Modeling strategies to analyse longitudinal biomarker data: An illustration on predicting immunotherapy non-response in non-small cell lung cancer
Frederik A. van Delft,
Milou Schuurbiers,
Mirte Muller,
Sjaak A. Burgers,
Huub H. van Rossum,
Maarten J. IJzerman,
Hendrik Koffijberg,
Michel M. van den Heuvel
Affiliations
Frederik A. van Delft
Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, Overijssel, 7522NH, the Netherlands
Milou Schuurbiers
Department of Respiratory Diseases, Radboud University Medical Center, Nijmegen, Gelderland, 6525GA, the Netherlands
Mirte Muller
Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, Noord-Holland, 1066CX, the Netherlands
Sjaak A. Burgers
Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, Noord-Holland, 1066CX, the Netherlands
Huub H. van Rossum
Department of Laboratory Medicine, Netherlands Cancer Institute, Amsterdam, Noord-Holland, 1066CX, the Netherlands
Maarten J. IJzerman
Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, Overijssel, 7522NH, the Netherlands; Centre for Cancer Research and Centre for Health Policy, University of Melbourne, Parkville, Melbourne, Victoria, Australia; Peter MacCallum Cancer Centre, Parkville, Melbourne, Victoria, Australia
Hendrik Koffijberg
Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, Overijssel, 7522NH, the Netherlands; Corresponding author.
Michel M. van den Heuvel
Department of Respiratory Diseases, Radboud University Medical Center, Nijmegen, Gelderland, 6525GA, the Netherlands
Serum tumor markers acquired through a blood draw are known to reflect tumor activity. Their non-invasive nature allows for more frequent testing compared to traditional imaging methods used for response evaluations. Our study aims to compare nine prediction methods to accurately, and with a low false positive rate, predict progressive disease despite treatment (i.e. non-response) using longitudinal tumor biomarker data. Bi-weekly measurements of CYFRA, CA-125, CEA, NSE, and SCC were available from a cohort of 412 advanced stage non-small cell lung cancer (NSCLC) patients treated up to two years with immune checkpoint inhibitors. Serum tumor marker measurements from the first six weeks after treatment initiation were used to predict treatment response at 6 months. Nine models with varying complexity were evaluated in this study, showing how longitudinal biomarker data can be used to predict non-response to immunotherapy in NSCLC patients.
