# Quantifying Multiscale Competitive Landscapes of Clonal Diversity in Glioblastoma

> **NIH NIH U01** · MAYO CLINIC ARIZONA · 2021 · $166,000

## Abstract

ABSTRACT
 Glioblastoma (GBM) exhibits profound intratumoral molecular heterogeneity that contributes to treatment
resistance and poor survival. Specifically, each tumor comprises multiple molecularly-distinct subpopulations
with different treatment sensitivities. This heterogeneity not only portends the pre-existence of resistant
molecular subpopulations, but also the communications between neighboring subpopulations that further
modulate tumorigenicity and resistance. In fact, a minority tumor subpopulation with EGFRvIII mutation has
been shown to potentiate a majority subpopulation with wild-type EGFR to increase tumor growth, cell survival,
and drug resistance. This type of cooperativity presents clear implications for improving GBM treatment. Yet
compared to other tumor types, the interactions in GBM remain critically understudied.
A significant barrier to studying the interactions between molecularly-distinct subpopulations is the
challenge of tissue sampling in GBM. In particular, contrast-enhanced MRI (CE-MRI) routinely guides surgical
biopsy and resection of the MRI enhancing core, but fails to address the diverse subpopulations of the
surrounding non-enhancing parenchyma (so called “brain around tumor” or BAT). These unresected residual
subpopulations in BAT represent the main contributors to tumor recurrence, which can exhibit different
therapeutic targets (and interactions) compared with enhancing biopsies. To address the limitations of tissue
sampling, imaging techniques can help quantitatively characterize tumors in their entirety, including unresected
BAT regions. Our group has used multi-parametric MRI and image-guided biopsies to develop and validate
machine-learning (ML) models of intratumoral genomic heterogeneity, with particular focus on the BAT zone.
In Aim 1, will we collect and molecularly profile a large set of image-recorded stereotactic biopsies in
primary GBM patients to quantify the diversity of molecularly-distinct subpopulations, as well as their
phenotypic niches, throughout the BAT zone. We will assess local heterogeneity at the biopsy level and also
co-localize regional patterns and rates of recurrence on serial MRI. In Aim 2, we will use these biopsies and
spatially matched MRI metrics to refine our existing ML predictive models. We will use these ML models to co-
localize spatial patterns of molecularly-distinct subpopulations (and their phenotypic niches) to quantify their
risk of regional recurrence. In Aim 3, we will functionally validate the subpopulation interactions observed in
Aims 1 and 2 using patient derived xenograft (PDX) models. We will also validate these interactions in human
GBM using a subset of spatially matched biopsies from primary and recurrent tumors in the same patients.
This proposal leverages our unique expertise in image-guided tissue analysis and MRI-based computational
modeling to study the diversity of molecularly-distinct subpopulations and the evolving competitive landscapes
in hum...

## Key facts

- **NIH application ID:** 10411429
- **Project number:** 3U01CA220378-05S1
- **Recipient organization:** MAYO CLINIC ARIZONA
- **Principal Investigator:** Leland Hu
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $166,000
- **Award type:** 3
- **Project period:** 2017-09-12 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10411429

## Citation

> US National Institutes of Health, RePORTER application 10411429, Quantifying Multiscale Competitive Landscapes of Clonal Diversity in Glioblastoma (3U01CA220378-05S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10411429. Licensed CC0.

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