Economic Analysis of Exclusionary EGFR Test Versus Up-Front NGS for Lung Adenocarcinoma in High EGFR Mutation Prevalence Areas

Authors: Szu-Chun Yang MD, PhD1, Yi-Chen Yeh MD2,3, Yi-Lin Chen MS4,5, and Chao-Hua Chiu MD3,6
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  • 1 Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan;
  • | 2 Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei;
  • | 3 College of Medicine, National Yang Ming Chiao Tung University, Taipei;
  • | 4 Molecular Diagnosis Laboratory, Department of Pathology, National Cheng Kung University Hospital, Tainan;
  • | 5 Department of Medical Laboratory Science and Biotechnology, College of Medicine, National Cheng Kung University, Tainan; and
  • | 6 Division of Thoracic Oncology, Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.

Background: This study sought to determine whether exclusionary EGFR mutation testing followed by next-generation sequencing (NGS) is a cost-efficient and timely strategy in areas with high prevalence rates of EGFR mutation. Methods: We developed a decision tree model to compare exclusionary EGFR testing followed by NGS and up-front NGS. Patients entered the model upon diagnosis of metastatic lung adenocarcinoma. Gene alterations with FDA-approved targeted therapies included EGFR, ALK, ROS1, BRAF, RET, MET, NTRK, and KRAS. Model outcomes were testing-related costs; time-to-test results; monetary loss, taking both costs and time into consideration; and percentage of patients who could be treated by FDA-approved therapies. Stacked 1-way and 3-way sensitivity analyses were performed. Results: Exclusionary EGFR testing incurred testing-related costs of US $1,387 per patient, a savings of US $1,091 compared with the costs of up-front NGS. The time-to-test results for exclusionary EGFR testing and up-front NGS were 13.0 and 13.6 days, respectively. Exclusionary EGFR testing resulted in a savings of US $1,116 in terms of net monetary loss, without a reduction of patients identified with FDA-approved therapies. The EGFR mutation rate and NGS cost had the greatest impact on minimizing monetary loss. Given that the tissue-based NGS turnaround time was shortened to 7 days, up-front NGS testing would become the best strategy if its price could be reduced to US $568 in Taiwan. Conclusions: In areas with high prevalence rates of EGFR mutation, exclusionary EGFR testing followed by NGS, rather than up-front NGS, is currently a cost-efficient strategy for metastatic lung adenocarcinoma.

Background

Targeted therapies have changed the landscape of treatments for advanced lung adenocarcinoma. Patients with tumors harboring actionable gene alterations experience a durable response to treatment. Timely identification of these alterations can facilitate early initiation of an appropriate therapy, which improves treatment response and survival outcomes.1,2 According to Version 2.2021 of the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Non–Small Cell Lung Cancer,3 patients with newly diagnosed metastatic lung adenocarcinoma are recommended to be tested for mutations in 8 driver genes, including EGFR, ALK, ROS1, BRAF, RET, MET, NTRK, and KRAS, because FDA-approved therapies are available for the treatment of patients with these gene alterations. Next-generation sequencing (NGS) can test all these actionable gene alterations and has emerged on the front line to guide appropriate therapy.

In Western countries, where none of these 8 driver mutations are predominant,4 up-front NGS testing is the most cost-efficient and timeliest strategy in testing actionable gene alterations for metastatic lung adenocarcinoma.5 However, because the presence of these driver mutations is usually mutually exclusive, this strategy may not be the best for a population in which there is an overwhelming major mutation. Accordingly, a stepwise approach may be a reasonable alternative. More specifically, an exclusionary testing of EGFR mutation could have identified more than half of the actionable gene alterations in East Asian patients,4 leaving patients who tested negative for an EGFR mutation to have subsequent NGS testing. The cost and turnaround time for EGFR testing are less than that for NGS testing, reducing the total costs and time associated with testing.

We hypothesized that in areas with high prevalence rates of EGFR mutations, exclusionary EGFR testing followed by NGS, as compared with up-front NGS testing, would be more cost-efficient and less time-consuming and would only underdiagnose a minimal percentage of patients with lung adenocarcinoma with actionable gene alterations. By conducting a decision tree analysis and addressing rebiopsy issues, we attempt to verify our hypothesis.

Methods

In this study, we conducted the analyses from a Taiwanese societal perspective. The target population was all patients newly diagnosed with metastatic lung adenocarcinoma. This model-based economic analysis received an exemption determination from the Institutional Review Board at Taipei Veterans General Hospital (2021-07-004CE).

Model Overview

We developed a decision tree model to compare 2 testing strategies for patients with newly diagnosed metastatic lung adenocarcinoma: (1) up-front NGS testing and (2) exclusionary testing of an EGFR mutation (Figure 1). Patients entered the model upon diagnosis of metastatic lung adenocarcinoma, and their tumor samples were sufficient for testing of an EGFR mutation. In the up-front NGS testing strategy, tissue-based NGS was used to test all gene alterations, which included an EGFR mutation, ALK gene rearrangement, ROS1 gene rearrangement, BRAF V600E mutation, RET gene rearrangement, MET exon 14 (METex14) skipping mutation, NTRK gene rearrangement, KRAS G12C mutation, and HER2 mutation. If specimens were insufficient for tissue-based NGS, then liquid-based NGS was performed according to the current recommendations.6 We considered tissue rebiopsy for negative results of liquid-based NGS. Patients who did not undergo rebiopsy or experienced rebiopsy failure (ie, insufficient specimens for tissue-based NGS) were tested for an EGFR mutation. Appropriate therapy was directed by testing results.

