Squamous trans-differentiation of pancreatic cancer cells promotes stromal inflammation

  1. Tim DD Somerville
  2. Giulia Biffi
  3. Juliane Daßler-Plenker
  4. Stella K Hur
  5. Xue-Yan He
  6. Krysten E Vance
  7. Koji Miyabayashi
  8. Yali Xu
  9. Diogo Maia-Silva
  10. Olaf Klingbeil
  11. Osama E Demerdash
  12. Jonathan B Preall
  13. Michael A Hollingsworth
  14. Mikala Egeblad
  15. David A Tuveson
  16. Christopher R Vakoc  Is a corresponding author
  1. Cold Spring Harbor Laboratory, United States
  2. University of Nebraska, United States
  3. University of Nebraska Medical Center, United States

Abstract

A highly aggressive subset of pancreatic ductal adenocarcinomas undergo trans-differentiation into the squamous lineage during disease progression. Here, we investigated whether squamous trans-differentiation of human and mouse pancreatic cancer cells can influence the phenotype of non-neoplastic cells in the tumor microenvironment. Conditioned media experiments revealed that squamous pancreatic cancer cells secrete factors that recruit neutrophils and convert pancreatic stellate cells into cancer-associated fibroblasts (CAFs) that express inflammatory cytokines at high levels. We use gain- and loss-of-function approaches to show that squamous-subtype pancreatic tumor models become enriched with neutrophils and inflammatory CAFs in a p63-dependent manner. These effects occur, at least in part, through p63-mediated activation of enhancers at pro-inflammatory cytokine loci, which includes IL1A and CXCL1 as key targets. Taken together, our findings reveal enhanced tissue inflammation as a consequence of squamous trans-differentiation in pancreatic cancer, thus highlighting an instructive role of tumor cell lineage in reprogramming the stromal microenvironment.

Data availability

The RNA-seq and ChIP-seq data in this study is available in the Gene Expression Omnibus database https://www.ncbi.nlm.nih.gov/geo/ with accession number GSE140484.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Tim DD Somerville

    Cancer and Molecular Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  2. Giulia Biffi

    Cancer and Molecular Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  3. Juliane Daßler-Plenker

    Cancer and Molecular Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  4. Stella K Hur

    Cancer and Molecular Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  5. Xue-Yan He

    Cancer and Molecular Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  6. Krysten E Vance

    Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska, Omaha, United States
    Competing interests
    No competing interests declared.
  7. Koji Miyabayashi

    Cancer and Molecular Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  8. Yali Xu

    Cancer and Molecular Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  9. Diogo Maia-Silva

    Cancer and Molecular Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  10. Olaf Klingbeil

    Cancer and Molecular Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  11. Osama E Demerdash

    Cancer and Molecular Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  12. Jonathan B Preall

    Cancer and Molecular Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  13. Michael A Hollingsworth

    Eppley Institute, University of Nebraska Medical Center, Omaha, United States
    Competing interests
    No competing interests declared.
  14. Mikala Egeblad

    Cancer and Molecular Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  15. David A Tuveson

    Cancer and Molecular Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    David A Tuveson, an advisor to Surface, Leap, and Cygnal and has stock ownership in Surface and Leap.
  16. Christopher R Vakoc

    Cancer and Molecular Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    For correspondence
    vakoc@cshl.edu
    Competing interests
    Christopher R Vakoc, has received funding from Boehringer-Ingelheim and is an advisor to KSQ Therapeutics..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1158-7180

Funding

New York State Department of Health (C150158)

  • Tim DD Somerville

National Cancer Institute (U10CA180944)

  • David A Tuveson

National Cancer Institute (U01CA210240)

  • David A Tuveson

National Cancer Institute (U01CA224013)

  • David A Tuveson

National Cancer Institute (1R01CA188134)

  • David A Tuveson

National Cancer Institute (1R01CA190092)

  • David A Tuveson

Pershing Square Foundation

  • Christopher R Vakoc

National Cancer Institute (5P01CA013106-Project 4)

  • Christopher R Vakoc

National Cancer Institute (CA229699)

  • Christopher R Vakoc

Pancreatic Cancer Action Network (16-20-25-VAKO)

  • Christopher R Vakoc

Lustgarten Foundation

  • David A Tuveson

National Cancer Institute (5P30CA45508)

  • David A Tuveson

National Cancer Institute (5P50CA101955)

  • David A Tuveson

National Cancer Institute (P20CA192996)

  • David A Tuveson

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All animal procedures and studies were approved by the Cold Spring Harbor Laboratory Animal Care and Use Committee (IACUC protocol number 19-16-8).

