Genomic Health, Redwood City, CA
M. Crager , G. Tang , S. Shak
Background: The 21-gene Oncotype DX recurrence score (RS) is widely used for assessment of distant recurrence risk and prediction of chemotherapy benefit in patients with early stage ER-positive breast cancer. The Recurrence Score-Clinical-Pathologic (RSPC) risk assessment tool assesses distant recurrence risk when tamoxifen (TAM) therapy is used without chemotherapy, integrating RS with tumor grade, tumor size, and patient age (Tang et al., J Clin Oncol 28:15s, 2010 [suppl; abstr 509]). Neither RS nor RSPC risk assessment have significant treatment interaction with TAM versus aromatase inhibitor (AI) use. Individualized risk assessment when AI therapy is planned would be desirable. Methods: A recent meta-analysis (Dowsett et al, JCO 2010) compares the relative efficacy of AIs and TAM in reducing the risk of distant recurrence. The AI:TAM hazard ratio from the meta-analysis was combined with 10-year risk of distant recurrence as assessed by RS or RSPC for individual N0, ER+ patients to assess the patient’s distant recurrence risk with planned AI therapy. Results: From log rank statistics reported in the AI meta-analysis, we derived a treatment hazard ratio estimate of 0.82 (AI:TAM). Assessments of recurrence risk with AI depend, as expected, on the distant recurrence risk with TAM alone as assessed by RS or RSPC. Confidence interval widths for the AI-alone and TAM-alone risk assessments did not differ greatly. The estimated absolute benefit of AI relative to TAM for 10-year distant recurrence risk in individual patients ranges from about 1% to about 5%. Conclusions: The AI:TAM meta-analysis hazard ratio estimate can be combined with individual patient’s RS and RSPC distant recurrence risk assessment to assess the patient’s distant recurrence risk with planned treatment with AI alone.
Disclaimer
This material on this page is ©2024 American Society of Clinical Oncology, all rights reserved. Licensing available upon request. For more information, please contact licensing@asco.org
Abstract Disclosures
2022 ASCO Annual Meeting
First Author: Christopher David Walden
2023 ASCO Annual Meeting
First Author: Charles Geyer
2024 ASCO Annual Meeting
First Author: Hongbing Liu
2023 ASCO Annual Meeting
First Author: Lauren Claire Brown