Figure 1.
Figure 1.

Decision tree analysis for minimizing monetary loss.

Abbreviation: NGS, next-generation sequencing.

aPatients were tested for EGFR mutation.

bMonetary loss included testing costs and productivity costs; the latter was the product of turnaround time and average wage (Table 1).

Citation: Journal of the National Comprehensive Cancer Network 20, 7; 10.6004/jnccn.2021.7120

In the exclusionary testing strategy of an EGFR mutation, patients were first tested for this gene alteration using allele-specific real-time PCR assays. Because the aforementioned gene alterations are mutually exclusive, a positive EGFR mutation would preclude further NGS testing; however, a negative EGFR mutation was followed by tissue-based NGS testing. Similarly, if specimens were insufficient for tissue-based NGS, then they were followed by liquid-based NGS, and tissue rebiopsy was considered for negative results of liquid-based NGS. For patients without driver mutations and those needing rebiopsy but not receiving it or for whom it failed, platinum doublet-based chemotherapy or immunotherapy based on the PD-L1 expression level was applied as a nontargeted therapy.

Model Inputs

The unit cost for EGFR testing was based on the reimbursement rate of National Health Insurance of Taiwan (Table 1).7 Two pulmonologists estimated the unit cost for rebiopsy, assuming a patient was admitted for CT-guided needle biopsy. Because both tissue- and liquid-based NGS are not currently reimbursed by Taiwan’s national health insurance, we conducted a survey among 3 pathology centers—2 in northern Taiwan and 1 in southern Taiwan—to determine the mean costs of NGS. In the questionnaire, we also surveyed the turnaround time of EGFR and tissue-based NGS testing. The turnaround time of liquid-based NGS was 7 days,8 whereas the median turnaround time for rebiopsy was 10 days according to the 2 pulmonologists’ experience. Because both testing strategies included immunohistochemistry testing of the PD-L1 expression level, we did not take the additional cost and time of PD-L1 testing into consideration. The average daily wage of patients was calculated using the average monthly income for all employees from Taiwan’s National Statistics database.9

Table 1.

Input Parameters

Table 1.

The EGFR-positive test rate was 55.7% according to a multicenter study in Taiwan.10 The rates of other gene alterations were abstracted from the related literature.1117 Gene alterations with FDA-approved therapies included an EGFR mutation (both classic mutations and exon 20 insertion), ALK gene rearrangement, ROS1 gene rearrangement, BRAF V600E mutation, RET gene rearrangement, METex14 skipping mutation, NTRK gene rearrangement, and KRAS G12C mutation3; meanwhile, HER2 mutation was denoted as a mutation with potential treatment but without FDA approval. We considered the false-negative rate of EGFR testing, which comprised common mutations (eg, exon 19 deletions and L858R) included in EGFR testing but not detected due to the low sensitivity of EGFR tests18 and mutations (eg, exon 19 insertions and uncommon exon 19 deletions and exon 20 insertions) not included in routine EGFR testing.19 Patients indicated for FDA-approved therapies were assumed to be 100% identified using tissue- or liquid-based NGS; we subtracted the false-negative rate of EGFR testing and the ALK, ROS1, BRAF V600E, RET, METex14, NTRK, and KRAS G12C alteration rates from 100% to derive the identification rate using EGFR testing alone.

For patients undergoing tissue-based NGS, 16.5% had an insufficient tissue sample.20 We considered patients without actionable gene alterations and sensitivity of liquid-based NGS21 to calculate the probability of patients needing rebiopsy when liquid-based NGS results were negative. Of those in need of rebiopsy, only 30.0% of patients actually underwent rebiopsy.5 We did not include the cost or time attributable to downstream complications of rebiopsy in this analysis.

Model Outcomes

We aimed to minimize the costs associated with testing and the time to test results per patient. In addition, to simultaneously take both costs and time into consideration, we borrowed the concept of net monetary benefit.22 Because the time to test results was a loss rather than a benefit, we calculated monetary loss using the following equation:
Monetaryloss=Testingcost+Timecosts                                       =Testingcosts+Time×Averagewage

That is, monetary loss captured not only the direct medical costs associated with testing but also the indirect productivity costs in waiting for test results. Productivity costs could be regarded as a lower bound of willingness-to-accept.22 All costs were made equivalent to 2020 US dollars.

Because some patients with negative liquid-based NGS test results did not undergo rebiopsy or experienced failure with rebiopsy, we also compared the percentages of patients identified with FDA-approved therapies between the 2 strategies.

