Spatial and genomic analysis of the glioblastoma tumor microenvironment
By: Andrew Chen
Publisher: [publisher not identified]
Published: 2020
Place of publication: [New York, N.Y.?]
Language: eng
Description: Glioblastoma (GBM) is an aggressive brain cancer with devastating outcomes and few effective treatments. Although immunotherapy has shown promise in treating a variety of cancers, it is still unclear if and how it can be effectively used in GBM. Elucidating this will require a better understanding of the mechanistic role of immune cells and their interactions in the GBM tumor microenvironment. This thesis utilizes recent technological developments in cancer genomics and imaging to study the mechanisms underlying immunotherapy and the tumor microenvironment. First, we will provide background on our current understanding of GBM, its immune microenvironment, as well as modern sequencing and imaging methods. Second, we will present a longitudinal study of GBM patients before and after treatment with PD-1 immunotherapy. Only a small fraction of GBM patients respond to this type of therapy, so we perform genomic, transcriptomic, and spatial analyses to compare the molecular features of these rare responders versus non-responders.

We show that clinical response to PD-1 immunotherapy in GBM is associated with specific molecular alterations and immune infiltration profiles that reflect the tumor’s clonal evolution during treatment. The most common infiltrating immune cells in GBM are macrophages, which are implicated in a wide variety of pro-tumor and anti-tumor roles. We then focus on this specific immune population by analyzing single-cell expression data from GBM tumors. We identify a novel macrophage subpopulation characterized by expression of the scavenger receptor MARCO, which drives tumor progression in GBM and is altered over the course of PD-1 immunotherapy. Next, we demonstrate that the methods we have developed for GBM are applicable to understanding the tumor microenvironments of other cancers as well. We analyze a cohort of melanoma cases to show that transcriptomic and imaging features can be combined to create a biomarker that stratifies patients into different risk groups. Finally, while most of the image analysis described so far has utilized histopathology, we include two appendices where we demonstrate new ways to process and analyze Magnetic Resonance Imaging (MRI) in GBM.
Notes: Department: Cellular, Molecular and Biomedical Studies.

Thesis advisor: Raul Rabadan.

Thesis (Ph.D.)--Columbia University, 2020.