardiac repair and simple FloWatch-PAB removal without PA reconstruction. Mean PA internal diameter with FloWatch-PAB removal increased from 3.0+/-0.8 to 12.4+/-4.5 mm (normal mean internal diameter for the age=9.9+/-1.6). No residual pressure gradient was recorded in correspondence of the site of the previous FloWatch-PAB implantation in 6/7 patients, 10 mmHg peak and 5 mmHg mean gradient in 1/7. CONCLUSIONS: The non-circular shape of FloWatch-PAB can replace conventional circular banding with the following advantages: (a) the pressure gradient will remain essentially the same as for conventional circular banding for any given cross-section, but with significantly smaller reduction of PA perimeter; and (b) PA reconstruction at the time of de-banding for intra-cardiac repair can be avoided.