Quantitative analysis of dynamic tumor cell phenotypes regulated by tumor associated macrophages.

Abstract

Cancer cells and the tumor microenvironment (TME) dynamically interact to promote cancer progression. One such cell type demonstrated to create an immunosuppressive TME are tumor-associated macrophages (TAMs). Furthermore, macrophage infiltration has been associated with disease progression, angiogenesis, and metastasis. Methods such as single-cell RNA sequencing and multiplexed histology provide a detailed image of the tumor composition, including spatial co-localization and global ligand-receptor expression between TAMs and tumor cells. Nonetheless, it remains challenging to translate static snapshots of tissue composition into understanding how communication networks operate to coordinate dynamic biological processes. In this dissertation, I aim to address this challenge by creating a computational pipeline to quantify dynamic phenotypes in vivo, focusing on understanding how TAMs influence cancer cell cytoskeletal dynamics and migration. To quantify cytoskeletal changes in individual cancer cells, I developed an integrated pipeline combining in vivo confocal (intravital) microscopy, automated tracking of individual microtubules, and multivariate statistics to study dynamics in live xenograft models of cancer. I discovered that in addition to the extracellular matrix, interaction with TAMs can lead to coherent microtubule alignment correlating with increased migration rates in individual cancer cells. Furthermore, I identified specific growth factors and cytokine signaling mechanisms underlying this phenomenon. Although in vivo imaging allows signaling pathways to be monitored and manipulated in real-time, its limited multiplexing prevents global characterization of intercellular communication affecting disease progression. Therefore, I also developed a computational method utilizing known ligand-receptor interactions and single-cell transcriptomic data to understand how intercellular communication changes during biological processes such as cancer progression. Altogether, this work aims to develop methods to capture and quantify cancer cell dynamics and understand how specific tumor microenvironment components regulate such dynamics. Developing new approaches that can accurately model the TME and detect subtle changes during cancer progression are essential to obtain a complete picture of how cancer cells evade treatment and metastasize.