Data availability Processed data from this study are available via Zenodo at https://doi.org/10.5281/zenodo.20329097 (ref. 68) for scRNA‑seq and https://doi.org/10.5281/zenodo.14907947 (ref. 69) for ST. Raw scRNA‑seq and ST data have been deposited in Genotypes and Phenotypes database under accession no. phs004079.v1.p1 and are available under controlled access. Access may be requested by eligible investigators through the database of Genotypes and Phenotypes Authorized Access System by submitting a Data Access Request, including a Research Use Statement, institutional approval and relevant personnel and data security information.

Requests are reviewed by the NCI Data Access Committee for consistency with the dataset’s data use limitation (research related to pancreatic and hepatobiliary cancer); approved investigators and authorized personnel under their direct supervision are granted access. Data Access Committee review typically takes approximately 2 weeks after institutional approval. Source data are provided with this paper. References Beatty, G.

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& Simeone, D. Spatial transcriptomics profiling of human primary pancreatic ductal adenocarcinoma tumors [Dataset]. Zenodo https://doi.org/10.5281/zenodo.14907947 (2025). Download references Acknowledgements We thank all the patients who participated in the study.

We thank members of the Center for Biospecimen Research and Development at NYU Langone Health (research resource identifier (RRID): SCR_017930) for support with tissue acquisition, the Experimental Pathology Research Lab (RRID: SCR_017928) at NYU Langone Health for the ST experiments, the Genome Technology Center (RRID: SCR_017929) at NYU Langone Health for expert library preparation and sequencing, and the Applied Bioinformatics Laboratories (RRID: SCR_019178) at NYU Langone Health for providing bioinformatics support and helping with the analysis and interpretation of the data. We thank P. Guo and the Nikon Imaging Center at UC San Diego for support with the microscopy experiments.

This work used computing resources at the NYU Grossman School of Medicine High Performance Computing Facility. Funding This project was supported by a National Institutes of Health grant no. R01CA245005 (D.M.S.) and a Sky Foundation seed grant award (D.K.). The shared resources and core facilities of NYU Langone Health are partially supported by a Cancer Center Support Grant no. P30CA016087 at the Laura and Isaac Perlmutter Cancer Center. Author information Author notes These authors contributed equally: Aristotelis Tsirigos, Diane M. Simeone. Authors and Affiliations Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY, USA Despoina Kalfakakou, Daniel C.

Cameron, Elektra Manolakos, Igor Dolgalev & Aristotelis Tsirigos Applied Bioinformatics Laboratories, NYU Grossman School of Medicine, New York, NY, USA Daniel C.

Cameron, Emily A. Kawaler & Aristotelis Tsirigos Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, OR, USA Motoyuki Tsuda, Hayley Zimny & Rosalie C. Sears Moores Cancer Center, UC San Diego Health, San Diego, CA, USA Lidong Wang, Qi Su, Sungsik Kim, Hannah Carter, Theodore H. Welling & Diane M. Simeone Division of Genomics and Precision Medicine, Department of Medicine, UC San Diego, La Jolla, CA, USA Sungsik Kim & Hannah Carter Perlmutter Cancer Center, NYU Grossman School of Medicine, New York, NY, USA Xiaohong Jing, Dylan L.

Tamayo, Yoona Shim, Amanda Ackermann, Daniel Weissinger & Rosmel Hernandez Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA Cristina Hajdu, Adriana Heguy, Amanda W.

Lund & Aristotelis Tsirigos Department of Surgery, NYU Grossman School of Medicine, New York, NY, USA Matthew Beier Genome Technology Center, Division of Advanced Research Technologies, Office of Science and Research, NYU Langone Health, New York, NY, USA Dacia Dimartino, Peter Meyn & Adriana Heguy Experimental Pathology Research Laboratory, Division of Advanced Research Technologies, Office of Science and Research, NYU Langone Health, New York, NY, USA Kalina Rice, Shanmugapriya Selvaraj & Cynthia Loomis Department of Dermatology, NYU Grossman School of Medicine, New York, NY, USA Amanda W. Lund Brenden-Colson Center for Pancreatic Care, Oregon Health and Science University, Portland, OR, USA Rosalie C.

Sears Knight Cancer Institute, Portland, OR, USA Rosalie C. Sears Department of Surgery, UC San Diego Health, San Diego, CA, USA Theodore H. Welling & Diane M. Simeone Cellular Analytics Laboratory, NYU Grossman School of Medicine, New York, NY, USA Igor Dolgalev Contributions D.K.,

I.D., A.T. and D.M.S. developed the study concept and were responsible for the study design. D.K. wrote the paper, which was then edited by all coauthors.

