Inferring gene expression profiles of tumor clones
- 1Faculty of Mathematics, Informatics and Mechanics, University of Warsaw , Warsaw, 02-097,
- 2Institute of AI for Health, Helmholtz Zentrum Munchen , Neuherberg, 85764,
Abstract
Motivation: Tumors are heterogeneous mixtures of clonal subpopulations of cells that co-evolve in time and space. Apart from genetic differences, cancer clones may reside in different locations in the tumor tissue and show various transcriptional phenotypes, with specific gene expression profiles and marker genes. Computational tools that simultaneously infer genotypes and transcriptional phenotypes of clones in tumor tissues are lacking.
Results: We introduce ClonalGE, a probabilistic graphical model identifying cancer clone genotypes, their gene expression profiles, and spatial mapping within tumor tissue. The model proceeds by probabilistic matching of single nucleotide variant sites in spatial transcriptomics to clone-specific variants inferred from DNA sequencing data, and by deconvoluting gene expression in spatial transcriptomics spots into contributions from individual clones. We propose a procedure that utilizes the Monte Carlo posterior samples for identification of differentially expressed genes between clones, using a Highest Density Interval plus Region of Practical Equivalence (HDI+ROPE) criterion that jointly requires credible and practically meaningful effect sizes. ClonalGE demonstrates superior performance on simulated data. Applied to a prostate cancer sample, it reveals clone-specific expression patterns and outperforms its predecessor model in accurately inferring gene expression profiles and explaining spatial transcriptomics signals. Our approach advances the functional and phenotypic characterization of clonal cell subpopulations in tumors.
Availability: The implementation of ClonalGE and the data used in this study can be accessed in the following GitHub repository https://github.com/szczurek-lab/ClonalGE/. The benchmarking data alongside the intermediate results are available on Zenodo at DOI: 10.5281/zenodo.20446991.
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BibTeX
@article{shafighi2026inferring,
title = {{Inferring gene expression profiles of tumor clones}},
author = {Shadi Shafighi and Barbara Jurzysta and Ewa Szczurek},
journal = {Bioinformatics},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag717},
url = {https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag717/8860686},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Inferring gene expression profiles of tumor clones
AU - Shadi Shafighi
AU - Barbara Jurzysta
AU - Ewa Szczurek
PY - 2026
DA - 2026/10/03
JO - Bioinformatics
DO - 10.1093/bioinformatics/btag717
UR - https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag717/8860686
AB - Motivation: Tumors are heterogeneous mixtures of clonal subpopulations of cells that co-evolve in time and space. Apart from genetic differences, cancer clones may reside in different locations in the tumor tissue and show various transcriptional phenotypes, with specific gene expression profiles and marker genes. Computational tools that simultaneously infer genotypes and transcriptional phenotypes of clones in tumor tissues are lacking. Results: We introduce ClonalGE, a probabilistic graphical model identifying cancer clone genotypes, their gene expression profiles, and spatial mapping within tumor tissue. The model proceeds by probabilistic matching of single nucleotide variant sites in spatial transcriptomics to clone-specific variants inferred from DNA sequencing data, and by deconvoluting gene expression in spatial transcriptomics spots into contributions from individual clones. We propose a procedure that utilizes the Monte Carlo posterior samples for identification of differentially expressed genes between clones, using a Highest Density Interval plus Region of Practical Equivalence (HDI+ROPE) criterion that jointly requires credible and practically meaningful effect sizes. ClonalGE demonstrates superior performance on simulated data. Applied to a prostate cancer sample, it reveals clone-specific expression patterns and outperforms its predecessor model in accurately inferring gene expression profiles and explaining spatial transcriptomics signals. Our approach advances the functional and phenotypic characterization of clonal cell subpopulations in tumors. Availability: The implementation of ClonalGE and the data used in this study can be accessed in the following GitHub repository https://github.com/szczurek-lab/ClonalGE/. The benchmarking data alongside the intermediate results are available on Zenodo at DOI: 10.5281/zenodo.20446991.
SN - 1367-4803
SN - 1367-4811
ER -