Cost-Effectiveness of DPYD Genotyping Prior to Capecitabine Administration for Metastatic Breast Cancer

Cost-Effectiveness of DPYD Genotyping Prior to Capecitabine Administration for Metastatic Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cost-Effectiveness of DPYD Genotyping Prior to Capecitabine Administration for Metastatic Breast Cancer Tanvi Chiddarwar, Anne Blaes, Karen Kuntz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7992444/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Mar, 2026 Read the published version in Breast Cancer Research and Treatment → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose Patients with a DPYD genetic deficiency who receive capecitabine are at increased risk of severe, potentially fatal toxicities due to impaired drug metabolism. Genetic testing for this deficiency allows for proactive dose adjustments to mitigate these risks. We evaluated the cost-effectiveness of DPYD genotyping prior to capecitabine administration, followed by dose modification for patients with metastatic breast cancer. Methods We developed a state-transition model to simulate health outcomes and costs for a cohort of 62-year-old women with metastatic breast cancer from the perspective of the U.S. healthcare payer. Costs and utilities were derived from the literature to calculate quality-adjusted life years (QALYs) and the incremental cost-effectiveness ratio (ICER) for DPYD genotyping compared to no DPYD genotyping. We conducted deterministic and probabilistic sensitivity analyses to identify factors influencing cost-effectiveness. Results The genotyping strategy was cost-effective, with a cost of $ 2,832 yielding 1.16 QALYs, compared to $ 2,677 and 1.15 QALYs for the no-genotyping strategy. This resulted in an ICER of $ 13,028/QALY and $ 9,973 per life-year-gained. In probabilistic sensitivity analysis, the genotyping strategy was cost-effective in 99% of the simulations, using a willingness-to-pay threshold of $ 100,000/QALY. Results from scenario analyses testing key assumptions also showed that genotyping is cost-effective. Conclusions Our findings support the implementation of DPYD genotyping prior to capecitabine initiation in metastatic breast cancer patients. This strategy exemplifies the value of personalized medicine and pharmacogenomics in improving treatment safety and effectiveness. As sequencing technologies advance and become affordable, integration of genotyping into routine oncology care is increasingly feasible. DPYD genotyping capecitabine metastatic breast cancer cost-effectiveness analysis pharmacogenomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Breast cancer is the most common cancer among women, accounting for approximately 15% of newly diagnosed cancer cases, with 6% of these cases being metastatic at diagnosis. 1 Among the treatment options for metastatic breast cancer, capecitabine stands out as a widely used monotherapy. This oral chemotherapy drug is converted into its active form, 5-fluorouracil (5-FU), a cornerstone of cancer treatment for decades due to its ability to effectively inhibit the growth and division of cancer cells. 2 However, the role of the DPYD gene in capecitabine metabolism is critical. The DPYD gene encodes dihydropyrimidine dehydrogenase (DPD), the enzyme responsible for the rate-limiting step in 5-FU metabolism. Genetic variations in DPYD can lead to reduced or absent DPD activity, causing an accumulation of 5-FU in the body and significantly increasing the risk of severe toxicity. 3 , 4 This heightened toxicity can manifest as life-threatening conditions, including diarrhea, bone marrow suppression, gastrointestinal toxicity, hand-foot syndrome, and neutropenia, often requiring hospitalization. 4 , 5 The body of evidence supporting the use of DPYD genotyping before initiating capecitabine treatment continues to grow. Meta-analyses across various cancer types have demonstrated a significant association between DPYD variant carriers and treatment-related mortality, underscoring the importance of identifying these mutations to predict and prevent serious side effects. 6 , 7 In metastatic breast cancer patients treated with capecitabine, a study on the clinical implementation of pre-treatment DPYD genotyping highlighted its successful integration into routine practice to reduce the risk of severe fluoropyrimidine toxicities. 8 Importantly, research has shown that using pharmacogenomics to guide treatment decisions—such as adjusting treatment intensity based on DPYD genotyping—does not adversely affect treatment efficacy in variant carriers. 9 , 10 Furthermore, the cost of managing toxicities in patients with DPYD variants far exceeds the cost of genetic testing, as these patients are more likely to require hospitalization. 9 Prior cost-effectiveness analyses in colorectal cancer have demonstrated that DPYD genotyping before treatment is a cost-effective strategy. 11 – 13 Despite growing evidence supporting the benefits of pre-treatment DPYD genotyping for improving patient outcomes and its cost-effectiveness, neither the American Society of Clinical Oncology (ASCO) nor the National Comprehensive Cancer Network (NCCN) currently includes specific guidelines for its use. While both organizations acknowledge the increased risk of toxicity in individuals with DPYD variants, they remain hesitant to fully endorse testing, citing concerns about the strength of the evidence. 14 In contrast, the Clinical Pharmacogenetics Implementation Consortium, which provides evidence-based guidelines for integrating pharmacogenetic information into clinical practice, recommends dose reductions for patients identified as slower fluorouracil metabolizers. 15 Similarly, the European Medicines Agency actively recommends testing for DPD deficiency before starting fluoropyrimidine-based chemotherapy, citing its potential to significantly reduce treatment-related toxicities. 16 Building on this momentum, in 2024, the FDA updated safety labels for fluorouracil injection products to warn of the increased risk of severe adverse effects in patients with DPD deficiency. 17 To ensure that patient care reaches its full potential, it is important that we use the power of pharmacogenomics to proactively prevent avoidable toxicities and fatalities. This paper looks at the use of capecitabine therapy for metastatic breast cancer, a strategy proven to significantly prolong progression-free survival and overall survival. 18 Our aim is to assess the cost-effectiveness of DPYD genotyping prior to initiating capecitabine treatment to adjust dosages in metastatic breast cancer patients. The findings from our analysis can provide valuable insights for decision-makers considering the adoption of genotyping as a standard practice in the United States. Model Model Overview We developed a cohort state-transition model to evaluate the cost-effectiveness of genotyping prior to administering capecitabine compared to no genotyping for a hypothetical cohort of 62-year-old women newly diagnosed with metastatic breast cancer. The model assessed lifetime costs and quality-adjusted life years (QALYs) from the perspective of the U.S. healthcare payer. It included four health states: Progression-Free (Starting Dose), Progression-Free (Reduced Dose), Progressed, and Death, as depicted in Figure 1. We built a cohort-state transition model with a monthly cycle length to simulate health outcomes and costs over a lifetime. Costs and QALYs were discounted at an annual rate of 3%, following recommendations from the Second Panel on Cost-Effectiveness in Health and Medicine. 19 These values were used to calculate the incremental cost effectiveness ratio (ICER) of genotyping followed by dose adjustments compared to no genotyping while using a commonly accepted willingness-to-pay threshold of $100,000/QALY. 20 All analyses were conducted using TreeAge Pro 2023 (TreeAge Software Inc., Williamstown, MA, USA), and results were reported in compliance with the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) guidelines, detailed in eTable 1 of the supplement. 21 Clinical Parameters In our simulated cohort, patients with advanced breast cancer started in the Progression-Free (Starting-Dose) state. If they experienced toxicity, they could transition to the Progression-Free (Reduced-Dose) state. Patients in both Progression-Free states—Starting-Dose and Reduced-Dose—could either progress or die. In the genotyping arm, patients were stratified by their DPYD genotype. People are typically classified as normal, intermediate, or poor metabolizers, with the latter two groups combined as DPYD variant carriers. Those with a DPYD variant received a reduced dose (75%) of capecitabine, while patients with the wild type received the standard dose. Conversely, in the no-genotyping strategy, all patients received the standard dose regardless of genotype. Patients who experienced a toxicity event had their dose reduced to 75% in subsequent cycles, and further reduced to 50% if they were already on a reduced-dose regimen. Once a patient progressed, treatment was discontinued as it was deemed ineffective at that stage. Death could result from disease progression, background mortality, or early-treatment-related toxicity; however, we assumed that metastatic breast cancer-related deaths occurred solely due to disease progression. A simplified framework for the model is presented in Figure 2. We assumed that dose reduction due to toxicity did not diminish drug effectiveness, as supported by evidence of this phenomenon in metastatic colon cancer, but we did test this assumption in sensitivity analysis. 