Sensitivity Analyses

To examine the robustness of the results, we performed a stacked 1-way sensitivity analysis by varying the testing costs, average wage, and turnaround time between 0 and twice the baseline values (Table 1); meanwhile, the EGFR-positive testing rate and rebiopsy probabilities were varied from 0% to 100%. We altered the tissue-based NGS turnaround time and EGFR mutation rate to determine the time to test results in both strategies. To apply the results to different circumstances, we also conducted a 3-way sensitivity analysis by varying the NGS testing costs, tissue-based NGS turnaround time, and EGFR mutation rate concomitantly. Notably, we assumed that the costs of tissue- and liquid-based NGS would change in the same manner, and we varied the NGS testing costs at between 0% and 200% of the baseline values. We used Amua software (version 0.3.0) to perform the analyses.

Scenario Analysis Using Data From US Caucasian Patients

Using parameter values from relevant research (supplemental eTable 1, available with this article at JNCCN.org),1,5,8,15,18,20,21,2326 we adopted the model to US Caucasian patients with different rates of gene alterations (supplemental eFigure 1).

Results

Main Results

For a patient newly diagnosed with metastatic lung adenocarcinoma, exclusionary EGFR testing followed by NGS incurred a test-related cost of US $1,387, a cost savings of US $1,091 compared with up-front NGS testing (Table 2). The time to test results for exclusionary testing of the EGFR mutation was 13.0 days, whereas for up-front NGS testing it was 13.6 days. If we converted the time to test results into productivity costs and compared the monetary loss, exclusionary EGFR testing followed by NGS resulted in a net monetary savings of US $1,116, without a reduction of patients identified with FDA-approved therapies.

Table 2.

Main Results per Patient

Table 2.

Sensitivity Analyses

Stacked 1-way sensitivity analysis (Figure 2A) revealed that the testing costs of NGS and the EGFR mutation rate had the greatest impact on minimizing monetary loss in Taiwan. Given that the baseline values of other parameters were not changed, up-front NGS testing would become the best strategy if the testing costs of NGS could be reduced to 16% of baseline values. Similarly, if the EGFR mutation rate was lower than 17.0%, then up-front NGS testing would also be preferable to exclusionary testing of the EGFR mutation. Varying the testing costs of EGFR mutation and rebiopsy, the turnaround time of different tests and rebiopsy, and the average wage between 0 and twice the baseline values did not change the results. As such, exclusionary EGFR testing followed by NGS remained the best strategy if the probabilities of patients needing rebiopsy and those needing and receiving rebiopsy were varied between 0% and 100%.

Figure 2.
Figure 2.

Stacked 1-way sensitivity analysis for minimizing monetary loss in (A) base case using Taiwanese data and (B) scenario analysis using data from US Caucasian patients. We performed a series of 1-way sensitivity analyses by varying parameter values in reasonable ranges (Table 1 and supplemental eTable 1) and determined the best strategy at each value. The dashed lines represent the baseline values.

Abbreviation: NGS, next-generation sequencing.

Citation: Journal of the National Comprehensive Cancer Network 20, 7; 10.6004/jnccn.2021.7120

The time to test results increased along with an increase in tissue-based NGS turnaround time or a decrease in the EGFR mutation rate in both strategies (supplemental eFigure 2). Given a baseline EGFR mutation rate of 55.7%, exclusionary EGFR testing was a timely strategy when the turnaround time of tissue-based NGS exceeded 21 days. A 3-way sensitivity analysis (Figure 3A) showed that up-front NGS testing was more likely to become the best strategy in minimizing monetary loss when the EGFR mutation rate, testing costs of NGS, and turnaround time of tissue-based NGS decreased. Given the EGFR mutation rate of 55.7% in Taiwan and the tissue-based NGS turnaround time shortened to 7 days, up-front NGS testing would be preferable to exclusionary testing of the EGFR mutation if its testing price was reduced to 28% of the baseline value, or US $568.

Figure 3.
Figure 3.

Three-way sensitivity analysis for the best strategy to minimize monetary loss in (A) base case using Taiwanese data and (B) scenario analysis using data from US Caucasian patients. The black dots represent the respective EGFR mutation rates and baseline NGS costs in Taiwan and the United States with different turnaround times of tissue-based NGS. (A) Exclusionary EGFR testing was the best strategy in Taiwan, irrespective of the tissue-based NGS turnaround time. Up-front NGS would be preferable if its testing price was reduced to 28%, 16%, and 6% of the baseline value when tissue-based NGS turnaround time was 7, 14, and 21 days, respectively (vertical arrows). (B) In general, up-front NGS was the best strategy in the United States, even if the tissue-based NGS turnaround time was up to 28 days. However, exclusionary EGFR testing would become a preferable strategy in a given population whose EGFR mutation rate was higher than 42%, 32%, 26%, and 22% when tissue-based NGS turnaround time was 7, 14, 21, and 28 days, respectively (horizontal arrows).

Abbreviation: NGS, next-generation sequencing.

Citation: Journal of the National Comprehensive Cancer Network 20, 7; 10.6004/jnccn.2021.7120

Scenario Analysis Using Data From US Caucasian Patients

Scenario analysis using parameter values from US Caucasian patients was also performed (Table 2). Up-front NGS, as compared with exclusionary EGFR testing, was not only more cost-efficient but also less time-consuming. It identified a similar percentage of patients with FDA-approved therapies. Up-front NGS became a preferable strategy in most circumstances using the data from US Caucasian patients (Figure 2B). However, we noted that exclusionary EGFR testing would be a preferable strategy in certain US populations, such as nonsmokers, whose EGFR mutation rate was expected to be higher (Figure 3B).

