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. 2020 Feb 1;112(2):154-160.
doi: 10.1093/jnci/djz068.

Expected Monetary Impact of Oncotype DX Score-Concordant Systemic Breast Cancer Therapy Based on the TAILORx Trial

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Expected Monetary Impact of Oncotype DX Score-Concordant Systemic Breast Cancer Therapy Based on the TAILORx Trial

Angela Mariotto et al. J Natl Cancer Inst. .

Abstract

Background: TAILORx demonstrated that women with node-negative, hormone receptor-positive, HER2-negative breast cancers and Oncotype DX recurrence scores (RS) of 0-25 had similar 9-year outcomes with endocrine vs chemo-endocrine therapy; evidence for women aged 50 years and younger and RS 16-25 was less clear. We estimated how expected changes in practice following the trial might affect US costs in the initial 12 months of care (initial costs).

Methods: Data from Surveillance, Epidemiology, and End Results (SEER), SEER-Medicare, and SEER-Genomic Health Inc datasets were used to estimate Oncotype DX testing and chemotherapy rates and mean initial costs pre- and post-TAILORx (in 2018 dollars), assuming all women received Oncotype DX testing and score-suggested therapy posttrial. Sensitivity analyses tested the impact on costs of assumptions about compliance with testing and score-suggested treatment and estimation methods.

Results: Pretrial mean initial costs were $2.816 billion. Posttrial, Oncotype DX testing costs were projected to increase from $115 to $231 million and chemotherapy use to decrease from 25% to 17%, resulting in initial care costs of $2.766 billion, or a net savings of $49 million (1.8% decrease). A small net savings was seen under most assumptions. The one exception was if all women aged 50 years and younger with tumors with RS 16-25 elected to receive chemotherapy, initial care costs could increase by $105 million (4% increase).

Conclusions: Personalizing breast cancer treatment based on tumor genetic profiles could result in small cost decreases in the initial 12 months of care. Studies are needed to evaluate the long-term costs and nonmonetary benefits of personalized cancer care.

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Figures

Figure 1.
Figure 1.
Effects of alternative assumptions on the US total initial care costs pretrial vs potential costs based on patterns of practice expected posttrial. This diagram illustrates the changes in the net costs of initial care of women with node-negative, hormone receptor-positive, HER2-negative, size 1.1–5.0 cm breast cancers from the pre-TAILORx trial (pretrial) to the post-TAILORx trial (posttrial) under differing scenarios based on various parameter values and alternative assumptions. Initial care costs are the total cancer costs in the first 12 months after diagnosis. The solid vertical line represents the base analysis savings comparing the initial care costs prior to the TAILORx trial to the initial care costs post-TAILORx trial. The diamond shapes indicate the savings when the one individual value used in the post-TAILORx scenario analysis was varied. If the diamond shape is to the right of the base case line, it indicates that the alternative value or assumption results in greater cost savings than the base case, whereas diamonds to the left indicate that the alternative value or assumption had less savings or increased costs compared to the base case costs. The scenarios include the following: 1) Subgroups: Chemotherapy use was varied from zero among women with tumors with recurrence scores of 16–25 to 100% for women in this score category who are aged 50 years or younger. 2) Adherence to score-suggested treatment: These analyses assume that, rather than 100% of women with scores of ≥26 and/or ≥31 receiving chemotherapy, levels remain the same as those seen in the pretrial era for women with tumors with scores of ≥31, or that women with tumors with scores of 0–25 continue to use chemotherapy at pretrial levels seen among women with tumors with scores 0–10. 3) Score category assignment: Because score categories were based on regression results to impute scores for women who were not tested, we varied the score category assignments by the upper or lower 95% CI such that overall assignment probability remained constant at 100%. Thus, if one category was assumed to be at the upper 95% CI, then other categories were adjusted downwards. CI = confidence interval.

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