22 For patients with reduced doses (75%), we also assumed a proportional reduction in both the probability of toxicity and the cost of the drug, the latter of which was evaluated in sensitivity analyses. Toxicity was defined as grade 3 or 4 chemotherapy-related events, and patients experiencing toxicities were assigned a disutility. Grade 3 toxicities were classified as severe or medically significant but not life-threatening, while grade 4 toxicities were considered life-threatening. 23 Hospitalizations due to toxicity, lasted five days and incurred additional costs and disutility. Our model incorporated parameter estimates from various sources, listed in Table 1. Prevalence of DPYD variants was drawn from existing literature, indicating a range of 2%–8%. Among individuals of European descent, the prevalence was 3%–5%, while it was higher at approximately 8% in individuals of African descent. 4,11,24 The probability of disease progression and dying subsequently were calibrated using the progression-free survival and overall survival curves from a randomized clinical trial evaluating capecitabine as a first-line chemotherapy for breast cancer. 18 Background mortality rates were sourced from the CDC US life tables. 25 The probabilities of experiencing grade 3 or 4 toxicities and hospitalizations were obtained from a prospective DPYD genotyping study involving patients initiating fluoropyrimidine-based therapy. That study provided separate estimates for individuals with DPYD variants receiving reduced or standard doses and those with wild-type DPYD receiving standard doses. 26 Finally, probabilities of early treatment-related death were derived from studies conducted by Sharma et al. and Brooks et al. 7,11 Costs We accounted for the costs of DPYD genotyping, capecitabine, and hospitalization for toxicities, as detailed in Table 1, with all costs reported in 2024 U.S. dollars. The cost of DPYD genotyping came from the Clinical Laboratory Fee Schedule of the Centers for Medicare and Medicaid Services (CMS) using the HCPCS code 81232. 27 Capecitabine costs for a monthly treatment cycle were derived from the CMS Part B Average Sales Price file (June 2024) using the HCPCS code J8521. 28 Dosing calculations were based on a body surface area of 1.8 m², following the FDA-recommended intermittent regimen of 1,250 mg/m² taken twice daily for 14 days of a 21-day cycle, with treatment continuing until disease progression. 29 Since capecitabine is administered orally, infusion-related costs were not included. Hospitalization costs associated with adverse events were estimated using data from a real-world study. 30 Costs related to disease management or death were assumed to be equivalent across both arms of the analysis. Utilities Utilities for the progression-free and progressed states of metastatic breast cancer were obtained from a study that used the standard gamble technique to elicit utilities from a general population. 31 Utilities for grade 3/4 toxicities were sourced from a study that employed EQ-5D scores to assess the quality of life in metastatic breast cancer patients treated with capecitabine. 32 The utility associated with hospitalization due to toxicity was estimated in a separate study. 11 Sensitivity Analysis To assess the impact of model parameters, we performed both deterministic and probabilistic sensitivity analyses. Parameter ranges were sourced from the literature; when unavailable, we varied values by ±25%. For the probabilistic sensitivity analysis, parameters were varied simultaneously using prespecified distributions in 10,000 Monte Carlo simulations, with results presented as an ICER scatterplot. Additionally, a one-way sensitivity analysis was conducted to evaluate the effect of population-level variant probabilities. We further examined our model assumptions through scenario analyses. These included testing the assumption that capecitabine costs decrease with dose reduction and assessing whether reduced efficacy from dose reduction impacts cost-effectiveness. Results Base Case Results The genotyping strategy was found to be more effective than the no-genotyping strategy. Base-case results from our analysis are presented in Table 2 . The estimated life expectancy was 2.23 years for the genotyping arm compared to 2.22 years for the no-genotyping arm. When incorporating quality of life, patients in the genotyping arm had 1.16 QALYs, compared to 1.15 QALYs in the no-genotyping arm. While the genotyping arm was $ 154 more costly, with total costs of $ 2,832 versus $ 2,677 for the no-genotyping arm, it was cost-effective. The analysis resulted in an ICER of $ 13,027 per QALY and $ 9,973 per life year gained, which is well below the commonly accepted willingness-to-pay threshold of $ 100,000/QALY in the United States. Sensitivity/Scenario Analysis We conducted a deterministic sensitivity analysis to examine the impact of varying the population probability of the DPYD variant. While the average probability is 6.3%, we tested a range between 3% and 8%, and the ICER remained below the $ 100,000/QALY threshold across the entire range, as shown in Fig. 3 . For the probabilistic sensitivity analysis, we conducted 10,000 Monte Carlo simulations using prespecified parameter distributions. The results, illustrated in the ICER scatterplot (Fig. 4 ), indicate that the genotyping strategy is cost-effective in 99% of simulations, including 12% of the simulations where the strategy is cost-saving—achieving greater effectiveness at a lower cost compared to no genotyping. In our first scenario analysis, we excluded drug costs, effectively assuming that capecitabine costs are not influenced by the chosen strategy. Without accounting for drug costs, the genotyping strategy cost $ 150 more than no genotyping, with an ICER of $ 12,707/QALY, remaining cost-effective at the $ 100,000/QALY threshold. Next, we modeled a scenario in which effectiveness declines with dose reduction, that is the probability of progression increases in proportion to the decrease in dosage. Under this assumption, we calculated an ICER of $ 34,200/QALY, also remaining cost-effective. Although, real-world studies indicate that dose adjustments may not necessarily compromise health outcomes. 9 , 10 Overall, the results appear robust to both parameter uncertainty and variations in key assumptions. Discussion To our knowledge, this is the first study to evaluate the cost-effectiveness of DPYD genotyping prior to capecitabine administration compared to no genotyping in metastatic breast cancer patients in the United States. We found that the genotyping strategy was both more effective and slightly more costly, with an ICER of $ 13,027/QALY, making it cost-effective at the commonly accepted US threshold of $ 100,000/QALY from the healthcare payer perspective. These findings remained robust across sensitivity analyses and assumption testing. Our results align with findings from a similar study conducted in the UK, the Netherlands, and Hungary, where DPYD testing prior to fluorouracil administration was also shown to be cost-effective. 33 As one of the oldest and most widely used chemotherapies, ensuring the safety of fluoropyrimidines should remain a priority. Our study adds to the growing body of evidence supporting the integration of genotyping into routine clinical practice to improve patient outcomes. Establishing DPYD genotyping as a standard practice in oncology is essential, particularly given that the US National Institutes of Health estimates approximately 1,300 annual deaths attributable to DPD deficiency—equivalent to 0.5% of patients treated with fluoropyrimidines. 24 While the overall probability of harboring DPYD variants is not exceedingly high, the associated risks, healthcare costs, and patient disutilities are substantial. Notably, African American individuals carry a disproportionate burden due to the higher prevalence of these variants, highlighting the urgency for widespread testing. A deeper analysis by race and gender further revealed that African American women exhibited the lowest DPD enzyme activity among all race-gender groups, suggesting that women of African descent may face an elevated risk of fluoropyrimidine-related toxicity. 34 Although the FDA has issued safety warnings emphasizing the elevated toxicity risk in individuals with DPD deficiency, translating this information into clinical practice remains a necessity. We urgently need increased awareness and better direction through professional society guidelines. A survey of oncologists revealed a clear gap between awareness and practice: while 98% agreed that patients with DPD deficiency are at higher risk of toxicity, and 96% indicated they would adjust fluoropyrimidine dosing for known deficiency, only 32% considered pretreatment DPYD testing useful for guiding treatment, and just 20% had ever ordered the test. The primary barriers to testing were the perceived low prevalence of DPD deficiency (54%) and the lack of strong clinical practice guideline recommendations (48%). 35 This disconnect highlights the urgent need for targeted educational initiatives, clearer clinical guidelines, and the integration of DPYD genotyping into standard oncology practice to improve patient safety and treatment outcomes. Uridine triacetate, an FDA-approved oral pyrimidine analog, serves as an antidote to mitigate the toxic effects of excessive fluoropyrimidines. It is believed to compete for receptors on normal cells, thereby reducing toxicity. 