Reviewing Editor

  1. Charles L Sawyers, Memorial Sloan Kettering Cancer Center, United States

Publication history

  1. Received: November 6, 2019
  2. Accepted: April 23, 2020
  3. Accepted Manuscript published: April 24, 2020 (version 1)
  4. Version of Record published: May 5, 2020 (version 2)
  5. Version of Record updated: May 12, 2020 (version 3)

Copyright

© 2020, Somerville et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 5,045
    Page views
  • 722
    Downloads
  • 31
    Citations

Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Tim DD Somerville
  2. Giulia Biffi
  3. Juliane Daßler-Plenker
  4. Stella K Hur
  5. Xue-Yan He
  6. Krysten E Vance
  7. Koji Miyabayashi
  8. Yali Xu
  9. Diogo Maia-Silva
  10. Olaf Klingbeil
  11. Osama E Demerdash
  12. Jonathan B Preall
  13. Michael A Hollingsworth
  14. Mikala Egeblad
  15. David A Tuveson
  16. Christopher R Vakoc
(2020)
Squamous trans-differentiation of pancreatic cancer cells promotes stromal inflammation
eLife 9:e53381.
https://doi.org/10.7554/eLife.53381

Further reading

    1. Cancer Biology
    2. Chromosomes and Gene Expression
    Ariel Ogran et al.
    Research Article

    The transformation of normal to malignant cells is accompanied by substantial changes in gene expression programs through diverse mechanisms. Here, we examined the changes in the landscape of transcription start sites and alternative promoter (AP) usage and their impact on the translatome in TCL1-driven chronic lymphocytic leukemia (CLL). Our findings revealed a marked elevation of APs in CLL B cells from Eµ-Tcl1 transgenic mice, which are particularly enriched with intra-genic promoters that generate N-terminally truncated or modified proteins. Intra-genic promoter activation is mediated by (1) loss of function of ‘closed chromatin’ epigenetic regulators due to the generation of inactive N-terminally modified isoforms or reduced expression; (2) upregulation of transcription factors, including c-Myc, targeting the intra-genic promoters and their associated enhancers. Exogenous expression of Tcl1 in MEFs is sufficient to induce intra-genic promoters of epigenetic regulators and promote c-Myc expression. We further found a dramatic translation downregulation of transcripts bearing CNY cap-proximal trinucleotides, reminiscent of cells undergoing metabolic stress. These findings uncovered the role of Tcl1 oncogenic function in altering promoter usage and mRNA translation in leukemogenesis.

    1. Cancer Biology
    Shouhao Zhou et al.
    Tools and Resources

    The median-effect equation has been widely used to describe the dose-response relationship and identify compounds that activate or inhibit specific disease targets in contemporary drug discovery. However, the experimental data often contain extreme responses, which may significantly impair the estimation accuracy and impede valid quantitative assessment in the standard estimation procedure. To improve the quantitative estimation of the dose-response relationship, we introduce a novel approach based on robust beta regression. Substantive simulation studies under various scenarios demonstrate solid evidence that the proposed approach consistently provides robust estimation for the median-effect equation, particularly when there are extreme outcome observations. Moreover, simulation studies illustrate that the proposed approach also provides a narrower confidence interval, suggesting a higher power in statistical testing. Finally, to efficiently and conveniently perform common lab data analyses, we develop a freely accessible web-based analytic tool to facilitate the quantitative implementation of the proposed approach for the scientific community.