A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma.

Neuroblastoma is a complex and aggressive type of cancer that affects children. Current treatments involve a combination of surgery, chemotherapy, radiotherapy, and stem cell transplantation. However, treatment outcomes vary due to the heterogeneous nature of the disease. Computational models have been used to analyse data, simulate biological processes, and predict disease progression and treatment outcomes. While continuum cancer models capture the overall behaviour of tumours, and agent-based models represent the complex behaviour of individual cells, multiscale models represent interactions at different organisational levels, providing a more comprehensive understanding of the system. In 2018, the PRIMAGE consortium was formed to build a cloud-based decision support system for neuroblastoma, including a multi-scale model for patient-specific simulations of disease progression. In this work we have developed this multi-scale model that includes data such as patient's tumour geometry, cellularity, vascularization, genetics and type of chemotherapy treatment, and integrated it into an online platform that runs the simulations on a high-performance computation cluster using Onedata and Kubernetes technologies. This infrastructure will allow clinicians to optimise treatment regimens and reduce the number of costly and time-consuming clinical trials. This manuscript outlines the challenging framework's model architecture, data workflow, hypothesis, and resources employed in its development.

Prostaglandin-endoperoxide synthase genes COX1 and COX2 - novel modifiers of disease severity in cystic fibrosis patients.

Cystic fibrosis (CF) is one of the most common autosomal recessive diseases among Caucasians caused by a mutation in the CFTR gene. However, the clinical outcome of CF pulmonary disease varies remarkably even in patients with the same CFTR genotype. This has led to a search for genetic modifiers located outside the CFTR gene. The aim of this study was to evaluate the effect of functional variants in prostaglandin-endoperoxide synthase genes (COX1 and COX2) on the severity of lung disease in CF patients. To the best of our knowledge, it is the first time when analysis of COX1 and COX2 as potential CF modifiers is provided. The study included 94 CF patients homozygous for F508del mutation of CFTR. To compare their clinical condition, several parameters were recorded, e.g. a unique clinical score: disease severity status (DSS). To analyse the effect of non-CFTR genetic polymorphisms on the clinical course of CF patients, the whole coding region of COX1 and selected COX2 polymorphisms were analysed. Statistical analysis of genotype-phenotype associations revealed a relationship between the heterozygosity status of identified polymorphisms and better lung function. These results mainly concern COX2 polymorphisms: -765G>C and 8473T>C. The COX1 and COX2 polymorphisms reducing COX protein levels had a positive effect on all analysed clinical parameters. This suggests an important role of these genes as protective modifiers of pulmonary disease in CF patients, due to inhibition of arachidonic acid conversion into prostaglandins, which probably reduces the inflammatory process.