Publications

Abstract (Expand)

Background and Objective: Predicting individual mutation and cancer risks is essential to assist genetic counsellors in clinical decision making for patients with a hereditary cancer predisposition. Worldwide a variety of statistical models and empirical data for risk prediction have been developed and published for hereditary breast and ovarian cancer (HBOC), and hereditary non-polyposis colorectal cancer (HNPCC / Lynch syndrome, LS). However, only few models have so far been implemented in convenient and easy-to-use computer applications. We therefore aimed to develop user-friendly applications of selected HBOC and LS risk prediction models, and to make them available through the "Leipzig Health Atlas" (LHA), a web-based multifunctional platform to share research data, novel ontologies, models and software tools with the medical and scientific community. LHA is a project funded within the BMBF initiative "i:DSem – Integrative data semantics in system medicine". Methods and Results: We selected a total of six statistical models and empirical datasets relevant for HBOC and LS: 1) the Manchester Scoring System, 2) the "Mutation Frequency Explorer" of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), 3) an extended version of the Claus model, 4) MMRpredict, 5) PREMM1,2,6, and 6) PREMM5. The Manchester Scoring System allows calculation of BRCA1/2 mutation probabilities based on aggregated family history. The "Mutation Frequency Explorer" allows flexible assessment of mutation risks in BRCA1/2 and other genes for different sets of familial cancer histories based on a large dataset from the GC-HBOC. The extended Claus model (as implemented in the commercial predigree drawing software Cyrillic 2.1.3, which is no longer supported and no longer works on newer operating systems) predicts both mutation and breast cancer risks based on structured pedigree data. MMRpredict, PREMM 1,2,6, and PREMM 5 predict mutation risks in mismatch repair genes for patients from families suspected of having LS. All models were implemented using the statistical software "R" and the R-package "Shiny". "Shiny" allows the development of interactive applications by incorporating "R" with HTML and other web technologies. The Shiny apps are accessible on the website of the "Leipzig Health Atlas" (https://www.health-atlas.de) for registered researchers and genetic counselors. Conclusions: The risk prediction apps allow convenient calculation of mutation or cancer risks for an advice-seeking individual based on pedigree data or aggregated information on the familial cancer history. Target users should be specialized health professionals (physicians and genetic counselors) and scientists to ensure correct handling of the tools and careful interpretation of results.

Authors: Silke Zachariae, Sebastian Stäubert, C. Fischer, Markus Löffler, Christoph Engel

Date Published: 8th Mar 2019

Journal: Not specified

Human Diseases: hereditary breast ovarian cancer syndrome, Lynch syndrome, colorectal cancer

Abstract (Expand)

The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and epidemiological studies. The development of computable phenotype algorithms to solve these tasks is a challenging problem, caused by various reasons. Firstly, the term ‘phenotype’ has no generally agreed definition and its meaning depends on context. Secondly, the phenotypes are most commonly specified as non-computable descriptive documents. Recent attempts have shown that ontologies are a suitable way to handle phenotypes and that they can support clinical research and decision making. The SMITH Consortium is dedicated to rapidly establish an integrative medical informatics framework to provide physicians with the best available data and knowledge and enable innovative use of healthcare data for research and treatment optimization. In the context of a methodological use case “phenotype pipeline” (PheP), a technology to automatically generate phenotype classifications and annotations based on electronic health records (EHR) is developed. A large series of phenotype algorithms will be implemented. This implies that for each algorithm a classification scheme and its input variables have to be defined. Furthermore, a phenotype engine is required to evaluate and execute developed algorithms. In this article we present a Core Ontology of Phenotypes (COP) and a software Phenotype Manager (PhenoMan), which implements a novel ontology-based method to model and calculate phenotypes. Our solution includes an enhanced iterative reasoning process combining classification tasks with mathematical calculations at runtime. The ontology as well as the reasoning method were successfully evaluated based on different phenotypes (including SOFA score, socioeconomic status, body surface area and WHO BMI classification) and several data sets.

