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Spatially resolved analysis of pancreatic cancer identifies therapy-associated remodeling of the tumor microenvironment

Nature Genetics (2024)Cite this article

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Abstract

In combination with cell-intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell–cell interactions in human pancreatic cancer associated with neoadjuvant chemotherapy and radiotherapy. We developed spatially constrained optimal transport interaction analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand–receptor gene expression. Our results uncovered a marked change in ligand–receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid coculture system. We identified enrichment in interleukin-6 family signaling that functionally confers resistance to chemotherapy. Overall, this study demonstrates that characterization of the tumor microenvironment using single-cell spatial transcriptomics allows for the identification of molecular interactions that may play a role in the emergence of therapeutic resistance and offers a spatially based analysis framework that can be broadly applied to other contexts.

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Fig. 1: SMI captures pancreatic tumor architecture at subcellular resolution.
Fig. 2: SMI uncovers cell-type diversity in pancreatic cancer.
Fig. 3: SMI reveals glandular heterogeneity and multicellular neighborhoods in pancreatic cancer.
Fig. 4: Deciphering cell–cell interactions using SCOTIA.
Fig. 5: Treatment-associated LR interactions between CAFs and malignant cells.
Fig. 6: Validation analysis of treatment-associated LR interactions between CAFs and malignant cells.
Fig. 7: Substantiation of LR interactions in mouse coculture tumoroids.
Fig. 8: IL-6 family signaling confers chemoresistance in human pancreatic cancer cell lines.

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Data availability

The raw and processed SMI and coculture snRNA-seq data are available via Mendeley Data at https://doi.org/10.17632/kx6b69n3cb.1 (ref. 81) and Zenodo https://doi.org/10.5281/zenodo.7963531 (ref. 82). The snRNA-seq and GeoMx datasets6 are available from GEO: GSE202051 and GSE199102, respectively. The known ligand–receptor databases are from FANTOM5 (https://fantom.gsc.riken.jp/5), CellChat (http://www.cellchat.org) and CellPhoneDB (https://www.cellphonedb.org). Downstream target gene information was from KEGG (https://www.genome.jp/kegg) and TRRUST (https://www.grnpedia.org/trrust). Source data are provided with this paper.

Code availability

The SCOTIA package and our analysis code have been uploaded to Zenodo at https://doi.org/10.5281/zenodo.12707341 (ref. 83) and GitHub (https://github.com/Caochris/SCOTIA).

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Acknowledgements

We thank the patients and families who contributed their time and surgical specimens to this study. We thank K. Cormier for assistance with tissue sectioning; T. Oni (Whitehead Institute/Massachusetts Institute of Technology), D. Tuveson (Cold Spring Harbor Laboratory) for providing mouse pancreatic stellate cell lines; P. Danaher, A. Wardhani, M. Rhodes, S. Wiegel, S. He, E. Rueckert, E. Piazza, S. Stephenson, A. Grootsky and E. Miller from NanoString for assistance with project coordination and data analysis; and D. Moschella, T. Balducci, S. McSorley, S. Sullivan, M. Pivovarov, M. Mues, K. Yee, K. Mercer, J. Teixeira and K. Anderson for administrative and technical support. This work was supported in part by National Institutes of Health/National Cancer Institute (NIH/NCI) grant no. K08CA270417 (W.L.H.), Burroughs Wellcome Fund Career Award for Medical Scientists (W.L.H.), Pancreatic Cancer Action Network Career Development Award (W.L.H.), NIH/NCI grant no. 2P50CA127003 (W.L.H.), Krantz Family Center for Cancer Research Quantum Award (W.L.H., D.T.T.), SU2C-Lustgarten Foundation (T.J., T.S.H., D.T.T.), Robert L. Fine Cancer Research Foundation (D.T.T.), the Evergrande Center (J.C., M.H.), National Institute of Arthritis and Musculoskeletal and Skin Diseases grant no. 5UC2AR081023-02 (J-W.C., M.H.), a Bisconti award from Harvard Medical School (M.H.) and the Helmsley Foundation (J-W.C., M.H.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Carina Shiau, Jingyi Cao.