36 The drug is approved for emergency treatment of severe, early-onset, or life-threatening toxicity within 96 hours of completing 5-FU or capecitabine administration in both adult and pediatric patients. 37 The timing of administration is critical. While most patients in clinical trials received treatment within the recommended 96-hour window, mortality was significantly higher among those treated beyond this period—half of these patients died. 36 However, the high cost of uridine triacetate presents a major challenge. The full 20-dose regimen can cost up to $ 89,000 in the US. 38 Cost barriers are further exacerbated by limited insurance coverage: CMS Part D plans do not cover the drug, and coverage among private insurers remains inconsistent. 39 Furthermore, there are currently no generic alternatives available, adding to the financial burden. We find ourselves in a pivotal moment where the tools of genetics allow us to precisely tailor medications to specific populations, particularly those who are severely ill. By harnessing the power of pharmacogenomics, we can achieve optimal therapeutic outcomes, preventing unnecessary suffering and loss of life. Some of the hesitation may come from the lack of prospective studies and trials supporting using genetics and the fear that reducing the dosage may reduce the effectiveness of the drugs. While critics of genetic testing often highlight cost as a barrier, advancements in technology have made genotyping faster and more affordable, with results typically available in under a week. Given the relatively low cost and the ability to prevent severe toxicities, integrating DPYD genotyping into routine oncology chemotherapy practices is increasingly practical and advantageous. Additionally with the advent of Next generation sequencing and increasing amount of people using it, additional DPYD genotyping may not prove to be an additional burden of genotyping. We are at a pivotal moment where advancements in genetics enable us to tailor medications with precision, particularly for critically ill populations. Pharmacogenomics offers a powerful opportunity to optimize therapeutic outcomes, preventing unnecessary suffering and loss of life. However, hesitation remains, often stemming from the lack of large-scale prospective studies and concerns that dose reductions based on genetic testing might compromise treatment efficacy. And, while cost is frequently cited as a barrier to genetic testing, technological advancements have made genotyping both faster and more affordable, with results typically available within a week. Moreover, with the growing adoption of next-generation sequencing and its widespread use, incorporating DPYD genotyping may not impose significant additional burdens on existing clinical workflows. Our analysis has some limitations that should be considered when interpreting the results. First, we faced challenges in obtaining all necessary parameters, which required us to make certain assumptions. For example, we assumed that reducing the dosage by a specific percentage would proportionally decrease both the risk of toxicity and the associated drug costs. Second, we did not account for cases of complete DPD deficiency, which could further underscore the importance of DPYD genotyping, as these patients are at a heightened risk of experiencing more severe toxicities. Third, our analysis did not incorporate the sensitivity and specificity of the genotyping test. Fourth, this analysis takes a US perspective, using Medicare prices and focusing primarily on older individuals. Finally, we did not include the costs of uridine triacetate, an expensive drug, that would likely make DPYD genotyping appear even more favorable in cost-effectiveness analyses. Future research in this area should focus on conducting more prospective studies to strengthen the evidence supporting routine DPYD genotyping prior to administering fluoropyrimidines. Conclusion Patients with a DPYD variant face an increased risk of toxicities from fluorouracil treatments. In our study, we found that implementing DPYD genotyping prior to initiating capecitabine treatment in metastatic breast cancer patients was cost-effective in the United States. Our findings offer valuable insights for decision-makers evaluating the adoption of genotyping as a standard practice in oncology. Embracing this approach marks a significant step toward personalized medicine, demonstrating a commitment to optimizing treatment efficacy while minimizing preventable harm. As we advance, it is crucial to prioritize and invest in this promising field, creating a future where every individual receives the safest and most effective treatment possible. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Tanvi Chiddarwar and Karen Kuntz. The first draft of the manuscript was written by Tanvi Chiddarwar and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The data that is used to build the model is available in publicly available peer-reviewed research. References Cancer of the Breast (Female) - Cancer Stat Facts. SEER. Accessed March 2, 2024. https://seer.cancer.gov/statfacts/html/breast.html Wagstaff AJ, Ibbotson T, Goa KL. Capecitabine. Drugs . 2003;63(2):217-236. doi:10.2165/00003495-200363020-00009 van Kuilenburg ABP. Dihydropyrimidine dehydrogenase and the efficacy and toxicity of 5-fluorouracil. 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Patterns of de novo metastasis and survival outcomes by age in breast cancer patients: a SEER population-based study. Front Endocrinol (Lausanne) . 2023;14:1184895. doi:10.3389/fendo.2023.1184895 Tables Table 1 Model Parameters Parameter Name Base Case Distributions for Probabilistic sensitivity analysis Source General Information Age 62 - Xiao et al. 40 Annual discount rate 3% - 2nd Panel 19 Body surface area 1.8 m2 - Background mortality - CDC Lifetables 25 Clinical Parameters Probability of carrying a DPYD gene variant 0.063 Beta (6.3, 93) Brooks et al. 11 Dean et al. 4 Monthly Probability of Grade 3 or 4 toxicity Henricks et al. 26 DPYD variant, standard dose 0.08 Beta (8.6, 94.5) DPYD variant, reduced-dose 0.044 Beta (3, 65.5) DPYD wild type, standard dose 0.026 Beta (23, 879) Monthly Probability of Hospitalization Henricks et al. 26 DPYD variant, standard dose 0.4 Beta (12, 18.9) DPYD variant, reduced-dose 0.54 Beta (41.6, 35.4) DPYD wild type, standard dose 0.6 Beta (28.4, 18.9) Monthly Probability of treatment-related death DPYD variant, standard dose 0.002 Triangular (0.001, 0.003, 0.002) Sharma et al. 7 DPYD variant, reduced-dose 0.0002 Triangular (0.0001, 0.0003, 0.0002) Brooks et al. 11 DPYD wild type, standard dose 0.0001 Triangular (0, 0.0002, 0.0001) Sharma et al. 7 Monthly Transition Probabilities Stockler et al. 18 Progression-free → Progressed 0.11 Beta (11, 89) Progressed → Death 0.051 Beta (5.1, 95) Costs ($) Cost of capecitabine [HCPCS:J8521] $ 0.64/500 mg - CMS ASP 2024 28 Monthly cost of capecitabine 115.56 Gamma (116, 29) CMS ASP 2024 28 Cost of DPYD genotyping test [CPT: 81232] 174.81 Gamma (174, 44) CMS Clinical Lab Fee schedule 27 Hospitalization cost 12907 Gamma (12907, 6450) Hassett et al. 30 Health State Utilities Progression-free 0.715 Beta (7.2, 2.9) Lloyd et al. 31 Progressed 0.443 Beta (4.4, 5.6) Lloyd et al. 31 Hospitalization (Disutility) Applied for one week -0.28 Beta (28,72) Brooks et al. 11 Grade 3/4 Toxicity -0.125 Beta (13.9, 97) Sherrill et al. 32 Table 2 Results of Cost-effectiveness Analysis Strategy No Genotyping Genotyping Total Cost $ 2,677 $ 2,832 Incremental total cost - $ 154 QALYs 1.15 1.16 Incremental QALY - 0.01 Life-years 2.22 2.23 Incremental life-years - 0.02 ICER $ /QALY - 13,027 $ /Life-year - 9,973 Additional Declarations No competing interests reported. Supplementary Files Supplement.docx Cite Share Download PDF Status: Published Journal Publication published 28 Mar, 2026 Read the published version in Breast Cancer Research and Treatment → Version 1 posted Editorial decision: Revision requested 12 Dec, 2025 Reviews received at journal 10 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviewers invited by journal 16 Nov, 2025 Editor assigned by journal 01 Nov, 2025 Submission checks completed at journal 01 Nov, 2025 First submitted to journal 30 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7992444","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":547548250,"identity":"bffb43fe-0ddf-46f7-bdae-b3040a54da31","order_by":0,"name":"Tanvi Chiddarwar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYDACCcYGCR4DCTl+CJeZWC0VNsaSDcRrASKeM2mJGw4Qq4V/dnPjjbdthxk338hOe8BQYZ3YQNCSOwebLee2HWY2u5G73YDhTDphLQYSiW3SvG2H2YBatkkwth0mXguP8QyQln/EagF6X8JAAqSlgQgtEjcSmy3nVNgYSJx5u00i4Vi6MUEt/DPSH954YyBR398OtOVDjbUsQS2oIIE05aNgFIyCUTAKcAEADvc/p3EJadYAAAAASUVORK5CYII=","orcid":"","institution":"University of Minnesota","correspondingAuthor":true,"prefix":"","firstName":"Tanvi","middleName":"","lastName":"Chiddarwar","suffix":""},{"id":547548251,"identity":"8f7801c3-8d88-41d7-8280-4ef209e8c20f","order_by":1,"name":"Anne Blaes","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"","lastName":"Blaes","suffix":""},{"id":547548252,"identity":"36a73552-124e-461a-a333-320db909b2e7","order_by":2,"name":"Karen