Discussion

Although a study based on Western populations demonstrated that up-front NGS testing, as compared with testing of single-gene alteration, was a cost-saving and time-efficient strategy,5 the results cannot be applied to East Asian populations with high rates of EGFR mutation.4 The current study widely searched the literature for gene alteration rates, false-negative rates of EGFR testing, and rebiopsy probabilities. A brief survey regarding the NGS costs and turnaround time was also conducted. By integrating cost and time together to compare the net monetary loss, we found that in areas with high prevalence rates of EGFR mutation, exclusionary EGFR testing followed by NGS appeared to be a better strategy than up-front NGS testing. Accordingly, the results from this study could help health administrations in countries with different prevalence rates of EGFR mutation plan their reimbursement policies for NGS testing.

Our study was in line with an investigation from Hong Kong, which showed that exclusionary testing of an EGFR mutation and ALK gene rearrangement was the best strategy in terms of reducing test-related cost and time to appropriate therapy.27 In contrast to our results, a Singaporean study found that the per-patient cost difference of up-front NGS versus exclusionary EGFR testing was only SGD $110 (US $80).28 Plausible explanations include a higher unit cost of EGFR testing (SGD $719 = US $521) and a lower unit cost of tissue-based NGS testing (SGD $1,235 = US $896) in that study compared with ours (Table 1). In the Singaporean study, the time to test results for exclusionary EGFR testing was 20 to 25 days, whereas the time for up-front NGS testing was 10 to 15 days. We supposed that they directly added the testing time for EGFR mutation to that for tissue-based NGS. However, patients who tested positive for EGFR mutation did not receive further NGS testing; therefore, the time in their exclusionary EGFR testing group might be overestimated.

We built a model using stacked 1-way and 3-way sensitivity analyses for minimizing monetary loss. The best strategy for molecular testing in a given population will depend on the results of interaction among the cost and turnaround time of the test and the prevalence of the predominant mutation. For example, given a tissue-based NGS cost of US $2,000 and a turnaround time of 14 days, up-front NGS testing might be the preferred strategy if the prevalence of EGFR mutations was <17.0% in Taiwan (Figure 3A). On the other hand, exclusionary EGFR testing might be the preferred strategy if the EGFR mutation rate was >32.0% in the United States (Figure 3B). In that regard, in general, exclusionary EGFR testing followed by NGS is the preferred molecular testing strategy for East Asian patients, but up-front NGS is the preferred strategy in Caucasian patients. However, exclusionary EGFR testing would be a preferred strategy in certain Caucasian populations, such as nonsmokers whose EGFR mutation rate was approximately 30% to 50%.29,30 Similarly, when the price of NGS decreases and the turnaround time shortens, given an increasing daily testing volume in the future, up-front NGS testing may become the best strategy, even in East Asian populations.

Based on prior evidence, we assumed that 16.5% of patients had insufficient specimens for tissue-based NGS20 and that 30% of those in need of rebiopsy underwent rebiopsy.5 The rebiopsy rate of 5.0% (=16.5% × 30%) exceeded the 1.8% rate reported in a previous study24 but was less than the 10% described in another.31 Nevertheless, in the stacked 1-way sensitivity analysis, we widely varied the probabilities between 0% and 100% to test the robustness of the results. Exclusionary testing of the EGFR mutation remained the best strategy for minimizing monetary loss (Figure 2A).

We did not treat HER2 as a targetable gene, despite several HER2-directed therapies3234 showing promising anticancer activity, because these agents had not been approved by the FDA. If we consider a HER2 mutation as an actionable gene alteration, the costs associated with testing, time-to-test results, and net monetary loss would not change, but the percentages of patients identified with FDA-approved therapies in exclusionary EGFR testing and up-front NGS strategies would decrease to 98.7% and 98.4%, respectively.

Several limitations must be acknowledged in this study. First, we did not consider posttesting treatments, which might bring about different survival and costs between the 2 testing strategies. In contrast to previous studies comparing NGS testing with the testing of just several gene alterations,35 nearly all negative EGFR results were directly followed by NGS testing in our study. Because exclusionary EGFR testing did not reduce the percentage of patients who should have been identified with FDA-approved therapies, the outcomes after 2 testing strategies are believed to be similar. Second, while calculating the time costs, we only considered the turnaround time of testing. This analysis did not consider the time spent in decision-making and treatment delays. Furthermore, we did not take transportation costs and payments to caregivers into account. Costs rendered for both groups of patients are believed to be underestimated. Third, we did not take downstream complications and related costs into consideration. Nevertheless, the estimated rebiopsy rate was 5.0%, and the complication rate after rebiopsy was approximately 4.4% according to a previous report.36 As such, the effect of downstream complications on the results seemed to be minimal. Last, an emerging biomarker of tumor mutation burden was not considered in our study. However, there is still no consensus on how to measure tumor mutation burden, and guidelines have not recommended its routine use in clinical practice.3

Conclusions

In areas with high prevalence rates of EGFR mutation, exclusionary testing of the EGFR mutation followed by NGS, rather than up-front NGS testing, is a cost-efficient strategy for patients with metastatic lung adenocarcinoma. In addition to the cost and turnaround time, the local prevalence of driver mutations should be taken into consideration when health administrations craft their NGS reimbursement policies.