  2. These authors jointly supervised this work: Martin Hemberg, William L. Hwang.

Authors and Affiliations

  1. Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Carina Shiau, Dennis Gong, Xunqin Yin, Peter L. Wang, Jennifer Su, Steven Wang, Jimmy A. Guo, Nicole A. Lester, Jung Woo Bae, Ryan Zhao, Ralph Weissleder & William L. Hwang

  2. Department of Radiation Oncology, Massachusetts General Hospital, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Carina Shiau, Dennis Gong, Xunqin Yin, Peter L. Wang, Jennifer Su, Steven Wang, Jimmy A. Guo, Nicole A. Lester, Jung Woo Bae, Ryan Zhao, Theodore S. Hong, Jennifer Y. Wo, Hannah Roberts & William L. Hwang

  3. Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Carina Shiau, Dennis Gong, Xunqin Yin, Peter L. Wang, Jennifer Su, Steven Wang, Jimmy A. Guo, Nicole A. Lester, Jung Woo Bae, Ryan Zhao, David T. Ting & William L. Hwang

  4. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Carina Shiau, Dennis Gong, Xunqin Yin, Peter L. Wang, Jennifer Su, Steven Wang, Jimmy A. Guo, Nicole A. Lester, Jung Woo Bae, Ryan Zhao, Martin Hemberg & William L. Hwang

  5. Gene Lay Institute of Immunology and Inflammation, Brigham and Women’s Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Jingyi Cao, Jae-Won Cho & Martin Hemberg

  6. Harvard-MIT Health Sciences and Technology Program, Cambridge, MA, USA

    Dennis Gong

  7. NanoString Technologies, Seattle, WA, USA

    Mark T. Gregory, Jason W. Reeves, Tae Kyung Kim, Youngmi Kim, Nathan Schurman & Joseph M. Beechem

  8. Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Nicholas J. Caldwell, Jamie L. Barth, Maria L. Ganci & Mari Mino-Kenudson

  9. Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Peter L. Wang, Jennifer Su, Steven Wang, Nicole A. Lester, Tyler Jacks & William L. Hwang

  10. Biological and Biomedical Sciences Program, Harvard Medical School, Boston, MA, USA

    Jimmy A. Guo

  11. Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Motaz Qadan & Carlos Fernandez-del Castillo

  12. Department of Medical Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    David T. Ting

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Contributions

C.S., J.C., M.H. and W.L.H. developed the study concept. T.K.K., Y.K., N.S. and J.M.B. performed spatial molecular imaging using a panel including custom probes designed by C.S. and W.L.H. C.S., J.C., M.T.G., J-W.C., P.L.W., J.W.R., M.H. and W.L.H. analyzed and interpreted the spatial molecular imaging data. J.C. and M.H. developed the SCOTIA method with input from C.S. and W.L.H. D.G., S.W., J.S., J.A.G., X.Y., P.L.W., N.A.L., J.W.B., R.Z. and W.L.H. designed, performed and analyzed the in vitro experiments, single-nucleus and bulk RNA-sequencing. M.M.-K., N.J.C., J.L.B. and M.L.G. performed histological analyses and guided sectioning and staining. M.Q., T.S.H., J.Y.W., H.R., C.F.-d.C. and M.M-K. provided clinical insights and access to patient specimens. C.S., J.C., D.G. and M.T.G. generated the tables and figures with guidance from M.H. and W.L.H. Funding for the work was provided by W.L.H., D.T.T., T.J., M.H. and R.W. The study was supervised by W.L.H. and M.H. The manuscript was written by J.C., C.S., D.G., P.L.W., M.H. and W.L.H. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Martin Hemberg or William L. Hwang.

Ethics declarations

Competing interests

W.L.H. and C.S. have received conference travel reimbursements from NanoString Technologies related to presentation of some work in this study. M.T.G., J.W.R., T.K.K., Y.K., N.S. and J.M.B. were employees of NanoString Technologies at the time of their contributions to this study. D.T.T. has received an honorarium from NanoString Technologies, which had technology used in this manuscript. D.T.T. has received consulting fees from ROME Therapeutics and Tekla Capital not related to this work. D.T.T. has received honoraria from Moderna, Ikena Oncology, Foundation Medicine, Inc. 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, Inc., which is not related to this work. D.T.T. receives research support from ACD-Biotechne, PureTech Health LLC, Ribon Therapeutics, AVA LifeScience GmbH and Incyte, which was not used in this work. W.L.H., J.A.G. and T.J. (U.S. Provisional Application No. 63/069,035) and W.L.H., J.A.G., C.S., J.S. and T.J. (U.S. Provisional Application No. 63/346,670) are coinventors on provisional patents related to the pancreatic cancer states used in this study. The interests of W.L.H. and D.T.T. were reviewed and are managed by Mass General Brigham in accordance with their conflict of interest policies. T.J. is a member of the Board of Directors of Amgen and Thermo Fisher Scientific, and a co-Founder of Dragonfly Therapeutics and T2 Biosystems. T.J. serves on the Scientific Advisory Board of Dragonfly Therapeutics, SQZ Biotech and Skyhawk Therapeutics. T.J. is also President of Break Through Cancer. His laboratory currently receives funding from Johnson & Johnson, but these funds did not support the research described in this manuscript. All other authors declare no interests related to this work.