D.M.S., X.J. and C.H. helped acquire the human biospecimens.

D.L.T.,

Y.S.,

D.W.,

R.H.,

D.D., P.M. and A.H. processed samples for scRNA-seq.

D.K.,

D.C.C.,

E.A.K., E.M. and I.D. analyzed the scRNA-seq data.

D.K.,

D.C.C., E.A.K. and I.D. were responsible for the bioinformatics analysis pipelines. X.J. collected patient information and maintained the database.

C.H.,

D.K., C.L. and D.M.S. analyzed the H&E staining.

K.R.,

S.S.,

C.L.,

D.D., P.M. and A.H. processed the samples for ST. D.K. analyzed the ST data. R.C.S. provided reagents and cell lines for the murine experiment. M.T. and H.Z. conducted the murine experiments. D.K. and M.T. analyzed the data from the murine experiments. S.K. designed and performed the whole-genome duplication analysis. L.W. and Q.S. conducted the human cell line, mIF and FISH experiments.

T.H.W.,

C.H.,

L.W.,

A.A.,

M.B., A.W.L. and H.C. provided study guidance and feedback. T.H.W. and R.C.S. assisted with study design and analysis. D.M.S. and A.T. directed the study. All coauthors approved the final version of the paper before submission. Corresponding authors Correspondence to Aristotelis Tsirigos or Diane M. Simeone. Ethics declarationsCompeting interests The authors declare no competing interests. Peer reviewPeer review information Nature Genetics thanks William Hwang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Peer reviewer reports are available. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Extended dataExtended Data Fig. 1 Cell type composition in PDAC. a, Non-batch corrected UMAP of all cells (n = 443,451), color-coded by major cell type.

b, Non-batch corrected UMAP of all cells, color-coded by more granular cell type.

c, Non-batch corrected UMAP of all cells, color-coded by patient, showing patient sample-specific clusters of most epithelial cells and clustering of other cell types across samples.

d, Non-batch corrected UMAP of epithelial cells, color-coded by patient.

e, Non-batch corrected UMAP of epithelial cells, color-coded by malignancy.

f, Patient specificity of transcriptional clusters in epithelial cells, based on entropy.

g-l, Non-batch corrected UMAP of all cells, showing the expression of the epithelial (KRT19-d), stromal (DCN-e), endothelial (PECAM1-f), T (CD3E-g), myeloid (CD68-h), and B (CD79A-i) cell markers.

m, Cell type composition per sample. Extended Data Fig. 2 Spatial transcriptomic cell-type annotation and deconvolution in PDAC. a, Histology and common cell type markers for representative samples; the same analysis was performed for all spatially profiled tumors (n = 11).

b, Histology and results of deconvolution for representative samples; the same analysis was performed for all spatially profiled tumors (n = 11).

c, UMAP of all spots, color-coded by the cell type with the highest inferred proportion in each spot, based on deconvolution with BayesPrism.

d, UMAP of all spots, color-coded by manually annotated clusters.

e, Top cell type composition per sample.

Scale bar, 1000 µm. Extended Data Fig. 3 Validation of copy number variation detection from single-cell RNA sequencing data. a-c, Representative FISH images from one section each of two independent MYC-amplified tumors (P332, P466.2) and one copy-neutral control tumor (P432). MYC (green) and control (red) FISH probes targeting MYC and the centromere of chromosome 8; yellow arrows point to nuclei showing amplified MYC.

Scale bar, 5 µm.

Inserts, magnified view of regions shown in the square Field of View (FOV).

d, Quantification of MYC copy number in the same sections. Dotted and violin plot of per-nuclei copy number ratio for MYC in each sample (N = 84,42, and 63 nuclei from n = 3 ROIs of samples P332, P466.2, P432, respectively). Statistics calculated by one-way ANOVA followed by multiple t-test.

e, Fraction of WGS-detected CNV recovered by three tools tested (inferCNV, copyKAT, Numbat). f. Recovery rate of WGS-detected CNVs by tool, per CNV type. Source data Extended Data Fig. 4 CNV-driven modulation of ligand–receptor interactions and spatial communication phenotypes in PDAC. a-d, Representative spatial expression and spatial interaction (computed with NICHES) maps for selected ligand-receptor (L-R) pairs affected by copy-number alterations (a-b, NLGN2 deletions suppress NRXN2-NLGN2 interaction, c-d, PDGFRA amplifications increase PDGFA-PDGFRA interaction).