References

  • 1.

    Dagogo-Jack I, Azzolli CG, Fintelmann F, et al. Clinical utility of rapid EGFR genotyping in advanced lung cancer [published online July 24, 2018]. JCO Precis Oncol, doi.org/10.1200/PO.17.00299

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Dagogo-Jack I, Robinson H, Mino-Kenudson M, et al. Expediting comprehensive molecular analysis to optimize initial treatment of lung cancer patients with minimal smoking history. J Thorac Oncol 2019;14:835843.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Ettinger DS, Wood DE, Aisner DL, et al. NCCN Clinical Practice Guidelines in Oncology: Non-Small Cell Lung Cancer. Version 2.2021. Accessed February 25, 2021. To view the most recent version, visit NCCN.org

    • Search Google Scholar
    • Export Citation
  • 4.

    Kohno T, Nakaoku T, Tsuta K, et al. Beyond ALK-RET, ROS1 and other oncogene fusions in lung cancer. Transl Lung Cancer Res 2015;4:156164.

  • 5.

    Pennell NA, Mutebi A, Zhou ZY, et al. Economic impact of next-generation sequencing versus single-gene testing to detect genomic alterations in metastatic non-small-cell lung cancer using a decision analytic model. JCO Precis Oncol 2019;3:19.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Rolfo C, Mack PC, Scagliotti GV, et al. Liquid biopsy for advanced non-small cell lung cancer (NSCLC): a statement paper from the IASLC. J Thorac Oncol 2018;13:12481268.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    National Health Insurance Administration. National Health Insurance medical service payment items and standards [in Mandarin]. Accessed December 13, 2021. Available at: https://www.nhi.gov.tw/Content_List.aspx?n=58ED9C8D8417D00B

    • Search Google Scholar
    • Export Citation
  • 8.

    Guardant360 CDx. How do you treat at the speed of cancer? The answers are in our blood. Accessed November 1, 2021. Available at: https://guardant360cdx.com

    • Search Google Scholar
    • Export Citation
  • 9.

    National Statistics, R.O.C. (Taiwan). Monthly income of major job for employees [in Mandarin]. Accessed December 13, 2021. Available at: https://www.stat.gov.tw/ct.asp?xItem=46590&ctNode=3579&mp=4

    • Search Google Scholar
    • Export Citation
  • 10.

    Hsu KH, Ho CC, Hsia TC, et al. Identification of five driver gene mutations in patients with treatment-naïve lung adenocarcinoma in Taiwan. PLoS One 2015;10:e0120852.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Wu YC, Chang IC, Wang CL, et al. Comparison of IHC, FISH and RT-PCR methods for detection of ALK rearrangements in 312 non-small cell lung cancer patients in Taiwan. PLoS One 2013;8:e70839.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Chen YF, Hsieh MS, Wu SG, et al. Clinical and the prognostic characteristics of lung adenocarcinoma patients with ROS1 fusion in comparison with other driver mutations in East Asian populations. J Thorac Oncol 2014;9:11711179.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Wu SG, Liu YN, Yu CJ, et al. Driver mutations of young lung adenocarcinoma patients with malignant pleural effusion. Genes Chromosomes Cancer 2018;57:513521.

  • 14.

    Gow CH, Hsieh MS, Wu SG, et al. A comprehensive analysis of clinical outcomes in lung cancer patients harboring a MET exon 14 skipping mutation compared to other driver mutations in an East Asian population. Lung Cancer 2017;103:8289.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15.

    Okamura R, Boichard A, Kato S, et al. Analysis of NTRK alterations in pan-cancer adult and pediatric malignancies: implications for NTRK-targeted therapeutics [published online November 15, 2018]. JCO Precis Oncol, doi.org/10.1200/PO.18.00183

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Wu SG, Liao WY, Su KY, et al. Prognostic characteristics and immunotherapy response of patients with nonsquamous NSCLC with KRAS mutation in East Asian populations: a single-center cohort study in Taiwan. JTO Clin Res Rep 2020;2:100140.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Gow CH, Chang HT, Lim CK, et al. Comparable clinical outcomes in patients with HER2-mutant and EGFR-mutant lung adenocarcinomas. Genes Chromosomes Cancer 2017;56:373381.

  • 18.

    Roche Diagnostics. cobas EGFR Mutation Test v2. Accessed January 21, 2022. Available at: https://diagnostics.roche.com/us/en/products/params/cobas-egfr-mutation-test-v2.html

    • Search Google Scholar
    • Export Citation
  • 19.

    Shen CI, Chiang CL, Luo YH, et al. The intrinsic limitation and clinical impact of mutant allele-specific real-time PCR-based EGFR mutation assay in NSCLC. J Thorac Oncol 2021;16:S966.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20.

    Goswami RS, Luthra R, Singh RR, et al. Identification of factors affecting the success of next-generation sequencing testing in solid tumors. Am J Clin Pathol 2016;145:222237.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Leighl NB, Page RD, Raymond VM, et al. Clinical utility of comprehensive cell-free DNA analysis to identify genomic biomarkers in patients with newly diagnosed metastatic non-small cell lung cancer. Clin Cancer Res 2019;25:46914700.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Drummond MF, Sculpher MJ, Claxton K, et al. Methods for the Economic Evaluation of Health Care Programmes. Oxford, UK: Oxford University Press; 2015.