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Extended data

Extended Data Fig. 1 Spatial molecular imaging experimental workflow and probe set.

a, Schematic of the spatial molecular imaging RNA assay workflow. RNA targets in the FFPE tissue slide that are bound to in situ hybridization (ISH) probes are subject to cyclic readout of 16 sets of reporters conjugated to four different fluorophores, which bind to the different reporter-landing domains on the ISH probes. High-resolution images are acquired during each round of reporter hybridization. Fluorophores are then UV cleaved and washed off the reporters before the slide is incubated with the next set of reporters. b, Gene overlap between the seven malignant lineage programs6, the base 960-plex probe set and the 30 custom probes (color legend).

Extended Data Fig. 2 Comparison of cell segmentation and cell type annotation results from different segmentation methods.

a, Overlap of cell boundaries identified by Cellpose (red) and Baysor (blue). b, Barplot showing the adjusted mutual information score across transcripts for each sample. U, untreated; T, treated; b, base panel; a, augmented panel. c, UMAP visualization of the non-malignant cells segmented by Cellpose (left) and Baysor (right). d, Two representative FOVs showing the spatial distributions of all annotated cells (left, Cellpose; right, Baysor). e, Bubble heatmap showing expression levels of select marker genes for annotated cell types (top, Cellpose; bottom, Baysor). f, Comparison of cell type counts (top) and proportions (bottom) between Cellpose (red) and Baysor (blue). g, Heatmap showing the confusion matrix of cell types annotated for cells segmented with Cellpose (x axis) and Baysor (y axis).

Extended Data Fig. 3 Cell type composition across untreated and treated pancreatic cancer samples.

a, UMAP showing subsets of vascular, lymphoid, and myeloid cells. b, Proportions of major cell types (from Fig. 2c-d) across untreated and treated tumors with (left) or without (right) malignant cells included. U, untreated; T, treated; b, base 960-plex panel; a, augmented 990-plex panel. c, Comparison of malignant/non-malignant cell numbers in treated and untreated FOVs (untreated: n = 164; treated: n = 136); two-sided Mann-Whitney U test. d, Proportions of major non-malignant cell types in treated and untreated FOVs (untreated: n = 164; treated: n = 136); Benjamini-Hochberg corrected two-sided Mann-Whitney U tests. e, Comparison of silhouette scores between subsetting malignant cells (n = 27,463) into two (CLS and BSL) or three (CLS, BSL, and NRP) subtypes. Two-sided Mann-Whitney U tests. f, Expression of chemokines with a role in neutrophil recruitment (CXCL1/2/3/5/6/8) from CAFs and malignant cells in treated and untreated FOVs (untreated: n = 164; treated: n = 136); Benjamini-Hochberg corrected two-sided Mann-Whitney U tests. For boxplots in panel c-f, the box limits denote the first and third quartiles, with the median shown in the center and whiskers covering data within 1.5× the interquartile range from the box, with diamonds representing outliers.

Extended Data Fig. 4 Glandular heterogeneity and multicellular neighborhoods in pancreatic cancer.

