Toward Personalized Network Biomarkers in Alzheimer's Disease: Computing Individualized Genomic and Protein Crosstalk Maps
We call for the AD field to move beyond discrete biomarkers and utilize the full power of informatics and big data approaches to build and test personalized markers at a pathway and network level. While we have chosen AD as an example, the issues we propose are also highly relevant to other neurodegenerative disorders, such as vascular dementia or dementia with Lewy bodies, where even less is known about how various biomarkers interact. Indeed, the availability of rich biomarker data across a range of neurodegenerative disorders would enable more accurate pathology-based classification of such conditions (as opposed to the current predominantly clinical classifications). We further hypothesize that building dynamic network biomarkers and pathway crosstalk reference maps using the combined power of several protein/gene-level knowledge priors could accelerate discovery of disease-specific mechanisms and novel drug targets by enrichment with patient specific genetic information. Application of this methodology to large public AD datasets is needed to test our hypotheses and refine the methods. Subsequent replication in independent datasets and population studies as well as functional validation of mechanisms in laboratory models will be the next steps. Ultimately, it is our hope that such novel methods may yield further insights into both disease mechanisms as well as novel targets for biomarker development and drug discovery.