Kuntz","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Karen","middleName":"","lastName":"Kuntz","suffix":""}],"badges":[],"createdAt":"2025-10-30 20:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7992444/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7992444/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10549-026-07948-y","type":"published","date":"2026-03-28T16:10:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96854844,"identity":"974f4d5e-d41c-46e9-8774-67385382674f","added_by":"auto","created_at":"2025-11-26 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18:53:41","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122337,"visible":true,"origin":"","legend":"","description":"","filename":"a588943f961d4da6ac7ebc0aff39bc541structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7992444/v1/2f4070bb9ef8cd784cbb1e96.xml"},{"id":96854855,"identity":"e28aaac7-7b9d-4f3a-b901-4e3598c539ca","added_by":"auto","created_at":"2025-11-26 18:53:41","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132506,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7992444/v1/0d4a3510f2bc92d85f97d9b4.html"},{"id":96854842,"identity":"37272957-318b-4f50-aa73-5b14095f2828","added_by":"auto","created_at":"2025-11-26 18:53:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHealth-states\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe model starts with a hypothetical cohort of patients with metastatic breast cancer, all initially in the progression-free (starting dose) state. From there, patients can either remain in the same state, have a dose reduction, experience disease progression, or die.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7992444/v1/e9b15c8a78b8d8e9ece45f81.png"},{"id":96854845,"identity":"518f85ca-83d5-4188-98ba-3aa6cd30e98b","added_by":"auto","created_at":"2025-11-26 18:53:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":175823,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSimplified Model Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the model, patients either underwent DPYD testing or did not, followed by capecitabine treatment. The diagram illustrates the sequence of events following diagnosis.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7992444/v1/3ed8174a2a723540323687d1.png"},{"id":96854843,"identity":"9f6c5228-ca76-441d-8c30-f036dba68187","added_by":"auto","created_at":"2025-11-26 18:53:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9867,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOne-way sensitivity analysis – Probability of DPYD Variant\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne-way sensitivity analysis evaluating the impact of varying the probability of a \u003cem\u003eDPYD\u003c/em\u003evariant on the ICER, with a baseline value of 6.3%. The genotyping strategy remained cost-effective across all tested values at a willingness-to-pay threshold of $100,000/QALY.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7992444/v1/2a8dc17fe366017823827a55.png"},{"id":96918087,"identity":"9a081de9-3259-4af1-b6e9-2b7d122cf236","added_by":"auto","created_at":"2025-11-27 14:11:09","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":423050,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eICER Scatterplot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe probabilistic sensitivity analysis, based on 10,000 Monte Carlo simulations, showed that DPYD genotyping was cost-effective in 99% of cases at a willingness-to-pay threshold of $100,000, as illustrated by the ICER scatterplot.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7992444/v1/8eb4278cf1c48ae4aa779097.jpeg"},{"id":105755510,"identity":"36acbada-5575-41a6-8f3c-3c4dc37a962c","added_by":"auto","created_at":"2026-03-30 16:27:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1349631,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7992444/v1/365b9942-ae27-4c4d-bad5-b98d230bc10a.pdf"},{"id":96854847,"identity":"2ae50794-6bb8-43fe-83a1-b9b56310911d","added_by":"auto","created_at":"2025-11-26 18:53:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19752,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-7992444/v1/62dabf004489cabebffdbeff.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cost-Effectiveness of DPYD Genotyping Prior to Capecitabine Administration for Metastatic Breast Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is the most common cancer among women, accounting for approximately 15% of newly diagnosed cancer cases, with 6% of these cases being metastatic at diagnosis.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Among the treatment options for metastatic breast cancer, capecitabine stands out as a widely used monotherapy. This oral chemotherapy drug is converted into its active form, 5-fluorouracil (5-FU), a cornerstone of cancer treatment for decades due to its ability to effectively inhibit the growth and division of cancer cells.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e However, the role of the DPYD gene in capecitabine metabolism is critical. The DPYD gene encodes dihydropyrimidine dehydrogenase (DPD), the enzyme responsible for the rate-limiting step in 5-FU metabolism. Genetic variations in DPYD can lead to reduced or absent DPD activity, causing an accumulation of 5-FU in the body and significantly increasing the risk of severe toxicity.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e This heightened toxicity can manifest as life-threatening conditions, including diarrhea, bone marrow suppression, gastrointestinal toxicity, hand-foot syndrome, and neutropenia, often requiring hospitalization.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe body of evidence supporting the use of DPYD genotyping before initiating capecitabine treatment continues to grow. Meta-analyses across various cancer types have demonstrated a significant association between DPYD variant carriers and treatment-related mortality, underscoring the importance of identifying these mutations to predict and prevent serious side effects.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e In metastatic breast cancer patients treated with capecitabine, a study on the clinical implementation of pre-treatment DPYD genotyping highlighted its successful integration into routine practice to reduce the risk of severe fluoropyrimidine toxicities.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Importantly, research has shown that using pharmacogenomics to guide treatment decisions\u0026mdash;such as adjusting treatment intensity based on DPYD genotyping\u0026mdash;does not adversely affect treatment efficacy in variant carriers.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Furthermore, the cost of managing toxicities in patients with DPYD variants far exceeds the cost of genetic testing, as these patients are more likely to require hospitalization.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Prior cost-effectiveness analyses in colorectal cancer have demonstrated that DPYD genotyping before treatment is a cost-effective strategy.\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e Despite growing evidence supporting the benefits of pre-treatment DPYD genotyping for improving patient outcomes and its cost-effectiveness, neither the American Society of Clinical Oncology (ASCO) nor the National Comprehensive Cancer Network (NCCN) currently includes specific guidelines for its use. While both organizations acknowledge the increased risk of toxicity in individuals with DPYD variants, they remain hesitant to fully endorse testing, citing concerns about the strength of the evidence.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e In contrast, the Clinical Pharmacogenetics Implementation Consortium, which provides evidence-based guidelines for integrating pharmacogenetic information into clinical practice, recommends dose reductions for patients identified as slower fluorouracil metabolizers.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Similarly, the European Medicines Agency actively recommends testing for DPD deficiency before starting fluoropyrimidine-based chemotherapy, citing its potential to significantly reduce treatment-related toxicities.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Building on this momentum, in 2024, the FDA updated safety labels for fluorouracil injection products to warn of the increased risk of severe adverse effects in patients with DPD deficiency.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTo ensure that patient care reaches its full potential, it is important that we use the power of pharmacogenomics to proactively prevent avoidable toxicities and fatalities. This paper looks at the use of capecitabine therapy for metastatic breast cancer, a strategy proven to significantly prolong progression-free survival and overall survival.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Our aim is to assess the cost-effectiveness of DPYD genotyping prior to initiating capecitabine treatment to adjust dosages in metastatic breast cancer patients. The findings from our analysis can provide valuable insights for decision-makers considering the adoption of genotyping as a standard practice in the United States.