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A gene–environment-induced epigenetic program initiates tumorigenesis

Nature volume 590, pages 642–648 (2021)Cite this article

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Abstract

Tissue damage increases the risk of cancer through poorly understood mechanisms1. In mouse models of pancreatic cancer, pancreatitis associated with tissue injury collaborates with activating mutations in the Kras oncogene to markedly accelerate the formation of early neoplastic lesions and, ultimately, adenocarcinoma2,3. Here, by integrating genomics, single-cell chromatin assays and spatiotemporally controlled functional perturbations in autochthonous mouse models, we show that the combination of Kras mutation and tissue damage promotes a unique chromatin state in the pancreatic epithelium that distinguishes neoplastic transformation from normal regeneration and is selected for throughout malignant evolution. This cancer-associated epigenetic state emerges within 48 hours of pancreatic injury, and involves an ‘acinar-to-neoplasia’ chromatin switch that contributes to the early dysregulation of genes that define human pancreatic cancer. Among the factors that are most rapidly activated after tissue damage in the pre-malignant pancreatic epithelium is the alarmin cytokine interleukin 33, which recapitulates the effects of injury in cooperating with mutant Kras to unleash the epigenetic remodelling program of early neoplasia and neoplastic transformation. Collectively, our study demonstrates how gene–environment interactions can rapidly produce gene-regulatory programs that dictate early neoplastic commitment, and provides a molecular framework for understanding the interplay between genetic and environmental cues in the initiation of cancer.

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Fig. 1: Tissue damage induces cancer-associated chromatin states in pre-malignancy.
Fig. 2: An in vivo approach to perturb chromatin output in regenerating and neoplastic pancreatic epithelia.
Fig. 3: Neoplastic and regenerative outcomes of injury rely on distinct BRD4-dependent programs.
Fig. 4: A chromatin switch that is induced by gene–environment interactions defines the neoplastic transition.
Fig. 5: Epigenetic dysregulation of IL-33 promotes neoplastic reprogramming.

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Data availability

All ATAC-seq, RNA-seq and scATAC-seq data generated in this study have been deposited in the Gene Expression Omnibus (GEO) database under the super-series GSE132330. Publicly available RNA-seq, gene expression microarray and ChIP–seq data reanalyzed for this study are available under the accession codes GSE86262 (PTF1A ChIP–seq), GSE34295 (NR5A2 ChIP–seq), GSE99311 (H3K27ac ChIP–seq), GSE62452 (human specimen gene expression microarray) and GSE71729 (human specimen gene expression microarray) and in the GTRD (https://gtrd.biouml.org). All other data supporting the findings of this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

Custom codes for processing, filtering, and visualization of scATAC-seq were performed using Python and are demonstrated in a Jupyter notebook available for download at https://github.com/dpeerlab/pdac-tumorigenesis-scATAC/.

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Acknowledgements

We thank M. Chalarca, S. Ackermann, J. Simon, A. Wuest and the MSKCC animal facility for technical support with animal colonies; S. Yang, S. Young, Z. Zhao and A. Kahn for assistance with the generation of ES-cell-derived mouse models; J. Valdés, S. Dhara, T. Baslan, S. Tian and A. Osterhoudt for support with profiling experiments; I. Masilionis and O. Chaduhary for support with scATAC-seq experiments; V. Lavallée for his input and discussion on scATAC-seq data analysis; R. Garner and the MSKCC Flow Cytometry Core facility staff for assistance with cell sorting; and J. Reyes and other members of the Lowe laboratory for advice and discussions. We also acknowledge the MSKCC Integrated Genomics Operation Core, funded by the NCI Cancer Center Support Grant (CCSG, P30 CA08748), Cycle for Survival, and the Marie-Josée and Henry R. Kravis Center for Molecular Oncology. D.A.-C. was supported by the Spanish Fundación Ramón Areces Postdoctoral Fellowship and is recipient of the La Caixa Postdoctoral Junior Leader Fellowship (LCF/BQ/PI20/11760006); C.B. is supported by an NIH F31 grant (F31CA246901); J.P.M. was supported by an American Cancer Society Postdoctoral Fellowship (126337-PF-14-066-01-TBE); R.C. is supported by the Pancreatic Cancer Action Network-AACR Pathway to Leadership Award; H.-A.C. is supported by a NIH F99 Grant (F99CA245797); K.M.T. is supported by the Jane Coffin Childs Memorial Fund for Medical Research; F.M.B. is supported by MSKCC’s Translational Research Oncology Training Fellowship (5T32CA160001-08); N.T. is supported by an NIH K99 grant (K99CA237736); G.L. was supported by an NIH F32 grant (1F32CA177072-01) and American Cancer Society Fellowship (PF-13-037-01-DMC); and E.A. is supported by a NIH K99 grant (K99CA230195). This work was also supported by MSKCC’s David Rubenstein Center for Pancreatic Research Pilot Project (to S.W.L.) and The Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center (to S.W.L./D.P.); the Lustgarten Foundation Research Investigator Award (to S.W.L.); the Agilent Thought Leader Program (to S.W.L.); and the NIH grants P01CA13106 (to S.W.L.), R01CA204228 and P30CA023108 (to S.D.L.) and U54CA209975 (to D.P.). S.W.L. is an investigator in the Howard Hughes Medical Institute and the Geoffrey Beene Chair for Cancer Biology.

Author information

Authors and Affiliations

  1. Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Direna Alonso-Curbelo, Yu-Jui Ho, John P. Morris IV, Hsuan-An Chen, Kaloyan M. Tsanov, Francisco M. Barriga, Wei Luan, Nilgun Tasdemir, Geulah Livshits & Scott W. Lowe

  2. Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Cassandra Burdziak, Elham Azizi, Jaeyoung Chun, Linas Mazutis & Dana Pe’er

  3. Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY, USA

    Cassandra Burdziak

  4. Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jesper L. V. Maag & Richard Koche

  5. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Rohit Chandwani & Steven D. Leach

  6. Department of Surgery, Weill Cornell Medical College, New York, NY, USA

    Rohit Chandwani

  7. Department of Cell and Developmental Biology, Weill Cornell Medical College, New York, NY, USA

    Rohit Chandwani

  8. Louis V. Gerstner Jr Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Hsuan-An Chen

  9. Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jaeyoung Chun & Linas Mazutis

