Evidence map›Paper›PMID 38194023›Full record

ArticlePharmacoEconomics2024

Exploring the Cost Effectiveness of a Whole-Genome Sequencing-Based Biomarker for Treatment Selection in Patients with Advanced Lung Cancer Ineligible for Targeted Therapy.

Zakile A Mfumbilwa, Martijn J H G Simons, Bram Ramaekers, Valesca P Retèl, Joanne M Mankor, Harry J M Groen, Joachim G J V Aerts, Manuela Joore, Janneke A Wilschut, Veerle M H Coupé

Open access · hybridAbstract read
In one paragraph

Article in PharmacoEconomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
2.5field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 6 institutions in 2 countries.

Zakile A MfumbilwaDepartment 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.ORCID 0000-0002-9705-7227
Martijn J H G SimonsDepartment of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, The Netherlands.ORCID 0000-0001-5248-1676
Bram RamaekersDepartment of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, The Netherlands.ORCID 0000-0001-5785-9228
Valesca P RetèlDepartment of Health Technology and Services Research, University of Twente, Enschede, The Netherlands.ORCID 0000-0002-5624-3234
Joanne M MankorDepartment of Pulmonary Medicine, Erasmus MC, Rotterdam, The Netherlands.ORCID 0000-0003-3653-9043
Harry J M GroenDepartment of Pulmonary Diseases, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.ORCID 0000-0002-2978-5265
Joachim G J V AertsDepartment of Pulmonary Medicine, Erasmus MC, Rotterdam, The Netherlands.ORCID 0000-0001-6662-2951
Manuela JooreDepartment of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, The Netherlands.ORCID 0000-0002-5649-6768
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.ORCID 0000-0002-4921-227X
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. v.coupe@amsterdamumc.nl.ORCID 0000-0002-9553-9791
Maastricht University Medical Centre · NLErasmus MC · NLGGD Amsterdam · NLAmsterdam Neuroscience · NLUniversity Medical Center Groningen · NLUniversity of Twente · NL

Funding

ZonMw 846001002
6 · The paper itself

Abstract

objectiveWe 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.

methodsA 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.

resultsStrategy 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.

conclusionsThe 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.

Indexed as

Antineoplastic Agents, ImmunologicalCarcinoma, Non-Small-Cell LungLung NeoplasmsB7-H1 AntigenBiomarkers, TumorCost-Benefit AnalysisCost-Effectiveness AnalysisHumansAntineoplastic Agents, ImmunologicalB7-H1 AntigenBiomarkers, Tumor

Identifiers

PMID38194023
PMCPMC10937799
OpenAlexW4390730475

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.