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970 Multiplex-immunofluorescence-based spatial characterization of the tumor-microenvironment of a large bicentric clinical non-small cell lung cancer cohort
  1. Simon Schallenberg1,
  2. Gabriel Dernbach1,2,
  3. Sharon Ruane2,
  4. Cornelius Böhm2,
  5. Lukas Ruff2,
  6. Kai Standvoss2,
  7. Sandip Ghosh2,
  8. Mihnea P Dragomir1,3,4,
  9. Rebecca Fritz1,
  10. Ines Koch1,
  11. Corinna Friedrich1,5,
  12. Sabine Merkelbach-Bruse6,
  13. Alexander Quaas6,
  14. Nikolaj Frost7,
  15. Kyrill Boschung8,
  16. Winfried Randerath8,
  17. Georg Schlachtenberger9,
  18. Matthias Heldwein9,
  19. Ulrich Keilholz10,11,
  20. Khosro Hekmat9,
  21. Jens-Carsten Rückert12,
  22. Reinhard Büttner6,
  23. Angela Vasaturo13,
  24. David Horst1,
  25. Maximilian Alber2,11 and
  26. Frederick Klauschen1,3,14,15,20
  1. 1Institute of Pathology-Charité Universitätsmedizin Berlin, Berlin, Berlin, Germany
  2. 2Aignostics, Berlin, Germany
  3. 3German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Partner Site Berlin, Heidelberg, Germany
  4. 4Berlin Institute of Health (BIH), Berlin, Germany
  5. 5Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Proteomics Platform, Berlin, Germany
  6. 6Institute of Pathology, University Hospital Cologne, Cologne, Germany
  7. 7Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
  8. 8Bethanien Hospital, Clinic of Pneumology and Allergology, Center for Sleep Medicine and Respiratory Care, Institute of Pneumology, University of Cologne, Solingen, Germany
  9. 9Department of Cardiothoracic Surgery, University Hospital Cologne, Cologne, Germany
  10. 10German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Partner Site Berlin, Berlin, Germany
  11. 11Charité Comprehensive Cancer Center, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
  12. 12Department of General, Visceral, Vascular and Thoracic Surgery, Charité – Universitätsmedizin Berlin, Berlin, Germany
  13. 13Ultivue, Cambridge, MA, USA
  14. 14BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
  15. 15Institute of Pathology, Ludwig-Maximilians-University Munich, Munich, Germany
  16. 20German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Munich, Heidelberg, Germany
  • Journal for ImmunoTherapy of Cancer (JITC) preprint. The copyright holder for this preprint are the authors/funders, who have granted JITC permission to display the preprint. All rights reserved. No reuse allowed without permission.


Background Non-small-cell lung cancer (NSCLC) is the leading cause for cancer death. Current targeted- and immuno-therapies are effective in a patient subset, but causes for resistance and the complexity of the immune reaction are difficult to be identified on a single patient level. It is hypothesized that the tumor microenvironment (TME) and its immune cell spatial composition are a key to alleviate that. Arguably, a sufficiently large study cohort, a high-plex (immune) cell characterization and an accordingly scalable analysis is required to advance the discovery of complex patterns. In this study, we tackle this by means of a large NSCLC cohort, a multiplex panel consisting of hematoxylin and eosin (H&E) and 12 immunofluorescence markers (mIF), and a scalable AI-based image analysis approach, combining histomorphological and multiplex immunofluorescence (mIF) data to characterize the TME for detection of prognostic and predictive biomarkers.

Methods We gathered formalin-fixed, paraffin-embedded tissue and clinicopathological data from 1168 patients with resected stage I-IV NSCLCs from two large German hospitals (Charité Berlin and University Hospital Cologne). Four 1.5 mm tissue cores were punched from tumor regions of each case for constructing a tissue microarray. Sections were stained with a 12-plex mIF panel followed by H&E. All stains were scanned and co-registered at single cell accuracy. Deep learning models were developed to detect the different tumor regions: carcinoma, stroma, and necrosis from H&E, and different IF markers from mIF and H&E. The marker-wise cell classification predictions were combined into different cell subtypes. The spatially resolved readouts characterized the TME and were correlated with clinical data.

Results The tissue segmentation model achieved a F1-score of 92%. The cell classification models predict positivity for 12 markers with an F1-score of at least 95% on hold-out data. The predictions were combined to quantify 42 different cell subtypes and overall 53 million cells were characterized. The extracted data confirms the known result of a high density of TREG in the tumor stroma indicating a shorter disease-free survival. Further analysis revealed that data-driven immune cell signatures can be identified separating patient subgroups with respect to overall survival.

Conclusions In our study we demonstrate that the combination of a large cohort, high-plex panel, and automated AI-based analysis has the potential to discover complex, predictive TME signatures. Initial results validate TREG as a known biomarker and suggest that the approach indeed allows for a data-driven TME pattern discovery with clinical impact as a potential stratification tool for future clinical trials.

Ethics Approval This study was approved by Charité Berlin’s Ethics Board with approval number EA1/243/21.

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

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