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Single-cell RNA sequencing reveals the effects of chemotherapy on human pancreatic adenocarcinoma and its tumor microenvironment

Author

Listed:
  • Gregor Werba

    (NYU Langone Health)

  • Daniel Weissinger

    (NYU Langone Health)

  • Emily A. Kawaler

    (NYU Langone Health)

  • Ende Zhao

    (NYU Langone Health)

  • Despoina Kalfakakou

    (NYU Langone Health)

  • Surajit Dhara

    (NYU Langone Health)

  • Lidong Wang

    (NYU Langone Health)

  • Heather B. Lim

    (NYU Langone Health)

  • Grace Oh

    (NYU Langone Health
    NYU Langone Health)

  • Xiaohong Jing

    (NYU Langone Health)

  • Nina Beri

    (NYU Langone Health
    NYU Langone Health)

  • Lauren Khanna

    (NYU Langone Health)

  • Tamas Gonda

    (NYU Langone Health)

  • Paul Oberstein

    (NYU Langone Health
    NYU Langone Health)

  • Cristina Hajdu

    (NYU Langone Health)

  • Cynthia Loomis

    (NYU Langone Health)

  • Adriana Heguy

    (NYU Langone Health)

  • Mara H. Sherman

    (Oregon Health Sciences University)

  • Amanda W. Lund

    (NYU Langone Health
    NYU Langone Health)

  • Theodore H. Welling

    (NYU Langone Health)

  • Igor Dolgalev

    (NYU Langone Health)

  • Aristotelis Tsirigos

    (NYU Langone Health
    NYU Langone Health)

  • Diane M. Simeone

    (NYU Langone Health
    NYU Langone Health
    NYU Langone Health)

Abstract

The tumor microenvironment (TME) in pancreatic ductal adenocarcinoma (PDAC) is a complex ecosystem that drives tumor progression; however, in-depth single cell characterization of the PDAC TME and its role in response to therapy is lacking. Here, we perform single-cell RNA sequencing on freshly collected human PDAC samples either before or after chemotherapy. Overall, we find a heterogeneous mixture of basal and classical cancer cell subtypes, along with distinct cancer-associated fibroblast and macrophage subpopulations. Strikingly, classical and basal-like cancer cells exhibit similar transcriptional responses to chemotherapy and do not demonstrate a shift towards a basal-like transcriptional program among treated samples. We observe decreased ligand-receptor interactions in treated samples, particularly between TIGIT on CD8 + T cells and its receptor on cancer cells, and identify TIGIT as the major inhibitory checkpoint molecule of CD8 + T cells. Our results suggest that chemotherapy profoundly impacts the PDAC TME and may promote resistance to immunotherapy.

Suggested Citation

  • Gregor Werba & Daniel Weissinger & Emily A. Kawaler & Ende Zhao & Despoina Kalfakakou & Surajit Dhara & Lidong Wang & Heather B. Lim & Grace Oh & Xiaohong Jing & Nina Beri & Lauren Khanna & Tamas Gond, 2023. "Single-cell RNA sequencing reveals the effects of chemotherapy on human pancreatic adenocarcinoma and its tumor microenvironment," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36296-4
    DOI: 10.1038/s41467-023-36296-4
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    References listed on IDEAS

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    1. Ludo Waltman & Nees Eck, 2013. "A smart local moving algorithm for large-scale modularity-based community detection," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(11), pages 1-14, November.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Nayoung Kim & Hong Kwan Kim & Kyungjong Lee & Yourae Hong & Jong Ho Cho & Jung Won Choi & Jung-Il Lee & Yeon-Lim Suh & Bo Mi Ku & Hye Hyeon Eum & Soyean Choi & Yoon-La Choi & Je-Gun Joung & Woong-Yang, 2020. "Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    4. Peter Bailey & David K. Chang & Katia Nones & Amber L. Johns & Ann-Marie Patch & Marie-Claude Gingras & David K. Miller & Angelika N. Christ & Tim J. C. Bruxner & Michael C. Quinn & Craig Nourse & L. , 2016. "Genomic analyses identify molecular subtypes of pancreatic cancer," Nature, Nature, vol. 531(7592), pages 47-52, March.
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    Cited by:

    1. Chao-Hui Chang & Feng Liu & Stefania Militi & Svenja Hester & Reshma Nibhani & Siwei Deng & James Dunford & Aniko Rendek & Zahir Soonawalla & Roman Fischer & Udo Oppermann & Siim Pauklin, 2024. "The pRb/RBL2-E2F1/4-GCN5 axis regulates cancer stem cell formation and G0 phase entry/exit by paracrine mechanisms," Nature Communications, Nature, vol. 15(1), pages 1-29, December.

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