AI-powered spatial cell phenomics enhances risk stratification in non-small cell lung cancer

Simon Schallenberg, Gabriel Dernbach, Sharon Ruane, Philipp Jurmeister, Cornelius Böhm, Kai Standvoss, Sandip Ghosh, Marco Frentsch, Mihnea P. Dragomir, Philipp G. Keyl, Corinna Friedrich, Il-Kang Na, Sabine Merkelbach-Bruse, Alexander Quaas, Nikolaj Frost, Kyrill Boschung, Winfried Randerath, Georg Schlachtenberger, Matthias Heldwein, Ulrich Keilholz, Khosro Hekmat, Jens-Carsten Rückert, Reinhard Büttner, Angela Vasaturo, David Horst, Lukas Ruff, Maximilian Alber, Klaus-Robert Müller () and Frederick Klauschen ()
Additional contact information
Simon Schallenberg: Humboldt-Universität zu Berlin and Berlin Institute of Health
Gabriel Dernbach: Humboldt-Universität zu Berlin and Berlin Institute of Health
Sharon Ruane: Aignostics
Philipp Jurmeister: Ludwig-Maximilians-Universität München
Cornelius Böhm: Aignostics
Kai Standvoss: Aignostics
Sandip Ghosh: Aignostics
Marco Frentsch: corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin
Mihnea P. Dragomir: Humboldt-Universität zu Berlin and Berlin Institute of Health
Philipp G. Keyl: BIFOLD - Berlin Institute for the Foundations of Learning and Data
Corinna Friedrich: Humboldt-Universität zu Berlin and Berlin Institute of Health
Il-Kang Na: corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin
Sabine Merkelbach-Bruse: University Hospital Cologne
Alexander Quaas: University Hospital Cologne
Nikolaj Frost: Humboldt-Universität zu Berlin and Berlin Institute of Health
Kyrill Boschung: Institute of Pneumology at the University of Cologne
Winfried Randerath: Institute of Pneumology at the University of Cologne
Georg Schlachtenberger: University Hospital Cologne
Matthias Heldwein: University Hospital Cologne
Ulrich Keilholz: Berlin Institute of Health (BIH)
Khosro Hekmat: University Hospital Cologne
Jens-Carsten Rückert: Charité - Universitätsmedizin Berlin
Reinhard Büttner: University Hospital Cologne
Angela Vasaturo: Ultivue
David Horst: Humboldt-Universität zu Berlin and Berlin Institute of Health
Lukas Ruff: Aignostics
Maximilian Alber: Humboldt-Universität zu Berlin and Berlin Institute of Health
Klaus-Robert Müller: BIFOLD - Berlin Institute for the Foundations of Learning and Data
Frederick Klauschen: Humboldt-Universität zu Berlin and Berlin Institute of Health

Nature Communications, 2025, vol. 16, issue 1, 1-25

Abstract: Abstract Risk stratification remains a critical challenge in non-small cell lung cancer patients for optimal therapy selection. In this study, we develop an artificial intelligence-powered spatial cellomics approach that combines histology, multiplex immunofluorescence imaging and multimodal machine learning to characterize the complex cellular relationships of 43 cell phenotypes in the tumor microenvironment in a real-world retrospective cohort of 1168 non-small cell lung cancer patients from two large German cancer centers. The model identifies cell niches associated with survival and achieves a 14% and 47% improvement in risk stratification in the two main non-small cell lung cancer subtypes, lung adenocarcinoma and squamous cell carcinoma, respectively, combining niche patterns with conventional cancer staging. Our results show that complex immune cell niche patterns identify potentially undertreated high-risk patients qualifying for adjuvant therapy. Our approach highlights the potential of artificial intelligence powered multiplex imaging analyses to better understand the contribution of the tumor microenvironment to cancer progression and to improve risk stratification and treatment selection in non-small cell lung cancer.

Date: 2025
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DOI: 10.1038/s41467-025-65783-z

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