SASKit Project

Senescence-Associated Systems Diagnostics Kit for cancer and stroke (SASKit)

a "Systems medicine research consortium" under the research funding scheme "e:Med - Paving the Way for Systems Medicine" of the Federa Ministry of Education and Research.

With aging comes cellular senescence, and (multi-)morbidity. Cellular senescence is a key driver of an interconnected disease network including cancer and stroke. We will 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 perform 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 will co-culture cancerous and stellate cells, and develop 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 that will 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.