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

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $363,862

## 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

## Key facts

- **NIH application ID:** 10499903
- **Project number:** 3R01CA260271-02S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Olivier Gevaert
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $363,862
- **Award type:** 3
- **Project period:** 2021-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10499903

## Citation

> US National Institutes of Health, RePORTER application 10499903, Multi-scale modeling of glioma for the prediction of treatment response, treatment monitoring and treatment allocation (3R01CA260271-02S1). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10499903. Licensed CC0.

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