Abstract
DNA damage is preferentially repaired in expressed genes; thus, genome-wide correlations between somatic mutation patterns and normal cell transcription may reflect tumor cell origins. Accordingly, we found that aggregate lung adenocarcinoma (LUAD) and squamous cancer (LUSC) somatic mutation density associated most strongly with distal (alveolar) and proximal (basal) lung cell-type-specific gene expression, respectively, consistent with presumed LUAD and LUSC cell origins. Analyzing individual genomes, 21% of LUADs bore mutational footprints of proximal airway origins, with 38% classified as ambiguous. Distal origin LUADs, enriched for KRAS and STK11 drivers, occurred mainly in smokers; proximal origin LUADs, enriched for EGFR drivers, were more common in never-smokers. Ambiguous origin LUADs showed APOBEC signatures and SMARCA4 alterations. TP53 mutant LUADs with non-distal cell origins preferentially exhibited non-distal transcriptional identity. Our study reveals a complex interplay between lineage and identity in LUAD evolution and offers a scalable strategy to infer tumor origins in human cancers.
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Data availability
Obtained data sources
Whole genome sequencing data were obtained from cases as part of TCGA Research Network consortium through the Genomic Data Commons (https://portal.gdc.cancer.gov). We obtained 205 cases with appropriate dbGaP permissions (study accession, phs000178.v11.p8). Additional WGS data were obtained for 90 cases from multiple studies publicly available through the EGA (https://ega-archive.org) and dbGap (https://dbgap.ncbi.nlm.nih.gov). These cohorts include 49 LUADs (accession no. EGAS00001002801)28, 13 LUADs and three LUSCs from Cancer Alliance (accession no. EGAS00001004013) and 25 LUADs (accession no. phs000488.v2.p1)25. The HLCA was downloaded from CellXGene (https://cellxgene.cziscience.com/collections/6f6d381a-7701-4781-935c-db10d30de293). ScRNA-seq data for the lung cancer atlas were obtained from several publicly available cohorts, including https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6149 (ref. 30), GEO accession GSE123904 (ref. 32), https://lungcancer.chenlulab.com (ref. 34), https://ega-archive.org/studies/EGAS00001004419 (ref. 33) and GSE133747 (ref. 31).
Generated data sources
Five LUAD samples were processed for both scRNA-seq and WGS (described in detail above). Raw count scRNA-seq data for these samples are available as Supplementary Data 1. WGS data for WCM-1 are available as Supplementary Data 2. Raw sequencing data available upon request by contacting the corresponding author and a 4–8 week review of the request by a data access committee and IRB. Source data are provided with this paper.
Code availability
Code for generating analyses and figures is provided in GitHub (https://github.com/mskilab-org/lung_coo_2025) and Zenodo (https://doi.org/10.5281/zenodo.17243535)59.
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Acknowledgements
We thank C. Kim, T. Tammela and D. Lyden for helpful discussions and the Weill Cornell Medicine Epigenomics and Histology core facilities for technical support. Project support for this research was provided in part by the Center for Translational Pathology at the Department of Pathology and Laboratory Medicine, Weill Cornell Medicine. M.I., S.P., P.M. and H.T. were supported by National Institutes of Health (NIH) award R37CA229861 to M.I. In addition, M.I., J.S.A-M., A.D., J.R. and H.T. were supported by Weill Cornell Medicine Department of Pathology and Laboratory Medicine startup funds, NYU Perlmutter Cancer Center startup funds, a Pershing Square Sohn Cancer Prize and a Burroughs Wellcome Fund Career Award for Medical Scientists awarded to M.I. K.E.J. was supported by a National Science Foundation Graduate Research Fellowship (1746886).
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M.I. designed and supervised the study. S.P., P.M., J.S.A.-M. and K.E.J. performed experiments and data analysis with contributions from M.I. and A.D. S.B., K.O. and J.M.M. coordinated sample collection and reviewed histopathology. M.S. and P.S. performed scRNA-seq experiments. H.T., M.S., J.S.A.-M. and K.E.J. performed sample processing and library preparation for scRNA-seq and WGS. S.P., P.M., J.S.A.-M. and K.E.J. performed data curation, scRNA-seq analysis, cancer genomics analyses and simulations, with assistance from A.L. on driver mutation analysis. S.R.Y. and W.T. conducted the expert pathology review. K.E.J., S.B., J.R. and J.M.M. performed immunohistochemistry analysis. S.P., P.M., K.E.J., J.S.A.-M., A.D., P.P. and M.I. interpreted data. S.P., P.M., J.S.A.-M., K.E.J. and M.I. wrote the manuscript with comments from all authors.