  10. Department of Pathology, University of Michigan School of Medicine, Ann Arbor, MI, USA

    John E. Wilkinson

  11. Dartmouth Norris Cotton Cancer Center, Hanover, NH, USA

    Steven D. Leach

  12. David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Steven D. Leach

  13. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Scott W. Lowe

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  1. Direna Alonso-Curbelo
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Contributions

D.A.-C. conceived the study, designed and performed experiments, analysed data and wrote the manuscript. Y.-J.H. analysed the RNA-seq data and integrated RNA-seq and ATAC-seq datasets. J.L.V.M. and R.K. analysed the bulk ATAC-seq data. C.B., J.C., E.A. and L.M. contributed to design and implementation of the scATAC-seq processing pipeline. C.B. analysed the scATAC-seq data. J.P.M., R.C., H.-A.C., K.M.T., F.M.B., W.L., N.T. and G.L. assisted in experiments, produced reagents or edited the manuscript. J.E.W. performed histological analyses of pancreatic lesions from mouse pancreases. S.D.L. provided input and supervised experiments. D.P. supervised the design and implementation of the scATAC-seq data analysis. S.W.L. conceived and supervised the study and wrote the manuscript. All authors read the manuscript.

Corresponding author

Correspondence to Scott W. Lowe.

Ethics declarations

Competing interests

A patent application (PTC/US2019/041670, international filing date 12 July 2019) has been submitted based in part on results presented in this manuscript covering methods for preventing or treating KRAS-mutant pancreatic cancer with inhibitors of type 2 cytokine signalling. D.A.-C. and S.W.L. are listed as the inventors. S.W.L. is a founder and member of the scientific advisory board of Blueprint Medicines, Mirimus, ORIC Pharmaceuticals and Faeth Therapeutics, and is on the scientific advisory board of Constellation Pharmaceuticals and PMV Pharmaceuticals. S.D.L. is on the scientific advisory board of Nybo Therapeutics and Episteme Prognostics.

Additional information

Peer review information Nature thanks Mark Dawson, Mara Sherman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Chromatin accessibility dynamics during pancreatic regeneration and early neoplasia.

a, Schematic representation of the allele configurations used to trace cre-recombined wild-type, Kras-mutant or Kras/p53-mutant pancreatic epithelial cells in transgenic mice. b, Representative H&E staining (top) or mKate2 IHC (bottom) of pancreases from the indicated mouse models and treatment conditions (n = 3 mice per group), illustrating the defined tissue states, spanning normal healthy (normal), regenerating (reversible metaplasia, injury), early neoplastic (Kras*, Kras* + injury) and malignant (PDAC) tissues, used for in vivo profiling of chromatin and transcriptional dynamics that underlie physiological or pathological exocrine pancreas plasticity. Mouse model genotype abbreviations are as follows: C, Ptf1a-cre;RIK; KC, Ptf1a-cre;RIK;LSL-KrasG12D; KPflC, Ptf1a-cre;RIK;LSL-KrasG12D;p53fl/+. The RIK allele enables tracing of cre-recombined pancreatic epithelial cells through the reporter mKate2. Scale bars, 100 μm. c, Example of gating strategy to isolate pancreatic epithelial cells expressing the lineage-tracing marker mKate2. Live mKate2+CD45−DAPI− cells were isolated from single-cell suspensions of pancreases from the autochthonous models of PDAC tumorigenesis (KC or KPflC) or normal pancreas counterparts (C) described in a, b (see also Supplementary Fig. 1). d, Correlation plot showing ATAC-seq size factors used for data normalization of the indicated experimental conditions with two different methods. n = 3, 5, 3, 6 or 4 mice (from top to bottom) per group. PeakNorm uses the in-built DESeq2 normalization for all filtered reads mapped to the peak atlas, whereas DepthNorm uses the number of filtered mapped reads irrespective of whether reads are within or outside the peak atlas. The shaded region represents the 95% confidence interval for the regression between normalization types. e, Overlap between the dynamic ATAC peaks lost (left) or gained (right) in the indicated tissue conditions versus normal. Numbers reflect peaks in each category.

Extended Data Fig. 2 Shared and distinctive features of ATAC gain and loss regions induced by mutant Kras, tissue damage or their combination.

a, Heat map representation of chromatin accessibility at ATAC peaks significantly gained or lost between normal, injury, Kras* and Kras* + injury conditions, as assessed by DESeq2 analyses of ATAC-seq data. Unsupervised clustering identified six major modules of peaks, that are either shared (S, A2) or specifically altered during physiological regenerative metaplasia (R) versus neoplastic transformation (N1, N2, A1). Each column represents one independent biological replicate (mouse). Figure 1e shows these same clusters plotted with the PDAC condition to illustrate their accessibility status in advanced disease. b, Metagene representation of the mean ATAC-seq signal for six ATAC cluster regions identified in the above analyses in the indicated epithelial states, with the number of mice analysed per condition indicated in brackets. c, Genomic annotations of dynamic peaks comprising each ATAC-seq cluster. Note accessibility dynamics predominantly occur at intronic and distal intergenic cis-regulatory elements, with an enriched contribution of promoter or TSS regions in regeneration-associated gained (R) or lost (A2) clusters. d, Top-scoring TF motifs identified by HOMER de novo motif analyses per ATAC-seq cluster. The number in the brackets indicates enrichment P values. e, Heat map representing the relative motif enrichment for the top-scoring motifs across the same clusters of peaks sensitive to effects of injury and/or mutant Kras shown in d. Injury and mutant Kras cooperatively produce gain (for example, AP-1, KLF, ETS, RUNX, SOX and MAF), loss (for example, HNF), and redistribution (for example, FOX and GATA) of accessible putative binding sites for a multi-pronged network of TF families, including TFs known to control pro-oncogenic (for example, AP-113, RUNX376, KLF4/577,78, SOX9 and SOX179,79) or tumour-suppressive programs (for example, HNF1A80, KLF1481, NR5A282 and PTF1A22) in PDAC. f, Correlation matrices showing the differential degree of co-occurrence of motifs from different classes of TFs at the peaks comprising each ATAC-seq cluster (defined in a), revealing TF modules (marked with black rectangles). Note that AP-1-motif-positive peaks gained uniquely during regenerative metaplasia (R cluster) show co-occurrence with pancreas lineage TF (GATA, FOX) motifs, whereas those gained during pro-neoplastic (Kras-mutant) metaplasia (S, N1, N2) do not. g, Bubble plots showing the relative enrichment of the indicated motifs (identified as top-scoring by HOMER analyses described in d) in the ATAC peaks that are significantly gained (left, right) or lost (blue, right) in the injury (I, n = 5 mice), Kras* (K, n = 3 mice), Kras* + injury (K + I, n = 6 mice) or advanced cancer (PDAC, n = 4 mice) conditions versus normal healthy pancreas (normal, n = 3), as defined in Fig. 1a.

