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INTRODUCTION Continence results from a complex interplay between anal canal (AC) muscles and sensory-motor feedback mechanisms. The AC's passive ability to withstand opening pressure - its compliance - has recently been shown to correlate with continence. Functional lumen imaging probe (FLIP) is used to assess AC compliance, although it provides no anatomical information. Therefore, compliance assessment of specific anatomical structures has not been possible, and the anatomical position of critical functional zones remains unknown. In addition, FLIP assumes a circular orifice cross-section, which has not been shown for the AC. To address those shortcomings, a technique combining FLIP with a medical imaging modality is needed. METHOD We implemented a new research method (MR-FLIP) that combines FLIP with MR-imaging. Twenty healthy volunteers underwent MR-FLIP and conventional FLIP assessment. MR-FLIP was validated by comparison with FLIP results. Anatomical markers were identified, and the cross-sectional shape of the orifice was investigated. RESULTS MR-FLIP provides compliance measurements identical to those obtained by conventional FLIP. Anatomical analysis revealed that the least compliant AC zone was located at the proximal end of the external anal sphincter. The AC cross-sectional shape was found to deviate only slightly from circularity in healthy volunteers. CONCLUSION The proposed method was equivalent to classical FLIP. It establishes for the first time a direct mapping between local tissue compliance and anatomical structure, which is key for gaining novel insights into (in)continence. In addition, MR-FLIP provides a tool for better understanding conventional FLIP measurements in the AC by quantifying its limitations and assumptions. This article is protected by copyright. All rights reserved.

The CHIC (Computational Horizons in Cancer) project develops computational models for the cancer domain, as well as a secure infrastructure for data and model access, and reuse. It addresses challenges related to the development, validation and maintenance of multi-scale models by proposing the creation of complex disease models as composition of reusable component models. We present a versatile component model for the simulation of bio-mechanical aspects of macroscopic tumor growth. The model computes mechanical stresses and strains, resulting from tumor growth or shrinkage in a patient-specific anatomy, from a map of cancer cell concentration. In iterative coupled execution with other component models, its output can be used, for example, to guide the directionality of tumour expansion, or to simulate the effect of increased pressure on blood perfusion. Simulation of the bio-mechanic interaction relies on the finite element method (FEM); it is based on a hyper-elastic material model, as well as organ-specific boundary conditions and material properties. A pre-processing pipeline has been developed to automate the configuration process. In combination with automatic segmentation tools, this pipeline permits rapid generation of patient-specific FEM models for personalized simulations, including the assignment of suitable material parameters and boundary conditions from simple configuration options. Model and pre-processing pipeline are implemented using Open Source libraries and software packages (CGAL, VTK, FEBio). The model can be parametrised easily for different organs and body sites of interests; it has been applied to the simulation of kidney, lung and brain cancers in the context of CHIC.

A proposal has been made to accelerate ion beams ranging from hydrogen to neon with magnetic rigidities up to 6.7 Tm for biomedical experiments at CERN using the Low Energy Ion Ring (LEIR), in parallel to its continued operation for LHC and for SPS fixed target physics experiments. The feasibility of a new slow extraction system for LEIR based on the third integer resonance was studied with two possible resonance driving mechanisms: quadrupole-driven extraction and RF-knockout extraction. The extraction of fully stripped carbon ions (20-440 MeV/u kinetic energies) has been studied in detail. The requirement to keep the present performance of the machine for physics experiments imposes tight space constraints for the upgrade. The extraction scheme and the hardware requirements are described in this paper., Proceedings of the 5th Int. Particle Accelerator Conf., IPAC2014, Dresden, Germany

In light of the recent European developments in ion beam therapy, there is a strong interest from the biomedical research community to have more access to clinically relevant beams. Beamtime for pre-clinical studies is currently very limited and a new dedicated facility would allow extensive research into the radiobiological mechanisms of ion beam radiation and the development of more refined techniques of dosimetry and imaging. This basic research would support the current clinical efforts of the new treatment centres in Europe (for example HIT, CNAO and MedAustron). This paper presents first investigations on the feasibility of an experimental biomedical facility based on the CERN Low Energy Ion Ring LEIR accelerator. Such a new facility could provide beams of light ions (from protons to neon ions) in a collaborative and cost-effective way, since it would rely partly on CERN’s competences and infrastructure. The main technical challenges linked to the implementation of a slow extraction scheme for LEIR and to the design of the experimental beamlines are described and first solutions presented. These include introducing new extraction septa into one of the straight sections of the synchrotron, changing the power supply configuration of the magnets, and designing a new horizontal beamline suitable for clinical beam energies, and a low-energy vertical beamline for particular radiobiological experiments.

