ITHindex: An integrated web-based platform for intratumor heterogeneity evaluation

Yutao Liu, Yuan Jiang, Wenchuan Xie, Feidie Duan, Shuiting Fu, Jing Zhao, Jinyu Yang, Jianchun Duan, Guoqiang Wang, Yuzi Zhang, Shangli Cai and Yongqin Wen

PLOS Digital Health, 2026, vol. 5, issue 8, 1-15

Abstract: Intratumor heterogeneity (ITH) is a critical factor influencing cancer progression, therapeutic response, and the development of drug resistance. Despite its importance, the lack of a definitive gold standard for ITH quantification has hindered consistent clinical application. To bridge this gap, we developed ITHindex, a web-based, research-enabling platform that integrates a pragmatic subset of 17 user-accessible ITH algorithms within the R Shiny framework. The tool supports diverse data modalities, including somatic mutation, copy number variation, transcriptomic, proteomic, and methylation profiles. By analyzing 11,242 samples across 32 cancer types from The Cancer Genome Atlas (TCGA) and 4,904 samples from cBioPortal, alongside 398 paired tissue and plasma samples, we validated the platform’s utility in quantifying ITH and characterizing the relationships between diverse metrics. ITHindex streamlines the computational pipeline, providing researchers with a robust tool for systematic ITH investigation and data-driven biomarker discovery. The ITHindex server is freely accessible at https://shinyapps.brbiotech.com/app/ithindex.Author summary: Prior knowledge indicated that ITH mirrors the complexity of tumor clones and holds promise as a predictive biomarker for the efficacy of immunotherapy. Several algorithms have been developed to quantify ITH based on omics data. However, implementing these algorithms often involves multiple software tools, advanced programming skills, and complex workflows, which can hinder accessibility and reproducibility. We introduced ITHindex, an integrated platform that integrated these algorithms to streamline the practice of ITH quantification for clinical researchers. ITHindex combines these algorithms into a user-friendly platform, seamlessly integrating computational methods with proprietary modules through an intuitive graphical interface. By integrating modality-specific algorithms, multi-omics data, and customizable parameters, the platform empowers researchers to conduct ITH quantification without coding. This platform reduces technical barriers, enhances reproducibility, and fosters collaborative, data-driven biomarker discovery.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0001654

DOI: 10.1371/journal.pdig.0001654

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