Multi-scale modeling of glioma for the prediction of treatment response, treatment monitoring and treatment allocation

Stanford University, Stanford CA

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Abstract

Project Summary/Abstract Novel molecular technologies such as single cell RNA seq and DNA methylation assays have now become routine techniques for gathering data at the molecular level including for Alzheimer’s disease (AD). Yet, these technologies are still expensive and require fresh tissue, which not feasible for large cohorts. Moreover, processing tissues for single cell analysis can distort gene expression profiles as well as the representation of different cell types. Computational deconvolution methods can infer proportions of cells from bulk tissue assays that have been minimally processed, retaining important information. We have previously developed and applied such methods in the context of cancer biology. Here we will bring them to the analysis of Alzheimer disease, interrogating unaffected vs early vs late affected