Abstract 6397: Understanding the glioblastoma microenvironment with spatial resolution in PDX models

Abstract

Abstract Glioblastoma multiforme (GBM) is the most common adult brain tumour, and despite aggressive treatment, it recurs fatally. GBM tumours include diverse populations of malignant and non-neoplastic cells with distinct molecular capabilities and with differential levels of sensitivity to treatment. Understanding the cell dynamics that occur during the development of GBM resistance to therapy could reveal key aspects of this process, including how resistance is acquired in time and how the diverse cell types of the tumour microenvironment (TME) contribute to this phenotype. In addition to the role of the TME, GBM exhibits significant tumour heterogeneity with diverse genetic clones coexisting in the same tumor, as well as cells with similar genetic backgrounds capable of adopting distinct transcriptional states and subtypes. The complex and dynamic interactions between tumor and TME remain to be fully studied. This work focuses on the in vivo spatial organization in GBM during disease progression. We generated spatial transcriptomic data from a set of adult GBM samples grown as patient-derived xenograft (PDX) models, profiled at different time points of the disease. Three PDX lines from one GBM patient (derived from tumor core, vascularized area, and infiltrating front) were used to recapitulate the genetic and phenotypic heterogeneity observed in the human disease. Two replicates from each of 8 PDX mice were collected from early, mid, and late time points of tumour growth and data was generated using the 10X Genomics Visium platform. We developed a robust computational pipeline capable of distinguishing admixture of human (tumour) and mouse cells (TME), using state-of-the-art tools. Human and mouse cell types and states were identified using pooled and separate single-cell references of human GBM states, and mouse brain cells from both normal and tumour conditions. With this approach we observe spatially distinct patterns of both (a) tumour infiltration patterns specific to the each PDX line that includes non-random distribution of GBM transcriptional states and genetic clones, and (b) spatially distinct infiltration of TME components including microglial and macrophage populations. Overall, our approach addresses the challenge of understanding the tumor-TME relationship by application of spatial profiling in PDX models, and provides a computational pipeline complex multi-species analysis in the spatial transcriptomic field. Citation Format: Aly O. Abdelkareem, Katalin Osz, Donna Senger, Jennifer A. Chan, Sorana Morrissy. Understanding the glioblastoma microenvironment with spatial resolution in PDX models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6397.