Abstract
Genetic interactions (GIs) drive carcinogenesis and treatment resistance via non-additive phenotypic effects between genes. Traditional bulk-based methods fail to capture cell-type-specific interactions in heterogeneous tumors like lung adenocarcinoma (LUAD), limiting precision oncology. Resolving cell-type-specific GIs at single-cell resolution persists as a major hurdle, hindered by limited analytical methodologies. Here, we develop scPGI-finder, a computational framework that identifies gene pairs whose coordinated high expression is associated with higher proliferation-related fitness at single-cell resolution, which we refer to operationally as single-cell positive genetic interactions (scPGIs). Using scPGI-finder, we identify 49,808 and 15,896 scPGIs spanning epithelial cells and T cells in LUAD, respectively. The predicted scPGIs display tighter junctions in the protein interaction network compared to non-scPGIs. Furthermore, we demonstrate the predictive power of scPGIs for malignancy and immunotherapy response through multi-omics validation across diverse cohorts. Notably, with a mean area under the ROC curve (AUROC) of 0.974 in bulk tissue validation, the epithelial-derived scPGI classifier enables concordant malignancy identification across scales ranging from epithelial single cells and lung cancer cell lines, through spatial transcriptomic maps, to bulk LUAD tissue profiles. Additionally, a six-scPGI T cell signature reliably forecasts immunotherapy efficacy, with AUROC values exceeding 0.80 across multiple datasets. Together, our research advances the understanding of underlying cancer-positive GIs at the single-cell level. scPGIs of epithelial and T cells serve as robust biomarkers for malignancy evaluation and treatment response, offering a translational framework for precision oncology.
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Data availability
All data analyzed during this study can be downloaded from public databases or retrieved from associated files of papers, such as The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/), Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), CCLE, DepMap (https://depmap.org/portal/) and the molecular signatures database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb), CTRP (https://portals.broadinstitute.org/ctrp/), GDSC (https://www.cancerrxgene.org/). Detailed information was provided in the Supplementary Tables.
Code availability
The scPGI-finder pipeline is available at GitHub (https://github.com/chencccchen/scPGI-finder-pipeline) with an archived version on Zenodo (https://zenodo.org/records/17817076). Because scPGI-finder is implemented as an analysis workflow rather than a standalone software package, the scripts can be executed directly in a standard R environment without installation. The repository provides step-by-step instructions to reproduce the analyses reported in this study. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
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Acknowledgments
This work was supported by grants from the National Key Research and Development Program of China (No. 2023YFF1204600), the National Natural Science Foundation of China (No. 32470702 and 32270710), the Scientific Research Project of Provincial Scientific Research Institutes of Heilongjiang Province (No. CZKYF2024-1-A010), National Multidisciplinary Innovation Team Project in Traditional Chinese Medicine (No. ZYYCXTD-D-202407), and Chunyan Team Program of Heilongjiang Province (No. CYQN24043). In addition, the authors acknowledge the efforts of all the researchers who have contributed the data to the public databases of TCGA, GEO, DepMap, CTRP, GDSC, COSMIC, and MSigDB. The interpretation and reporting of these data are the sole responsibility of the authors.
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Y.Y.G., B.C., and J.J.L. designed the project. B.C., M.Y.L., and Q.D. wrote the paper. B.C., K.D.L., and C.L. collected data and performed bioinformatic analysis. H.M.H., L.Z.W., N.Z., and W.Y.Z. provided valuable suggestions for the paper. All authors read and approved of the final paper.
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Chen, B., Liu, M., Dong, Q. et al. Charting cell-type-specific positive genetic interaction at single-cell resolution for lung adenocarcinoma. npj Precis. Onc. (2026). https://doi.org/10.1038/s41698-026-01328-x
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DOI: https://doi.org/10.1038/s41698-026-01328-x
