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Mfumbilwa ZA, Simons MJHG, Ramaekers B, Retèl VP, Mankor JM, Groen HJM, Aerts JGJV, Joore M, Wilschut JA, Coupé VMH. Exploring the Cost Effectiveness of a Whole-Genome Sequencing-Based Biomarker for Treatment Selection in Patients with Advanced Lung Cancer Ineligible for Targeted Therapy. PHARMACOECONOMICS 2024; 42:419-434. [PMID: 38194023 PMCID: PMC10937799 DOI: 10.1007/s40273-023-01344-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/06/2023] [Indexed: 01/10/2024]
Abstract
OBJECTIVE We aimed to perform an early cost-effectiveness analysis of using a whole-genome sequencing-based tumor mutation burden (WGS-TMB), instead of programmed death-ligand 1 (PD-L1), for immunotherapy treatment selection in patients with non-squamous advanced/metastatic non-small cell lung cancer ineligible for targeted therapy, from a Dutch healthcare perspective. METHODS A decision-model simulating individual patients with metastatic non-small cell lung cancer was used to evaluate diagnostic strategies to select first-line immunotherapy only or the immunotherapy plus chemotherapy combination. Treatment was selected using PD-L1 [A, current practice], WGS-TMB [B], and both PD-L1 and WGS-TMB [C]. Strategies D, E, and F take into account a patient's disease burden, in addition to PD-L1, WGS-TMB, and both PD-L1 and WGS-TMB, respectively. Disease burden was defined as a fast-growing tumor, a high number of metastases, and/or weight loss. A threshold of 10 mutations per mega-base was used to classify patients into TMB-high and TMB-low groups. Outcomes were discounted quality-adjusted life-years (QALYs) and healthcare costs measured from the start of first-line treatment to death. Healthcare costs includes drug acquisition, follow-up costs, and molecular diagnostic tests (i.e., standard diagnostic techniques and/or WGS for strategies involving TMB). Results were reported using the net monetary benefit at a willingness-to-pay threshold of €80,000/QALY. Additional scenario and threshold analyses were performed. RESULTS Strategy B had the lowest QALYs (1.84) and lowest healthcare costs (€120,800). The highest QALYs and healthcare costs were 2.00 and €140,400 in strategy F. In the base-case analysis, strategy A was cost effective with the highest net monetary benefit (€27,300), followed by strategy B (€26,700). Strategy B was cost effective when the cost of WGS testing was decreased by at least 24% or when immunotherapy results in an additional 0.5 year of life gained or more for TMB high compared with TMB low. Strategies C and F, which combined TMB and PD-L1 had the highest net monetary benefit (≥ €76,900) when the cost of WGS testing, immunotherapy, and chemotherapy acquisition were simultaneously reduced by at least 47%, 39%, and 43%, respectively. Furthermore, strategy C resulted in the highest net monetary benefit (≥ €39,900) in a scenario where patients with both PD-L1 low and TMB low were treated with chemotherapy instead of immunotherapy plus chemotherapy. CONCLUSIONS The use of WGS-TMB is not cost effective compared to PD-L1 for immunotherapy treatment selection in non-squamous metastatic non-small cell lung cancer in the Netherlands. WGS-TMB could become cost effective provided there is a reduction in the cost of WGS testing or there is an increase in the predictive value of WGS-TMB for immunotherapy effectiveness. Alternatively, a combination strategy of PD-L1 testing with WGS-TMB would be cost effective if used to support the choice to withhold immunotherapy in patients with a low expected benefit of immunotherapy.
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Affiliation(s)
- Zakile A Mfumbilwa
- Department of Epidemiology and Data Science, Disease Modelling and Health Care Evaluation, Amsterdam UMC, Location Vrije Universiteit Amsterdam, PO Box 7057, 1007 MB, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
- Department of Mathematics and Statistics, Sokoine University of Agriculture, Morogoro, Tanzania
| | - Martijn J H G Simons
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Bram Ramaekers
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Valesca P Retèl
- Department of Health Technology and Services Research, University of Twente, Enschede, The Netherlands
| | - Joanne M Mankor
- Department of Pulmonary Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Harry J M Groen
- Department of Pulmonary Diseases, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Manuela Joore
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Janneke A Wilschut
- Department of Epidemiology and Data Science, Disease Modelling and Health Care Evaluation, Amsterdam UMC, Location Vrije Universiteit Amsterdam, PO Box 7057, 1007 MB, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
| | - Veerle M H Coupé
- Department of Epidemiology and Data Science, Disease Modelling and Health Care Evaluation, Amsterdam UMC, Location Vrije Universiteit Amsterdam, PO Box 7057, 1007 MB, Amsterdam, The Netherlands.
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands.
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