External validation and clinical utility assessment of PREDICT breast cancer prognostic model in young, systemic treatment-naïve women with node-negative breast cancer.

Background: The validity of the PREDICT breast cancer prognostic model is unclear for young patients without adjuvant systemic treatment. This study aimed to validate PREDICT and assess its clinical utility in young women with node-negative breast cancer who did not receive systemic treatment.

Methods: We selected all women from the Netherlands Cancer Registry who were diagnosed with node-negative breast cancer under age 40 between 1989 and 2000, a period when adjuvant systemic treatment was not standard practice for women with node-negative disease. We evaluated the calibration and discrimination of PREDICT using the observed/expected (O/E) mortality ratio, and the area under the receiver operating characteristic curve (AUC), respectively. Additionally, we compared the potential clinical utility of PREDICT for selectively administering chemotherapy to the chemotherapy-to-all strategy using decision curve analysis at predefined thresholds.

Results: A total of 2264 women with a median age at diagnosis of 36 years were included. Of them, 71.2% had estrogen receptor (ER)-positive tumors and 44.0% had grade 3 tumors. Median tumor size was 16 mm. PREDICT v2.2 underestimated 10-year all-cause mortality by 33% in all women (O/E ratio:1.33, 95%CI:1.22-1.43). Model discrimination was moderate overall (AUC 10-year :0.65, 95%CI:0.62-0.68), and poor for women with ER-negative tumors (AUC 10-year :0.56, 95%CI:0.51-0.62). Compared to the chemotherapy-to-all strategy, PREDICT only showed a slightly higher net benefit in women with ER-positive tumors, but not in women with ER-negative tumors.

Conclusions: PREDICT yields unreliable predictions for young women with node-negative breast cancer. Further model updates are needed before PREDICT can be routinely used in this patient subset.

Competing Interests: Declaration of Competing Interest Sabine C. Linn has been an advisory board member for AstraZeneca, Cergentis, IBM, Novartis, Pfizer, Roche and Sanofi, and has received unrestricted institutional research support or unrestricted educational funding from Agendia, Amgen, AstraZeneca, Bayer, Daiichi Sankyo, Eurocept Pharmaceuticals, Genentech, Immunomedics (now Gilead), Merck, Roche, Sanofi and TESARO (now GSK), and has a pending patent application for a BRCA-like ovarian cancer classifier. Paul J. van Diest has a pending patent application for DDX3 as a biomarker for cancer and its related methods. Gabe Sonke has received institutional research support from Agendia, AstraZeneca, Merck, Novartis, Roche and Seagen. Other authors claim no conflict of interest.

(Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)