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Extended Data Fig. 3 Early chromatin accessibility changes affect cell-identity genes associated with experimentally validated enhancers of normal and malignant pancreatic epithelial cells.

a, Representative ATAC-seq tracks showing dynamic accessibility at gene loci previously described to contain active enhancers (top) in normal acinar (left, Il22ra183) or advanced PDAC (right, Trim2917) cells across mKate2+ sorted cells freshly isolated from the indicated tissue states as defined in Fig. 1. n = 3, 5, 3, 6 or 4 (from top to bottom) mice per group. b, Metagene representation of the mean ATAC-seq signal for regions bound by the acinar lineage-determining TF PTF1A in normal pancreas (left) (defined from GSE8626215) or H3K27ac gain regions of metastasis-derived PDAC organoids versus healthy pancreas counterparts (defined from GSE9931117) in mKate2+ sorted cells freshly isolated from the indicated tissue states. The number of mice analysed per condition is indicated in the brackets. c, Proportion of genomic regions showing a significant gain of H3K72ac signal in metastasis-derived cultured organoids compared to their healthy counterparts (H3K27ac ChIP–seq data from GSE99311) that gain accessibility in pancreatic epithelial (mKate2+) cell populations freshly isolated from injury (n = 5 mice), Kras* (n = 3 mice), Kras* + injury (n = 6 mice) or PDAC (n = 4 mice) tissue states as compared to normal pancreas (n = 3 mice), as defined by overlapping ChIP–seq and ATAC-seq datasets. d, Schematic representation of the genetic approach used to induce exocrine pancreas-specific suppression of the chromatin reader BRD4. We generated mice (KCsh) containing the following alleles: (i) a pancreas-specific (C)re driver (Ptf1a-cre), (ii) a Cre-activatable LSL-KrasG12D allele, and (iii) two additional alleles (LSL-rtTA3-IRES-mKate (RIK) and the collagen homing cassette (CHC)) that allow for inducible expression of a GFP-linked shRNA targeting Brd4 (shBrd4) or a neutral shRNA (shRenilla) in cre-recombined cells labelled by the fluorescent reporter mKate2. After receiving a doxycycline-containing diet, a GFP-linked shRNA targeting Brd4 (or Renilla, control) is induced selectively in mKate2-labelled pancreatic epithelial cells. Analogous models containing the doxycycline-inducible shRNAs without the LSL-KrasG12D allele (referred to as Csh) were generated to compare and contrast epigenetic requirements of pro-neoplastic versus regenerative pancreas plasticity. e, Representative H&E, IHC or immunofluorescence (IF) analyses of the indicated proteins in pancreases from Csh (top) or KCsh (bottom) mice (n = 3 per group) placed on a doxycycline diet at 5 weeks old and analysed 9 days later. mKate2 staining marks Kras-wild-type (top) or Kras-mutant (bottom) pancreatic exocrine cells in which Ptf1a-cre has been expressed. GFP staining corresponds to shRNA expression and is coupled with BRD4 suppression in that same compartment (but not in surrounding stroma) in mice containing shRNAs that target Brd4 (shown for the shBrd4.552 strain) but not Renilla (control). Dashed lines demark boundaries between epithelium and stroma, and arrows point to the BRD4-suppressed exocrine pancreas compartment of shBrd4 mice. The same BRD4 IHC panels are shown in Fig. 2b. Scale bars, 50 μm. f, GSEA and metagene plots showing the relative expression (top) and accessibility (bottom) status, respectively, of the same loci defining normal acinar state (left, with the top 500 PTF1A-bound peaks) or containing activated enhancers in metastatic PDAC cells (right) shown in b in shBrd4 versus shRen mKate2+ metaplastic epithelial cells isolated from KCsh mice (Kras* + injury) described above (n = 3 mice per genotype). BRD4 suppression selectively impairs the transcription of lineage-specific and PDAC enhancer-associated genes in the Kras-mutant metaplastic epithelium without impairing chromatin accessibility at those loci. Genome-wide profiles of these same conditions are shown in Extended Data Fig. 6l. g, Top, representative ATAC-seq and RNA-seq tracks of genes known to be associated with active lineage-specific enhancers of acinar (for example, Il22ra183) or pancreatic progenitor cells (for example, Fgfr221) in shRen.713 (black) or shBrd4.552 (blue) mKate2+ epithelial cells freshly isolated from metaplastic pancreases from KCsh mice (n = 3 per genotype) at the 48 h after caerulein treatment time point (thus matching the Kras* + injury condition). BRD4 suppression impairs transcription of pancreatic enhancer-associated genes without altering chromatin accessibility at that same locus. Bottom, tracks of housekeeping genes are shown as specificity controls. Mice were placed on doxycycline six days before the caerulein treatment, to induce ADM in the presence or absence of BRD4 (as summarized in Extended Data Fig. 4a). See also Extended Data Fig. 6k, l.

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Extended Data Fig. 4 BRD4 suppression is dispensable for both regenerative and pre-neoplastic ADM.

