Report OPERRA Workshop: Modelling of pathogenesis

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1 Report OPERRA Workshop: Modelling of pathogenesis Markus Eidemüller a, Christian Kaiser a, E. Georg Luebeck b, William Paul Accomando c, Kristian Unger a, Mark van de Wiel d, Jonas Behr e, Marc Chadeau-Hyam f, Fieke Dekkers g, Heiko Enderling h Helmholtz Zentrum München, January 12-13, 2015 a Helmholtz Zentrum München, Germany b Fred Hutchinson Cancer Research Center, Seattle, USA c Universitätsklinikum Freiburg, Germany d Vrije Universiteit Amsterdam, Netherlands e ETH Zürich, Switzerland f Imperial College London, UK g Rijksinstituut voor Volksgezondheid en Milieu (RIVM), Netherlands h Moffitt Cancer Center, USA Executive Summary: Research Priorities A major activity of the OPERRA platforms is the establishment of Strategic Research Agendas (SRA's) for radiation protection in Europe. Modelling of pathogenesis constitutes an essential element of the MELODI SRA priorities to integrate radiobiology and epidemiology for improved health risk estimates. The following future research priorities have been identified: 1. Multi scale modelling: Integration of modelling approaches at various levels of disease development in space and time to link radiobiology and epidemiology 2. Modelling sporadic pathogenesis from incipient stages to clinical relevance as a basis to understand the effect of radiation on disease development 3. Integration with molecular data from radio-epidemiological cohorts to 1.) An integrative approach to predict long-term human health effects from ionizing radiation requires multiscale mechanism-based models of disease development. Models for short-term cellular processes should be combined with tissue-level effects and long-term models that provide the link to epidemiology. So far, various modelling approaches have been developed for different levels of disease development. The challenge is the integration of the different approaches to robust, cell- and tissue-specific mathematical multiscale models that link the essential elements of disease progress to population risk. Such models would be a major step forward for linking radiobiology with human health. to 2.) Different radiation mechanisms in the development of a disease are possible. Does radiation initiate or accelerate the sporadic pathogenic processes, or does it open a distinct molecular pathway (possibly connected with a bio-marker of radiation), which is not observed in the sporadic phenotype? Therefore it is important to have a good understanding of sporadic pathogenesis. In many cases the amount and quality of data on genotyping from sporadic tumors (e.g. from the The Cancer Genome Atlas, TCGA) exceeds that of data from radio-epidemiological cohorts. It is a challenge to describe

2 how cells with early genetic damage turn into pre-malignant clones, and how these clones grow in tissue. Radio-biological experiments have mainly focused on cellular processes up to some weeks, but observations over longer time periods are necessary. Models must be developed that identify radiation targets along the full course of disease progression for both cancer and cardiovascular diseases. Such models can generate hypotheses to steer the implementation of molecular measurements and experiments. to 3.) Analysis of biological samples from (cancer and non-cancer) tissue of radio-epidemiological cohorts will contribute to a better understanding of the effects of radiation on disease development. There is a need for generating and analysing OMICs trajectories (profiling at different time points). From systems biology the changes in the molecular pathways can be investigated. Effects and doseresponse relationship on the mechanisms of disease development should be analyzed. Such mechanisms can be implemented in models of carcinogenesis or atherosclerosis. This would allow the integration of molecular, clinical and epidemiologic data across diverse length and time scales to estimate the effects on health risks. Workshop Summary The workshop brought together experts from different areas of modelling of pathogenesis. The models covered different levels of disease development. It was agreed that a major step forward would be the integration of the different approaches to a better mechanistic multistage description of disease progress. Here the contributions from the participants are summarized, together with an outlook for future developments. This complements and specifies the more general research priorities of the executive summary. Colon carcinogenesis Three talks of the workshop had as subject colon carcinogenesis. William Paul Accomando (Uniklinik Freiburg) focused on genetic and epigenetic aberrations in colorectal cancer. E. Georg Luebeck (FHCRC) and Christian Kaiser (HMGU) presented multistage models of colon carcinogenesis. Carcinogenesis in the colon is a multistage process of initiating healthy stem cells by about two mutations, growth of pre-neoplastic lesions in polyps which harbor genomically unstable cells. These cells are eventually transformed into cancer stem cells which grow into clinically detectable tumors. The molecular origins of colon carcinogenesis are well understood in comparison with tumorigenesis in other organs. In colorectal cancer the tumor suppressor gene APC, an antagonist of the Wnt signaling pathway, is often mutated. APC mutations are early events and tend to cluster at multiple sites in particular regions of the colon. KRAS and BRAF are driver mutations and overexpressed in tumor tissue, BRAF regulates MAP kinase and ERK signaling which is responsible for cell proliferation. Localized epigenetic alterations such as silencing of the MLH1 gene or the CpG island methylator phenotype (CIMP) have been detected predominantly in carcinoma of the right colon. These epigenetic alterations are considered as molecular markers of a distinct pathway to colorectal cancer which is associated with microsatellite instability (MSI). APC mutations are the initial events in a second molecular pathway of chromosomal instability (CIN). In both pathways adenoma of different size and morphology appear as precursor lesions. Mechanistic models suggest that the growth dynamics of precursor lesions is markedly different. In the MSI (or serrated) pathway polyps are flat and grow slower compared to the adenoma in the CIN pathway. The sojourn time of CIN adenoma is defined from the first appearance of cells with two hits in the APC gene to the development of cancer stem cells which do not become extinct. This time has been estimated to about 50 years from models results pertaining to the North American SEER cohort and the Japanese cohort of a-bomb survivors.

