TUSCAN: Tumor Segmentation and Classification Analysis in Spatial Transcriptomics

Authors

Chenxuan Zang
Charles C Guo
Yaohong Wang
Peng Wei
Ziyi Li

Language

English

Publication Date

3-1-2026

Journal

PLOS Computational Biology

DOI

10.1371/journal.pcbi.1014058

PMID

41843898

PMCID

PMC12995303

PubMedCentral® Posted Date

3-17-2026

PubMedCentral® Full Text Version

Post-print

Abstract

The identification of tumor cells is pivotal for understanding tumor heterogeneity and the tumor microenvironment. Recent advances in spatially resolved transcriptomics (SRT) have revolutionized the way that transcriptomic profiles are characterized and have enabled the simultaneous quantification of transcript locations in intact tissue samples. SRT is a promising alternative method to study gene expression patterns in spatial domains. Nevertheless, the precise detection of tumor regions within intact tissue remains a great challenge. A common strategy for identifying tumor cells is via tumor-specific marker gene expression signatures, which are highly dependent on marker accuracy. Another effective approach is through aneuploid copy number alterations, as most types of cancer exhibit copy number abnormalities. Here, we introduce a novel computational method, called TUSCAN (TUmor Segmentation and Classification ANalysis in spatial transcriptomics), which constructs a spatial copy number variation profile to improve the accuracy of tumor region identification. TUSCAN combines gene information from SRT data and hematoxylin-and-eosin-staining image to annotate tumor sections and other benign tissues. We benchmark the performance of TUSCAN and several existing methods through the application to multiple datasets from different SRT platforms. We demonstrate that TUSCAN can effectively delineate tumor regions, with improved accuracy compared to other approaches. Additionally, the output of TUSCAN provides interpretable clonal evolution inferences that may lead to novel insights into disease development and potential druggable targets.

Keywords

Humans, Neoplasms, Gene Expression Profiling, Transcriptome, Computational Biology, Algorithms, DNA Copy Number Variations, Tumor Microenvironment

Published Open-Access

yes

Recommended Citation

Zang, Chenxuan; Guo, Charles C; Wang, Yaohong; et al., "TUSCAN: Tumor Segmentation and Classification Analysis in Spatial Transcriptomics" (2026). Faculty, Staff and Student Publications. 6776.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6776