CAREER: Integrative modeling of intercellular interactions in the tumor microenvironment

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Breast cancer is the second leading cause of cancer deaths among women overall and the leading cause of cancer deaths among African American and Hispanic women in the United States. The goal of this project is to study interactions between cells inside and around breast cancer tumors to better understand how aggressive tumors evade the body’s immune defenses. New computational methods will be developed to analyze the interactions between immune cells and breast cancer. This new framework will reveal how cell organization in breast tumors impacts immune response and will inform approaches for improving anti-tumor immunity. The developed tools will be made publicly available to benefit the broader scientific community for use in exploring other biological systems and cancers. The research program will be closely coupled with an educational mission of bridging the gap between computational sciences and biomedical engineering. Underrepresented high school and undergraduate students will be engaged through partnerships with the AI4ALL and Hypothekids Bioforce programs. Graduate students will also learn about computational techniques for analyzing high-dimensional data through a new course in machine learning. This CAREER project will develop novel machine learning methods in the form of deep generative frameworks for analyzing high-resolution and high-dimensional spatial transcriptomic data to uncover heterogeneous and interacting cell states in the context of the breast tumor microenvironment. The scalable models will provide interpretability for interactions between diverse cell states, in particular between tumor-associated fibroblasts and immune cells. Semi-supervised models will be constructed for inferring the composition of cell states from spatial transcriptomic data through incorporating prior biological knowledge. This model will then be extended to systematically infer cell-cell interaction networks by integrating transcriptional data with histology imaging of the tissue in the aggressive subtype of triple-negative breast cancer. Additionally, multivariate models will be developed to learn gene-gene regulatory interactions and master regulators underlying spatially dynamic immune cell populations by integrating single-cell epigenetic data. The findings of this project will advance our understanding of immune evasion mechanisms and will guide strategies for modulating immune response to breast cancer.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.