3 Radiation risk in the MSI pathway is predicted lower compared to the CIN pathway. Due to their flat size and slow growth MSI polyps are difficult to detect by screening measures such as colonoscopy. Moreover, once malignant cells have developed in the MSI pathway within two to three years they grow into a detectable tumor whereas tumor growth in the CIN pathway takes about five to six years. Based on material presented in these three talks future research could aim at improving screening strategies to reduce colon cancer incidence. In an innovative approach, simulation models to optimise the effectiveness of screening strategies can be developed. These models gain predictive power by linking molecular systems biology with epidemiology. Differential genotyping protocols of precursor lesions in both molecular pathways should be developed. The protocols include drawing tissue samples from polyps which have been removed from patients during colonoscopy. By appropriate techniques of molecular investigation the number of initiated cells in the extracted polyps will be estimated. Based on the estimates the shape of the age-dependent growth curves for adenoma in both pathways will be estimated. From the growth curves parameters for clonal expansion of adenoma in the mechanistic models of carcinogenesis will be derived. These estimates can be compared with independent estimates of model parameters which have been derived from fits to epidemiological data of colon cancer incidence, which are available in Europe from Danish cancer registries. In case of good agreement for model parameters pertaining to adenoma growth, which have been obtained independently from measurements and fits to epidemiological data, the mechanistic model for colon cancer will be considered as validated and can be used to investigate the impact of different screening strategies on colon cancer incidence. This will be done in simulation studies, where the size distribution of polyps in patients is predicted depending on their age. Screening is introduced in these models by removing large clones in patients. This measure is expected to reduce the probability of a malignancy. If the simulation is performed in a large number of patients the epidemiological effect of screening on a study population can be investigated. Molecular networks and Integrative radiation systems biology Mark van de Wiel presented molecular network models. Network models provide an essential link between cellular processes and disturbed pathways that can induce carcinogenesis. Different levels of expression data are available (DNA copy number, mrna, micro RNA, methylation) that can be integrated to derive disturbed molecular networks. Different methods have been presented to reconstruct causal links of the pathways. Information from different expression levels is very important for good network reconstruction. For understanding radiation effects it is essential to understand how radiation affects the molecular networks. From disturbed networks the cellular functions might be derived, e.g. the effect of radiation on cellular growth. Such information might be integrated in models of carcinogenesis. Kristian Unger gave a presentation on the general approach of integrative biology and systems biology. The main question is: how can biological data on an individual molecular level be integrated with the population level. This is a multi-scale task ranging from genes, cells organs to patients and cohorts of patients. He showed conceptual models of radiation-induced thyroid cancer which can integrate data on over-expression of the CLIP2 bio-marker in papillary thyroid cancer to explain the incidence in a radio-epidemiological cohort. The task in systems biology is to identify relevant biomarkers for key carcinogenic processes. He emphasized that these markers can found in data bases such as The Cancer Genome Atlas (TCGA) and can be generated by molecular measurements. Mechanistic modeling can help to generate hypothesis to guide those measurements. One yet unaddressed challenge in the field is the consideration of the time dimension which would be key for efficient integration of biology based models into mechanistic models.