a, Experimental strategy to address the functional effect of spatiotemporally controlled perturbation of BRD4 during injury-accelerated tumorigenesis or physiological regeneration in KCsh or Csh mice, respectively. Four-week-old mice were placed on a doxycycline diet to induce the expression of shRNA targeting Brd4 or Ren (control) in the pancreatic epithelium, and pancreatic injury was induced by caerulein treatment six days thereafter to trigger synchronous ADM throughout the organ in the presence (shRen) or absence (shBrd4) of epithelial BRD4 function, respectively. Tissue responses were evaluated at the indicated days (d) or weeks (w) post-caerulein or PBS (control) treatment. Specifically, to match our previous profiling experiments, we examined pancreatic ADM at 48 h after caerulein treatment, a time point corresponding to the distinct, genotype-specific chromatin accessibility profiles identified above. Subsequent regeneration (Kras-wild-type context) or neoplasia (Kras-mutant context) were evaluated at five days or two to three weeks thereafter, respectively. In addition, separate cohorts of doxycycline-treated KCsh mice were analysed at six weeks and one year of age to track effects in the context of stochastic Kras-driven neoplasia. Mouse illustrations were made using BioRender. b, c, shBrd4 perturbation does not impair mutant Kras-driven ADM. Representative immunofluorescence stains of the acinar markers CPA1 (b, top) and amylase (c) or the ductal metaplasia marker SOX9 (b, bottom) co-stained with lineage-tracer markers (mKate2 and GFP) in Kras-mutant pancreases from six-week-old KC-shRen or KC-shBrd4 mice (n = 6 per group) in the stochastic tumorigenesis setting. d–g, shBrd4 perturbation does not blunt injury-induced ADM but impairs subsequent acinar regeneration. Representative immunofluorescence staining of pancreases from Kras-wild-type Csh mice expressing shRen or shBrd4 that were treated with caerulein or PBS control and analysed at the indicated days after treatment for protein expression of the acinar marker CPA1 (d), metaplasia markers KRT19 (e), SOX9 (f) or clusterin (g) co-stained with GFP (marking shRNA-expressing cells) and DAPI (nuclei). n = 5 mice per group. Scale bars, 100 μm.

Extended Data Fig. 5 BRD4 suppression impairs regenerative and neoplastic fate outcomes of injury-driven pancreas plasticity.

a, Representative bright-field and fluorescence images showing gross morphology of pancreases of C-shRen and C-shBrd4 mice treated with caerulein or PBS control and analysed at the indicated time points. Lineage-traced pancreatic epithelial cells expressing shRNA are marked by the fluorescent reporters mKate2 and GFP. Reduced mKate2 and GFP signals denote loss of pancreatic tissue expressing shBrd4. Scale bar, 5 mm. b, Representative bright-field and fluorescence images showing gross morphology of pancreases of KC-shRen and KC-shBrd4 mice placed on doxycycline from postnatal day 10 to induce shRNA expression and analysed at 1 year of age. Reduced mKate2 and GFP signals denote loss of shBrd4-expressing Kras-mutant pancreatic epithelial cells. Scale bar, 5 mm. c, Quantification of pancreatic weight normalized to mouse body weight by genotype. Data are mean ± s.e.m.; n = 8, 7 or 5 mice (top, from left to right), or n = 3, 4 or 2 mice (bottom, from left to right); unpaired two-tailed Student’s t-test. d, Representative IHC stains of mKate2 (top) and MYC (bottom) in pancreases from six-week-old mice of the indicated genotypes that were placed on a doxycycline diet at day 10 after birth (stochastic tumorigenesis setting). Bottom, high-magnification images of regions marked with dashed boxes, for visualization of the nuclear localization of MYC. Although oncogenic expression of MYC can require BRD4-associated enhancers in some settings35,84,85 and is suppressed by systemic BET inhibition in KC mice86, epithelial-specific BRD4 suppression did not reduce MYC protein in our model. Scale bars, 100 μm. e, Representative co-immunofluorescence stains of mKate2 (red) and the acinar marker CPA1 (green) in pancreases from six-week-old KCsh mice of the indicated genotypes that were placed on a doxycycline diet at day 10 after birth, as above. Right, high-magnification images of regions marked with dashed boxes. KCsh mice that contain a validated shRNA targeting Myc (instead of Brd4) exhibited impaired rather than accelerated ADM. The reduction of CPA1 that was observed in KC-shBrd4 mice is not phenocopied in KC-shMyc mice, which retain CPA1 expression. Scale bar, 100 μm. f, Schematic representation of the phenotypic output of pancreas-specific suppression of BRD4 during mutant-Kras-driven neoplasia and tissue-injury-driven regeneration: BRD4 is dispensable for acinar-to-ductal metaplasia induction in both contexts but mediates subsequent neoplastic progression to PanIN or regenerative plasticity, respectively.

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Extended Data Fig. 6 BRD4 suppression reveals distinct chromatin-associated transcriptional programs in normal versus Kras-mutant injured pancreases.