a, Ten representative side-by-side comparisons between gland assignments manually annotated by a board-certified pathologist (outlined in red) versus those extracted by the DBSCAN algorithm (outlined in different colors). White arrows highlight differences in gland annotations for six highly concordant example FOVs. b, Distribution of the number of cells across malignant glands. c, Observed (black) and expected (gray) distributions of the interspersion of malignant glands, subset to glands in the top quartile of heterogeneity. d, Log2 fold change (y axis) between the observed and expected summed exponential-transformed distances between CAF subtypes and malignant cells across varying decay radii (x axis) for n = 1000 permutations. e, Left: Depiction of the summed exponential functions (z axis height and color bar, decay radius r = 50 μm) that are generated from CD8 T cells for a representative FOV, with spatial locations of malignant subtypes shown as colored dots. Right: Log2 fold change (y axis) between the observed and expected summed exponential-transformed distances between malignant subtypes and CD8 T cells (using a malignant-centric model) for n = 1000 permutations6. f-g, Log2 fold change between the observed and expected summed exponential-transformed distances between malignant subtypes and CD8 T cells (using a malignant-centric model), for varying decay radii (f) and varying quantile thresholds (g), for n = 1000 permutations. Data for (d-g) are presented as mean values ± 95% confidence interval. Color legends for (f) are shared with (g). Significant results for one-sided permutation (p < 0.001) and two-sided K-S (p < 10−16) tests in panels (c-g) are indicated with square and circle symbols, respectively. Statistical test legends for panels (d-g) are shared with panel (c). h-i, Depiction of the summed exponential functions that are generated from CD8 T cells for varying decay radii r (h) and with spatial locations of shuffled malignant subtype annotations, which serves as the null distribution (i).

Extended Data Fig. 5 Performance evaluation of SCOTIA using simulated datasets from SRTsim.

a, The ability of different permutation strategies to identify LR interactions using reference-based (top) and reference-free (bottom) simulated datasets. Strategy A: shuffle gene expression within each cell type; strategy B: shuffle gene expression across all cell types; strategy C: shuffle gene expression across all cell types and permute cell locations. For reference-based simulations, the PDAC SMI dataset was used as the reference. Each scenario contains 3,000 cells across 8 cell types. Gene expression profiles were simulated using negative binomial models with parameters estimated based on the specific cell type in the reference data. To simulate cell type A interacting with cell type B via ligand (L) on A and receptor (R) on B, we randomly assigned the 422 known L-R pairs to specific cell type pairs (A-B), then we increased the expression of the L gene in cell type A and the R gene in adjacent cell type B to a fixed fold change (ranging from 1 to 7). Adjacent cells were defined as the nearest four cells. For reference-free simulations, we used SRTsim to randomly generate cell locations and gene expression profiles with default settings. Each simulation scenario was replicated 5 times. Performance was measured by sensitivity, specificity, F1 score, and precision. Reg is the entropy regularization term, the default is 1.0; regm is the marginal relaxation term, the default is 2.0; Dist_cutff is the distance cutoff for defining ‘adjacent’ cell clusters, the default is 50 µm. b, Performance evaluation of SCOTIA with varied parameters using reference-based simulation datasets from panel a. Fold change of true ligand receptor pairs was set to 2 (top) and 5 (bottom). Error bars in panel a and b indicating standard deviations.

Extended Data Fig. 6 The permutation test strategy used in SCOTIA.

a, Pearson correlation of receptor gene expression between malignant subtypes or ligand gene expression between CAF subtypes (two-sided t test). b, Schematic of the permutation test model used for spatial molecular imaging data. The null distribution was established by randomizing cell locations within a small range (from -20 to 20 µm) while shuffling gene expression in each FOV. c, Neighborhood composition for each cell type from one example FOV with the original (left), permuted (middle) and shuffled negative control (right) data. The negative control was constructed by shuffling cell type labels without any constraints. Neighborhood cells were defined as cells within a radius of 30 µm. d, Boxplots showing the Jaccard index of the top 5% most likely interacting LRs inferred between CAF and malignant cells with varying reg (left) and regm (right) parameters, n = 16. For boxplots, the box limits denote the first and third quartiles, with the median shown in the center and whiskers covering data within 1.5× the interquartile range from the box, with diamonds representing outliers. e, The top five strongest interacting cell type pairs inferred by using cost function Eq. 6 (left) and 7 (right) (Methods). Dot size represents the number of permutation test-significant LR pairs, colored based on the average LR interaction score. Bar plot indicates the average interaction strengths of each cell type pair for the treated and untreated groups. U, untreated; T, treated; b, base 960-plex panel; a, augmented 990-plex panel. f, The top five strongest interacting cell type pairs inferred by using different permutation strategies.

