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Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and treatment-refractory cancer. Molecular stratification in pancreatic cancer remains rudimentary and does not yet inform clinical management or therapeutic development. Here, we construct a high-resolution molecular landscape of the cellular subtypes and spatial communities that compose PDAC using single-nucleus RNA sequencing and whole-transcriptome digital spatial profiling (DSP) of 43 primary PDAC tumor specimens that either received neoadjuvant therapy or were treatment naive. We uncovered recurrent expression programs across malignant cells and fibroblasts, including a newly identified neural-like progenitor malignant cell program that was enriched after chemotherapy and radiotherapy and associated with poor prognosis in independent cohorts. Integrating spatial and cellular profiles revealed three multicellular communities with distinct contributions from malignant, fibroblast and immune subtypes: classical, squamoid-basaloid and treatment enriched. Our refined molecular and cellular taxonomy can provide a framework for stratification in clinical trials and serve as a roadmap for therapeutic targeting of specific cellular phenotypes and multicellular interactions.

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Fig. 1: snRNA-seq of untreated and treated PDAC captures representative diversity of cell types.
Fig. 2: Epithelial cell-type composition and inferred pseudotemporal trajectory includes putative acinar-ductal metaplasia and atypical ductal intermediates.
Fig. 3: Molecular stratification of malignant cells in PDAC reveals a NRP program that is enriched in genes associated with the nervous system and perineural invasion (PNI).
Fig. 4: Molecular stratification of CAFs in PDAC.
Fig. 5: The NRP program is enriched in residual tumor and patient-derived organoids after cytotoxic therapy and is associated with poor clinical outcomes.
Fig. 6: Spatial mapping of malignant and CAF programs reveals program-specific associations with intra- versus intertumor heterogeneity.
Fig. 7: Spatial analysis of malignant programs, CAF programs and immune cell composition reveals three distinct multicellular communities and treatment-associated receptor–ligand interactions.
Fig. 8: Refined molecular taxonomy of PDAC.

Data availability

All figures are associated with raw data. Raw images for MIBI and NanoString GeoMx experiments are publicly available on Science Data Bank (https://doi.org/10.57760/sciencedb.01706). Raw and processed sequencing data (single-nucleus RNA-seq) for patient-derived tumors and organoids, and NanoString GeoMx reads and count matrices have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession numbers GSE202051 and GSE199102, respectively. Raw snRNA-seq data are also available in the controlled access repository DUOS (https://duos.broadinstitute.org/) under dataset ID 000139. Processed snRNA-seq data are also available on the Single Cell Portal at https://singlecell.broadinstitute.org/single_cell/study/SCP1089 (untreated) and https://singlecell.broadinstitute.org/single_cell/study/SCP1096 (treated)

Code availability

All code has been made available in GitHub at https://github.com/karthikj89/humanpdac116 and https://github.com/whwanglab/PDAC117.