a, Representative IHC of BRD4 in pancreases from Csh (left) or KCsh (right) mice (n = 3 per group) containing shRen.713 or shBrd4.1448 at 48 h after caerulein treatment, placed on a doxycycline diet 6 days before the start of caerulein treatment. b, Overlap of DEGs that are downregulated after BRD4 suppression in the injury (regeneration) or Kras* + injury (neoplastic transformation) settings. Examples of BRD4-dependent genes that are shared or unique to each context are shown. DN-DEGs, downregulated genes. c, Heat map representation of normalized enrichment scores comparing the mRNA expression of genes associated with the ATAC-seq clusters identified in Fig. 1 between shBrd4.1448 and shRen.713 pancreatic epithelial cells (mKate2+GFP+) isolated from Kras-wild-type (injury, left) or Kras-mutant (Kras* + injury, right) metaplastic tissues, as analysed by GSEA at the 48 h after caerulein treatment time point. Negative normalized enrichment scores indicate downregulation of the gene set in shBrd4 cells as compared to their shRen counterparts. Consistent with the accelerated ADM but blunted neoplastic transformation phenotype (Fig. 3), BRD4 suppression impairs the expression of genes linked to the acinar ATAC-seq clusters (A1 and A2) in both wild-type and Kras-mutant cells and, in addition, of genes linked to the neoplasia-specific ATAC-seq clusters (N1 and N2) in Kras-mutant cells. Shared (S) and regeneration-specific (R) and ATAC-seq clusters are not blunted in either context, suggesting that these reflect injury-driven ADM states that can be induced in the absence of BRD4 in both wild-type and Kras-mutant contexts. d, GSEA comparing the expression of known PTF1A-dependent genes22 between shBrd4 and shRen cells isolated from Kras-wild-type (Csh; top) or Kras-mutant (KCsh; bottom) mice triggered to undergo regenerative (injury) or pro-neoplastic (Kras* + injury) metaplasia, respectively. e, f, Effects of BRD4 suppression on the protein (e) or mRNA and DNA accessibility (f) levels of known drivers of pancreatic tumorigenesis linked to ATAC gain loci specific to early neoplasia (Kras* + injury; K + I) that remain in a closed chromatin state in both regenerative metaplasia (injury) and normal pancreas. Panels in e show representative immunofluorescence stains of the indicated neoplasia-activated factors (red) co-stained with GFP (green, marking epithelial cells) in pancreases from wild-type or Kras-mutant shRNA-expressing mice two days after tissue injury (caerulein) or control (PBS). Nuclei are counterstained with DAPI (blue). Representative ATAC-seq and RNA-seq tracks of these and other neoplasia-activated genes herein identified to be induced during pancreatitis-induced neoplasia (Kras* + injury condition) in a BRD4-independent manner are shown in f (left panels). Examples of classic metaplasia genes that are not neoplasia-specific (i.e. induced by injury also during physiological regeneration) do not display injury-driven chromatin accessibility changes nor BRD4-dependent expression, as shown in f (right panels). g, GSEA comparing the expression of MYC-activated genes between Kras-mutant shBrd4 and shRen cells (Kras* + injury condition), showing retained expression in shBrd4 populations. Similar results were obtained with additional MYC signatures87,88 (not shown). h, GSEA comparing the expression of a mutant Kras-associated FOSL1 gene signature between shBrd4 and shRen cells isolated from KCsh mice (Kras* + injury condition). i, GSEA comparing the expression of genes upregulated in samples of human PDAC versus samples of healthy human pancreas (Human PDAC UP-DEGs) between shBrd4 and shRen cells isolated from KCsh mice (Kras* + injury condition). Similar results were obtained with the GSE62452 dataset. j, Representative immunofluorescence staining for the proliferation marker Ki67 (green) co-stained with mKate2 (red, marking epithelial cells) in pancreases from Kras-wild-type or Kras-mutant shRNA-expressing mice two days after treatment with caerulein (tissue injury) or PBS (control). Nuclei were counterstained with DAPI (blue). BRD4 suppression induces aberrant activation of Cdkn1a and other stress response p53-activated genes in both Kras-wild-type and Kras-mutant metaplastic cells (see Supplementary Table 4), which, accordingly, showed reduced proliferation. k, Metagene and GSEA plots showing the relative accessibility (left) and expression (right) status, respectively, of ATAC gain regions induced by tissue damage in Kras-mutant pancreases (Kras* + injury versus Kras*) in Kras-mutant shBrd4 versus shRen cells isolated from the same Kras* + injury tissue condition. l, Scatter plots comparing the genome-wide chromatin accessibility (left) and transcriptional (right) landscapes of Kras-mutant shBrd4 versus shRen cells isolated from the same Kras* + injury tissue condition (n = 3 mice per genotype). Each dot represents an ATAC-seq peak (left) or transcript (right); differentially accessible loci (log2-transformed fold change ≥ 0.58, FDR ≤ 0.1) or differentially expressed genes (fold change > 2, P < 0.05) between genotypes are marked in red (gained) or blue (lost). shBrd4 populations display ATAC-seq profiles indistinguishable from those of shRen controls, ruling out the possibility that the observed BRD4-dependent transcriptional changes result from confounding secondary effects of acute BRD4 perturbation on chromatin state or epithelial tissue cell composition. Scale bars, 50 μm.

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Extended Data Fig. 7 Early dysregulation of chromatin regulatory features of advanced PDAC.

a, Heat map representation of RNA-seq data showing the relative expression of gene sets associated with the ATAC-seq clusters identified in Fig. 1 across mKate2+ pancreatic epithelial cells isolated from normal, injury, Kras*, Kras* + injury and PDAC tissue states (as defined in Fig. 1a). Heat map colour represents median expression of all genes associated with each cluster, z-scored for comparison across conditions. Each column represents an independent mouse. b, Chromatin dynamics at ATAC peaks at promoter, distal or intron regions associated with differentially expressed genes (DEGs; RNA-seq fold change > 2, adjusted P < 0.05) between mKate2+ pancreatic epithelial cells isolated from the indicated tissue states versus normal pancreas (n = independent mice per condition as in a). DEGs were classified depending on whether they exhibit significant chromatin accessibility change (chromatin-dynamic DEGs) or no accessibility change (chromatin-stable DEGs, in grey) at associated peaks in the respective experimental condition versus normal pancreas. UP-DEGs, upregulated genes; DN-DEGs, downregulated genes. c, Heat map of RNA-seq data showing top upregulated pathways in the Kras* + injury condition, separated depending on whether they exhibit ATAC gains at associated peaks (promoter or distal). Upregulated genes associated with ATAC gain (left) are linked to distinct biological traits commonly acquired in PDAC (for example, differentiation, inflammation, fibrosis, signalling), whereas those with no ATAC change (that is, ‘primed’ in normal pancreas) are linked to general cellular processes (for example, cell proliferation, translation) (right). See Supplementary Table 6 for additional tissue states and pathways. d, Relative enrichment of the indicated gene sets in shBrd4.1448 versus shRen.713-expressing pancreatic epithelial cells (mKate2+GFP+) isolated from KCsh mice (n = 3 per shRNA genotype, Kras* + injury) as determined by GSEA. UP-DEGs (left bars) and DN-DEGs (right bars) between the Kras* + injury and normal conditions were classified depending on whether they exhibit or not significant accessibility changes (ATAC gain or ATAC loss) at associated ATAC peaks. Negative normalized enrichment scores (NES) indicate downregulation of gene sets in shBrd4 cells as compared to shRen counterparts. e, Heat map representation of ATAC–RNA combined scores for the indicated TFs and tissue states. The ATAC–RNA combined score infers the probability of differential binding of a specific TF to a motif significantly enriched in the ATAC gain or loss regions of each condition versus normal, based on a consistent gene expression change in the same comparison (see Methods for details). Top TFs scoring for the Kras* + injury or PDAC conditions versus normal are shown. DN-TFs, downregulated TFs (versus normal); UP-TFs, upregulated TFs (versus normal). f, Heat map of RNA-seq data from lineage-traced (mKate2+) pancreatic epithelial cells isolated from the indicated tissue states, showing the relative expression of TFs whose binding motifs are enriched in ATAC gain or loss loci by effects of tissue damage, mutant Kras or the combination of both (see Extended Data Fig. 2a, ATAC clusters) or in the transition to full-blown adenocarcinoma PDAC (PDAC versus Kras* + injury). Each column represents an individual mouse. The boxes highlight modules of TFs that are: (i) similarly expressed in normal regeneration and cancer context (green, black); (ii) selectively induced in early neoplasia and PDAC (red); (iii) selectively overexpressed in late disease (dark blue); (iv) that become increasingly suppressed by effects of injury, mutant Kras (light blue) or both (orange); or (v) selectively induced in early-stage but not late-stage disease (purple), with names of TF examples to the right. Injury and mutant Kras differentially induce diverse members of the same TF families, including several AP-1 JUN–FOS complex members (marked with arrows) and other TFs known known to also bind AP-1 motifs. In addition, note that the Kras* and injury combination suppresses the expression of master regulators of acinar differentiation (marked with asterisks) more potently than either insult alone. g, Representative IHC of two AP-1 family members in Kras-mutant or wild-type pancreatic tissues in the presence and absence of tissue damage (48 h after caerulein treatment) and compared to advanced PDAC (n = 4 mice per group). Whereas the AP-1 family member FOSL1 is induced in non-injured Kras-mutant pancreases, JUNB protein levels increase only after injury, with a potent co-activation occurring in the presence of both stimuli, suggesting that cooperative gene–environment interactions modulate the expression of AP-1 TF complex members. Scale bars, 100 μm.