    • Search Google Scholar
    • Export Citation
  • 23.

    Centers for Medicare & Medicaid Services. Clinical laboratory fee schedule files. Accessed November 1, 2021. Available at: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/ClinicalLabFeeSched/Clinical-Laboratory-Fee-Schedule-Files

    • Search Google Scholar
    • Export Citation
  • 24.

    Yu TM, Morrison C, Gold EJ, et al. Budget impact of next-generation sequencing for molecular assessment of advanced non-small cell lung cancer. Value Health 2018;21:12781285.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    U.S. Bureau of Labor Statistics. Occupational employment and wage statistics. Accessed November 1, 2021. Available at: https://www.bls.gov/oes/2020/may/oes_nat.htm

    • Search Google Scholar
    • Export Citation
  • 26.

    Calvayrac O, Pradines A, Pons E, et al. Molecular biomarkers for lung adenocarcinoma. Eur Respir J 2017;49:1601734.

  • 27.

    Loong H, Wong CKH, Leung LKS, et al. Economic impact of next-generation sequencing (NGS) versus single-gene testing modalities to detect genomic alterations (GAs) in metastatic non-small cell lung cancer (mNSCLC) in Asia. Ann Oncol 2020;31(Suppl 6):S13941395.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 28.

    Tan AC, Lai GGY, Tan GS, et al. Utility of incorporating next-generation sequencing (NGS) in an Asian non-small cell lung cancer (NSCLC) population: incremental yield of actionable alterations and cost-effectiveness analysis. Lung Cancer 2020;139:207215.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 29.

    Midha A, Dearden S, McCormack R. EGFR mutation incidence in non-small-cell lung cancer of adenocarcinoma histology: a systematic review and global map by ethnicity (mutMapII). Am J Cancer Res 2015;5:28922911.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30.

    Esteban E, Majem M, Martinez Aguillo M, et al. Prevalence of EGFR mutations in newly diagnosed locally advanced or metastatic non-small cell lung cancer Spanish patients and its association with histological subtypes and clinical features: the Spanish REASON study. Cancer Epidemiol 2015;39:291297.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Signorovitch J, Zhou Z, Ryan J, et al. Budget impact analysis of comprehensive genomic profiling in patients with advanced non-small cell lung cancer. J Med Econ 2019;22:140150.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32.

    Li BT, Shen R, Buonocore D, et al. Ado-trastuzumab emtansine for patients with HER2-mutant lung cancers: results from a phase II basket trial. J Clin Oncol 2018;36:25322537.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Tsurutani J, Iwata H, Krop I, et al. Targeting HER2 with trastuzumab deruxtecan: a dose-expansion, phase I study in multiple advanced solid tumors. Cancer Discov 2020;10:688701.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Prelaj A, Bottiglieri A, Proto C, et al. Poziotinib for EGFR and HER2 exon 20 insertion mutation in advanced NSCLC: results from the expanded access program. Eur J Cancer 2021;149:235248.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35.

    Schluckebier L, Caetano R, Garay OU, et al. Cost-effectiveness analysis comparing companion diagnostic tests for EGFR, ALK, and ROS1 versus next-generation sequencing (NGS) in advanced adenocarcinoma lung cancer patients. BMC Cancer 2020;20:875.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Yang SC, Lai CH, Kuo CW, et al. Downstream complications and healthcare expenditure after invasive procedures for lung lesions in Taiwan. Int J Environ Res Public Health 2021;18:4040.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation

Submitted August 4, 2021; final revision received December 8, 2021; accepted for publication December 8, 2021. Published online April 6, 2022.

Author contributions: Study design: Chiu. Data curation: Yeh, Chen. Model development and data analysis: Yang. Data interpretation: Yang, Chiu. Funding acquisition: Yang, Chiu. Investigation: Yang, Chiu. Project administration: Chiu. Manuscript preparation: All authors.

Disclosures: Dr. Chiu has disclosed receiving honoraria from AstraZeneca, Boehringer-Ingelheim, Bristol-Myers Squibb, Chugai Pharmaceutical, Eli Lilly, Merck Sharp & Dohme, Novartis, Ono Pharmaceutical, Pfizer, Roche, and Takeda, and serving on the advisory board for Boehringer-Ingelheim, Bristol-Myers Squibb, Eli Lilly, Merck Sharp & Dohme, Novartis, and Roche. The remaining authors have disclosed that they have not received any financial consideration from any person or organization to support the preparation, analysis, results, or discussion of this article.

Funding: This work was supported by Taipei Veterans General Hospital (V110C-106) and the Ministry of Science and Technology (110-2314-B-006-100-MY2).

Correspondence: Chao-Hua Chiu, MD, Division of Thoracic Oncology, Department of Chest Medicine, Taipei Veterans General Hospital, 201, Section 2, Shih-Pai Road, Taipei 112, Taiwan. Email: jhchiou@vghtpe.gov.tw

Supplementary Materials

  • View in gallery

    Decision tree analysis for minimizing monetary loss.