4 Cancer progression models Progression of cancer may be viewed as an evolutionary process in which the accumulation of genomic alterations leads to certain capabilities of the cancer tissue. Jonas Behr presented Bayesian network models that are able to infer the temporal order of acquired cellular capabilities and cancer progression paths for 21 cancer sites from TCGA data. These models can include clinical data together with genetic data. This makes it possible to compare progression paths between different groups of patients of e.g. different cancer types, or sporadic versus radiation induced cancers in the same tissue. Thus these models could identify similar underlying cancer progressions and possible similar mechanisms of radiation-induced effects. Models for solid tumor dynamics Models for cancer stem cell-driven solid tumor dynamics were presented by Heiko Enderling. The models describe the properties of individual cells and the interactions with each other and their immediate environment under different external conditions. Simulations are performed to obtain cell population dynamics and growth rates or mutation fixation times at the tissue level. Thus these models could provide a necessary link between cellular properties and more general models of carcinogenesis. Further research is required to investigate the consequences of individual variability in tumor growth to population risk. Another important question is the biological target of radiationinduced perturbations. Are heritable DNA mutations in single cells prevalent, or is disruption of tissuelevel homeostatic regulation leading to malignant progression? The implications for radiation protection could be significant. Smoking-induced lung cancer Marc Chadeau-Hyam presented a hidden Markov model for the natural history of smoking-induced lung cancer. Assuming that disease risk is not only driven by exposure itself but by its full history and its increment, the model has been developed to include a dynamic component to risk assessment, where both age, and calendar time are included. The longitudinal model accounts for individual smoking history and age. It aims to disentangle the time co-linearity of attained age, age at first exposure and exposure duration, and time since exposure cessation. The model has been applied to data of a nested case control study in the European EPIC initiative. The exposure history has been reconstructed based on questionnaires and biomarkers for smoking. Main findings of the model are that age does not impact lung cancer risk after adjustment for exposure duration. Significant risk factors are age at begin of smoking and time since cessation of smoking. The duration from the first appearance of malignant cells to the diagnosis of lung cancer is estimated between one and five years. The model captures accurate dynamic features of lung carcinogenesis without explicitly modelling cellular mechanisms. Future lines of research will focus on the inclusion of omics data such as smoking-related epigenetic markers to assess the added predictive power obtained by the use biomarker trajectories into the model and to identify the pathogenic stages at which these markers play a role. Mechanistic models of carcinogenesis Markus Eidemüller presented an introduction to the principles of models of carcinogenesis and the application to breast cancer in the Swedish hemangioma cohort. The models included an explicit path of genomic instabilty. It was found that the path with genomic instability was induced by radiation at a very early stage and lead to a different temporal dynamics than the spontaneous path. As a consequence radiation-induced breast cancer risk remains elevated until very old ages, even with exposures at infant age. Models of carcinogenesis thus provide a way to link mechanistic processes

5 to epidemiological data and derive risk estimates. Future development of these models should aim for a better integration with mechanistic models closer related to radiobiology. These could include e.g. network models from systems biology to identify relevant pathways, or models for intercellular interactions and growth dynamics. Models of atherosclerosis The mechanisms of atherosclerotic development are still poorly understood. A model was presented by Fieke Dekkers that is based on a system of coupled differential equations describing levels of LDL, macrophages, plaque volume and collagen content. Radiation acts on this process by inducing plaque initiation and affecting subsequent growth. The model was fitted to mouse data from ApoE-/- mice. Preliminary results showed that in the model plaque growth characteristics is identical for all plaques, i.e. the plaque size is determined by time since creation. Few other modelling approaches exist (e.g. multistage model by Schöllnberger and Simonetto, or spatial model by Little). However, since the epidemiological data for cardiovascular risk is partially inconsistent and under debate (e.g. whether lower doses relate to smaller risk, or even a threshold model) a better mechanistic understanding of atherosclerosis is strongly desirable. This requires better knowledge on the dynamics of disease progression for spontaneous as well as radiation-induced disease. Of particular interest is the distribution of number, size and composition of atherosclerotic plaques at different ages. A closer interaction between different modelling approaches would be important for future progress.