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Extended Data Fig. 8 Single-cell analysis of chromatin dynamics in early-stage neoplasia.

a, UMAP representation of scATAC-seq profiles of mKate2+ cells isolated from Kras* and Kras* + injury tissue conditions (n = 1 mice each) and co-embedded together, revealing chromatin heterogeneity across Kras-mutant pancreatic epithelial cells from pre-malignant tissues. Dots represent individual cells (n = 6,369) and colours indicate cluster identity based on initial phenograph clustering (left). The heat map shows the degree of intersection of significantly enriched peaks (Fisher’s exact test, adjusted P < 0.05) between each pair of phenograph cluster (coloured matching UMAP plot), normalized by the total number of enriched peaks in the cluster for that row (left). Rows and columns are ordered according to their grouping into seven larger subpopulations derived from the merging of phenograph clusters on the basis of the overlap of their differentially accessible peak sets (see Methods for details). b, UMAP representation of the same mKate2+ scATAC-seq profiles shown in a, coloured by major subpopulations (see Methods for details). c, Heat maps showing patterns of accessibility at subpopulation-defining peaks, shown across each of the major subpopulations defined in b separated by tissue injury (+/−) condition. Colour illustrates the proportion of all cells in each subpopulation and condition with an accessible peak, where values have been z-scored. The complete list of subpopulation-defining peaks is provided in Supplementary Table 8. d, Visualization of differential chromatin opening for the indicated peaks associated with known pancreatic cell-state-defining markers or the housekeeping gene Gapdh, illustrated by opened-peak density plots for nearby proximal or distal elements within 50 kb of the TSS. Colour scale indicates a Gaussian kernel density estimate of cells containing the open peak in the UMAP visualization, with yellow signal marking increased density of cells with open chromatin at that specific locus. e, UMAP projection of scATAC-seq profiles of Kras-mutant (mKate2+) epithelial cells shown in a–d, coloured by the indicated tissue states. f, Correlation analysis comparing normalized accessibility signals per peak captured in scATAC- and bulk ATAC-seq analyses of the indicated conditions. For scATAC-seq data, values representing pooling of all individual cells to generate depth-normalized accessibility signals per condition (pseudo-bulk) are shown. For bulk ATAC-seq data, values from a representative sample (independent mouse) of a total of n = 3 (Kras*) or n = 6 (Kras* + injury) are shown. g, Volcano plot showing dynamic peaks identified between PDAC and normal conditions in bulk ATAC-seq analyses (Fig. 1), coloured according to their relative accessibility fold change detected between Kras* + injury and Kras* samples in scATAC-seq analyses. Peaks gained or lost in PDAC versus normal are found differentially represented in scATAC-seq data from early-stage neoplasia, correlating with tissue injury status. h, UMAP projection illustrating examples of peaks exhibiting chromatin closing (left) or opening (right) within the same Kras-mutant cell cluster upon tissue injury (+), visualized by opened-peak density plots in which colour indicates a Gaussian kernel density estimate of cells containing the open peak in the UMAP visualization. i, scATAC-seq tracks of the indicated loci showing chromatin accessibility patterns across the indicated subpopulations, marked with colour labels matching b and separated by experimental condition. The first two rows (aggregate, in grey) show global patterns from pooling all cells from each condition, regardless of subpopulation identity, and population-specific dynamics are shown below. Blue- and red-coloured boxes mark ATAC gains or losses detected in aggregate populations, and dashed boxes highlight examples of peaks displaying injury-associated accessibility changes between Kras-mutant cells from the same subpopulation. j, AP-1 and NR5A2 activity scores are anticorrelated across single-cell epigenetic profiles, separated by subpopulation. Logged activity scores are plotted as a heat map, with cells (columns) ordered by ratio of AP-1/NR5A2 activity within each subpopulation. k, Heat maps showing accessibility signals for the indicated cluster of peaks (columns) identified from bulk ATAC-seq analyses (see Extended Data Fig. 2a) across each major subpopulation of Kras-mutant cells, separated by experimental condition. The colour scale represents the proportion of all cells in each subpopulation and condition with an accessible peak, where values have been z-scored. As above, the first two rows (aggregate) show global accessibility patterns from pooling all individual cells in each condition, regardless of subpopulation; and subpopulation-specific dynamics are shown below. l, Proportion of mKate2+ cells per cluster (marked with colour labels matching Extended Data Fig. 8c) derived from Kras* (grey) or Kras* + injury (orange) tissue conditions.

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Extended Data Fig. 9 Epigenetic dysregulation of IL-33 during injury-facilitated neoplastic transformation.

