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. 2024 Aug 21:16:17588359241266164.
doi: 10.1177/17588359241266164. eCollection 2024.

Early evaluation of the effectiveness and cost-effectiveness of ctDNA-guided selection for adjuvant chemotherapy in stage II colon cancer

Astrid Kramer  1 , Marjolein J E Greuter  2 , Suzanna J Schraa  3 , Geraldine R Vink  3   4 , Jillian Phallen  5 , Victor E Velculescu  5 , Gerrit A Meijer  6 , Daan van den Broek  7 , Miriam Koopman  3 , Jeanine M L Roodhart  3 , Remond J A Fijneman  6 , Valesca P Retèl  8   9 , Veerle M H Coupé  2
Affiliations

Early evaluation of the effectiveness and cost-effectiveness of ctDNA-guided selection for adjuvant chemotherapy in stage II colon cancer

Astrid Kramer et al. Ther Adv Med Oncol. .

Abstract

Background: Current patient selection for adjuvant chemotherapy (ACT) after curative surgery for stage II colon cancer (CC) is suboptimal, causing overtreatment of high-risk patients and undertreatment of low-risk patients. Postoperative circulating tumor DNA (ctDNA) could improve patient selection for ACT.

Objectives: We conducted an early model-based evaluation of the (cost-)effectiveness of ctDNA-guided selection for ACT in stage II CC in the Netherlands to assess the conditions for cost-effective implementation.

Methods: A validated Markov model, simulating 1000 stage II CC patients from diagnosis to death, was supplemented with ctDNA data. Five ACT selection strategies were evaluated: the current guideline (pT4, pMMR), ctDNA-only, and three strategies that combined ctDNA status with pT4 and pMMR status in different ways. For each strategy, the costs, life years, quality-adjusted life years (QALYs), recurrences, and CC deaths were estimated. Sensitivity analyses were performed to assess the impact of the costs of ctDNA testing, strategy adherence, ctDNA as a predictive biomarker, and ctDNA test performance.

Results: Model predictions showed that compared to current guidelines, the ctDNA-only strategy was less effective (+2.2% recurrences, -0.016 QALYs), while the combination strategies were more effective (-3.6% recurrences, +0.038 QALYs). The combination strategies were not cost-effective, since the incremental cost-effectiveness ratio was €67,413 per QALY, exceeding the willingness-to-pay threshold of €50,000 per QALY. Sensitivity analyses showed that the combination strategies would be cost-effective if the ctDNA test costs were lower than €1500, or if ctDNA status was predictive of treatment response, or if the ctDNA test performance improved substantially.

Conclusion: Adding ctDNA to current high-risk clinicopathological features (pT4 and pMMR) can improve patient selection for ACT and can also potentially be cost-effective. Future studies should investigate the predictive value of post-surgery ctDNA status to accurately evaluate the cost-effectiveness of ctDNA testing for ACT decisions in stage II CC.

Keywords: adjuvant chemotherapy; colon cancer; cost-effectiveness; ctDNA; prognostic biomarker.

Plain language summary

Effectiveness and cost-effectiveness of circulating tumour DNA-guided selection for adjuvant chemotherapy in patients with stage II colon cancer Most patients with stage II colon cancer (CC) are cured by surgery. Therefore, guidelines recommend to only offer adjuvant chemotherapy to patients who have a tumor with high-risk features. However, current selection is suboptimal, leading to recurrence of cancer in 13% of low-risk patients and unnecessary administration of chemotherapy in some high-risk patients. Previous studies indicate that a biomarker, so-called circulating tumour DNA (ctDNA), could improve the selection of high-risk patients for adjuvant chemotherapy, as patients who have detectable ctDNA in their blood after surgery are likely to develop a recurrence. Despite its potential, implementation is still pending. Our study assessed the long-term effectiveness and costs associated with various ctDNA-guided strategies for selecting high-risk patients for adjuvant chemotherapy in stage II CC. We used an health-economic model to simulate a cohort of 1000 Dutch patients with stage II CC from diagnosis to death. Next, we compared the health outcomes and costs of the ctDNA-guided strategies to those when selection is based on the Dutch guideline. We found that a combination of the Dutch guideline and ctDNA was the most effective strategy, but not cost-effective. Additional analyses showed that ctDNA-guided selection were cost-effective if the costs of the ctDNA test were below 1500 euros, if the ctDNA test performed significantly better, or if patients with detectable ctDNA responded better to chemotherapy. Thus, while post-surgery ctDNA status is a good indicator for recurrence risk, specific criteria related to ctDNA test performance and costs, in addition to combining ctDNA with current high-risk features, should be met to achieve cost-effective implementation. Looking ahead, future studies should explore how patients with detectable ctDNA respond to chemotherapy for next assessments of the cost-effectiveness of ctDNA-guided strategies in selecting patients with stage II CC for adjuvant chemotherapy.

