Abstract 6614: Spatial transcriptomic driven mechanistic model to investigate and predict pathogenesis of oligodendroglioma

Abstract

Abstract Rationale: Oligodendrogliomas represent the second most common glioma in adults after glioblastoma. Recent studies conjecture that oligodendrocyte progenitor cells (OPCs) are precursors for glioma. The etiology of OPC derived oligodendrogliomas, the corresponding genetic program and genetic aberrations, and the underlying mechanisms leading to the pathogenesis of oligodendroglioma is yet to be explored. We hypothesize that genetic mechanisms mediating oligodendrogenesis (the process of OPCs maturing to oligodendrocytes (OL)) are temporally and spatially coordinated. In this study, we spearhead a comprehensive computational framework that utilizes a) Machine learning (ML) to interpret spatial transcriptomic (ST) images of a normal whole mouse brain dataset obtained from Vizgen’s MERFISH Mouse Brain Receptor Map and b) mathematical modeling to perturb the cellular mechanisms in the normal brain to understand the abnormalities leading to the pathogenesis of oligodendroglioma. Methods: Using ML for clustering and trajectory inference, we create a comprehensive spatial catalog of cell types. The cell types are validated using canonical genes from the scRNAseq mouse brain data from CZI Biohub’s Tabula Muris Consortium. From the cell types, we extract potential OL pseudotime differentiation (from immature to mature OLs) trajectories. These trajectories are used to develop a phenotype structured partial differentiation equation (PDE) model of oligodendrogenesis. This can be used to simulate both spatial and phenotypic interactions, differentiation status, and the pathogenesis of oligodendroglioma by mathematically perturbing the process of cellular proliferation and differentiation using genes known to cause oligodendroglioma: PDGFRA, OLIG1, MAS1. Results: We mathematically perturb cell differentiation lineages in normal oligodendrogenesis to simulate, and predict the effects of perturbation and how this leads to abnormal (cancer aiding) cell states. Our PDE simulations reinforce that OPC proliferation and OL differentiation occur more in the white matter in the normal mouse brain, and further postulate that OPC and OL differentiation occur more in gray matter in malignant mouse brains. Conclusions: Our study is one of the earliest attempts to integrate qualitative and scalable ML techniques with quantitative and interpretable mathematical modeling at the cellular, omics, and organ level using ST imaging and sequencing data. We develop and calibrate PDE mathematical models for robustly simulating, predicting, and studying the effects of perturbation from sparse, noisy single cell spatial measurements to forecast abnormal behavior in OLs based on spatial and (pseudo)temporal changes. In future work, we will apply our framework to jointly perturb differentiation trajectories of multiple cell types to understand their cumulative effects in tumor progression. Citation Format: Sandhya Prabhakaran, Heyrim Cho, Maximilian Strobl, Russell Rockne, Alexander R. Anderson. Spatial transcriptomic driven mechanistic model to investigate and predict pathogenesis of oligodendroglioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6614.