a, UMAP visualization of the number of total open peaks at the Il33 (bottom) or Cpa1 (top) loci per individual Kras-mutant cell in the scATAC-seq analyses applied to 6,369 individual cells freshly isolated from Kras* or Kras* + injury conditions (n = 1 mice each) and co-embedded together. Peaks nearby proximal or distal elements within 50 kb of the TSS were counted. Colour scale indicates log-transformed counts of open peaks in the vicinity of the TSS. Note increased accessibility at Il33 gene-regulatory loci in dedifferentiated populations, but not in the more differentiated acinar chromatin state or neuroendocrine-like subpopulations. b, scATAC-seq analyses identifies accessibility changes strongly correlated with AP-1/NR5A2 activity ratio across individual Kras-mutant cells isolated from pancreases undergoing early neoplastic cell-fate transitions (Kras* and Kras* + injury conditions). Bottom panels show normalized accessibility values for peaks (rows) displaying a strong (r > 0.1) positive (for example, five Il33-associated peaks) or negative (r < −0.1) (for example, acinar Cpa1-associated peak) correlation with AP-1/NR5A2 activity scores (as in Fig. 4f) across individual Kras-mutant cells (columns, marked with colour labels matching Extended Data Fig. 8b). The identified five switch-correlated peaks (n1–n5) at the Il33 locus overlap with those captured as sensitive to effects of injury and/or mutant Kras in ATAC-seq analyses of bulk populations (see e). c, Signal tracks of the Il33 loci showing rapid ATAC gain (in grey boxes) in the mutant-Kras epithelium after tissue injury in single-cell populations, separated by cluster (three clusters shown) and condition (Kras* versus Kras* + injury). Note that ATAC gains are detected upon injury even within a defined cell cluster (examples marked with dashed lines), supporting bona fide chromatin remodelling at these loci. The five chromatin switch-correlated peaks identified in b are labelled as n1–n5. All tracks show accessibility signals downsampled to same coverage to correct for cell count and sequencing depth disparities across conditions. d, Violin plots showing the AP-1/NR5A2 activity scores of Kras-mutant (mKate2+) pancreatic cells displaying an opened (blue) state for the indicated acinar Cpa1-associated peak, or any of the five chromatin switch-correlated Il33 peaks versus those that do not (green). Il33-accessible cell populations exhibit an enhanced AP-1 activity, whereas Cpa1-accessible cells do not. n = 288, 6,081, 2,228 or 4,141 (from left to right) individual cells obtained from n = 2 mice (Kras*, Kras* + injury conditions). Significance was assessed by unpaired two-tailed Student’s t-test. e, Top, representative ATAC-seq tracks of the Il33 locus in lineage-traced (mKate2+) pancreatic epithelial cells isolated from normal (normal, n = 3 mice), regenerating (injury, n = 5 mice), stochastic neoplasia (Kras*, n = 3 mice), synchronous injury-accelerated neoplasia (Kras* + injury, n = 6 mice) or cancer (PDAC, n = 4 mice) experimental conditions, as described in Fig. 1a. Bottom, independent ChIP–seq experiments (lines) from the 2019 GTRD database summarizing experimentally validated binding of certain AP-1 subunits (and other top-scoring TFs associated with injury transitions) across different cellular contexts to the indicated dynamic ATAC-seq peaks associated with Il33. f, Relative mRNA levels (RNA-seq DESeq2-normalized counts) of Il33 in FACS-sorted mKate2+CD45− cell populations isolated from the indicated tissue states. Data are mean ± s.e.m. of n = 4, 5, 3, 4 or 3 (from left to right) independent biological replicates (mice) per group. g, qRT–PCR analyses validating downregulation of Il33 mRNA in BRD4-suppressed Kras-mutant pancreatic cell populations (mKate2+) isolated from mice (n = 2 per genotype) triggered to undergo synchronous pro-neoplastic transitions after caerulein-induced tissue damage in KCsh mice placed on a doxycycline diet 6 days earlier (as in Extended Data Fig. 4a). Cells were isolated for expression analysis at 48 h after caerulein treatment, that is, matching the Kras* + injury condition of the omics analyses revealing rapid gain in accessibility and expression at the Il33 locus. h, Representative IHC stains of IL-33 protein in normal (left) or metaplastic (middle, right) pancreases expressing shRen or shBrd4 from Kras-wild-type (Csh) or Kras-mutant (KCsh) mice (n = 4 per condition). Metaplasia was induced by treated with caerulein-induced pancreatic injury and analysed 48 h thereafter (as in Extended Fig. 4a). Scale bar, 50 μm. i, Relative Il33 mRNA levels in pancreatic acinar 266-6 (left) or KPflC PDAC (right) cultured cells stably transduced with vectors encoding for the indicated proteins, as assessed by qRT–PCR and normalized to β-actin housekeeping control. Representative results of 2 independent experiments performed with n = 2 biological replicates (wells) each with individual data points shown. j, Multiplexed immunoassay detecting the indicated cytokines or chemokines in protein lysates from normal or Kras-mutant pancreases, two days after the induction of caerulein-induced tissue injury or treatment with PBS (control). n = 2, 3, 4, 2 or 5 (from left to right) independent mice per condition. The bar graphs to the right display pooled data (mean ± s.e.m.) from n = 5 independent mice (Kras* + injury condition), revealing IL-33 as a major pancreas injury ‘alarmin’ induced by the combined effects of Kras gene mutation and tissue damage.

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Extended Data Fig. 10 IL-33 cytokine signalling shapes the transcriptional, chromatin accessibility and histological state of the Kras-mutant pancreatic epithelium.