Authors: Alexandr Uciteli, Christoph Beger, Toralf Kirsten, Frank A. Meineke, Heinrich Herre

Date Published: 20th Dec 2019

Journal: CEUR Workshop Proceedings

Human Diseases: Not specified

Abstract (Expand)

PURPOSE: The aim of this study was to test psychometric properties of the Satisfaction with Life Scale (SWLS), to provide normative values, and to analyze associations between life satisfaction and sociodemographic and behavioral data. METHODS: A German community sample (n = 9711) with an age range of 18-80 years was surveyed using the SWLS and several other questionnaires. Confirmatory factor analysis (CFA) was used to test the dimensionality of the SWLS. Invariance across gender and age groups was tested with multiple-group CFA. Associations between SWLS, sociodemographic variables, and behavioral variables were tested with ANOVAs. RESULTS: Confirmatory factorial analysis results confirmed that the SWLS is a one-dimensional scale. Measurement invariance across gender was completely confirmed, while concerning age strict measurement invariance was confirmed. The effects of gender and age on satisfaction with life were weak. Satisfaction with life was associated with fatigue (r = - .49), the mental component of quality of life (r = .45), anxiety (r = - .42), dispositional optimism (r = .41), pessimism (r = - .34), sleep quality (r = - .32), and sociodemographic factors such as marital status, income, and occupational status. Non-smokers reported higher life satisfaction than smokers. CONCLUSIONS: Because of the good psychometric properties, the SWLS can be recommended for use in epidemiological research. Normative values based on a large community sample are provided.

Authors: Andreas Hinz, I. Conrad, M. L. Schroeter, H. Glaesmer, E. Brahler, M. Zenger, R. D. Kocalevent, P. Y. Herzberg

Date Published: 29th Mar 2018

Journal: Qual Life Res

Human Diseases: Not specified

Abstract (Expand)

PURPOSE: The Sniffin' Sticks Screening 12 test is a test of olfactory performance based on pen-like odor dispensing devices. The aims of this study were to analyze the performance of this test in a general population sample and to explore associations between olfactory dysfunction and quality of life. METHODS: A large community sample (n = 7267) completed the Sniffin' Sticks Screening 12 test and several questionnaires measuring quality of life, anxiety, dispositional optimism, social support, and satisfaction with life. RESULTS: According to the criteria recommended by the test manufacturer, 5.1% of the participants were anosmic (score </= 6), 52.4% were dysosmic (7 </= score </= 10), and 42.5% were normosmic (score >/= 11). While frequencies of correct identification differed between the 12 sticks, all sticks contributed positively to the test results. The associations between olfactory functioning and quality of life variables were negligible. In the multivariate analyses, none of the associations reached the 1% significance level. CONCLUSIONS: While studies with patients in otorhinolaryngological clinics often report substantial detriments to their quality of life in relation to olfactory dysfunction, the present epidemiological study cannot confirm this association for the general population.

Authors: Andreas Hinz, Tobias Luck, Steffi Gerlinde Riedel-Heller, P. Y. Herzberg, C. Rolffs, K. Wirkner, Christoph Engel

Date Published: 21st Nov 2018

Journal: Eur Arch Otorhinolaryngol

Human Diseases: Not specified

Abstract (Expand)

AIM: We present here a novel method that enables unraveling the interplay between gene expression and DNA methylation in complex diseases such as cancer. MATERIALS & METHODS: The method is based on self-organizing maps and allows for analysis of data landscapes from 'governed by methylation' to 'governed by expression'. RESULTS: We identified regulatory modules of coexpressed and comethylated genes in high-grade gliomas: two modes are governed by genes hypermethylated and underexpressed in IDH-mutated cases, while two other modes reflect immune and stromal signatures in the classical and mesenchymal subtypes. A fifth mode with proneural characteristics comprises genes of repressed and poised chromatin states active in healthy brain. Two additional modes enrich genes either in active or repressed chromatin states. CONCLUSION: The method disentangles the interplay between gene expression and methylation. It has the potential to integrate also mutation and copy number data and to apply to large sample cohorts.