    Abbreviation: NGS, next-generation sequencing.

    aPatients were tested for EGFR mutation.

    bMonetary loss included testing costs and productivity costs; the latter was the product of turnaround time and average wage (Table 1).

  • View in gallery

    Stacked 1-way sensitivity analysis for minimizing monetary loss in (A) base case using Taiwanese data and (B) scenario analysis using data from US Caucasian patients. We performed a series of 1-way sensitivity analyses by varying parameter values in reasonable ranges (Table 1 and supplemental eTable 1) and determined the best strategy at each value. The dashed lines represent the baseline values.

    Abbreviation: NGS, next-generation sequencing.

  • View in gallery

    Three-way sensitivity analysis for the best strategy to minimize monetary loss in (A) base case using Taiwanese data and (B) scenario analysis using data from US Caucasian patients. The black dots represent the respective EGFR mutation rates and baseline NGS costs in Taiwan and the United States with different turnaround times of tissue-based NGS. (A) Exclusionary EGFR testing was the best strategy in Taiwan, irrespective of the tissue-based NGS turnaround time. Up-front NGS would be preferable if its testing price was reduced to 28%, 16%, and 6% of the baseline value when tissue-based NGS turnaround time was 7, 14, and 21 days, respectively (vertical arrows). (B) In general, up-front NGS was the best strategy in the United States, even if the tissue-based NGS turnaround time was up to 28 days. However, exclusionary EGFR testing would become a preferable strategy in a given population whose EGFR mutation rate was higher than 42%, 32%, 26%, and 22% when tissue-based NGS turnaround time was 7, 14, 21, and 28 days, respectively (horizontal arrows).

    Abbreviation: NGS, next-generation sequencing.

  • 1.

    Dagogo-Jack I, Azzolli CG, Fintelmann F, et al. Clinical utility of rapid EGFR genotyping in advanced lung cancer [published online July 24, 2018]. JCO Precis Oncol, doi.org/10.1200/PO.17.00299

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Dagogo-Jack I, Robinson H, Mino-Kenudson M, et al. Expediting comprehensive molecular analysis to optimize initial treatment of lung cancer patients with minimal smoking history. J Thorac Oncol 2019;14:835843.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Ettinger DS, Wood DE, Aisner DL, et al. NCCN Clinical Practice Guidelines in Oncology: Non-Small Cell Lung Cancer. Version 2.2021. Accessed February 25, 2021. To view the most recent version, visit NCCN.org

    • Search Google Scholar
    • Export Citation
  • 4.

    Kohno T, Nakaoku T, Tsuta K, et al. Beyond ALK-RET, ROS1 and other oncogene fusions in lung cancer. Transl Lung Cancer Res 2015;4:156164.

  • 5.

    Pennell NA, Mutebi A, Zhou ZY, et al. Economic impact of next-generation sequencing versus single-gene testing to detect genomic alterations in metastatic non-small-cell lung cancer using a decision analytic model. JCO Precis Oncol 2019;3:19.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Rolfo C, Mack PC, Scagliotti GV, et al. Liquid biopsy for advanced non-small cell lung cancer (NSCLC): a statement paper from the IASLC. J Thorac Oncol 2018;13:12481268.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    National Health Insurance Administration. National Health Insurance medical service payment items and standards [in Mandarin]. Accessed December 13, 2021. Available at: https://www.nhi.gov.tw/Content_List.aspx?n=58ED9C8D8417D00B

    • Search Google Scholar
    • Export Citation
  • 8.

    Guardant360 CDx. How do you treat at the speed of cancer? The answers are in our blood. Accessed November 1, 2021. Available at: https://guardant360cdx.com

    • Search Google Scholar
    • Export Citation
  • 9.

    National Statistics, R.O.C. (Taiwan). Monthly income of major job for employees [in Mandarin]. Accessed December 13, 2021. Available at: https://www.stat.gov.tw/ct.asp?xItem=46590&ctNode=3579&mp=4

    • Search Google Scholar
    • Export Citation
  • 10.

    Hsu KH, Ho CC, Hsia TC, et al. Identification of five driver gene mutations in patients with treatment-naïve lung adenocarcinoma in Taiwan. PLoS One 2015;10:e0120852.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Wu YC, Chang IC, Wang CL, et al. Comparison of IHC, FISH and RT-PCR methods for detection of ALK rearrangements in 312 non-small cell lung cancer patients in Taiwan. PLoS One 2013;8:e70839.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Chen YF, Hsieh MS, Wu SG, et al. Clinical and the prognostic characteristics of lung adenocarcinoma patients with ROS1 fusion in comparison with other driver mutations in East Asian populations. J Thorac Oncol 2014;9:11711179.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Wu SG, Liu YN, Yu CJ, et al. Driver mutations of young lung adenocarcinoma patients with malignant pleural effusion. Genes Chromosomes Cancer 2018;57:513521.

  • 14.

    Gow CH, Hsieh MS, Wu SG, et al. A comprehensive analysis of clinical outcomes in lung cancer patients harboring a MET exon 14 skipping mutation compared to other driver mutations in an East Asian population. Lung Cancer 2017;103:8289.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15.

