U01CA294551
Cooperative Agreement
Overview
Grant Description
Understanding the role of tumor microenvironment in low grade glioma progression to malignancy - Abstract
Our PCA Research Center brings together the UCLA Neurosurgery team with the Caltech Spatial Single Cell team to construct a comparative spatial atlas of low-grade gliomas.
The collaboration between the three components of the PCA Research Center has already generated preliminary data in several glioma samples.
We will expand this effort to generate a comparative atlas of gliomas with distinct progression outcomes using integrated spatial transcriptomics, proteomics, and chromosome profiling.
By comparing the low-grade gliomas that eventually transform with ones that stay indolent or do not recur, and with IDH-mutant high-grade gliomas, we aim to understand the molecular and cellular mechanisms at the low-grade stage that are predictive of malignant transformation (MT) and to suggest intervention strategies to prevent MT.
The comparative analysis will examine three types of changes in low-grade gliomas with different outcomes: cell type composition, tumor microenvironment, and pathway specific gene expression.
From UCLA’s Brain Tumor Translation Resource (BTTR) Center, we have already collected 99 fresh-frozen low-grade glioma samples and will collect approximately an additional 100 samples of low-grade glioma with different outcomes (MT, indolent, and no-recurrence).
38% of the current cohort of patients are from under-represented minority groups; we will continue to recruit from a diverse patient pool in order to better understand which patients may be at higher risk for malignant transformation and therefore need more frequent surveillance or earlier intervention.
We will then generate an integrated multi-modal spatial atlas targeting 2500 mRNAs, 10 proteins and 10 DNA CNVs and translocations.
From the high sensitivity and multiplexed RNA SeqFISH assays, we will be able to capture not only cell type and microenvironment information, but also genes and pathways that could be causal for progression to malignancy.
Lastly, we will use the data to 1) predict tumor progression based on the cell type compositions and microenvironments; 2) design intervention strategies based on the spatial data, using counterfactual inference models to affect immune infiltrating and other predictors of progression; and 3) build a model of tumor progression dynamics based on gene expression and mechanics of the tissue.
Our comprehensive low-grade glioma tissue collection, the integrated spatial dataset with transcriptomics, proteomics and chromosomal abnormalities, and the models built using advanced machine-learning tools will extend the existing capabilities of the HTAN consortium and be interoperable.
The atlas and the computational tools will be used by us and the wider scientific community to further understand the mechanisms leading to malignant transformation.
Our PCA Research Center brings together the UCLA Neurosurgery team with the Caltech Spatial Single Cell team to construct a comparative spatial atlas of low-grade gliomas.
The collaboration between the three components of the PCA Research Center has already generated preliminary data in several glioma samples.
We will expand this effort to generate a comparative atlas of gliomas with distinct progression outcomes using integrated spatial transcriptomics, proteomics, and chromosome profiling.
By comparing the low-grade gliomas that eventually transform with ones that stay indolent or do not recur, and with IDH-mutant high-grade gliomas, we aim to understand the molecular and cellular mechanisms at the low-grade stage that are predictive of malignant transformation (MT) and to suggest intervention strategies to prevent MT.
The comparative analysis will examine three types of changes in low-grade gliomas with different outcomes: cell type composition, tumor microenvironment, and pathway specific gene expression.
From UCLA’s Brain Tumor Translation Resource (BTTR) Center, we have already collected 99 fresh-frozen low-grade glioma samples and will collect approximately an additional 100 samples of low-grade glioma with different outcomes (MT, indolent, and no-recurrence).
38% of the current cohort of patients are from under-represented minority groups; we will continue to recruit from a diverse patient pool in order to better understand which patients may be at higher risk for malignant transformation and therefore need more frequent surveillance or earlier intervention.
We will then generate an integrated multi-modal spatial atlas targeting 2500 mRNAs, 10 proteins and 10 DNA CNVs and translocations.
From the high sensitivity and multiplexed RNA SeqFISH assays, we will be able to capture not only cell type and microenvironment information, but also genes and pathways that could be causal for progression to malignancy.
Lastly, we will use the data to 1) predict tumor progression based on the cell type compositions and microenvironments; 2) design intervention strategies based on the spatial data, using counterfactual inference models to affect immune infiltrating and other predictors of progression; and 3) build a model of tumor progression dynamics based on gene expression and mechanics of the tissue.
Our comprehensive low-grade glioma tissue collection, the integrated spatial dataset with transcriptomics, proteomics and chromosomal abnormalities, and the models built using advanced machine-learning tools will extend the existing capabilities of the HTAN consortium and be interoperable.
The atlas and the computational tools will be used by us and the wider scientific community to further understand the mechanisms leading to malignant transformation.
Funding Goals
TO PROVIDE SUPPORT FOR INITIATIVES FUNDED UNDER THE 21ST CENTURY CURES ACT TO SUPPORT CANCER RESEARCH, SUCH AS THE DEVELOPMENT OF CANCER VACCINES, THE DEVELOPMENT OF MORE SENSITIVE DIAGNOSTIC TESTS FOR CANCER, IMMUNOTHERAPY AND THE DEVELOPMENT OF COMBINATION THERAPIES, AND RESEARCH THAT HAS THE POTENTIAL TO TRANSFORM THE SCIENTIFIC FIELD, THAT HAS INHERENTLY HIGHER RISK, AND THAT SEEKS TO ADDRESS MAJOR CHALLENGES RELATED TO CANCER.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Pasadena,
California
911250001
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 393% from $1,069,188 to $5,275,940.
California Institute Of Technology was awarded
Tumor Microenvironment Analysis Low-Grade Glioma Malignancy Prediction
Cooperative Agreement U01CA294551
worth $5,275,940
from National Cancer Institute in September 2024 with work to be completed primarily in Pasadena California United States.
The grant
has a duration of 5 years and
was awarded through assistance program 93.353 21st Century Cures Act - Beau Biden Cancer Moonshot.
The Cooperative Agreement was awarded through grant opportunity Pre-Cancer Atlas (PCA) Research Centers (U01 Clinical Trial Not Allowed).
Status
(Ongoing)
Last Modified 9/24/25
Period of Performance
9/1/24
Start Date
8/31/29
End Date
Funding Split
$5.3M
Federal Obligation
$0.0
Non-Federal Obligation
$5.3M
Total Obligated
Activity Timeline
Transaction History
Modifications to U01CA294551
Additional Detail
Award ID FAIN
U01CA294551
SAI Number
U01CA294551-3174791433
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Private Institution Of Higher Education
Awarding Office
75NC00 NIH National Cancer Institute
Funding Office
75NC00 NIH National Cancer Institute
Awardee UEI
U2JMKHNS5TG4
Awardee CAGE
80707
Performance District
CA-28
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Modified: 9/24/25