SBI – Department of Systems Biology and Bioinformatics
Faculty of Computer Science and Electrical Engineering
University of Rostock
Ulmenstrasse 69 | 18057 Rostock
+49 381 498-7571
SASKit: Senescence-Associated Systems diagnostics Kit for cancer and stroke
With aging comes cellular senescence, and (multi-)morbidity. Cellular senescence is a key driver of an interconnected disease network including cancer and stroke. We wish to utilize systems modeling and bioinformatics, learning from omics and other lab data, to design and develop a biomarker + software kit with a focus on measuring and interpreting senescence-related signatures for precise (and early) diagnosis, prognosis, and, ultimately, therapy, of pancreatic cancer and ischemic stroke/thromboembolism. We build upon publications describing how cellular senescence and the senescence-associated secretory phenotype are directly involved in the comorbidity of pancreatic cancer, ischemic stroke, and more generally, of cancer and coagulation problems. We propose observational human studies for pancreatic cancer and ischemic stroke, measuring senescence markers in particular, preparing the power analyses and the companion diagnostics for larger interventional trials of, e.g., patient-specific natural-compound senolytics such as quercetin. For pancreas, we propose co-culture studies of cancerous and stellate cells, and a mouse cancer allograft model. For stroke, we study brain slices and stroke recovery in mice. In both cases, to mimic the human cohorts, we study young and old wild-type mice as well as senescence-prone strains already investigated in our past ROSAge GerontoSys project; data and tissues from then provide a valuable reference. High-throughput gene expression and protein array data are taken from blood and tissue of mice, and from blood of human, to allow the bioinformatics to extrapolate protein expression and pathway activation for the inaccessible tissue of humans, providing optimized input to machine learning of the best sets of biomarkers. Biomarker learning is also aided by sensitivity analyses based on dynamical models, which are in turn based on integrating mechanistic insights into disease and senescence based on public and consortium data.