Proteomics, RAdiomics & Machine learning-integrated strategy for precision medicine for Alzheimer’s
Recent clinical trials on Alzheimer's disease (AD) have been devised on the basis of hypotheses on the pathogenesis of the disease that nowadays are considered partly outdated and following approximative criteria for patient selection. As a consequence, patients affected by heterogeneous forms of AD with probable different sensitivity to active ingredients were considered. However, recent studies have suggested that several clinical phenotypes of AD exist and that the differentiation between disease subtypes can be due to the pathway followed by the AD precursor beta-amyloid (Aβ) peptide when self-assembles into amyloid aggregates in the brain. An integrated survey taking advantage of multiple marker modalities selected on the basis of the scientific evidence available today such as brain imaging and molecular biomarker analysis is perceived as a preferred solution to supply doctors in the identification of the different disease subtypes even in the early stages and therefore to develop a personalized treatment for each patient group.
In the PRAMA project we intend to build up a strategy for personalized prediction of the disease based on the hypothesis that the main precursors of AD can form specific aggregates responsible for distinct clinical pictures of the disease with consequent differential sensitivity to drugs. In detail, a combined biochemical, biophysical and optical spectroscopy characterization of molecular biomarkers (mainly Ab peptide and tau protein) found in the cerebrospinal fluid (CSF) of 100 individuals including patients with progressive clinical signs of AD will be carried out. These data will provide information on biomarker composition, structure, aggregation level and toxicity that will constitute the proteomic profile of the biomarker content of each individual. The same patients will be subjected to magnetic resonance imaging (MRI) followed by multiple features radiomic image analysis. The entire set of biochemical, optical, MRI data including clinical parameters and neuropsychological evaluation of patients will be elaborated through Big Data analytics techniques to, firstly, discover correlations among novel and gold-standard biomarkers and, then, to mine and identify different AD phenotypes. The newest Artificial Intelligence and Machine Learning techniques will be studied to model and process the complex high-dimensional data gathered in PRAMA. Data analyses will also aim at discovering specific diagnostic, prognostic or predictive responses of the different disease stages and on a personalized basis.Overall, the PRAMA project proposal represents a perspective of high human and socio-economic impact, with significant advantages including reducing healthcare costs and improving the well-being of the world population in the immediate future.
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