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Pancreas
Original research
Immune landscape, evolution, hypoxia-mediated viral mimicry pathways and therapeutic potential in molecular subtypes of pancreatic neuroendocrine tumours
  1. Kate Young1,2,
  2. Rita T Lawlor3,
  3. Chanthirika Ragulan1,4,
  4. Yatish Patil1,
  5. Andrea Mafficini3,5,
  6. Samantha Bersani3,5,
  7. Davide Antonello6,
  8. David Mansfield7,
  9. Sara Cingarlini8,
  10. Luca Landoni6,
  11. Antonio Pea6,
  12. Claudio Luchini3,5,
  13. Liliana Piredda3,
  14. Nagarajan Kannan9,
  15. Gift Nyamundanda1,
  16. Daniel Morganstein2,
  17. Ian Chau2,
  18. Bertram Wiedenmann10,
  19. Michele Milella8,
  20. Alan Melcher7,
  21. David Cunningham2,
  22. Naureen Starling2,
  23. Aldo Scarpa3,5,
  24. Anguraj Sadanandam1,4,9
  1. 1 Division of Molecular Pathology, Institute of Cancer Research, London, UK
  2. 2 Department of Medicine, Royal Marsden Hospital, London and Surrey, UK
  3. 3 ARC-Net Research Centre, University of Verona, Verona, Italy
  4. 4 Centre for Molecular Pathology, Royal Marsden Hospital, London, UK
  5. 5 Department of Diagnostics and Public Health, University and Hospital Trust of Verona, Verona, Italy
  6. 6 General and Pancreatic Surgery Department, Pancreas Institute, University and Hospital Trust of Verona, Verona, Italy
  7. 7 Division of Radiotherapy and Imaging, Institute of Cancer Research, London, UK
  8. 8 Department of Medicine, Medical Oncology, University and Hospital Trust of Verona, Verona, Italy
  9. 9 Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
  10. 10 Institut für Pathologie, Charite, Campus Virchow-Klinikum, University Medicine, Berlin, Germany
  1. Correspondence to Dr Anguraj Sadanandam, Division of Molecular Pathology, Institute of Cancer Research, London, Surrey, UK; anguraj.sadanandam{at}icr.ac.uk; Professor Aldo Scarpa, ARC-Net Centre for Applied Research on Cancer, University of Verona, Verona, Italy; aldo.scarpa{at}univr.it

Abstract

Objective A comprehensive analysis of the immune landscape of pancreatic neuroendocrine tumours (PanNETs) was performed according to clinicopathological parameters and previously defined molecular subtypes to identify potential therapeutic vulnerabilities in this disease.

Design Differential expression analysis of 600 immune-related genes was performed on 207 PanNET samples, comprising a training cohort (n=72) and two validation cohorts (n=135) from multiple transcriptome profiling platforms. Different immune-related and subtype-related phenotypes, cell types and pathways were investigated using different in silico methods and were further validated using spatial multiplex immunofluorescence.

Results The study identified an immune signature of 132 genes segregating PanNETs (n=207) according to four previously defined molecular subtypes: metastasis-like primary (MLP)-1 and MLP-2, insulinoma-like and intermediate. The MLP-1 subtype (26%–31% samples across three cohorts) was strongly associated with elevated levels of immune-related genes, poor prognosis and a cascade of tumour evolutionary events: larger hypoxic and necroptotic tumours leading to increased damage-associated molecular patterns (viral mimicry), stimulator of interferon gene pathway, T cell-inflamed genes, immune checkpoint targets, and T cell-mediated and M1 macrophage-mediated immune escape mechanisms. Multiplex spatial profiling validated significantly increased macrophages in the MLP-1 subtype.

Conclusion This study provides novel data on the immune microenvironment of PanNETs and identifies MLP-1 subtype as an immune-high phenotype featuring a broad and robust activation of immune-related genes. This study, with further refinement, paves the way for future precision immunotherapy studies in PanNETs to potentially select a subset of MLP-1 patients who may be more likely to respond.

