Faculty, Staff and Student Publications
Language
English
Publication Date
6-18-2026
Journal
Computer and Structural Biotechnological Journal
DOI
10.34133/csbj.0061
Abstract
Intratumor heterogeneity (ITH) is a fundamental driver of therapeutic failure and disease progression. However, the complexity of the tumor ecosystem is a critical yet underexplored aspect, making its precise quantification essential for fully deciphering ITH and its clinical implications. To address this, we developed Quantifying Ecosystem Intratumor Heterogeneity (QeITH), a computational framework that applies Shannon entropy to quantify ecosystem heterogeneity by measuring the diversity and distributional entropy of cellular compositions and functional states across single-cell, bulk, and spatial transcriptomics. At the single-cell resolution, QeITH identifies elevated ITH as intrinsic markers of malignant transformation, yet enhanced sensitivity to therapy. Pan-cancer bulk analyses further link elevated QeITH scores to increased neoantigen burden, PD-L1 expression, and unfavorable prognosis. Notably, spatial transcriptomics reveals that ecological complexity is nonuniformly distributed, peaking at invasive fronts and within tertiary lymphoid structures (TLS), where enhanced diversity within TLS modulates therapeutic vulnerability. Thus, QeITH reveals a dual role for ITH: While high scores associated with tumor aggressiveness, they also predict favorable treatment responses by capturing an immunologically active tumor ecosystem state. By integrating single-cell precision with spatial context, this framework elucidates the biological drivers of cancer progression and serves as a robust tool for optimizing personalized therapeutic strategies in precision oncology.
Published Open-Access
yes
Recommended Citation
Liu, Qiqi; Luo, Jiangti; Wang, Jiawei; et al., "QeITH: Quantifies Tumor Ecosystem Heterogeneity to Predict Cancer Progression and Treatment Benefit" (2026). Faculty, Staff and Student Publications. 1015.
https://digitalcommons.library.tmc.edu/uthshis_docs/1015
Graphical Abstract