\u003c/p\u003e"},{"header":"Model","content":"\u003cp\u003e\u003cstrong\u003eModel Overview\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe developed a cohort state-transition model to evaluate the cost-effectiveness of genotyping prior to administering capecitabine compared to no genotyping for a hypothetical cohort of 62-year-old women newly diagnosed with metastatic breast cancer. The model assessed lifetime costs and quality-adjusted life years (QALYs) from the perspective of the U.S. healthcare payer. It included four health states: Progression-Free (Starting Dose), Progression-Free (Reduced Dose), Progressed, and Death, as depicted in Figure 1.\u003c/p\u003e\n\u003cp\u003eWe built a cohort-state transition model with a monthly cycle length to simulate health outcomes and costs over a lifetime. Costs and QALYs were discounted at an annual rate of 3%, following recommendations from the Second Panel on Cost-Effectiveness in Health and Medicine.\u003csup\u003e19\u003c/sup\u003e These values were used to calculate the incremental cost effectiveness ratio (ICER) of genotyping followed by dose adjustments compared to no genotyping while using a commonly accepted willingness-to-pay threshold of $100,000/QALY.\u003csup\u003e20\u003c/sup\u003e\u0026nbsp; All analyses were conducted using TreeAge Pro 2023 (TreeAge Software Inc., Williamstown, MA, USA), and results were reported in compliance with the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) guidelines, detailed in eTable 1 of the supplement.\u003csup\u003e21\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Parameters\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our simulated cohort, patients with advanced breast cancer started in the Progression-Free (Starting-Dose) state. If they experienced toxicity, they could transition to the Progression-Free (Reduced-Dose) state. Patients in both Progression-Free states\u0026mdash;Starting-Dose and Reduced-Dose\u0026mdash;could either progress or die. In the genotyping arm, patients were stratified by their DPYD genotype. People are typically classified as normal, intermediate, or poor metabolizers, with the latter two groups combined as DPYD variant carriers. Those with a DPYD variant received a reduced dose (75%) of capecitabine, while patients with the wild type received the standard dose. \u0026nbsp;Conversely, in the no-genotyping strategy, all patients received the standard dose regardless of genotype. Patients who experienced a toxicity event had their dose reduced to 75% in subsequent cycles, and further reduced to 50% if they were already on a reduced-dose regimen. Once a patient progressed, treatment was discontinued as it was deemed ineffective at that stage. Death could result from disease progression, background mortality, or early-treatment-related toxicity; however, we assumed that metastatic breast cancer-related deaths occurred solely due to disease progression. A simplified framework for the model is presented in Figure 2.\u003c/p\u003e\n\u003cp\u003eWe assumed that dose reduction due to toxicity did not diminish drug effectiveness, as supported by evidence of this phenomenon in metastatic colon cancer, but we did test this assumption in sensitivity analysis.\u003csup\u003e22\u003c/sup\u003e For patients with reduced doses (75%), we also assumed a proportional reduction in both the probability of toxicity and the cost of the drug, the latter of which was evaluated in sensitivity analyses. Toxicity was defined as grade 3 or 4 chemotherapy-related events, and patients experiencing toxicities were assigned a disutility. Grade 3 toxicities were classified as severe or medically significant but not life-threatening, while grade 4 toxicities were considered life-threatening.\u003csup\u003e23\u003c/sup\u003e Hospitalizations due to toxicity, lasted five days and incurred additional costs and disutility.\u003c/p\u003e\n\u003cp\u003eOur model incorporated parameter estimates from various sources, listed in Table 1. Prevalence of DPYD variants was drawn from existing literature, indicating a range of 2%\u0026ndash;8%. Among individuals of European descent, the prevalence was 3%\u0026ndash;5%, while it was higher at approximately 8% in individuals of African descent.\u003csup\u003e4,11,24\u003c/sup\u003e The probability of disease progression and dying subsequently were calibrated using the progression-free survival and overall survival curves from a randomized clinical trial evaluating capecitabine as a first-line chemotherapy for breast cancer.\u003csup\u003e18\u003c/sup\u003e Background mortality rates were sourced from the CDC US life tables.\u003csup\u003e25\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe probabilities of experiencing grade 3 or 4 toxicities and hospitalizations were obtained from a prospective DPYD genotyping study involving patients initiating fluoropyrimidine-based therapy. That study provided separate estimates for individuals with DPYD variants receiving reduced or standard doses and those with wild-type DPYD receiving standard doses.\u003csup\u003e26\u003c/sup\u003e Finally, probabilities of early treatment-related death were derived from studies conducted by Sharma et al. and Brooks et al.\u003csup\u003e7,11\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCosts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe accounted for the costs of DPYD genotyping, capecitabine, and hospitalization for toxicities, as detailed in Table 1, with all costs reported in 2024 U.S. dollars. The cost of DPYD genotyping came from the Clinical Laboratory Fee Schedule of the Centers for Medicare and Medicaid Services (CMS) using the HCPCS code 81232.\u003csup\u003e27\u003c/sup\u003e Capecitabine costs for a monthly treatment cycle were derived from the CMS Part B Average Sales Price file (June 2024) using the HCPCS code J8521.\u003csup\u003e28\u003c/sup\u003e Dosing calculations were based on a body surface area of 1.8 m\u0026sup2;, following the FDA-recommended intermittent regimen of 1,250 mg/m\u0026sup2; taken twice daily for 14 days of a 21-day cycle, with treatment continuing until disease progression.\u003csup\u003e29\u003c/sup\u003e Since capecitabine is administered orally, infusion-related costs were not included.\u003c/p\u003e\n\u003cp\u003eHospitalization costs associated with adverse events were estimated using data from a real-world study.\u003csup\u003e30\u003c/sup\u003e Costs related to disease management or death were assumed to be equivalent across both arms of the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUtilities\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUtilities for the progression-free and progressed states of metastatic breast cancer were obtained from a study that used the standard gamble technique to elicit utilities from a general population.\u003csup\u003e31\u003c/sup\u003e Utilities for grade 3/4 toxicities were sourced from a study that employed EQ-5D scores to assess the quality of life in metastatic breast cancer patients treated with capecitabine.\u003csup\u003e32\u003c/sup\u003e The utility associated with hospitalization due to toxicity was estimated in a separate study.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the impact of model parameters, we performed both deterministic and probabilistic sensitivity analyses. Parameter ranges were sourced from the literature; when unavailable, we varied values by \u0026plusmn;25%. For the probabilistic sensitivity analysis, parameters were varied simultaneously using prespecified distributions in 10,000 Monte Carlo simulations, with results presented as an ICER scatterplot. Additionally, a one-way sensitivity analysis was conducted to evaluate the effect of population-level variant probabilities.\u003c/p\u003e\n\u003cp\u003eWe further examined our model assumptions through scenario analyses. These included testing the assumption that capecitabine costs decrease with dose reduction and assessing whether reduced efficacy from dose reduction impacts cost-effectiveness.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eBase Case Results\u003c/h2\u003e\u003cp\u003eThe genotyping strategy was found to be more effective than the no-genotyping strategy. Base-case results from our analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The estimated life expectancy was 2.23 years for the genotyping arm compared to 2.22 years for the no-genotyping arm. When incorporating quality of life, patients in the genotyping arm had 1.16 QALYs, compared to 1.15 QALYs in the no-genotyping arm. While the genotyping arm was \u003cspan\u003e$\u003c/span\u003e154 more costly, with total costs of \u003cspan\u003e$\u003c/span\u003e2,832 versus \u003cspan\u003e$\u003c/span\u003e2,677 for the no-genotyping arm, it was cost-effective. The analysis resulted in an ICER of \u003cspan\u003e$\u003c/span\u003e13,027 per QALY and \u003cspan\u003e$\u003c/span\u003e9,973 per life year gained, which is well below the commonly accepted willingness-to-pay threshold of \u003cspan\u003e$\u003c/span\u003e100,000/QALY in the United States.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSensitivity/Scenario Analysis\u003c/h3\u003e\n\u003cp\u003eWe conducted a deterministic sensitivity analysis to examine the impact of varying the population probability of the DPYD variant. While the average probability is 6.3%, we tested a range between 3% and 8%, and the ICER remained below the \u003cspan\u003e$\u003c/span\u003e100,000/QALY threshold across the entire range, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor the probabilistic sensitivity analysis, we conducted 10,000 Monte Carlo simulations using prespecified parameter distributions. The results, illustrated in the ICER scatterplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e), indicate that the genotyping strategy is cost-effective in 99% of simulations, including 12% of the simulations where the strategy is cost-saving\u0026mdash;achieving greater effectiveness at a lower cost compared to no genotyping.