Thursday, November 21, 2024
11/21/2024
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.
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