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BEHANDLING-God og treffsikker diagnostikk, behandling og rehabilitering

A multiomics approach towards personalized medicine in rheumatoid arthritis

Alternative title: Molekylærtilnærming til revmatoid artritt for å muliggjøre persontilpasset behandling

Awarded: NOK 11.1 mill.

Rheumatoid arthritis is a multifactorial disease where both the biological background and the clinical characteristics of the patients vary largely. Clinical treatment of rheumatoid arthritis patients relies on a trial-error process, and it can take time to find the optimal treatment regime for each patient. The diverse patient biology is a likely cause of the variability in treatment response. Therefore, drug management tailored to the patient's biology, including molecular tools to predict the drug response, is a priority. We study patient cohorts at different stages: newly diagnosed, treatment naïve patients and rheumatoid arthritis patients in remission on methotrexate treatment, and also follow there disease development and monitor their treatment response. We use next generation sequencing technology to profile molecular signatures of relevant immune cells either in bulk or at single cell resolution. The complex biology of rheumatoid arthritis is more accurately mimicked by integrating several layers of molecular data. We will perform integrative analyses on envirome, genome, methylome, miRNAome and transcriptome separately harvested from RA pathogenic immune cells. Laboratory analyses and data collection for this part of the project is completed. In addition, we will also obtain molecular signatures from extracellular vesicles important in the cell-cell communication. We have so far optimized the laboratory protocols for vesicle isolation and characterization, as well as proteomics analyses and miRNA sequencing protocol. The PhD student is currently writing her first article. By studying molecular profiles from both immune cells and extracellular vesicles, we hope to capture key molecular players in the pathogenesis and biomarkers for treatment response. We aim to use the integrated molecular layers to dissect the complex pathogenic processes and obtain molecular signatures of distinct RA subtypes that are predictive of treatment response. Finally, we will transfer the obtained distinct molecular signatures to a translational digital platform combined with clinical data in order to aid personlized drug decisions.

Clinical treatment of rheumatoid arthritis (RA) patients relies on a trial-error process, usually starting with methotrexate, as no tool to predict drug response is available. RA is a heterogeneous and multifactorial disease where more than 100 genetic risk variants are involved, together with other, less studied, molecular layers. The diverse RA biology is a likely cause of the variable treatment response. Therefore, drug management tailored to the patient’s biology, including molecular tools to predict the drug response, is a priority. The complex biology of RA patients is more accurately mimicked by integration of several disease relevant omics data. We will perform integrative analyses on envirome, genome, methylome, miRNAome and transcriptome separately harvested from RA pathogenic immune cells (CD4 naïve, CD4 memory, Th17 T cells and CD19 B cells) and extracellular vesicles, using next generation deep sequencing. We have two clinically homogeneous patient cohorts, newly diagnosed, treatment naïve patients and RA patients in remission on methotrexate treatment. Several omics data have already been generated. We have included analyses of extracellular vesicles to also capture disturbances in the cell-cell communication, as their composition has been reported to be associated with RA. Extracellular vesicles and their RNA cargo is of particular interest because they have been proposed to be utilized therapeutically in RA. With the current proposal, we aim to integrate several layers of omics signatures and explore the origin and RNA cargo in extracellular vesicles in order to dissect the complex pathogenic processes and obtain molecular signatures of distinct RA subtypes that are predictive of treatment response. Finally, we will transfer the obtained molecular signatures to a translational digital platform combined with clinical data in order to aid personlized drug decisions.

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BEHANDLING-God og treffsikker diagnostikk, behandling og rehabilitering