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Acknowledgements

We are grateful to the patients and families who contributed their time and surgical specimens to this study. We thank L. Gaffney for assistance with preparing figures; E. Rueckert from NanoString for assistance with instrumentation; J. Ptacek from Ionpath for assistance with MIBI; and K. Yee, J. Teixeira, K. Anderson, M. Magendantz, K. Mercer, W. Rideout, B. Li, P. Westcott, C. McCabe, N. Sharif and J. Pfiffner-Borges for administrative and technical support. This work was supported in part by the Ludwig Institute for Cancer Research (A.R.), Klarman Cell Observatory (A.R. and R.X.), Lustgarten Foundation (T.J.), American Society for Clinical Oncology/Conquer Cancer Foundation Young Investigator Award (W.L.H), Hopper-Belmont Foundation Inspiration Award (W.L.H.), American Cancer Society/Massachusetts General Hospital Institutional Research Grant (W.L.H.), UCSF Dean’s Yearlong Fellowship (J.A.G.), Early Postdoc Mobility Fellowship (no. P2ZHP3 181475) from the Swiss National Science Foundation (D.S.), SU2C-Lustgarten Foundation (T.S.H. and D.T.T.) and the Robert L. Fine Cancer Research Foundation (D.T.T.). This study was also conducted with support of the Ontario Institute for Cancer Research (PanCuRx Translational Research Initiative) through funding provided by the Government of Ontario, the Wallace McCain Centre for Pancreatic Cancer supported by the Princess Margaret Cancer Foundation, the Terry Fox Research Institute, the Canadian Cancer Society Research Institute and the Pancreatic Cancer Canada Foundation. W.L.H. is an Andrew L. Warshaw, M.D. Institute for Pancreatic Cancer Research Fellow. D.S. is a Damon Runyon Cancer Research Fellow (DRQ-03-20). R.K.J. receives support from an NCI Cancer Moonshot grant (U01-CA224348) and the Ludwig Cancer Center at Harvard. T.J. and A.R. were investigators of the Howard Hughes Medical Institute (HHMI) during the time this work was performed but are no longer affiliated with HHMI. T.J. is David H. Koch Professor of Biology and a Daniel K. Ludwig Scholar. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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W.L.H., K.A.J., J.A.G., H.I.H., C.F.C., A.S.L., O.R.R., T.J. and A.R. developed the study concept and designed the experiments. W.L.H., E.D. and N.V.W. developed and optimized the snRNA-seq method for pancreatic cancer specimens. W.L.H., J.A.G., H.I.H., E.D., N.V.W., D.D., J.W., M.C., L.N., T.D. and D.P. performed snRNA-seq experiments, with guidance from O.R.R. K.A.J., W.L.H., J.A.G., H.I.H., C.S. and A.R. analyzed the snRNA-seq data with computational assistance from P.Y., R.M., O.A., J.G., C.L., C.P., A.T. and Z.E. and scientific insights from J.M.S., W.A.F-P., A.M., D.T.T., A.J.A. and R.K.J. W.L.H., G.E., J.A.G., H.I.H., C.R., D.C., J.R., P.Z., C.F.C. and A.S.L. acquired patient-derived tumor specimens with clinical input from M.M-K., A.J.A., T.S.H., J.Y.W., C.R.F., M.Q., D.T.T., C.D.W., D.P.R and J.S.L. W.L.H., C.F.C. and A.S.L. curated clinical data. K.A.J., W.L.H. and H.I.H. performed survival analyses on bulk RNA-seq data from TCGA and PanCuRx/ICGC with statistical assistance from D.S.N. S.L.F. and D.A. performed MIBI experiments, and D.S., P.Y., K.A.J., W.L.H., J.A.G. and H.I.H. analyzed the data. H.I.H., W.L.H., J.A.G., P.D., E.M., D.R.Z., K.F., J.M.B. and R.F. performed the DSP experiments with assistance from V.J. and J.H.C. P.Y., J.W.R., D.R.Z., W.L.H, H.I.H. and P.D. analyzed the DSP data with input from M.J.A. J.L.B., M.K. and M.M.-K. provided histological sections for MIBI and DSP experiments. M.M-K. performed histopathological analyses. J.S., W.L.H. and J.A.G. performed the multiplexed immunofluorescence. J.A.G., W.L.H., G.E., C.S. and P.Y. performed and analyzed the patient-derived organoid experiments. K.A.J., H.I.H., J.W.R., P.Y., J.A.G., C.S. and W.L.H. generated the tables and figures. Funding for the work was provided by A.R., T.J., R.X., D.T.T. and W.L.H. W.L.H., K.A.J., J.A.G., H.I.H., P.Y., J.W.R., D.T.T., T.J. and A.R. wrote the manuscript, and all authors reviewed the manuscript.

Corresponding authors

Correspondence to Tyler Jacks or Aviv Regev.