Extended Data Fig. 7 Pathway enrichment and target gene analysis for the SMI data.

a, Ligand–receptor (LR) interactions significantly up- or down- regulated in treated samples between CAF and malignant cells, related to Fig. 5a. The cost functions used were Eq. 6 (left) and 7 (right) (Methods). Benjamini-Hochberg corrected two-sided Mann-Whitney U tests. b, Differentially enriched LR interactions inferred with a mixed effects model test (two-sided, Benjamini-Hochberg adjusted p value, Methods). c, Comparison of ligand and receptor gene expression between SCOTIA-inferred interactions versus non-interacting CAF–malignant cell pairs. Two-sided Mann-Whitney U tests (CLCF1–CNTFR: untreated, n = 158; treated: n = 89. WNT5A–FZD5: untreated, n = 170; treated: n = 98). d, Pathway enrichment analysis with ligand (left) or receptor (right) gene sets from Fig. 5b. Top pathways enriched in untreated (purple) and treated (red) tumors are shown. Benjamini-Hochberg corrected one-sided Fisher’s exact test. e, Boxplots summarizing the difference in target gene expression between significantly up- and down-regulated LR groups for five other abundant cell type pairs. Two-sided Mann-Whitney U test. Malignant–CAF, up: n = 82; down: n = 6. Malignant–macrophage, up: n = 30; down: n = 3. CAF–macrophage, up: n = 20; down: n = 19. Macrophage–CAF, up: n = 56; down: n = 29. Macrophage–malignant, up: n = 10; down: n = 3. For boxplots in panel c and e, the box limits denote the first and third quartiles, with the median shown in the center and whiskers covering data within 1.5× the interquartile range from the box, with diamonds representing outliers.

Extended Data Fig. 8 Pathway enrichment analysis for the co-culture tumoroid data.

a, Pathway enrichment analysis with the ligand gene set (from CAFs, left) or receptor gene set (from malignant cells, right) that were significantly higher (red) or lower (purple) in the treated versus treatment-naïve co-culture tumoroids, related to Fig. 7c. Benjamini-Hochberg corrected one-sided Fisher’s exact test.

Extended Data Fig. 9 Association between IL6 family signaling, inflammatory CAFs and specific malignant subtypes.

a, Log2 fold change (y axis) between the observed and expected summed exponential-transformed distances between malignant subtypes and iCAFs across varying decay radii (x axis) for n = 1000 permutations. Data are presented as mean values ± 95% confidence interval. Significant results of one-sided permutation (p < 0.001) and two-sided K-S (p < 10−16) tests are indicated with square and circle symbols, respectively. b, Proportions of CAF subtypes per FOV (n = 320) in SMI (left) and per sample (n = 43) in single-nucleus RNA-seq6 (right) datasets, stratified by treatment. Box limits denote the first and third quartiles, median shown in the center, and whiskers cover data within 1.5× interquartile range, with diamonds representing outliers. Two-sided two-sample Mann-Whitney U test. c, Number of transcripts of specific IL6 family ligands expressed in CAFs (y axis) in the SMI dataset, stratified by CAF subtype. iCAF: n = 102934. myCAF: n = 149349. Data are presented as mean values ± 95% confidence interval. Two-sided two-sample Mann-Whitney U test.

Extended Data Fig. 10 Performance evaluation of SCOTIA with varied parameters using PDAC SMI and co-culture snRNA-seq datasets.

a, The number of significant LRs enriched in treated samples as a function of varying SCOTIA parameters; adjusted p < 0.05, two-sided Mann-Whitney U. b, The power (y axis) of SCOTIA with varied parameters for identifying treatment-enriched ligand-receptor (LR) interactions. Ground truth was defined with the co-culture snRNA-seq dataset. True LR interactions were defined as those exhibiting enrichment in treated tumoroids. Both ligand and receptor genes were required to display higher expression in treated compared to untreated samples, with at least one gene having a fold change >1.5. Conversely, false LR interactions were defined by enrichment in untreated samples with the same criteria. Performance was measured by sensitivity, specificity, F1 score, and precision. Reg is the entropy regularization term, the default is 1.0; Regm is the marginal relaxation term, the default is 2.0; Dist_cutoff is the distance cutoff for defining ‘adjacent’ cell clusters, the default is 50 µm.

Supplementary information

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Supplementary Figs. 1–4 and Tables 1–4.

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Supplementary Data 1

Full SCOTIA results.

Supplementary Data 2

SMI probe sequences.

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Shiau, C., Cao, J., Gong, D. et al. Spatially resolved analysis of pancreatic cancer identifies therapy-associated remodeling of the tumor microenvironment. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01890-9

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