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn our first scenario analysis, we excluded drug costs, effectively assuming that capecitabine costs are not influenced by the chosen strategy. Without accounting for drug costs, the genotyping strategy cost \u003cspan\u003e$\u003c/span\u003e150 more than no genotyping, with an ICER of \u003cspan\u003e$\u003c/span\u003e12,707/QALY, remaining cost-effective at the \u003cspan\u003e$\u003c/span\u003e100,000/QALY threshold. Next, we modeled a scenario in which effectiveness declines with dose reduction, that is the probability of progression increases in proportion to the decrease in dosage. Under this assumption, we calculated an ICER of \u003cspan\u003e$\u003c/span\u003e34,200/QALY, also remaining cost-effective. Although, real-world studies indicate that dose adjustments may not necessarily compromise health outcomes.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Overall, the results appear robust to both parameter uncertainty and variations in key assumptions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to evaluate the cost-effectiveness of DPYD genotyping prior to capecitabine administration compared to no genotyping in metastatic breast cancer patients in the United States. We found that the genotyping strategy was both more effective and slightly more costly, with an ICER of \u003cspan\u003e$\u003c/span\u003e13,027/QALY, making it cost-effective at the commonly accepted US threshold of \u003cspan\u003e$\u003c/span\u003e100,000/QALY from the healthcare payer perspective. These findings remained robust across sensitivity analyses and assumption testing. Our results align with findings from a similar study conducted in the UK, the Netherlands, and Hungary, where DPYD testing prior to fluorouracil administration was also shown to be cost-effective.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e As one of the oldest and most widely used chemotherapies, ensuring the safety of fluoropyrimidines should remain a priority. Our study adds to the growing body of evidence supporting the integration of genotyping into routine clinical practice to improve patient outcomes.\u003c/p\u003e\u003cp\u003eEstablishing DPYD genotyping as a standard practice in oncology is essential, particularly given that the US National Institutes of Health estimates approximately 1,300 annual deaths attributable to DPD deficiency\u0026mdash;equivalent to 0.5% of patients treated with fluoropyrimidines.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e While the overall probability of harboring DPYD variants is not exceedingly high, the associated risks, healthcare costs, and patient disutilities are substantial. Notably, African American individuals carry a disproportionate burden due to the higher prevalence of these variants, highlighting the urgency for widespread testing. A deeper analysis by race and gender further revealed that African American women exhibited the lowest DPD enzyme activity among all race-gender groups, suggesting that women of African descent may face an elevated risk of fluoropyrimidine-related toxicity.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAlthough the FDA has issued safety warnings emphasizing the elevated toxicity risk in individuals with DPD deficiency, translating this information into clinical practice remains a necessity. We urgently need increased awareness and better direction through professional society guidelines. A survey of oncologists revealed a clear gap between awareness and practice: while 98% agreed that patients with DPD deficiency are at higher risk of toxicity, and 96% indicated they would adjust fluoropyrimidine dosing for known deficiency, only 32% considered pretreatment DPYD testing useful for guiding treatment, and just 20% had ever ordered the test. The primary barriers to testing were the perceived low prevalence of DPD deficiency (54%) and the lack of strong clinical practice guideline recommendations (48%).\u003csup\u003e35\u003c/sup\u003e This disconnect highlights the urgent need for targeted educational initiatives, clearer clinical guidelines, and the integration of DPYD genotyping into standard oncology practice to improve patient safety and treatment outcomes.\u003c/p\u003e\u003cp\u003eUridine triacetate, an FDA-approved oral pyrimidine analog, serves as an antidote to mitigate the toxic effects of excessive fluoropyrimidines. It is believed to compete for receptors on normal cells, thereby reducing toxicity.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e The drug is approved for emergency treatment of severe, early-onset, or life-threatening toxicity within 96 hours of completing 5-FU or capecitabine administration in both adult and pediatric patients.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e The timing of administration is critical. While most patients in clinical trials received treatment within the recommended 96-hour window, mortality was significantly higher among those treated beyond this period\u0026mdash;half of these patients died.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e However, the high cost of uridine triacetate presents a major challenge. The full 20-dose regimen can cost up to \u003cspan\u003e$\u003c/span\u003e89,000 in the US.\u003csup\u003e38\u003c/sup\u003e Cost barriers are further exacerbated by limited insurance coverage: CMS Part D plans do not cover the drug, and coverage among private insurers remains inconsistent.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e Furthermore, there are currently no generic alternatives available, adding to the financial burden.\u003c/p\u003e\u003cp\u003eWe find ourselves in a pivotal moment where the tools of genetics allow us to precisely tailor medications to specific populations, particularly those who are severely ill. By harnessing the power of pharmacogenomics, we can achieve optimal therapeutic outcomes, preventing unnecessary suffering and loss of life. Some of the hesitation may come from the lack of prospective studies and trials supporting using genetics and the fear that reducing the dosage may reduce the effectiveness of the drugs. While critics of genetic testing often highlight cost as a barrier, advancements in technology have made genotyping faster and more affordable, with results typically available in under a week. Given the relatively low cost and the ability to prevent severe toxicities, integrating DPYD genotyping into routine oncology chemotherapy practices is increasingly practical and advantageous. Additionally with the advent of Next generation sequencing and increasing amount of people using it, additional DPYD genotyping may not prove to be an additional burden of genotyping.\u003c/p\u003e\u003cp\u003eWe are at a pivotal moment where advancements in genetics enable us to tailor medications with precision, particularly for critically ill populations. Pharmacogenomics offers a powerful opportunity to optimize therapeutic outcomes, preventing unnecessary suffering and loss of life. However, hesitation remains, often stemming from the lack of large-scale prospective studies and concerns that dose reductions based on genetic testing might compromise treatment efficacy. And, while cost is frequently cited as a barrier to genetic testing, technological advancements have made genotyping both faster and more affordable, with results typically available within a week. Moreover, with the growing adoption of next-generation sequencing and its widespread use, incorporating DPYD genotyping may not impose significant additional burdens on existing clinical workflows.\u003c/p\u003e\u003cp\u003eOur analysis has some limitations that should be considered when interpreting the results. First, we faced challenges in obtaining all necessary parameters, which required us to make certain assumptions. For example, we assumed that reducing the dosage by a specific percentage would proportionally decrease both the risk of toxicity and the associated drug costs. Second, we did not account for cases of complete DPD deficiency, which could further underscore the importance of DPYD genotyping, as these patients are at a heightened risk of experiencing more severe toxicities. Third, our analysis did not incorporate the sensitivity and specificity of the genotyping test. Fourth, this analysis takes a US perspective, using Medicare prices and focusing primarily on older individuals. Finally, we did not include the costs of uridine triacetate, an expensive drug, that would likely make DPYD genotyping appear even more favorable in cost-effectiveness analyses. Future research in this area should focus on conducting more prospective studies to strengthen the evidence supporting routine DPYD genotyping prior to administering fluoropyrimidines.