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M.I. reports receiving personal or consultancy fees from ImmPACT Bio outside of the scope of the submitted work. M.S. and P.S. report receiving personal fees from 10× Genomics outside of the scope of the submitted work. P.P. reports receiving personal fees from C2I-Genomics outside of the scope of the submitted work. S.R.Y. reports receiving speaking fees from Medscape, Onclive, Medical Learning Institute, PRIME Education, AstraZeneca and Roche outside of the scope of the submitted work. S.R.Y. reports consultancy fees from AstraZeneca, AbbVie, Merus, Eli Lilly, Boehringer Ingelheim, Roche, Amgen and Sanofi outside of the scope of the submitted work. The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Identification of carcinoma cells.
(a) Average gene expression of selected cell type specific markers across lung cell atlas scRNA-seq clusters (see Fig. 1b). Each circle i,j represents the expression of gene i in cell cluster j. The size of each circle i,j represents the percentage of cells in cluster i expressing gene j. The color represents the average expression of gene i in cluster j. (b) Scatter plot of Shannon’s diversity index (quantifying uniformity of cluster membership across patient samples) and cluster-level aneuploidy score used to label epithelial cell (EPCAM + ) clusters as carcinoma vs. non-carcinoma (see Methods and Supplementary Note 5). Carcinoma cells are EPCAM+ clusters with low diversity (Shannon’s diversity index < 10) and high aneuploidy (aneuploidy score > 40, thresholds indicated by dotted lines in the plot). (c) Heatmap of chromosome arm-level aneuploidy scores (average of normalized gene expression across each chromosome arm, see Methods and Supplementary Note 5 for details) showing gains (red) and losses (blue) across individual cells from EPCAM+ clusters (rows). Bars (right) delineate carcinoma and non-carcinoma labels for clusters to which cells were assigned.
Extended Data Fig. 2 Hierarchical representation of epithelial cell-type composition across five levels of annotation in the healthy human lung atlas.
Hierarchy of human lung cell atlas cell types across five levels, from broadest (level 1) to most detailed (finest level). The “finest level” annotation comprises 23 cell types. Cell types in level 4 and “finest level” are denoted: b = bronchial, n = nasal, nn = non-nasal, ss = subsegmental, SMG = submucosal gland, TB = terminal bronchiole, and AT = alveolar type.
Extended Data Fig. 3 Benchmarking label transfer and COO inference.
(a) Bar plot showing label concordance between auto-encoder based label transfer to the human lung cell atlas (scANVI) and marker-based cell annotation (Garnett) for benign cells from tumor and adjacent normal tissue samples (see Methods). (b) Gene-specific SNV density (SNVs/Mbp) in LUAD (n = 242 independent tumor/normal pairs) and LUSC (n = 53 independent tumor/normal pairs). Densities are shown across tertiles of AT2 and basal-resting gene expression. (c) Gene-specific LUAD SNV density of tobacco, aging, and APOBEC SNVs (SNVs/Mbp) across three tertiles of average lung epithelial cell gene expression (n = 242, as in b). (d) Accuracy of COO inference (see Methods) across levels of the HLCA lung epithelial cell hierarchy (see Extended Data Fig. 2). Cell types are denoted: b = bronchial, n = nasal, nn = non-nasal, ss = subsegmental, SMG = submucosal gland, TB = terminal bronchiole, and AT = alveolar type. (e) Accuracy of COO inference at various levels of cell type resolution (see Extended Data Fig. 2) and tumor mutational burden (TMB) (for a single tumor sample and/or aggregated cohort). Error bars in (b-c) represent standard error of the mean. Accuracy for (d) and (e) was calculated as the fraction of simulations which the inferred COO matched the true COO at the specified cell type taxonomy level (see Methods).
