Subject Area
Biostatistics, Immunology, Health Sciences
Abstract
T cells play a central role in cancer immunity. Successful anti-tumor immunotherapy relies on the effective infiltration of cytotoxic T cells. However, the tumor microenvironment presents physical and immunosuppressive barriers to prevent T cell entry. Here, we developed two novel computational tools, ReMITT and SIBERT, to search for migrating T cells and T cell entry regions (TERs) into the tumor core. SIBERT combines a novel change point detection method and a spatial imputation model to detect regions with signatures of recent T cell entry using the spatial transcriptomics data. Applying SIBERT to multiple human cancer samples revealed several TERs for each sample, which were dominantly distributed near the tumor margins rich in blood vessels. Unbiased gene expression analysis revealed key pathways and cytokine/receptors involved in T cell transmigration. Under the rationale of immunoediting, we also predicted potentially novel tumor antigens excluded from the TERs. This work provided insights in T cell exclusion for future immunotherapy development
Degree Date
Spring 5-17-2025
Document Type
Dissertation
Degree Name
Ph.D.
Department
Statistical Science
Advisor
Guanghua Xiao
Second Advisor
Qiwei Li
Acknowledgements
To my parents, my husband and my children.
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Zhong, Lin, "COMPUTATIONAL IDENTIFICATION OF INFILTRATING T LYMPHOCYTES INTO SOLID TUMOR" (2025). Statistical Science Theses and Dissertations. 50.
https://scholar.smu.edu/hum_sci_statisticalscience_etds/50