    Okamura R, Boichard A, Kato S, et al. Analysis of NTRK alterations in pan-cancer adult and pediatric malignancies: implications for NTRK-targeted therapeutics [published online November 15, 2018]. JCO Precis Oncol, doi.org/10.1200/PO.18.00183

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Wu SG, Liao WY, Su KY, et al. Prognostic characteristics and immunotherapy response of patients with nonsquamous NSCLC with KRAS mutation in East Asian populations: a single-center cohort study in Taiwan. JTO Clin Res Rep 2020;2:100140.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Gow CH, Chang HT, Lim CK, et al. Comparable clinical outcomes in patients with HER2-mutant and EGFR-mutant lung adenocarcinomas. Genes Chromosomes Cancer 2017;56:373381.

  • 18.

    Roche Diagnostics. cobas EGFR Mutation Test v2. Accessed January 21, 2022. Available at: https://diagnostics.roche.com/us/en/products/params/cobas-egfr-mutation-test-v2.html

    • Search Google Scholar
    • Export Citation
  • 19.

    Shen CI, Chiang CL, Luo YH, et al. The intrinsic limitation and clinical impact of mutant allele-specific real-time PCR-based EGFR mutation assay in NSCLC. J Thorac Oncol 2021;16:S966.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20.

    Goswami RS, Luthra R, Singh RR, et al. Identification of factors affecting the success of next-generation sequencing testing in solid tumors. Am J Clin Pathol 2016;145:222237.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Leighl NB, Page RD, Raymond VM, et al. Clinical utility of comprehensive cell-free DNA analysis to identify genomic biomarkers in patients with newly diagnosed metastatic non-small cell lung cancer. Clin Cancer Res 2019;25:46914700.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Drummond MF, Sculpher MJ, Claxton K, et al. Methods for the Economic Evaluation of Health Care Programmes. Oxford, UK: Oxford University Press; 2015.

    • Search Google Scholar
    • Export Citation
  • 23.

    Centers for Medicare & Medicaid Services. Clinical laboratory fee schedule files. Accessed November 1, 2021. Available at: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/ClinicalLabFeeSched/Clinical-Laboratory-Fee-Schedule-Files

    • Search Google Scholar
    • Export Citation
  • 24.

    Yu TM, Morrison C, Gold EJ, et al. Budget impact of next-generation sequencing for molecular assessment of advanced non-small cell lung cancer. Value Health 2018;21:12781285.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    U.S. Bureau of Labor Statistics. Occupational employment and wage statistics. Accessed November 1, 2021. Available at: https://www.bls.gov/oes/2020/may/oes_nat.htm

    • Search Google Scholar
    • Export Citation
  • 26.

    Calvayrac O, Pradines A, Pons E, et al. Molecular biomarkers for lung adenocarcinoma. Eur Respir J 2017;49:1601734.

  • 27.

    Loong H, Wong CKH, Leung LKS, et al. Economic impact of next-generation sequencing (NGS) versus single-gene testing modalities to detect genomic alterations (GAs) in metastatic non-small cell lung cancer (mNSCLC) in Asia. Ann Oncol 2020;31(Suppl 6):S13941395.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 28.

    Tan AC, Lai GGY, Tan GS, et al. Utility of incorporating next-generation sequencing (NGS) in an Asian non-small cell lung cancer (NSCLC) population: incremental yield of actionable alterations and cost-effectiveness analysis. Lung Cancer 2020;139:207215.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 29.

    Midha A, Dearden S, McCormack R. EGFR mutation incidence in non-small-cell lung cancer of adenocarcinoma histology: a systematic review and global map by ethnicity (mutMapII). Am J Cancer Res 2015;5:28922911.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30.

    Esteban E, Majem M, Martinez Aguillo M, et al. Prevalence of EGFR mutations in newly diagnosed locally advanced or metastatic non-small cell lung cancer Spanish patients and its association with histological subtypes and clinical features: the Spanish REASON study. Cancer Epidemiol 2015;39:291297.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Signorovitch J, Zhou Z, Ryan J, et al. Budget impact analysis of comprehensive genomic profiling in patients with advanced non-small cell lung cancer. J Med Econ 2019;22:140150.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32.

    Li BT, Shen R, Buonocore D, et al. Ado-trastuzumab emtansine for patients with HER2-mutant lung cancers: results from a phase II basket trial. J Clin Oncol 2018;36:25322537.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Tsurutani J, Iwata H, Krop I, et al. Targeting HER2 with trastuzumab deruxtecan: a dose-expansion, phase I study in multiple advanced solid tumors. Cancer Discov 2020;10:688701.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Prelaj A, Bottiglieri A, Proto C, et al. Poziotinib for EGFR and HER2 exon 20 insertion mutation in advanced NSCLC: results from the expanded access program. Eur J Cancer 2021;149:235248.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35.

    Schluckebier L, Caetano R, Garay OU, et al. Cost-effectiveness analysis comparing companion diagnostic tests for EGFR, ALK, and ROS1 versus next-generation sequencing (NGS) in advanced adenocarcinoma lung cancer patients. BMC Cancer 2020;20:875.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Yang SC, Lai CH, Kuo CW, et al. Downstream complications and healthcare expenditure after invasive procedures for lung lesions in Taiwan. Int J Environ Res Public Health 2021;18:4040.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
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