a, Schematic representation of the experimental design to interrogate the effects of recombinant IL-33 (rIL-33) on the transcriptional, chromatin and phenotypic state of the pancreatic epithelium from Kras-mutant (KC) or wild-type (C) mice. Molecular analyses were performed in lineage-traced (mKate2+) pancreatic epithelial cells purified by FACS sorting from rIL-33- or vehicle-treated mice at day 0 (ATAC-seq), or day 0 and day 21 (RNA-seq) after treatment. b, GSEA comparing the expression of the early chromatin activated gene program identified in the analyses in Fig. 4 (left), or of genes overexpressed in human PDAC specimens compared to healthy human pancreas23 (right), in Kras-mutant cells isolated from rIL-33-treated versus PBS-treated mice (day 21 time point). The chromatin activated neoplasia genes queried are the chromatin-dynamic DEGs identified to be upregulated during injury-accelerated neoplasia (Kras* + injury) and in advanced disease (PDAC) but not during normal regeneration (injury alone) and blunted by BRD4 suppression in metaplastic Kras-mutant cells (KCsh: Kras* + injury). c, d, GSEA comparing the expression of genes induced by the combination of mutant Kras + rIL-33 either in shBrd4 versus shRen Kras-mutant pancreatic epithelial cells (mKate2+) isolated from KCsh (Kras* + injury) (c) or in Kras-mutant populations isolated from caeruelin-treated (Kras* + injury) versus resting (Kras*) KC mice (d). The queried gene sets were identified as significantly upregulated in Kras-mutant pancreatic epithelial cell populations (mKate2+) isolated from rIL-33-treated (versus PBS-treated) mice (KC + rIL-33 versus KC + Veh) at either day 0 (d0) or day 21 (d21) time points. UP, upregulated genes; DN, downregulated genes. e, qRT–PCR analysis of rIL-33 effects in the mRNA levels of acinar differentiation (Cpa1), metaplasia (Sox9) and mutant Kras-dependent neoplasia (Agr2, Muc6) markers in pancreatic epithelial cell (mKate2+) populations isolated from Kras-wild-type (C) or Kras-mutant (KC) mice (n = 2 each) treated with rIL-33 or vehicle (PBS) and analysed 21 days thereafter. f, GSEA comparing the expression of genes induced by the combination of mutant Kras + rIL-33 in samples of human PDAC compared to healthy human pancreas23. g, Volcano plots comparing the chromatin accessibility landscape of Kras-mutant pancreatic epithelium of rIL-33-treated versus vehicle-treated mice, as assessed by ATAC-seq performed at the day 0 time-point. h, Top-scoring motifs identified by HOMER de novo analysis in ATAC gain peaks identified in Kras-mutant pancreatic epithelial cells (mKate2+) isolated from rIL-33-treated mice versus from PBS-treated counterparts, assessed by ATAC-seq analyses performed at the day 0 time point. The significance of the enrichment is shown in brackets. i, Metagene representation of the mean ATAC-seq signal (n = 3 mice per condition) at ATAC gain regions driven by injury in the Kras-mutant pancreatic epithelium (Kras* + injury versus Kras*) (top) or at ATAC gain regions linked to the neoplasia-specific gene activation program (identified in Fig. 4b analyses; right) in Kras-mutant pancreatic epithelial cells (mKate2+) isolated from rIL-33 treated versus PBS-treated mice (n = 3 each, day 0 time point). rIL-33 treatment promotes accessibility at injury-sensitive sites. P values were determined by Kolmogorov–Smirnov test. j, Quantification of the relative number of ADM and PanIN lesions in pancreases from Kras-wild-type (C) mice or Kras-mutant (KC) mice treated with rIL-33 or vehicle (PBS) and analysed at the indicated time points after treatment. Data are mean ± s.e.m. and significance was assessed by unpaired two-tailed Student’s t-test. n = 3, 4, 4, 5, 3 or 4 (from left to right) independent mice per experimental condition. k, Representative immunofluorescence stains of IL-33 protein (green) co-stained with the lineage-tracer marker mKate2 (red) marking pancreatic epithelial cells from mice (n = 3 per group) containing wild-type (normal) or mutant Kras in the indicated tissue states. Scale bar, 100 μm. l, Relative mRNA levels (RNA-seq transcripts per million (TPM) counts) of Il33 (left) or the indicated mutant Kras effector (Agr289, middle) or acinar-specific gene (Cpa1, right) in FACS-sorted mKate2+ pancreatic epithelial cell populations isolated from rIL-33-treated or PBS-treated mice containing wild-type or mutant Kras at the indicated days (d) after treatment. n = 3, 4, 4, 4, 5 or 4 (from left to right) biological replicates (independent mice) per group; median (middle line) and upper and lower quantile values (box limits) per group are indicated.

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Supplementary information

Supplementary Information

This file contains Supplementary Fig. 1 - gating strategies for flow cytometric experiments from genetically-engineered mice, a Supplementary Discussion and Supplementary References.

Reporting Summary

Supplementary Table 1

Description of mouse models and experimental conditions analyzed in genome-wide profiling experiments (RNA-seq, ATAC-seq and scATAC-seq datasets).

Supplementary Table 2

Dynamic peaks between regenerating (injury), early neoplastic (Kras*, Kras* + injury) or malignant (PDAC) epithelial states versus normal counterparts (normal), identified by ATAC-seq in lineage-traced pancreatic epithelial cells isolated from C, KC, KPflC-GEMMs or organoid-transplant models, including ATAC-seq cluster peak annotation of injury and/or mutant Kras-sensitive loci displayed in Extended Data Fig. 2a.

Supplementary Table 3

Pathway enrichment analysis of genes associated with injury and/or mutant Kras-sensitive ATAC-seq clusters.

Supplementary Table 4

DEGs between Kras-wild-type or Kras-mutant pancreatic epithelial cells expressing shBrd4.1448 vs shRen.713 (control) triggered to undergo regenerative metaplasia (Csh: injury) or injury-accelerated neoplastic transformation (KCsh: Kras* + injury), respectively, as identified by RNA-seq analyses in lineage-traced (mKate2+;GFP+) cells. See Extended Data Fig. 4a for experimental details.

Supplementary Table 5

DEGs between regenerating (injury), early neoplastic (Kras*, Kras* + injury) or malignant (PDAC) epithelial states versus healthy normal counterparts (normal), identified by RNA-seq analyses in pancreatic epithelial cells isolated from C, KC, KPflC-GEMMs. Upregulated (UP), downregulated (DN) or non-differentially expressed (NS) genes in each tissue state are annotated depending on whether they exhibit parallel accessibility gain (ATAC-GAIN), accessibility loss (ATAC-LOSS), or  no accessibility change (ATAC-NC) at associated loci in that same condition versus normal.

Supplementary Table 6

Pathway enrichment analysis of ‘chromatin-dynamic’ or ‘chromatin-stable’ DEGs between regenerating (injury), early neoplastic (Kras*, Kras* + injury) or malignant (PDAC) epithelial states versus healthy normal pancreas (normal), separated by ATAC-seq dynamics category. For each experimental tissue state, DEGs were classified into upregulated (DEG-UP) or downregulated (DEG-DN) categories, and then further subdivided depending on whether they exhibit parallel accessibility gain (ATAC-GAIN), accessibility loss (ATAC-LOSS), or no accessibility change (ATAC-NC) at associated loci in that same condition versus normal.

Supplementary Table 7

HOMER analyses of motifs enriched in dynamic cis-regulatory elements associated with ‘chromatin-dynamic DEGs’ between the indicated experimental conditions versus normal. The numbers in the brackets indicate the total number of peaks per category.

Supplementary Table 8

Subpopulation-defining peaks identified in single-cell ATAC-seq (scATAC-seq) analyses. Significantly enriched scATAC-seq peaks in the indicated cell subpopulations of mKate2+ cells isolated from Kras* and Kras* + injury tissue states analyzed together. Subpopulations are labeled S1-S7, with numbers matching those shown in Extended Data Fig. 8b.

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Alonso-Curbelo, D., Ho, YJ., Burdziak, C. et al. A gene–environment-induced epigenetic program initiates tumorigenesis. Nature 590, 642–648 (2021). https://doi.org/10.1038/s41586-020-03147-x

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