Understanding prion diseases: can we use data-driven statistical and computational models for differential and personalized diagnosis, prognosis, monitoring and treatment?

Alberto Bizzi

M.D. Professionista di Eccellenza, Neuroradiology Unit, Fondazione IRCCS Istituto NeurologicoCarlo B

Friday 1st July 2016

14:30:00

Aula Seminari "Saleri" VI Piano MOX -Dipartimento di Matematica, POLITECNICO DI MILANO

Prion diseases or transmissible spongiform encephalopathies (TSEs) are a family of rare progressive neurodegenerative disorders caused by prions, a type of protein that can trigger normal proteins in the brain to fold abnormally. Prion diseases can affect sporadically both humans and animals, and are sometimes transmitted to humans by infected meat products. One of the most studied human prion desease is Creutzfeldt-Jakob disease (CJD) that accounts for more than 90% of all cases of sporadic prion diseases; it is commonly categorised into twelve subtypes that can be distinguished according to leading clinical signs, histological lesions, and molecular traits of the pathogenic prion protein. Prognosis, survival rate and therapy are different among sCJD subtypes. An accurate and timely classification of the subtype depends on careful clinical examination and early performance and interpretation of diagnostic tests, including electroencephalography, quantitative assessment of the surrogate markers 14-3-3, tau, and of the prion protein in the CSF, and Magnetic Resonance diffusion imaging. In clinical practice signal hyperintensity in the cortex and/or in the striatum on magnetic resonance (MR) diffusion-weighted images (DWI) is a marker of sCJD. MR diagnostic accuracy is greater than 90%, but the biophysical mechanisms underpinning the signal abnormality are unknown. The aim of this seminar is to illustrate the clinical, pathological and MR diffusion imaging biomarkers of sporadic CJD and make a call for development of new powerful data-driven statistical and computational models of prion disease that may open new avenues for understanding the complexity of clinical phenotypes. Mathematical models offer innovation potential by underpinning support systems for clinical and drug-development applications. They may enable “precision Medicine” by providing differential and personalized diagnosis, fine-grained staging, and personalised prognosis. contact: simone.vantini@polimi.it