For each tumor sample, the left panel shows the spatial distribution of the partner gene expression (that is, the ligand or receptor not affected by the CNV), and the right panel shows the corresponding spatial NICHES interaction score for the L-R pair. Expression and interaction scores are visualized on matched spatial grids.

e, Summary of CNV-associated changes in interaction scores across all spatial clones and samples for high-confidence deletion- and amplification-associated L-R pairs. Each point summarizes the fold-change in (i) interaction strength (color; NICHES log2 fold-change between clones with and without a CNV) and (ii) partner-gene expression (size; mean expression of the partner gene in the neighborhood of clones with and without a CNV). Shapes indicate the direction of partner-gene expression change (circle, up; diamond, down). Ligands or receptors encoded within the CNV region are labeled in color (deletions: blue, amplifications:red). Extended Data Fig.

5 Validation of NECTIN2–TIGIT interactions by multiplex immunofluorescence. a-b, Representative multiplex immunofluorescence images of PDAC tissue sections with (CNV+; P466.2) or without (CNV⁻; P432) a NECTIN2 copy-number variation.

a, Co-localization of NECTIN2 + CDK19+ cells with TIGIT + CD3+ cells.

b, Co-localization of NECTIN2 + CDK19+ cells with TIGIT + CD8+ cells. Sections were stained for CK19 (magenta), NECTIN2 (yellow), TIGIT (red), and CD3 or CD8 (green)), with nuclei counterstained using DAPI (blue). Scale bars: 100 μm (overview), 30 μm (ROI1), and 20 μm (ROI2).

c,d, Quantification of the percentage of CK19+ cells co-expressing NECTIN2 (c) and CD3+ cells co-expressing TIGIT (d) in PDAC tissue sections shown in a (n = 10 ROIs per tumor; data are mean ±s.d.; two-sided t-test).

e,f, Quantification of the percentage of CK19+ cells co-expressing NECTIN2 (e) and CD8+ cells co-expressing TIGIT (f) in PDAC tissue sections shown in b (n = 11 ROIs per tumor; data are mean ±s.d.; two-sided t-test). Source data Extended Data Fig. 6 Patterns of clonal dynamics following chemotherapy. a, Cell type content in matched samples before and after treatment.

b, Number of clones that were eradicated, retained (expanded, contracted, remained stable), or emerged after treatment.

c, Pathway activation in matched samples before and after treatment (Nnaive=24 clones, Ntreated = 31 clones).

d-e, Interaction number between retained clones across samples and lymphatic cells (d) or stromal cells (e).

f, Interaction number between eradicated clones and stromal cells.

g-i, Boxplots showing the number of interactions between myCAFs (g), iCAFs (h), and endothelial cells (i) and three clone groups: eradicated, retained (pre-treatment), and retained (post-treatment) (Neradicated=16 clones, Nretained_pre = 16 clones, Nretained_post = YY clones.)

j, Interactions between eradicated (left), retained pre-treatment (middle), and retained post-treatment (right) clones and stromal cells. Boxplots: median (center line), 25th–75th percentiles (box), 1.5×IQR (whiskers); points beyond whiskers are outliers; c,g,h: two-sided Wilcoxon rank-sum test; i: two-sided Wilcoxon signed-rank test). Source dataSource Data (download XLSX ) Fig. 2e,j: Statistical source data.

Fig. 4b,d,e: CNVs per locus (b), number of pathway-activated clones per sample (d) and correlation source data (e).

Fig. 7c,e,f: Statistical source data (c), pathway enrichment data (e) and RNA-seq data (f).

Fig. 8i,j–m: Statistical source data (i) and pathway enrichment data (j–m). Extended Data Fig. 3b: Ratio MYC/Cen8. Extended Data Fig. 5c–f: %Num NECTIN2: CK19/Total (c), %Num CD3 (d), TIGIT/Total (e) and %Num CD8: TIGIT/Total (f). Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Reprints and permissions About this article Cite this article Kalfakakou, D.,

Cameron, D.C.,

Kawaler, E.A. et al. Clonal heterogeneity in human pancreatic ductal adenocarcinoma and its impact on tumor progression. Nat Genet (2026). https://doi.org/10.1038/s41588-026-02779-5 Download citation Received: 10 February 2025 Accepted: 01 September 2026 Published: 13 October 2026 Version of record: 13 October 2026 DOI: https://doi.org/10.1038/s41588-026-02779-5