Extended Data Fig. 4 Inferring patient-specific LUSC COO from passenger mutational patterns uncovers proximal origins.
(a) Hierarchically clustered heatmap showing association between benign cell type-specific gene expression and SNV density across individual LUSC WGS samples (n = 53 independent tumor/normal pairs). Each heatmap pixel i, j represents the strength of correlation (RR) for tumor sample i and benign lung cell type j, with values below 1 (blue) representing anti-correlation. (b) Bar plot of benign cell type-specific gene expression vs SNV counts regression results across the aggregated mutation calls from each of the LUSC clusters 1–3 (n = 6, 25, 22 cases respectively). Lime green bars represent proximal cell types, and orange bars represent distal cell types. Relative risk (RR) and 95% confidence interval (error bars) from the maximum likelihood regression fit is plotted for each aggregate cluster sample and benign lung cell type combination. Error bars represent 95% confidence intervals on the Bernoulli trial parameter. Cell types are labeled with the following abbreviations: b = bronchial, n = nasal, nn = non-nasal, ss = subsegmental, SMG = submucosal gland, TB = terminal bronchiole, and AT = alveolar type.
Extended Data Fig. 5 Relationship between identity, origin, histology and TP53 mutations in LUAD.
(a) Bar plot comparing the fraction of cases with distal/non-distal lineage plasticity (that is those with distal COO and non-distal identity or vice versa) grouped by inferred LUAD COO and TP53 mutation status (b) Alluvial plot linking origin, identity and histology with TP53 wildtype (WT) and mutant status across n = 75 LUAD cases. Bar height is proportional to the number of cases with the given feature, and ribbons indicate the number of cases with the given feature pair. (c-d) Bar plots comparing fraction of (c) papillary histology and (d) NSCLC, NOS histology between non-distal (ND, n = 34 cases) and distal (D, n = 41 cases) identity groups. (e) Bar plots comparing fraction of NSCLC, NOS between TP53 mutant (n = 44) and wild-type (n = 31) groups. (f) Bar plots comparing fraction of TP53 mutant NSCLC, NOS (n = 19 cases) and other TP53 mutant histologies (n = 56 cases). (g) Oncoprint of genomic alterations in LUAD, LUSC and Pan NSCLC drivers in NSCLC-NOS tumor samples. Error bars in a,c-f represent 95% confidence intervals on the Bernoulli trial parameter. P values in c-f obtained by two-sided Fisher’s exact test.
Extended Data Fig. 6 Identification of two distinct carcinoma cell populations in WCM-1.
(a) Euclidean distance of the (gene-wise cell type expression vs. SNV density) regression result vector for WCM-1 to regression vector centroids for distal, proximal, and ambiguous groups (Fig. 3, see Methods) (b-c) UMAP projection highlighting WCM-1 carcinoma cells (b) and two major WCM-1 carcinoma cell clusters (WCM-1-A, n = 618 cells; WCM-1-B, n = 1,297 cells) (c) against the backdrop of scRNA-seq transcriptomes of carcinoma cells from other patients’ tumor samples.
Supplementary information
Supplementary Information
Supplementary Notes.
Supplementary Table
Supplementary Table 1 contains lung cancer single-cell atlas cases; Supplementary Table 2 contains cell-type-specific markers; Supplementary Table 3 contains LUAD WGS cases; Supplementary Table 4 contains antibody information; Supplementary Table 5 contains reagents and instruments used in the study.
Supplementary Data 1
Zip file with contents: highly_variable_genes.txt, most highly variable genes across lung epithelial cell types; WCMcounts.csv, scRNA-seq counts matrix for LUAD patients profiled as part of this study; and WCMMetaData.csv, metadata for LUAD patients profiled as part of this study.
Supplementary Data 2
Zip file with contents: WCM-1_non-apobec_data.csv, per gene count of Non-APOBEC mutation in LUAD patient WCM-1.
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Panja, S., Mantri, P., Johnson, K.E. et al. Passenger mutations link cellular origin and transcriptional identity in human lung adenocarcinomas. Nat Genet 57, 3066–3074 (2025). https://doi.org/10.1038/s41588-025-02418-5
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DOI: https://doi.org/10.1038/s41588-025-02418-5