Biomechanical forces are known to affect tumor growth and evolution [1]. Likewise, tumor growth drives physical changes in the micro-environment that affect tissue solid and fluid mechanics. Tumor mass effect, resulting from rapid tumor cell proliferation, has been shown to be prognostic for poor outcome in glioblastoma (GBM) patients and to be associated with the expression of gene signatures consistent with proliferative growth phenotype [2]. Similarly, elevated interstitial fluid flow (IFF) has been shown to drive GBM invasion [3]. This study investigates the relationship between tumor mass effect, diffusion, perfusion and IFF in GBM using anatomical (pre- and post-contrast T1 weighted, T2/FLAIR) and quantitative MR imaging (Dynamic Contrast Enhanced (DCE) MRI, and Diffusion Weighted Imaging (DWI)). We use data from 39 patients from the Ivy Glioblastoma Atlas Project (Ivy GAP)[4] which provides matched imaging, ISH, RNA, gene expression and clinical data over the course of treatment. We analyze pre-operative anatomic imaging data to determine the tumor-induced mass effect in each patient using quantitative measures such as ‘Lateral ventricle displacement’ [2]. Perfusion and diffusion measures are derived from pre-operative DCE and DWI imaging. Additionally, we estimate IFF velocities in the tumor region using DCE imaging data in combination with a computational model of fluid flow [5]. References: [1] R.K. Jain et al. Annu. Rev. Biomed. Eng., 2014, 16, 321–346. [2] T.C. Steed et al. Scientific Reports, 2018, 8, 2827. [3] K.M. Kingsmore et al. Integr. Biol., 2016, 8 1246-1260 [4] N. Shah et al. Data from Ivy GAP. The Cancer Imaging Archive 2016. [5] K.M. Kingsmore et al. APL Bioengineering, 2018, 2, 031905.

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor T-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, proliferation, exhaustion, and persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parametrize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit.

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator–prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the chimeric antigen receptor T-cell treatment response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, CAR T-cell proliferation and exhaustion, and CAR T-cell persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parameterize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit.

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator–prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the chimeric antigen receptor T-cell treatment response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, CAR T-cell proliferation and exhaustion, and CAR T-cell persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parameterize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit.

The Low Energy Ion Ring (LEIR) at CERN has been proposed to provide ion beams with magnetic rigidities up to 6.7 Tm for biomedical research, in parallel to its continued operation for LHC and SPS fixed target physics experiments. In the context of this project, two beamlines are proposed for transporting the extracted beam to future experimental end-stations: a vertical beamline for specific low-energy radiobiological research, and a horizontal beamline for radiobiology and medical physics experimentation. This study presents a first linear-optics design for the delivery of 1-5 mm FWHM pencil beams and 5 cm x 5 cm homogeneous broad beams to both endstations. High field uniformity is achieved by selection of the central part of a strongly defocused Gaussian beam, resulting in low beam utilisation., Proceedings of the 5th Int. Particle Accelerator Conf., IPAC2014, Dresden, Germany

The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model-informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real-world setting. We developed a tumor growth inhibition model based on real-world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image-based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high-dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates.

The Clinical Data Repository (CDR) is a research collaboration tool that serves as unique access-point for all clinical data in the CHIC (http://chic-vph.eu/) project.

Access to high-quality clinical data is a prerequisite for medical data analysis and in-silico medicine. Data drives the development of research questions, feeds computational algorithms, and provides the evidence base for validating complex disease models which form the basis for personalized simulations in the future. However, provision of uniform access to, and secondary use of, clinical treatment or study data is hampered by the data's intrinsic characteristics: its confidential nature, and its heterogeneity in terms of sources, quality and information. The clinical data repository (CDR) has been designed to address these critical issues by providing a unique access-point to clinical data in compliance with the European medico-legal framework. CDR has been initiated as an exchange platform for medical images, but was quickly extended to other health-related data, in particular clinical treatment & follow-up, histological and genetic information. Data access is granted based on a role-based policy within a single-sign-on security framework. CDR also supports the (pseudo-)anonymization process and provides facilities for semantic annotation of data during and after the upload process, enabling semantically mediated queries for improved data discovery. CDR has been developed by SICAS (http://www.si-cas.com) and the University of Bern (http://www.istb.unibe.ch/) with support from the EU CHIC (http://chic-vph.eu/) and Co-Me (http://co-me.ch) projects. This presentation outlines the principal requirements and main challenges related to the sharing of heterogeneous clinical data for research purposes, discusses properties of a generic solution, and details implementation and application in the CHIC context.