Early Cost Effectiveness of Whole-Genome Sequencing as a Clinical Diagnostic Test for Patients with Inoperable Stage IIIB,C/IV Non-squamous Non-small-Cell Lung Cancer

Abstract

Background Advanced non-small-cell lung cancer (NSCLC) harbours many genetic aberrations that can be targeted with systemic treatments. Whole-genome sequencing (WGS) can simultaneously detect these (and possibly new) molecular targets. However, the exact added clinical value of WGS is unknown. Objective The objective of this study was to determine the early cost effectiveness of using WGS in diagnostic strategies compared with currently used molecular diagnostics for patients with inoperable stage IIIB,C/IV non-squamous NSCLC from a Dutch healthcare perspective. Methods A decision tree represented the diagnostic pathway, and a cohort state transition model represented disease progression. Three diagnostic strategies were modelled: standard of care (SoC) alone, WGS as a diagnostic test, and SoC followed by WGS. Treatment effectiveness was based on a systematic review. Probabilistic cost-effectiveness analyses were performed, and threshold analyses (using €80,000 per quality-adjusted life-year [QALY]) was used to explore the early cost effectiveness of WGS. Results WGS as a diagnostic test resulted in more QALYs (0.002) and costs (€1534 [incremental net monetary benefit -€1349]), and SoC followed by WGS resulted in fewer QALYs (-0.002) and more costs (€1059 [-€1194]) compared with SoC alone. WGS as a diagnostic test was only cost effective if it was priced at €2000 per patient and identified 2.7% more actionable patients than SoC alone. Treating these additional identified patients with new treatments costing >€4069 per month decreased the probability of cost effectiveness. Conclusions Our analysis suggests that providing WGS as a diagnostic test is cost effective compared with SoC followed by WGS and SoC alone if costs for WGS decrease and additional patients with actionable targets are identified. This costeffectiveness model can be used to incorporate new findings iteratively and to support ongoing decision making regarding the use of WGS in this rapidly evolving field.

Modelling of Lung Cancer Therapy; Modelled And Real-World Impact of Targeted Therapy And Immunotherapy In The Netherlands

“…For patients with PD-L1 ≥ 50% who were treated with pembrolizumab monotherapy, the proposed model with 23 percent long-term survivor fraction [54] had twice the mean life years (63.8 months) compared to the reported results by Chouaid et al [11] (34 months) as well as by Simons et al [14] (29 months). But when the 0 percent long-term survivor fraction was assumed, the proposed model had lower mean life years (21.1 months) compared to Chouaid et al and Simons et al Similarly, for patients with PD-L1 ≥ 50% treated with a combination of pembrolizumab and chemotherapy, the mean life years were three times higher compared to Simons et al when the long-term survivor fraction was assumed (66.5 months vs 23 months), and about 4 months higher when long-term survivor fraction was not assumed (27.9 months vs 23 months).…”

“…The mean life years (mean OS) of the currently proposed model for EGFR-TKI subgroup is four month lower compared to the mean life years reported by Holleman et al [10] that is, 20.3 months in proposed model compared to 24 (25) months for ge tinib (erlotinib) treatment in Holleman et al The difference can partly be explained by limiting the bene t of ge tinib (erlotinib) to 15 months to correct for treatment resistance as also demonstrated in the real-world data. Meanwhile, for the ALK subgroup treated with alectinib, a durable treatment bene t was assumed resulting a mean life years that were twice as high in the proposed model compared to the mean life years reported by Simons et al [14] (Strategy A, online supplementary) that is, 91.4 months versus 45.6 months. Durability of treatment bene t of alectinib in patients with ALK mutation was supported by updated results of the ALEX study [61].…”

“…HTA decision models for advanced (inoperable) non-squamous NSCLC have been developed before [10], [11], [14]. These studies were mainly based on RCTs.…”

“…The differences between the currently proposed model and the three models mentioned above are partly due to different modeling strategies, that is, microsimulation DES model versus Markov cohort models [10], [14] and Partition Survival model [11]; as well as different modelling assumptions adopted in the proposed model such as assuming the mixture cure time to event distribution for immunotherapy. But also, partly due to different underlying population.…”

“…Thus, RCTs on single interventions are no longer su cient. Simons et al [14] and van Amerongen et al [15] modelled the diagnostic and treatment pathways in multiple-lines based on data from several RCTs. However, RCTs represent highly selective populations that have in general better prognosis than patients not participating in RCTs.…”