  • neuroendocrine tumors
  • pancreatic cancer
  • immune response
  • immunotherapy
  • macrophages

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information. Data are available in GEO Omnibus (GEO Omnibus with IDs GSE73338 and GSE73339): (1) the data include transcriptome profiles from patient samples; (2) GEO Omnibus is the repository; and (3) there are no reuse conditions. We have included the information and GEO Omnibus IDs in the Materials and methods and supplemental information.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/gutjnl-2020-321016

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    Data availability statement

    All data relevant to the study are included in the article or uploaded as supplementary information. Data are available in GEO Omnibus (GEO Omnibus with IDs GSE73338 and GSE73339): (1) the data include transcriptome profiles from patient samples; (2) GEO Omnibus is the repository; and (3) there are no reuse conditions. We have included the information and GEO Omnibus IDs in the Materials and methods and supplemental information.

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    Footnotes

    • NS, AS and AS are joint senior authors.

    • KY, RTL and ASa are joint first authors.

    • Twitter @antonio pea

    • Presented at This work was previously presented partly at European Society for Medical Oncology (ESMO) Congress 2017 and partly at European NeuroEndocrine Tumor Society (ENETS) Conference 2019.

    • Correction notice This article has been corrected since it published Online First. A second corresonding author has been added.

    • Contributors ASa conceived the idea. ASa, KY, RTL, ASc and NS developed the idea. RTL and ASc organised the entire patient study protocol, collected the samples, and curated the clinical data for training and validation cohort 2 samples, and profiled gene expression for training cohort. YP and ASa performed all the bioinformatics analysis. GN modified certain bioinformatics tools for custom use and wrote the methods description for the same. KY assisted with enrichment analysis and multiplex spatial profile quantitation, and coordinated the data analysis. RTL, KY, NK and CR assisted with RNAseq experiments. KY and CR performed NanoString experiments and analysis. AM, SC, DA, SC, LL, AP, CL, LP and MM assisted in collecting samples, pathology analysis and/or RNA isolation. DM performed multiplex spatial profiling and quantitation, and AM supervised these experiments. BW provided validation cohort 2 samples’ RNA and critically read the manuscript. DM, IC, DC and NS assisted with the manuscript writing and coordination of the study. ASa, KY, RL and ASc interpreted the data. KY, ASa, RTL, AM and ASc wrote the manuscript. ASa, RTL, ASc and NS supervised the project. All the authors have read the manuscript.

    • Funding This study was supported by Associazione Italiana Ricerca Cancro (5×1000 grant 12 182) Fondazione Italiana Malattie Pancreas (FIMP, Ministero Salute, CUP_J33G13000210001); Fondazione Cariverona, Oncology Biobank Project 'Antonio Schiavi' (prot. 2 03 885–2017) for ASc and RTL. This paper represents independent research partly funded (BRC Reference #A144) by the National Institute for Health Research Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and the Institute of Cancer Research for ASa and NS.

    • Disclaimer The views expressed are those of the authors and not necessarily those of the National Health Service England, the National Institute for Health Research or the Department of Health and Social Care.

    • Competing interests ASa, ASc, CR, GN and KY have ownership interest as patent inventors for a patent entitled 'Patient classification and prognostic method' (international patent application number PCT/EP2019/053845). DC have research funding, 4SC (Inst), Amgen (Inst), AstraZeneca (Inst), Bayer (Inst), Celgene (Inst), Clovis Oncology (Inst), Janssen (Inst), Lilly (Inst), MedImmune (Inst), Merck (Inst), Merrimack (Inst) and Sanofi (Inst). NS has research funding: AstraZeneca, Bristol-Myers-Squibb, Merck Serono and Pfizer. ASa - research funding: Bristol-Myers Squibb, Merck KGaA and Pierre Fabre. Patents: (1) ‘Colorectal cancer classification with differential prognosis and personalized therapeutic responses’ (patent number PCT/IB2013/060416) and (2) ‘Prognostic and treatment response predictive method’ (European (EP) patent application number 18792565.6).

    • Patient and public involvement Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

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