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePatients with a DPYD variant face an increased risk of toxicities from fluorouracil treatments. In our study, we found that implementing DPYD genotyping prior to initiating capecitabine treatment in metastatic breast cancer patients was cost-effective in the United States. Our findings offer valuable insights for decision-makers evaluating the adoption of genotyping as a standard practice in oncology. Embracing this approach marks a significant step toward personalized medicine, demonstrating a commitment to optimizing treatment efficacy while minimizing preventable harm. As we advance, it is crucial to prioritize and invest in this promising field, creating a future where every individual receives the safest and most effective treatment possible.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Tanvi Chiddarwar and Karen Kuntz. The first draft of the manuscript was written by Tanvi Chiddarwar and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eData Availability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data that is used to build the model is available in publicly available peer-reviewed research. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCancer of the Breast (Female) - Cancer Stat Facts. SEER. Accessed March 2, 2024. https://seer.cancer.gov/statfacts/html/breast.html\u003c/li\u003e\n\u003cli\u003eWagstaff AJ, Ibbotson T, Goa KL. Capecitabine. \u003cem\u003eDrugs\u003c/em\u003e. 2003;63(2):217-236. doi:10.2165/00003495-200363020-00009\u003c/li\u003e\n\u003cli\u003evan Kuilenburg ABP. Dihydropyrimidine dehydrogenase and the efficacy and toxicity of 5-fluorouracil. \u003cem\u003eEuropean Journal of Cancer\u003c/em\u003e. 2004;40(7):939-950. doi:10.1016/j.ejca.2003.12.004\u003c/li\u003e\n\u003cli\u003eDean L, Kane M. Capecitabine Therapy and DPYD Genotype. 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Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Dihydropyrimidine Dehydrogenase Genotype and Fluoropyrimidine Dosing: 2017 Update. \u003cem\u003eClin Pharma and Therapeutics\u003c/em\u003e. 2018;103(2):210-216. doi:10.1002/cpt.911\u003c/li\u003e\n\u003cli\u003eEMA recommendations on DPD testing prior to treatment with fluorouracil, capecitabine, tegafur and flucytosine | European Medicines Agency. Accessed March 2, 2024. https://www.ema.europa.eu/en/news/ema-recommendations-dpd-testing-prior-treatment-fluorouracil-capecitabine-tegafur-and-flucytosine\u003c/li\u003e\n\u003cli\u003eResearch C for DE and. FDA approves safety labeling changes regarding DPD deficiency for fluorouracil injection products. \u003cem\u003eFDA\u003c/em\u003e. Published online March 21, 2024. Accessed December 11, 2024. https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-safety-labeling-changes-regarding-dpd-deficiency-fluorouracil-injection-products\u003c/li\u003e\n\u003cli\u003eStockler MR, Harvey VJ, Francis PA, et al. Capecitabine Versus Classical Cyclophosphamide, Methotrexate, and Fluorouracil As First-Line Chemotherapy for Advanced Breast Cancer. \u003cem\u003eJCO\u003c/em\u003e. 2011;29(34):4498-4504. doi:10.1200/JCO.2010.33.9101\u003c/li\u003e\n\u003cli\u003eSanders GD, Neumann PJ, Basu A, et al. Recommendations for Conduct, Methodological Practices, and Reporting of Cost-effectiveness Analyses: Second Panel on Cost-Effectiveness in Health and Medicine. \u003cem\u003eJAMA\u003c/em\u003e. 2016;316(10):1093-1103. doi:10.1001/jama.2016.12195\u003c/li\u003e\n\u003cli\u003eNeumann PJ, Cohen JT, Weinstein MC. Updating Cost-Effectiveness \u0026mdash; The Curious Resilience of the $50,000-per-QALY Threshold. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2014;371(9):796-797. doi:10.1056/NEJMp1405158\u003c/li\u003e\n\u003cli\u003eHusereau D, Drummond M, Augustovski F, et al. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. \u003cem\u003eBMC Medicine\u003c/em\u003e. 2022;20(1):23. doi:10.1186/s12916-021-02204-0\u003c/li\u003e\n\u003cli\u003eMunker S, Gerken M, Fest P, et al. Chemotherapy for metastatic colon cancer: No effect on survival when the dose is reduced due to side effects. \u003cem\u003eBMC Cancer\u003c/em\u003e. 2018;18(1):455. doi:10.1186/s12885-018-4380-z\u003c/li\u003e\n\u003cli\u003eCommon Terminology Criteria for Adverse Events (CTCAE). Published online 2017.\u003c/li\u003e\n\u003cli\u003eInnocenti F, Mills SC, Sanoff H, Ciccolini J, Lenz HJ, Milano G. All You Need to Know About DPYD Genetic Testing for Patients Treated With Fluorouracil and Capecitabine: A Practitioner-Friendly Guide. \u003cem\u003eJCO Oncol Pract\u003c/em\u003e. 2020;16(12):793-798. doi:10.1200/OP.20.00553\u003c/li\u003e\n\u003cli\u003eArias E. United States Life Tables, 202.\u003c/li\u003e\n\u003cli\u003eHenricks LM, Lunenburg CATC, de Man FM, et al. DPYD genotype-guided dose individualisation of fluoropyrimidine therapy in patients with cancer: a prospective safety analysis. \u003cem\u003eLancet Oncol\u003c/em\u003e. 2018;19(11):1459-1467. doi:10.1016/S1470-2045(18)30686-7\u003c/li\u003e\n\u003cli\u003eCLFS Files | CMS. Accessed March 2, 2024. https://www.cms.gov/medicare/payment/fee-schedules/clinical-laboratory-fee-schedule-clfs/files\u003c/li\u003e\n\u003cli\u003eASP Pricing Files | CMS. Accessed December 9, 2024. https://www.cms.gov/medicare/payment/part-b-drugs/asp-pricing-files\u003c/li\u003e\n\u003cli\u003eU.S. Food and Drug Administration. Xeloda (capecitabine) prescribing information. Published 2022. Accessed December 9, 2024. https://www.accessdata.fda.gov/drugsatfda_docs/label/2022/020896s044s045s046s047s048s049s050s051lbl.pdf\u003c/li\u003e\n\u003cli\u003eHassett MJ, O\u0026rsquo;Malley AJ, Pakes JR, Newhouse JP, Earle CC. Frequency and Cost of Chemotherapy-Related Serious Adverse Effects in a Population Sample of Women With Breast Cancer. \u003cem\u003eJNCI: Journal of the National Cancer Institute\u003c/em\u003e. 2006;98(16):1108-1117. doi:10.1093/jnci/djj305\u003c/li\u003e\n\u003cli\u003eLloyd A, Nafees B, Narewska J, Dewilde S, Watkins J. Health state utilities for metastatic breast cancer. \u003cem\u003eBr J Cancer\u003c/em\u003e. 2006;95(6):683-690. doi:10.1038/sj.bjc.6603326\u003c/li\u003e\n\u003cli\u003eSherrill B, Amonkar MM, Stein S, Walker M, Geyer C, Cameron D. Q-TWiST analysis of lapatinib combined with capecitabine for the treatment of metastatic breast cancer. \u003cem\u003eBr J Cancer\u003c/em\u003e. 2008;99(5):711-715. doi:10.1038/sj.bjc.6604501\u003c/li\u003e\n\u003cli\u003eKoleva-Kolarova R, Vellekoop H, Huygens S, et al. Budget Impact and Transferability of Cost\u0026ndash;effectiveness of DPYD Testing in Metastatic Breast Cancer in Three Health Systems. \u003cem\u003ePersonalized Medicine\u003c/em\u003e. 2023;20(4):357-374. doi:10.2217/pme-2022-0133\u003c/li\u003e\n\u003cli\u003eMattison LK, Fourie J, Desmond RA, Modak A, Saif MW, Diasio RB. Increased Prevalence of Dihydropyrimidine Dehydrogenase Deficiency in African-Americans Compared with Caucasians. \u003cem\u003eClinical Cancer Research\u003c/em\u003e. 2006;12(18):5491-5495. doi:10.1158/1078-0432.CCR-06-0747\u003c/li\u003e\n\u003cli\u003eKoo K, Pasternak AL, Henry NL, Sahai V, Hertz DL. Survey of US Medical Oncologists\u0026rsquo; Practices and Beliefs Regarding DPYD Testing Before Fluoropyrimidine Chemotherapy. \u003cem\u003eJCO Oncol Pract\u003c/em\u003e. 2022;18(6):e958-e965. doi:10.1200/OP.21.00874\u003c/li\u003e\n\u003cli\u003eIson G, Beaver JA, McGuinn WD Jr, et al. FDA Approval: Uridine Triacetate for the Treatment of Patients Following Fluorouracil or Capecitabine Overdose or Exhibiting Early-Onset Severe Toxicities Following Administration of These Drugs. \u003cem\u003eClinical Cancer Research\u003c/em\u003e. 2016;22(18):4545-4549. doi:10.1158/1078-0432.CCR-16-0638\u003c/li\u003e\n\u003cli\u003eMa WW, Saif MW, El-Rayes BF, et al. Emergency use of uridine triacetate for the prevention and treatment of life-threatening 5-fluorouracil and capecitabine toxicity. \u003cem\u003eCancer\u003c/em\u003e. 2017;123(2):345-356. doi:10.1002/cncr.30321\u003c/li\u003e\n\u003cli\u003eVistogard Prices, Coupons, Copay Cards \u0026amp; Patient Assistance. Drugs.com. Accessed December 18, 2024. https://www.drugs.com/price-guide/vistogard\u003c/li\u003e\n\u003cli\u003eVistogard Medicare Coverage and Co-Pay Details. GoodRx. Accessed December 18, 2024. https://www.goodrx.com/vistogard/medicare-coverage\u003c/li\u003e\n\u003cli\u003eXiao Q, Zhang W, Jing J, et al. Patterns of de novo metastasis and survival outcomes by age in breast cancer patients: a SEER population-based study. \u003cem\u003eFront Endocrinol (Lausanne)\u003c/em\u003e. 2023;14:1184895. doi:10.3389/fendo.2023.1184895\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eModel Parameters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBase Case\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistributions for Probabilistic sensitivity analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eGeneral Information\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXiao et al.\u003csup\u003e40\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual discount rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2nd Panel\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody surface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8 m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBackground mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCDC Lifetables\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eClinical Parameters\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProbability of carrying a DPYD gene variant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta (6.3, 93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrooks et al.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eDean et al.