Date on Master's Thesis/Doctoral Dissertation

5-2024

Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department

Interdisciplinary and Graduate Studies

Degree Program

Interdisciplinary Studies (Individualized Degree), PhD

Committee Chair

Frieboes, Herman

Committee Member

El-Baz, El-Baz

Committee Member

Altiparmak, Nihat

Committee Member

Chen, Joseph

Committee Member

Hood, Joshua

Author's Keywords

cancer; mathematical modeling; immunotherapy; machine learning; oncology; tumor microenvironment

Abstract

The tumor microenvironment (TME) represents the complex outcome of numerous tumor, stromal, and immune interactions, and whose composition can significantly affect treatment response. Particularly, immunotherapeutic efficacy is subject to multiple tumor-specific TME interactions that may be difficult to evaluate/predict clinically. Mathematical modelling has been formulated to evaluate specific aspects of the TME, including vasculature, ECM deposition, and immune-tumor interactions. However, the computational challenge of simulating multiple TME interactions has led to sacrificing varying degrees of model generalizability and clinical relevance. This work describes increased computational performance of a 3D continuum model that simulates tumor tissue, ECM, and vasculature using a Message Passing Interface (MPI) CUDA-accelerated framework (Chapter 2) and expanding biological scope to include TME immune interactions (Chapter 3). Model performance is scaled to 2.56x2.56x2.56 cm3 domain sizes while preserving mm-resolution interactions. The model’s host tissue phase is expanded to include an immune component. This component includes multiple innate and adaptive immune species whose local activation influences the TME into varying degrees of pro- or anti-tumor states. This model is applied to simulate the effect of a macrophage-mediated immunotherapeutic regimen against multiple breast cancer liver metastases (BCLM) simultaneously in a simulated mouse liver lobe (Chapter 4). The model results indicate that tumor burden could be potentially curbed with treatment intervals lasting less than 7 days. The effects of anti-Programmed Death Ligand 1 and antigen-loaded chitosan nanoparticle immunotherapies were quantified against primary and liver-metastatic pancreatic ductal adenocarcinoma (PDAC), finding that applying both therapies simultaneously may synergistically decrease tumor burden (Chapter 5). Lastly, as a first step towards evaluating the patient-specific TME immune landscape for BCLM, a machine learning workflow is presented that classifies expression of BCLM imaging mass cytometry (IMC) data from paired primary IMC data, with validation subset AUROC ≥0.75. Longer-term, this overall work could be applied across a broad spectrum of tumor types and therapeutic approaches to identify optimal strategies tailored to specific tumors. Chapter 2 is published in Computers in Biology and Medicine. Chapter 3 is published in the Journal of Theoretical Biology. Chapter 4 is published in Immunology. Chapters 5 and 6 are in preparation for submission.

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