Computational Methods for Decoding the Spatial Tumor Immune Microenvironment

TOPICAL COLLECTIONS

Guest Editor

Todd Aguilera

UT Southwestern Medical Center

Todd Aguilera, MD, PhD, is an associate professor of radiation oncology at UT Southwestern Medical Center and a physician-scientist specializing in gastrointestinal malignancies, with a particular focus on pancreatic and colorectal cancers. His research investigates the tumor immune microenvironment and mechanisms of therapeutic response and resistance. A major focus of his laboratory is spatial biology, integrating multiplex tissue imaging, single-cell profiling, and computational approaches to quantify the spatial organization and interactions of tumor, immune, and stromal cells within the tumor microenvironment.Dr. Aguilera leads translational studies integrating preclinical models, patient specimens, and clinical investigation to understand how radiation therapy can be combined with immunotherapy and other novel agents. His group develops and applies computational methods to characterize tissue architecture and spatial cellular relationships and to identify biomarkers associated with therapeutic response. He is actively involved in the development and conduct of clinical trials evaluating innovative radiation approaches and systemic therapies for gastrointestinal cancers, particularly pancreatic cancer.Through this integration of mechanistic cancer biology, spatial and computational analysis, and clinical investigation, Dr. Aguilera seeks to identify determinants of treatment response and translate them into biomarker-driven clinical trials and more personalized therapeutic strategies for patients with pancreatic and other gastrointestinal cancers.

Collection Overview

Spatial technologies and artificial intelligence are rapidly transforming tissue-based research, but generating high-dimensional images is only the first step. Extracting reproducible biological insight requires computational methods capable of identifying cells, defining phenotypes, mapping spatial relationships, recognizing multicellular neighborhoods, and integrating tissue architecture with molecular and clinical information. This Topical Collection will highlight practical and reproducible computational approaches for transforming histology, multiplex imaging, and spatial omics data into quantitative representations of tissue biology. Emphasis will be placed on methods that characterize the tumor immune microenvironment, including cell segmentation and phenotyping, spatial statistics, neighborhood analysis, multimodal image registration, deep learning, computational pathology, and integration of spatial proteomic and transcriptomic data.