Authors: Lydia Hopp, Henry Löffler-Wirth, J. Galle, Hans Binder

Date Published: 12th Jun 2018

Journal: Epigenomics

Human Diseases: glioblastoma multiforme

Abstract (Expand)

BACKGROUND: Haematotoxicity of conventional chemotherapies often results in delays of treatment or reduction of chemotherapy dose. To ameliorate these side-effects, patients are routinely treated with blood transfusions or haematopoietic growth factors such as erythropoietin (EPO) or granulocyte colony-stimulating factor (G-CSF). For the latter ones, pharmaceutical derivatives are available, which differ in absorption kinetics, pharmacokinetic and -dynamic properties. Due to the complex interaction of cytotoxic effects of chemotherapy and the stimulating effects of different growth factor derivatives, optimal treatment is a non-trivial task. In the past, we developed mathematical models of thrombopoiesis, granulopoiesis and erythropoiesis under chemotherapy and growth-factor applications which can be used to perform clinically relevant predictions regarding the feasibility of chemotherapy schedules and cytopenia prophylaxis with haematopoietic growth factors. However, interactions of lineages and growth-factors were ignored so far. RESULTS: To close this gap, we constructed a hybrid model of human granulopoiesis and erythropoiesis under conventional chemotherapy, G-CSF and EPO applications. This was achieved by combining our single lineage models of human erythropoiesis and granulopoiesis with a common stem cell model. G-CSF effects on erythropoiesis were also implemented. Pharmacodynamic models are based on ordinary differential equations describing proliferation and maturation of haematopoietic cells. The system is regulated by feedback loops partly mediated by endogenous and exogenous EPO and G-CSF. Chemotherapy is modelled by depletion of cells. Unknown model parameters were determined by fitting the model predictions to time series data of blood counts and cytokine profiles. Data were extracted from literature or received from cooperating clinical study groups. Our model explains dynamics of mature blood cells and cytokines after growth-factor applications in healthy volunteers. Moreover, we modelled 15 different chemotherapeutic drugs by estimating their bone marrow toxicity. Taking into account different growth-factor schedules, this adds up to 33 different chemotherapy regimens explained by the model. CONCLUSIONS: We conclude that we established a comprehensive biomathematical model to explain the dynamics of granulopoiesis and erythropoiesis under combined chemotherapy, G-CSF, and EPO applications. We demonstrate how it can be used to make predictions regarding haematotoxicity of yet untested chemotherapy and growth-factor schedules.

Authors: S. Schirm, Christoph Engel, Markus Löffler, Markus Scholz

Date Published: 26th May 2014

Journal: Theor Biol Med Model

Human Diseases: leukemia, anemia

Abstract (Expand)

Anaemia is a common haematologic side effect of dose-dense multi-cycle cytotoxic polychemotherapy requiring erythrocyte transfusions or erythropoietin (EPO) administration. To simulate the effectiveness of different EPO application schedules, we performed both modelling of erythropoiesis under chemotherapy and pharmacokinetic and dynamic modelling of EPO applications in the framework of a single comprehensive biomathematical model. For this purpose, a cell kinetic model of bone marrow erythropoiesis was developed that is based on a set of differential compartment equations describing proliferation and maturation of erythropoietic cell stages. The system is regulated by several feedback loops comprising those mediated by EPO. We added a model of EPO absorption after injection at different sites and a pharmacokinetic model of EPO derivatives to account for the effects of external EPO applications. Chemotherapy is modelled by a transient depletion of bone marrow cell stages. Unknown model parameters were determined by fitting the predictions of the model to data sets of circulating erythrocytes, haemoglobin, haematocrit, percentage of reticulocytes or EPO serum concentrations derived from the literature or cooperating clinical study groups. Parameter fittings resulted in a good agreement of model and data. Depending on site of injection and derivative (Alfa, Beta, Delta, Darbepoetin), nine groups of EPO applications were distinguished differing in either absorption kinetics or pharmacokinetics. Finally, eight different chemotherapy protocols were modelled. The model was validated on the basis of scenarios not used for parameter fitting. Simulations were performed to analyze the impact of EPO applications on the risk of anaemia during chemotherapy. We conclude that we established a model of erythropoiesis under chemotherapy that explains a large set of time series data under EPO and chemotherapy applications. It allows predictions regarding yet untested EPO schedules. Prospective clinical studies are needed to validate model predictions and to explore the feasibility and effectiveness of the proposed schedules.

Authors: S. Schirm, Christoph Engel, Markus Löffler, Markus Scholz

Date Published: 12th Jun 2013

Journal: PLoS One

Human Diseases: anemia

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