\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly Probability of Grade 3 or 4 toxicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHenricks et al.\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDPYD variant, standard dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta (8.6, 94.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDPYD variant, reduced-dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta (3, 65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDPYD wild type, standard dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta (23, 879)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly Probability of Hospitalization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHenricks et al.\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDPYD variant, standard dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta (12, 18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDPYD variant, reduced-dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta (41.6, 35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDPYD wild type, standard dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta (28.4, 18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly Probability of treatment-related death\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDPYD variant, standard dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriangular (0.001, 0.003, 0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSharma et al.\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDPYD variant, reduced-dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriangular (0.0001, 0.0003, 0.0002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrooks et al.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDPYD wild type, standard dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriangular (0, 0.0002, 0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSharma et al.\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly Transition Probabilities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStockler et al.\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProgression-free \u0026rarr; Progressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta (11, 89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProgressed \u0026rarr; Death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta (5.1, 95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eCosts ($)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost of capecitabine [HCPCS:J8521]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0.64/500 mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCMS ASP 2024\u003csup\u003e28\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly cost of capecitabine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGamma (116, 29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCMS ASP 2024\u003csup\u003e28\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost of DPYD genotyping test [CPT: 81232]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGamma (174, 44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCMS Clinical Lab Fee schedule\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospitalization cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGamma (12907, 6450)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHassett et al.\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eHealth State Utilities\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProgression-free\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta (7.2, 2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLloyd et al.\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProgressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta (4.4, 5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLloyd et al.\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospitalization (Disutility)\u003c/p\u003e\n \u003cp\u003eApplied for one week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta (28,72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrooks et al.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrade 3/4 Toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta (13.9, 97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSherrill et al.\u003csup\u003e32\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of Cost-effectiveness Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eStrategy\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo Genotyping\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenotyping\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Cost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e2,677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e2,832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncremental total cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQALYs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncremental QALY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLife-years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncremental life-years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eICER\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e/QALY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e/Life-year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"DPYD genotyping, capecitabine, metastatic breast cancer, cost-effectiveness analysis, pharmacogenomics","lastPublishedDoi":"10.21203/rs.3.rs-7992444/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7992444/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003ePatients with a DPYD genetic deficiency who receive capecitabine are at increased risk of severe, potentially fatal toxicities due to impaired drug metabolism. Genetic testing for this deficiency allows for proactive dose adjustments to mitigate these risks. We evaluated the cost-effectiveness of DPYD genotyping prior to capecitabine administration, followed by dose modification for patients with metastatic breast cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe developed a state-transition model to simulate health outcomes and costs for a cohort of 62-year-old women with metastatic breast cancer from the perspective of the U.S. healthcare payer. Costs and utilities were derived from the literature to calculate quality-adjusted life years (QALYs) and the incremental cost-effectiveness ratio (ICER) for DPYD genotyping compared to no DPYD genotyping. We conducted deterministic and probabilistic sensitivity analyses to identify factors influencing cost-effectiveness.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe genotyping strategy was cost-effective, with a cost of \u003cspan\u003e$\u003c/span\u003e2,832 yielding 1.16 QALYs, compared to \u003cspan\u003e$\u003c/span\u003e2,677 and 1.15 QALYs for the no-genotyping strategy. This resulted in an ICER of \u003cspan\u003e$\u003c/span\u003e13,028/QALY and \u003cspan\u003e$\u003c/span\u003e9,973 per life-year-gained. In probabilistic sensitivity analysis, the genotyping strategy was cost-effective in 99% of the simulations, using a willingness-to-pay threshold of \u003cspan\u003e$\u003c/span\u003e100,000/QALY. Results from scenario analyses testing key assumptions also showed that genotyping is cost-effective.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur findings support the implementation of DPYD genotyping prior to capecitabine initiation in metastatic breast cancer patients. This strategy exemplifies the value of personalized medicine and pharmacogenomics in improving treatment safety and effectiveness. As sequencing technologies advance and become affordable, integration of genotyping into routine oncology care is increasingly feasible.\u003c/p\u003e","manuscriptTitle":"Cost-Effectiveness of DPYD Genotyping Prior to Capecitabine Administration for Metastatic Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 18:53:36","doi":"10.21203/rs.3.rs-7992444/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-12T20:08:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-10T20:31:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T18:13:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226583250938020388219788365798781005708","date":"2025-12-09T17:31:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207263926921528434764968161526605107263","date":"2025-12-09T15:41:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187303607219548910825965279108136771554","date":"2025-11-18T22:33:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-16T22:30:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-01T08:28:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-01T08:27:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research and Treatment","date":"2025-10-30T19:54:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6f2b3f53-5488-4261-a68f-c2ea99a38080","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:21:08+00:00","versionOfRecord":{"articleIdentity":"rs-7992444","link":"https://doi.org/10.1007/s10549-026-07948-y","journal":{"identity":"breast-cancer-research-and-treatment","isVorOnly":false,"title":"Breast Cancer Research and Treatment"},"publishedOn":"2026-03-28 16:10:20","publishedOnDateReadable":"March 28th, 2026"},"versionCreatedAt":"2025-11-26 18:53:36","video":"","vorDoi":"10.1007/s10549-026-07948-y","vorDoiUrl":"https://doi.org/10.1007/s10549-026-07948-y","workflowStages":[]},"version":"v1","identity":"rs-7992444","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7992444","identity":"rs-7992444","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}