Optimization and Validation of an Indicator Cell Assay for Blood-Based Diagnosis of Alzheimers Disease

DESCRIPTION provided by applicant We are developing the Indicator Cell Assay Platform iCAP a broadly applicable and inexpensive platform for blood based diagnostics that can be used for early detection of disease and as a companion diagnostic for drug development The iCAP uses cultured cells as biosensors capitalizing on the ability of cells to respond differently to signals present in the serum or other biofluid from normal or diseased subjects with exquisite sensitivity as opposed to traditional assays that rely on direct detection of molecules in blood Developing the iCAP involves exposing cultured cells to serum from normal or diseased subjects measuring a global differential response pattern and using it to build a reliable disease classifier comprised of a small number of features Deploying the iCAP involves measuring only expression genes that are features of the disease classifier using cost effective tools Indicator cells are chosen based on the disease application and those typically selected have known relationships to the disease pathology The iCAP can overcome barriers to blood based diagnostics like broad dynamic range of blood components low abundance of specific markers and high levels of noise We have demonstrated proof of concept for an iCAP for the early detection of Alzheimerandapos s disease AD that can identify AD from plasma at two stages of progression presymptomatic AD no clinical symptoms but markers of AD pathophysiology present and early stage AD with accuracy compared to normal controls The focus of this proposal is to optimize the assay to generate a robust and accurate classifier of AD that can distinguish three sample classes presymptomatic AD early stage AD and normal controls and rigorously validate the assay with extended cohorts To do this we will Identify optimal indicator cells and experimental conditions for the AD iCAP Generate new data with the optimized assay conditions and use them to train and test an optimized classifier of presymptomatic and early AD that can also distinguish AD from non AD dementia Rigorously validate the robustness and accuracy of the classifier by training and testing on extended cohorts from two different clinical sources Our goal is to generate a robust final classifier base on expression levels of genes that can distinguish two stages of AD progression with blind predictive accuracy of with sensitivity and specificity over normal samples and those with non AD dementia that can be used for early detection of AD and for clinical trials PUBLIC HEALTH RELEVANCE We are developing a non invasive diagnostic tool to detect Alzheimerandapos s disease AD from blood samples called the indicator cell assay platform iCAP In the assay patient serum is applied to specifically selected standardized indicator cells and the multicomponent response of the cell provides the readout The iCAP will succeed where other methods have failed by exploiting cellsandapos natural capability to amplify and integrate multi analyte signals We aim to experimentally optimize and rigorously validate the AD iCAP to generate a robust and accurate assay for early detection of AD and for use in clinical trials