Biomarkers and systems biology

Despite several previous targeted efforts, no predictive biomarker has been identified for incident VTE.

Leader: Professor Jacob Odeberg

In order to predict the risk of disease, a biomarker needs to be present prior to the development of the disease, rather than being a consequence of the disease. Plasma isolated from blood is easily accessible and therefore an attractive source for measuring potential biomarkers. Despite several previous targeted efforts, no predictive biomarker has been identified for incident VTE. Therefore, a wider focus on the plasma proteome is presumably a more efficient strategy to identify novel biomarkers that are associated with VTE. Using untargeted mass spectrometry (MS)-based proteomics, we have recently identified 54 candidate proteins that were differently expressed in plasma from VTE cases (n=100) and matched controls (n=100) (Discovery). We are about to conduct a targeted affinity-based proteome study of these candidate plasma proteins in a large population-based case-cohort derived from the Tromsø study (VTEs=950, sub-cohort=3000, Replication). The next steps will be to develop quantitative assays for plasma proteins that remain differently expressed after replication, and then we will externally validate these candidates in a population-based case-cohort derived from the HUNT study.

In collaboration with the University of California San Diego (UCSD), we have conducted whole exome sequencing on DNA isolated from blood samples in the Tromsø study (VTEs=465, controls=455, Discovery). We are now conducting an individual-level meta-analysis on whole exome sequencing data from the Tromsø study, the Framingham study and the Geisinger study to identify common and rare coding and non-coding variants associated with VTE (VTEs=2100, controls=5300, Replication). Next, we will validate identified gene variants associated with VTE risk in the case-cohorts derived from the Tromsø and HUNT studies (Validation).

Genetic variation underlying molecular phenotypes, such as proteins and transcript expression levels, may be important tools to unravel biological pathways mediating disease processes, ultimately resulting in physiological understanding of diseases. Several studies, including our own recent work (Solomon et al. Circ Cardiovasc Genet 2016;9:375-383), have systematically identified genetic variations associated with protein levels and isoforms, and revealed novel pathways that could be tested in experimental laboratory studies. However, no study has used this approach to identify pathways in the pathogenesis of VTE. In this project, we will combine the genotype data with the MS-based proteomic data to perform protein quantitative trait loci (pQTL) studies to identify molecular pathways involved in the pathogenesis of VTE.

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