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Conflict of interest statement

S.J.S. received an institutional research grant from Personal Genome Diagnostics (PGDx). G.R.V. reported grants and/or nonfinancial support from BMS, Merck, Servier, Personal Genome Diagnostics, Bayer, Sirtex, Pierre Fabre, Lilly, Delfi Diagnostics, all outside the submitted work, and all financial supports transferred to the institute. J.P. is a founder of Delfi Diagnostics and owns Delfi Diagnostics stock. V.E.V. is a founder of Delfi Diagnostics, serves on the Board of Directors and as an officer for this organization, and owns Delfi Diagnostics stock, which is subject to certain restrictions under university policy. In addition, Johns Hopkins University owns equity in Delfi Diagnostics. V.E.V. divested his equity in Personal Genome Diagnostics (PGDx) to LabCorp in February 2022. V.E.V. is an inventor on patent applications submitted by Johns Hopkins University related to cancer genomic analyses and cell-free DNA for cancer detection that have been licensed to one or more entities, including Delfi Diagnostics, LabCorp, Qiagen, Sysmex, Agios, Genzyme, Esoterix, Ventana, and ManaT Bio. Under the terms of these license agreements, the University and inventors are entitled to fees and royalty distributions. V.E.V. is an advisor to Viron Therapeutics and Epitope. These arrangements have been reviewed and approved by Johns Hopkins University in accordance with its conflict-of-interest policies. D.v.d.B. has provided lectures, expert testimony, and advisory board presence, for Roche Diagnostics, all outside the submitted work and all financial supports transferred to the institute. M.K. reports having an advisory role for Eisai, Nordic Farma, Merck-Serono, Pierre Fabre, and Servier (vergoedingen naar instituut). Institutional scientific grants from Bayer, Bristol Myers Squibb, Merck, Personal Genome Diagnostics (PGDx), Pierre Fabre, Roche, Sirtex, and Servier. PI from the international cohort study PROMETCO with Servier as a sponsor. Non-financial interests: chair of the ESMO RWD-DH working group, co-chair DCCG, PI PLCRC (national observational cohort study), involved in several clinical trials as PI or co-investigator in CRC. G.A.M. is co-founder and board member (CSO) of CRCbioscreen BV, he has a research collaboration with CZ Health Insurances (cash matching to ZonMW grant), he has research collaborations with Exact Sciences, Sysmex, Sentinel Ch. SpA, Personal Genome Diagnostics (PGDx), DELFi, and Hartwig Medical Foundation; these companies provide materials, equipment, and/or sample/genomic analyses, and he has several patents pending/issued. J.M.L.R. is an advisory board and/or speaker at Bayer, BMS, Merck-Serono, Pierre Fabre, Servier, AMGEN, GSK, received research funding paid to the institution from BMS, Pierre Fabre, GSK, Servier, Cleara, HUB organoids B.V., Xilis, and is a board member of the Foundation Hubrecht Organoid Biobank. R.J.A.F. reports grants and nonfinancial support from Personal Genome Diagnostics (PGDx), DELFI Diagnostics, and Cergentis BV; grants from MERCK BV; and nonfinancial support from Pacific Biosciences, outside the submitted work. In addition, R.J.A.F. has several patents pending. V.P.R. received in the past 3 years an unrestricted grant from Intuitive BV, outside of the current work. The remaining authors declare no potential competing interests.

Figures

Figure 1.
Figure 1.
Structure of the PATTERN model. ctDNA, circulating tumor DNA; dMMR, deficient mismatch repair; pMMR, proficient mismatch repair.
Figure 2.
Figure 2.
Cost-effectiveness plane with base-case results. Cost-effectiveness plane depicting the average discounted costs in € and quality-adjusted life-years (QALYs) of each strategy per patient. The black line represents the cost-effectiveness frontier. Note that the ctDNA-only strategy and combination strategies 1 and 2 are not on the cost-effectiveness frontier. Current guideline: pT4, pMMR patients; ctDNA-only: ctDNA positive patients; Combination 1: pT4, pMMR patients and ctDNA positive, pMMR patients; Combination 2: pT4, pMMR patients and ctDNA-positive patients; Combination 3: pT4 patients and ctDNA-positive patients.
Figure 3.
Figure 3.
Results of the sensitivity analyses in terms of iNMB compared to the no ACT strategy. (a) Varying the costs of ctDNA testing. The X-axis depicts the costs per test per patient. (b) Strategy adherence. The X-axis depicts the adherence to the strategy. (c) Varying the treatment effect of ACT. The X-axis depicts three assumptions for the treatment effect of ACT for ctDNA-positive patients and ctDNA-negative patients. (d) Varying ctDNA test performance. The X-axis depicts the percentage of patients with a ctDNA-positive test result. Note the different scales used for the Y-axes in Figures a–d. Current guideline, pT4, pMMR patients; ctDNA-only, ctDNA positive patients; Combination 1, pT4, pMMR patients and ctDNA positive, pMMR patients; Combination 2, pT4, pMMR patients and ctDNA-positive patients; Combination 3, pT4 patients and ctDNA-positive patients; iNMB, incremental net monetary benefit.
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