ArticleCell systems2025
Stochastic modeling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones.
Article in Cell systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Computational approaches for multimodal lineage tracing.Nature reviews. Genetics · 2026Review
- Cancer drug response and resistance: molecular mechanisms and combating strategies.Signal transduction and targeted therapy · 2026Review
- Long-read sequencing of single cell-derived melanoma sublines reveals divergent and parallel genomic and epigenomic evolutionary trajectories.Nature communications · 2026Article
- Lineage-aware stochastic modeling reveals gene-expression dynamics in development and disease.bioRxiv : the preprint server for biology · 2026Article
- Knowledge-guided contextual gene set analysis with large language models.Bioinformatics (Oxford, England) · 2026Article
- Evolutionary dynamics under phenotypic uncertainty.bioRxiv : the preprint server for biology · 2026Article
- Spatial transcriptomics and artificial intelligence: a scoping review of emerging applications in head and neck pathology.Head and neck pathology · 2026Article
- Comparison of THP-1 Subtypes Reveals Differences on Transcriptomic and Protein Expression Level Throughout Their Immunological Differentiation.Journal of immunology research · 2026Article
- SCOUT: Ornstein-Uhlenbeck modelling of gene expression evolution on single-cell lineage trees.bioRxiv : the preprint server for biology · 2025Article
- Long-read sequencing of single cell-derived melanoma subclones reveals divergent and parallel genomic and epigenomic evolutionary trajectories.bioRxiv : the preprint server for biology · 2025Article
- GeneAgent: self-verification language agent for gene-set analysis using domain databases.Nature methods · 2025Article
- Stochastic gene expression in auxin signaling in the floral meristem of Arabidopsis thaliana.Nature communications · 2025Article
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Abstract
Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present a formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein-Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Sublines previously observed to be resistant to anti-CTLA4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression. A record of this paper's transparent peer review process is included in the supplemental information.
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