Spatial and molecular analysis of growth patterns in the lung adenocarcinoma microenvironment
Abstract/Contents
- Abstract
- Non-small cell lung cancer adenocarcinoma tumors are composed of heterogeneous cell populations that contribute to the development and dissemination of malignancies. Lung adenocarcinoma tumors have been observed to progress through a well-defined series of histologic growth patterns, including lepidic, acinar, papillary, and solid designations. These patterns are routinely utilized clinically and have been shown to correlate with clinical prognosis. Fibroblast cells are among the most prevalent within this microenvironment, but their relationship to growth patterns in lung adenocarcinoma is understudied. Despite the close association of lung adenocarcinoma growth patterns with prognosis and growing interest in the roles of pathological fibroblasts, the predominant fibroblast subtypes and their interactions within the context of each lung adenocarcinoma growth pattern are poorly defined. To understand the many cell types present in the lung adenocarcinoma tumor microenvironment, with a focus on resident lung fibroblasts, I generated an imaging dataset comprising whole-slide images of 8 lung adenocarcinoma tumors and analyzed spatially bound growth pattern regions annotated by an expert pathologist. I initialized a novel multiplexed immunofluorescence microfluidics instrument, developed a tumor marker panel for use in said instrument, and collected an in-house lung tumor tissue sample bank. I utilized this pipeline and methods to investigate specific biological hypotheses in lung adenocarcinoma fibroblast subtype localization with other cell types in varying histological growth patterns. In particular, I observed that colocalization of fibroblasts expressing the surface marker CD90 with CD3+ T cells are more associated with invasive acinar than noninvasive lepidic growth patterns. I utilized an independent lung tumor microarray dataset to perform concurrent analyses and found that this spatial cell association signature is correlated with prognosis and survival. I also leveraged multiple sources of imaging in these samples, revealing the potential of extracellular matrix features in predicting nodal status of individual samples. I observed differential changes in the localization of specific cell types and calculated extracellular matrix features in lung adenocarcinoma samples depending on nodal involvement. By defining pathological lung fibroblast subtypes through their shared markers, interactions with other cells, and functions, I shed light on these crucial components of the tumor microenvironment and ultimately define fibroblast- and microenvironment-based treatment targets for lung adenocarcinoma. These studies contribute to the field's understanding of spatial fibroblast interactions with the lung adenocarcinoma microenvironment and offer a conceptual framework for future investigations into the tumor microenvironment.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Li, Irene |
|---|---|
| Degree supervisor | Plevritis, Sylvia |
| Thesis advisor | Plevritis, Sylvia |
| Thesis advisor | Gentles, Andrew J |
| Thesis advisor | Graves, Edward (Edward Elliot), 1974- |
| Thesis advisor | Mallick, Parag, 1976- |
| Degree committee member | Gentles, Andrew J |
| Degree committee member | Graves, Edward (Edward Elliot), 1974- |
| Degree committee member | Mallick, Parag, 1976- |
| Associated with | Stanford University, School of Medicine |
| Associated with | Stanford University, Cancer Biology Program |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Irene Li. |
|---|---|
| Note | Submitted to the Cancer Biology Program. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/gm923wn6738 |
Access conditions
- Copyright
- © 2023 by Irene Li
- License
- This work is licensed under a Creative Commons Attribution Non Commercial No Derivatives 3.0 Unported license (CC BY-NC-ND).
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