Molecular biomarkers play an increasing role for the diagnosis and prediction of progression or therapy response in complex diseases such as cancer. In modern Systems Medicine approaches the aim is to look at increasingly complex interactions of complete signaling pathways in order to get a more holistic view for individualized treatment decisions. Individualized treatment decisions and newly developed specialized drugs warrant the need to broaden the focus in individualized medicine from singular biomarkers to pathways. Furthermore, the Omics-era enables research to incorporate whole genome, transcriptome and proteome views of the patient status. On the one hand genomics technologies allow the parallel measurement of many different components of the system. On the other hand pathway databases offer vast amounts of knowledge on biological networks, freely available and encoded in semi-structured formats. However, the vast amount of published data on molecular interactions makes it increasingly challenging for life science researchers to find and extract the most relevant information. Currently, the tools to use this information and integrate it in a clinical context are still lacking.
This project aims at providing more efficient data use in Systems Medicine by integrating patient clinical and genomics data with pathway knowledge. The goal is to present the most relevant, meaningful and interpretable patient-specific pathways to clinicians and researchers in order to enable further medical and pharmaceutical insights. In particular our approach will generate a knowledge base and methods to generate context-specific pathways, i.e. patient-specific, disease-specific or cohort-specific pathways. The project will deliver condensed knowledge of molecular networks in order to stratify and analyze sample groups in a clinical research environment. The knowledge base will utilize an innovative Software-as-a- Service architecture to receive, store and deliver data. The module-based architecture facilitates an inter- play with the local clinical information system, the cancer registry and the geneXplain platform for bioinformatics analyses. Internally, a nanopublication-inspired metadata store will be created to dynamically link data of multiple knowledge domains. This project systematically integrates established ontologies, databases, tools and unstructured patient data using metadata annotations in order to offer a refined view on patient-specific pathway knowledge. Specifically our aims are:
With this we aim to reduce the gap between patient-centered routine documentation and ontology-driven pathway and gene annotation. Thus, we establish a seamless data-flow from single patient data to Systems Medicine as a clinical resource and as a tool for knowledge discovery. We will implement and validate a software that offers a consistent, intuitive annotation of this data for the following user groups:
The developed software will be used and validated in projects with a clinical research aim first. Here the goal will be to test the developed tools in a clinical setting on data from patients with colorectal carcinomas collected and annotated within the clinical research group 179 (KFO179) and on data from patients with metastases collected in the MetastaSys project. In the long term we aim to establish this as a tool that can be used in clinical research as well as in clinical routine. For example, the tumor conference, a local board where the specific data and indications of individual cancer patients are discussed, would greatly benefit from this tool. This vision would be a first step on the way towards providing services similar to the online service Patients like me1, where patients can enter searches like “show me a summary of the existing data and tell me what it means in the context of the current literature”.