Abstract
The tumor microenvironment (TME) plays a critical role in driving cancer progression. However, the complexity and heterogeneity of the TME—in terms of cell types and spatial context—have posed significant challenges for cancer characterization. In this dissertation, I present several tools and methods for spatial and molecular quantification of the TME across several cancer types. First, I introduce Pollock, a deep learning-based classifier for single-cell data that offers high accuracy, broad cross-platform compatibility, and biologically interpretable outputs. I then apply single-cell and bulk transcriptomic analyses to pancreatic ductal adenocarcinoma (PDAC), identifying tumor ecotypes and a continuum of developmental stemness in malignant and stromal cells that predict patient survival. Finally, I spatially extend this approach to breast and prostate cancer, integrating spatial transcriptomics, multiplexed imaging, and 3D volumetric reconstruction to uncover genes that characterize the precancer-to-cancer transition.
Committee Chair
Li Ding
Committee Members
Aadel Chaudhuri; Kooresh Shoghi; Stephen Oh; Tau Ju
Degree
Doctor of Philosophy (PhD)
Author's Department
Biology & Biomedical Sciences (Computational & Systems Biology)
Document Type
Dissertation
Date of Award
5-6-2025
Language
English (en)
DOI
https://doi.org/10.7936/n0g1-kx82
Author's ORCID
https://orcid.org/0000-0002-8041-0864
Recommended Citation
Storrs, Erik, "Needle in the Z-stack: Mapping the 3D Transition to Malignancy" (2025). Arts & Sciences Theses and Dissertations. 3529.
The definitive version is available at https://doi.org/10.7936/n0g1-kx82