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Competing interests

A.R. is a cofounder and equity holder of Celsius Therapeutics, an equity holder in Immunitas, and was an SAB member of Thermo Fisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics and Asimov. From 1 August 2020, A.R. is an employee of Genentech. T.J. is a member of the Board of Directors of Amgen and Thermo Fisher Scientific. He is also a cofounder of Dragonfly Therapeutics and T2 Biosystems. T.J. serves on the Scientific Advisory Board of Dragonfly Therapeutics, SQZ Biotech and Skyhawk Therapeutics. He is President of Break Through Cancer. None of these affiliations represent a conflict of interest with respect to the design or execution of this study or interpretation of data presented in this manuscript. T.J.’s laboratory also currently receives funding from Johnson & Johnson Lung Cancer Initiative, but this funding did not support the research described in this manuscript. D.T.T. has received consulting fees from NanoString Technologies, which was used in this work. D.T.T. has received consulting fees from ROME Therapeutics, Foundation Medicine, EMD Millipore Sigma and Pfizer that are not related to this work. D.T.T. is a founder and has equity in ROME Therapeutics, PanTher Therapeutics and TellBio, which is not related to this work. D.T.T. receives research support from ACD-Biotechne, PureTech Health and Ribon Therapeutics, which was not used in this work. M.M.-K. has served as a compensated consultant for H3 Biomedicine and AstraZeneca and received a research grant (to institution) from Novartis that is not related to this work. R.K.J. received consultant fees from Elpis, Pfizer, SPARC and SynDevRx; owns equity in Accurius, Enlight and SynDevRx; serves on the Board of Trustees of Tekla Healthcare Investors, Tekla Life Sciences Investors and Tekla World Healthcare Fund; and received a Research Grant from Boehringer Ingelheim (all not related to this work). The interests of D.T.T., M.M.-K. and R.K.J. were reviewed and are managed by Massachusetts General Hospital and Mass General Brigham in accordance with their conflict of interest policies. O.R.R. is a co-inventor on patent applications filed by the Broad Institute for inventions related to single-cell genomics. O.R.R. is an employee of Genentech since 19 October 2020. W.L.H., K.A.J., J.A.G., H.I.H., T.J. and A.R. are co-inventors on U.S. Provisional Patent Application no. 63/313,596 (related to this work). All other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Cell type and inferred CNA composition across PDAC tumors.

a, UMAP embeddings of single-nucleus profiles (dots) from individual tumors (panels) from untreated (left) and treated (right) patients colored by post hoc cell-type annotations (color legend). b, Example inferCNV analysis of the epithelial subset from a study specimen. Inferred amplifications (red) and deletions (blue) based on expression (color bar) of sliding 100-gene window in each chromosomal locus (columns) from each cell (rows) labeled by its annotated cell type (color code). c, Inferred CNA frequencies in the snRNA-seq cohort have similar distribution as those derived from TCGA genomic study1. Frequency (y axis) of CNAs on each chromosome arm (x axis) as inferred across the patients in the snRNA-seq cohort (light green bars) and from genome analysis of PDAC (dark green bars) from the TCGA cohort. d, Proportion of cells (y axis) in each of the four major compartments (color legend, top) or immune cell subsets (color legend, bottom) as estimated by snRNA-seq or MIBI (x axis) in each matched untreated (left; n = 5) or treated (right; n = 2) tumor.

Extended Data Fig. 2 Treatment associated with distinct cell type proportions across compartments.

Proportions (y axis) of cell types (x axis) in untreated (n = 18), CRT (n = 14), or CRTL (n = 5) tumors out of all nonmalignant cells (top left) or in specific nonmalignant cell compartments in the tumor. The boxes indicate upper and lower quartiles, with the horizontal lines marking the means. The lines extending vertically from the boxes (whiskers) indicate the maximum and minimum values excluding outliers. Data points are plotted as circles. * Bonferroni adjusted p < 0.05, two-sided Mann–Whitney U test. Exact p-values for significant comparisons were nonmalignant myeloid CRT-CRTL = 0.016344, epithelial (nonmalignant) ductal CRT-CRTL = 0.031683, lymphoid Treg untreated-CRT = 0.033156, immune CD8+ T CRT-CRTL = 0.03507, immune Treg untreated-CRT = 0.022686.

