Exploring Microenvironmental Heterogeneity in Cancer and Renal Biology Through Spatial Transcriptomics and Multiplexed Immunohistochemistry

Abstract

Heterogeneity within solid tissue microenvironments has been well established as a contributor to patient outcome for a variety of different diseases and their etiology. With the advent of next-generational artificial intelligence algorithms for image segmentation and classification, current digital pathology workflows stand to gain considerable benefits by incorporating such tools. As pathological assessment is still widely regarded as the gold standard for numerous patient stratification and disease progression endpoints, it is important to incorporate artificial intelligence in a manner that works alongside pathologist’s classification, rather than aim to replace them. In this dissertation, I have investigated approaches for the development and application of computational methods for studying tissue microenvironments through the lens of spatial biology. First, we present an overview of hypoxia, one of the main determinants of microenvironmental heterogeneity, the means of measuring and targeting it, and the value of spatially-oriented analysis of hypoxia in high dimensionality histopathology datasets. Chapter 1 demonstrates the development of computational methods for the quantitative assessment of hypoxia and proliferation spatial gradients within histological tissue sections. In chapter 2, we apply these methods to two separate studies: evaluating the efficacy of the hypoxia activated prodrug evofosfamide in head and neck squamous cell carcinoma, and understanding the role and therapeutic potential of the autophagy activating kinase ULK1 in pancreatic cancer. In chapter 3, we build upon these methods to characterize spatial heterogeneity in glioblastoma microenvironments through the analysis of a high dimensionality dataset using the multiplexed immunohistochemistry imaging modality, Imaging Mass Cytometry (IMC). We explore the differential localization of specific cell subsets to regions of hypoxia and perform a comparison of the spatial distribution and localization of hypoxia biomarkers. To further test the robustness of the IMC analysis method developed in the previous chapter, we next apply this workflow in chapter 4 to characterize the renal localization of immune cells contributing to acute kidney injury (AKI) following treatment of cancers with immune checkpoint inhibitors (ICI). We identify specific cell subpopulations associated with AKI caused by ICI versus healthy tissue and corroborate our findings with supplementary patient information. Further building upon this pathologist-in-the-loop IMC analysis methodology, we use these tools to explore the single cell landscape of renal transplant rejection in chapter 5, incorporating further spatial characterizations such as the localization of immune cells to renal structures across disease types, to building a patient classifier capable of identifying spatially-derived features indicative of transplant rejection. In chapter 6, we delve back into the characterization of glioblastoma heterogeneity, this time using spatial transcriptomics. We develop a novel spatial transcriptomic signature of hypoxia in glioblastoma using registration of paired hypoxia immunohistochemistry images and piloting a spatial analysis method using open-source tools. This work highlights the critical role of spatial dimension in gene expression research, introducing a workflow that tackles batch variation and augments the spatial feature extraction of gene expression, thus unveiling novel perspectives on the transcriptional microenvironment of glioblastoma.