Comparing modeling strategies combining changes in multiple serum tumor biomarkers for early prediction of immunotherapy non-response in non-small cell lung cancer.

Autor:	van Delft FA; Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, The Netherlands., Schuurbiers MMF; Department of Respiratory Diseases, Radboud University Medical Center, Nijmegen, the Netherlands., Muller M; Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands., Burgers SA; Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands., van Rossum HH; Department of Laboratory Medicine, Netherlands Cancer Institute, Amsterdam, The Netherlands., IJzerman MJ; Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, The Netherlands.; Erasmus School of Health Policy and Management, Rotterdam, The Netherlands.; Centre for Cancer Research and Centre for Health Policy, University of Melbourne, Parkville, Melbourne, Australia.; Peter MacCallum Cancer Centre, Parkville, Melbourne, Australia., van den Heuvel MM; Department of Respiratory Diseases, Radboud University Medical Center, Nijmegen, the Netherlands., Koffijberg H; Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
Jazyk:	angličtina
Zdroj:	Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine [Tumour Biol] 2024; Vol. 46 (s1), pp. S269-S281.
DOI:	10.3233/TUB-220022
Abstrakt:	Background: Patients treated with immune checkpoint inhibitors (ICI) are at risk of adverse events (AEs) even though not all patients will benefit. Serum tumor markers (STMs) are known to reflect tumor activity and might therefore be useful to predict response, guide treatment decisions and thereby prevent AEs. Objective: This study aims to compare a range of prediction methods to predict non-response using multiple sequentially measured STMs. Methods: Nine prediction models were compared to predict treatment non-response at 6-months (n = 412) using bi-weekly CYFRA, CEA, CA-125, NSE, and SCC measurements determined in the first 6-weeks of therapy. All methods were applied to six different biomarker combinations including two to five STMs. Model performance was assessed based on sensitivity, while model training aimed at 95% specificity to ensure a low false-positive rate. Results: In the validation cohort, boosting provided the highest sensitivity at a fixed specificity across most STM combinations (12.9% -59.4%). Boosting applied to CYFRA and CEA achieved the highest sensitivity on the validation data while maintaining a specificity >95%. Conclusions: Non-response in NSCLC patients treated with ICIs can be predicted with a specificity >95% by combining multiple sequentially measured STMs in a prediction model. Clinical use is subject to further external validation.
Databáze:	MEDLINE
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