Immune signatures predict development of autoimmune toxicity in immune-checkpoint-inhibitor-treated cancer patients
Bibliographic record
Abstract
Immune signatures predict development of autoimmune toxicity in immune-checkpoint-inhibitor-treated cancer patients Nicolas Gonzalo Nuñez1*, Fiamma Berner2*, Ekaterina Friebel1*, Susanne Unger1, Mette-Triin Purde2, Rebekka Niederer2,3, Maximilian Porsch4, Christa Lichtensteiger2, Julia Martinez Gomez5, Mariaelena Capone6, Gabriele Madonna6, Lacin Cevhertas7,8, Teresa Amaral9,10, Omar Hasan Ali2,3,5,11, David Bomze2,12, Marie-Therese Abdou2, Stefan Diem13, Paolo Antonio Ascierto6, Reinhard Dummer5, Christoph Driessen13, Mitch Levesque5, Willem van de Veen7, Markus Jörger13, Martin Früh13,14, Burkhard Becher1**, Lukas Flatz2,3,5,13,15** */** these authors contributed equally Affiliations 1. Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland 2. Institute of Immunobiology, Medical Research Center, Kantonsspital St. Gallen, St.Gallen, Switzerland 3. Department of Dermatology, Kantonsspital St. Gallen, St.Gallen, Switzerland 4. Department of Radiology, Kantonsspital St. Gallen, St.Gallen, Switzerland 5. Department of Dermatology, University Hospital Zurich, Zurich, Switzerland 6. Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, Napoli, Italy 7. Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland 8. Department of Medical Immunology, Institute of Health Sciences, Bursa Uludag University, Bursa, Turkey 9. Skin Cancer Center, Department of Dermatology, University Hospital Tübingen, Tübingen, Germany 10. iFIT Cluster of Excellence (EXC 2180), University of Tübingen, Tübingen, Germany 11. Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, Canada 12. Sackler Faculty of Medicine, Tel-Aviv University, Israel 13. Department of Oncology, Kantonsspital St. Gallen, St.Gallen, Switzerland 14. Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland 15. Universitäts-Hautklinik, University of Tübingen, Tübingen, Germany Corresponding authors: Burkhard Becher, Prof. Dr. Institute of Experimental Immunology University of Zurich Winterthurerstrasse 190 8057 Zurich, Switzerland Phone: +41 44 635 37 03 Email: becher@immunology.uzh.ch Lukas Flatz, Prof. M.D. Universitäts-Hautklinik University of Tübingen 72016 Tübingen, Germany Phone: +49 7071 2984620 Email: lukas.flatz@med.uni-tuebingen.de Immune checkpoint inhibitors (ICIs) have emerged as one of the most promising treatment options for melanoma and non-small cell lung cancer (NSCLC). While ICIs can induce effective anti-tumour responses, their use is also frequently associated with immune-related adverse events (irAEs). Identifying biomarkers to predict which patients will suffer from irAEs would enable more accurate clinical risk-benefit-analysis for ICI treatment and may also shed light on common or distinct mechanisms underpinning treatment success and irAEs. In this prospective study we used a multiomics approach including unbiased single-cell profiling and serum analysis to characterise the systemic immune compartment of patients with melanoma or NSCLC before and during treatment with ICIs. We identified predictive immune signatures and early changes during ICI therapy that were significantly associated with the subsequent development of irAEs and were distinguished from markers of response to ICI therapy. These biomarkers may help to predict which patients are likely to benefit most from ICI therapy and those requiring intensive monitoring for irAEs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".