Extended Data Fig. 3 Impact of treatment on differential gene expression in immune cells, malignant cells, and CAFs.

a, Differential expression (β-value, x axis, Poisson mixed-effect linear regression model, lme4 R package) and its significance (-log10(adjusted p-value), y axis) for CD8+ T cells (top row), dendritic cells (second row), Tregs (third row) and macrophages (bottom row, color legend) in CRT vs. untreated (left), CRTL vs. untreated (middle), and CRTL vs. CRT (right) tumors. Selected enriched or depleted genes are labeled. Bonferroni adjusted p-value < 0.05 is indicated with a dotted horizontal line. b, Differential expression (β-value, x axis, Poisson mixed-effect linear regression model, lme4 R package) and its significance (-log10(adjusted p-value), y axis) for malignant cells (top row) and CAFs (bottom row, color legend) in CRT vs. untreated (left), CRTL vs. untreated (middle), and CRTL vs. CRT (right) tumors. Selected enriched or depleted genes are labeled. Bonferroni adjusted p-value < 0.05 is indicated with a dotted horizontal line.

Extended Data Fig. 4 Prior signatures derived primarily from the bulk setting insufficiently delineate cells from snRNA-seq.

a, Malignant cell signatures. UMAP embeddings of single nucleus profiles (dots) from all tumor nuclei (top panels) or only malignant cells (bottom panels) colored by expression score (color bar, Methods) of signatures derived from the Bailey2, Collisson3, Moffitt4, and Chan-Seng-Yue5 studies. b, CAF signatures. UMAP embeddings of single nucleus profiles (dots) from all fibroblast nuclei colored by normalized expression score (color bar, Methods) of myCAF, apCAF, and iCAF signatures6 and well as cross-tissue fibroblast lineage signatures (COL3A1+ myofibroblast, LRRC15+ myofibroblast, CCL19+ colitis, ADAMDEC1+ colitis, NPNT+ alveolar, and PI16+ adventitial)7.

Extended Data Fig. 5 Stability, error and power in selection of programs in consensus NMF.

a, Estimated stability (blue, left y axis) and error (red, right y axis) in the cNMF solution learned with different numbers of programs (k, x axis) for malignant cells (left) and CAFs (right). b, Number of malignant (out of 14; left) and CAF (out of 4; right) programs recovered in the cNMF solution learned with a different proportion of samples (x axis) subsampled from our cohort.

Extended Data Fig. 6 Correlation among malignant cell or CAF expression programs.

Correlation (color bar) among expression scores of malignant state and lineage programs across all malignant nuclei (a) or fibroblast programs across all fibroblast nuclei (b).

Extended Data Fig. 7 Enrichment of malignant cell and CAF programs in genes differentially expressed with treatment regimen.

Fold enrichment of overlap (x axis) between gene program signatures (top 200 genes; rows) and genes differentially expressed (q < 0.05) in CRT (n = 14) vs. untreated (n = 18) (left), CRTL (n = 5) vs. untreated (middle), or CRTL vs. CRT (right). * Bonferroni adjusted p < 0.05, two-sided hypergeometric test. Exact p-values for significant comparisons were untreated-CRT: CYS-untreated = 8.54 × 10−6, CYS-CRT = 7.36 × 10−4, CYG-untreated = 9.53 × 10−7, CYG-CRT = 5.96 × 10−4, MYC-CRT = 3.72 × 10−2, ADH-M-CRT = 2.95 × 10−2, RBS-untreated = 2.25 × 10−2, TNF-CRT = 5.61 × 10−3, ACN-untreated = 2.87 × 10−2, ACN-CRT = 1.51 × 10−6, CLS-untreated = 4.77 × 10−5, CLS-CRT = 3.72 × 10−2, BSL-untreated = 2.44 × 10−2, BSL-CRT = 2.65 × 10−5, SQM-CRT = 5.10 × 10−3, MES-untreated = 2.16 × 10−3, MES-CRT = 1.09 × 10−4, NEN-untreated = 4.83 × 10−3, NRP-untreated = 2.58×10−3, NRP-CRT = 6.06 × 10−10, ADH-F-untreated = 1.03 × 10−8, ADH-F-CRT = 3.68 × 10−43, IMM-untreated = 6.12 × 10−6, IMM-CRT = 5.24 × 10−8, MYO-untreated = 3.66 × 10−72, MYO-CRT = 5.60 × 10−4, NRT-untreated = 4.56 × 10−4, NRT-CRT = 5.60 × 10−7; untreated-CRTL: CYS-untreated = 9.45 × 10−19, CYS-CRT = 4.91 × 10−3, CYG-untreated = 1.04 × 10−13, ADH-M-CRT = 1.10 × 10−21, RBS-untreated = 7.60 × 10−3, RBS-CRT = 4.91 × 10−3, IFN-CRT = 4.49 × 10−2, ACN-untreated = 9.62 × 10−3, ACN-CRT = 4.14 × 10−6, SQM-untreated = 2.65 × 10−16, MES-CRT = 9.59 × 10−6, NEN-untreated = 1.43 × 10−2, NRP-untreated = 6.79 × 10−3, NRP-CRT = 2.74 × 10−20, ADH-F-untreated = 7.60 × 10−21, ADH-F-CRT = 1.54 × 10−151, IMM-untreated = 2.58 × 10−9, MYO-untreated = 4.80 × 10−6, MYO-CRT = 2.09 × 10−5, NRT-untreated = 2.57 × 10−10; CRT-CRTL: MYC-CRT = 3.07 × 10−2, ADH-M-CRTL = 4.17 × 10−8, IFN-CRTL = 1.99 × 10−2, ADH-F-CRT = 7.16 × 10−7, ADH-F-CRTL = 5.88 × 10−60, IMM-CRTL = 2.56 × 10−2.

