A Novel Framework for Characterization of Tumor-Immune Spatial Relationships in Tumor Microenvironment
Year: 2022
Type: preprint
Abstract: Understanding the impact of tumor biology on the composition of nearby cells often requires characterizing the impact of biologically distinct tumor regions. Biomarkers have been developed to label biologically distinct tumor regions, but challenges arise because of differences in the spatial extent and distribution of differentially labeled regions. In this work, we present a framework for systematically investigating the impact of distinct tumor regions on cells near the tumor borders, accounting their cross spatial distributions. We apply the framework to multiplex immunohistochemistry (mIHC) studies of pancreatic cancer and show its efficacy in demonstrating how biologically different tumor regions impact the immune response in the tumor microenvironment. Furthermore, we show that the proposed framework can be extended to largescale whole slide image analysis.
Source: arXiv (Cornell University)
Authors: Hasan, Mahmudul, Kaczmarzyk, Jakub R., Paredes, David, Oblein, Lyanne, Oentoro, Jaymie, Abousamra, Shahira, Horowitz, Michael, Samaras, Dimitris, Chen, Chao, Kurc, Tahsin, Shroyer, Kenneth R., Saltz, Joel
Institution: Stony Brook University
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Roche Diagnostics
OpenAlex ID: W4225105444