Project Area 2: Application Development And Validation


We will provide medical applications in two fields:

Application Group A: Supporting clinical decision making

Here, we decided to focus on examples from the cancer field where the translational effect can be illustrated. We plan to expand the program in the second funding phase.

Application Group B: Novel Phenotypes (“LIFE-Atlas”)

In this group of applications we address an emerging issue in system medicine. In the field of epidemiological research dedicated to investigate the risk for civilization diseases we are presently gaining data from novel assessment technologies (e.g. 3D-body shape, MRI, optical coherence tomography, liver elastography, vessel stiffness, transcriptome, metabolome, multichannel FACS cytomics). These methods provide quantitative and qualitative traits (QTs). They need to be characterized on a population level. The next challenging task is to investigate from a systems medicine perspective which of these QTs can be combined into reliable phenotype-profiles. It would e.g. be relevant to find out which obese persons have a low risk profile for onset of diabetes, hypertension or vessel disease (low risk “healthy obese”). We will utilize the LIFE ADULT and the LIFE HEART cohorts.

In the LIFE-ADULT cohort we have 10.000 participants from the Leipzig population. The have undergone extensive assessments: e.g. 3D-body shapes (10000), OCT-retina measurements (10000), metabolic traits (10000), vessel status assessments (8000), Brain MRI-scans (3000), WBC transcriptomes, genotypes and MS-metaboloms (3500). On the HEART cohort we have phenotype status including coronarography and WBC transcriptoms / genotypes / metaboloms on 7000 patients [86, 87-95, 105].

Project Area Projects

Project 2 A1: Integrative toolbox for clinical risk assessment in hereditary cancer syndromes

Individuals with pathogenic germline mutations in DNA repair genes have a substantially increased cancer risk (e.g. for breast and ovarian cancer in case of BRCA1/2 mutations or for colorectal and endometrial cancer in case of mutations in genes of the mismatch repair system). For these mutation carriers, individually tailored prevention strategies are important (e.g. a regular, close surveillance or prophylactic surgery).

Project 2 A2: Molecular disease classification and risk profiling of lymphomas

We are members of two German consortia working on lymphomas and have access to unique data sets and models. We coordinate the database and biometry of the German aggressive lymphoma study group (DSHNHL) conducting large clinical trials to optimize clinical outcome. We have derived and validated several prognostic factor models for treatment outcome, for type of recurrence and for risks of toxicity. Within systems biology consortia we molecularly profiled fresh tumor tissue and derived omic-based disease classifiers for several subgroups.

Project 2 A3: Molecular disease classification and risk profiling of brain tumors

Background: We coordinate the database and biometry of the German Glioma network (GGN) conducting an observational trial to investigate the role of molecular-genetic profiles for clinical outcome. 

Objective: It is our objective to provide the current knowledge of our established and published models for brain tumors to the LHA and give the medical user community usable access to the models, data and metadata. 

Users: Clinicians and cancer scientists, international scope

Project 2 A4: Molecular classification and risk profiling of Head and Neck Cancer

In the LIFE-project we determined molecular profiles of about 300 tumor specimen of head and neck cancers. In a publication by Wichmann et al. 2015, we identified factors contributing to the clinical, genetic and molecular heterogeneity of our cohort. The data set includes clinical phenotypes and molecular profiles including transcriptomes, papilloma viral infection status, candidate gene sequencing (Ion Torrent) and immunohistochemistry. Our data set is one of the largest data collections on head and neck cancer in the world. 

Project 2 A5: General cancer signatures querying tool

Applications A2-A4 deal with cancer entities for whom we provide specific signatures. We will query these and other signatures to identify overall cancer characteristics (e.g. inflammatory or proliferative signatures or signatures associated with specific genomic lesions) and to assign them to molecular cancer subtypes of different cancer entities. We plan to extend our application step-by-step into a more comprehensive ‘SOM cancer atlas’ by consecutively adding other cancer entities from external data sources such as TCGA (The Cancer Genome Atlas) and from own studies.

Project 2 A6: Simulation models of the blood formation system

Background: In the last years we built up quantitative models of the human blood formation system for white blood cells, red blood cells and platelets which permit to simulate a series of chemotherapies and growth factor applications [74-85]. In the BMBF-funded SB-project HaematoOpt validated models are made public.

Objective: It is the objective of this linked subproject to integrate these models into the LHA and to extend the range of applications.

Users: Clinicians and modelers

Project 2 A7: Severity and risk prediction for hospitalized community acquired pneumonia

Background: Stratification of hospitalized patients with community acquired pneumonia in regard to disease severity and expected clinical progression remains a challenge in modern medicine. A solution to this problem would help to optimize treatment and significantly reduce human suffering and cost to society. The BMBF-funded consortium project “PROGRESS” is seeking clinical markers and biomarkers in a large cohort. PROGRESS has successfully recruited patients (# 1000) and generated longitudinal multi-‘omics molecular data which are currently analyzed.

Project 2 B2: LIFE phenotype atlas

The LIFE study program collects several thousand phenotypic features as quantitative traits per proband together with the comprehensive characteristics of its health and social status and life styles. In addition, high-throughput molecular genome and transcriptome data were measured for sub-cohorts of a few thousand individuals each. Typically, this data is analyzed in hypothesis-driven analysis studies using subsets of probands and QTs.