Extended Data Fig. 8 Multivariable Cox regression analysis for overall survival in TCGA and PanCuRx/ICGC PDAC cohorts.

Hazard ratios ± 95% confidence interval (middle) and p-values (right) for each variable (clinicopathologic and program expression score in bulk RNA-seq, rows) in multivariable Cox regression model for overall survival (OS), based on a cohort of 269 patients with untreated, resected primary PDAC profiled by RNA-seq in TCGA and PanCuRx/ICGC.

Extended Data Fig. 9 Digital spatial profiling with whole-transcriptome atlas (WTA) enables accurate mapping of cell type signatures in space as a complement to snRNA-seq.

a, Immunofluorescence images of FFPE sections from all PDAC specimens analyzed using whole-transcriptome DSP (n = 21 independent tumors) separated by treatment status (top, untreated; bottom, treated). Color legend indicates target of fluorophore-conjugated antibodies. b, Expression (z-score of normalized counts across segments; purple/yellow color bar) of signature genes (rows) from different cell types (color legend 3 and left color bar 3) across segments (columns, color legend 2 and horizontal color bar 2) and treatment regimens (columns, grayscale legend 1 and horizontal grayscale bar 1) profiled by WTA, capturing epithelial (green), fibroblasts (blue) and immune (red) cells. Columns and rows are clustered by unsupervised hierarchical clustering. c, Pearson correlation coefficient (color bar) of the scores of each CAF, malignant, and immune feature in snRNA-seq (rows, columns) across patient tumors. Rows and columns are ordered by hierarchical clustering.

Extended Data Fig. 10 snRNA-seq captures a greater diversity and abundance of cell types relative to prior single-cell approaches.

Number of nuclei/cells per untreated tumor that passed quality control filters (y axis) in our study (n = 18) vs. Peng et al. study (n = 24)69 (grayscale legend), in total (left) and partitioned by cell type (right). The boxes indicate upper and lower quartiles, with the horizontal lines marking the means. The lines extending vertically from the boxes (whiskers) indicate the maximum and minimum values excluding outliers. Data points are plotted as solid circles. *Bonferroni adjusted p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, two-sided Mann–Whitney U test. Exact p-values for significant comparisons were all cell types = 5.76 × 10−9, lymphoid = 5.54 × 10−5, myeloid = 7.25 × 10−3, CAF = 3.70 × 10−3, pericyte = 5.22 × 10−11, vascular smooth muscle = 1.09 × 10−9, endocrine = 1.84 × 10−2, endothelial = 4.00 × 10−2.

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Hwang, W.L., Jagadeesh, K.A., Guo, J.A. et al. Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment. Nat Genet 54, 1178–1191 (2022). https://doi.org/10.1038/s41588-022-01134-8

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