Incorporating progesterone receptor expression into the PREDICT breast prognostic model. (September 2022) - Grootes, Isabelle; Keeman, Renske; Blows, Fiona M.; Milne, Roger L.; Giles, Graham G.; Swerdlow, Anthony J.; Fasching, Peter A.; Abubakar, Mustapha; Andrulis, Irene L.; Anton-Culver, Hoda; Beckmann, Matthias W.; Blomqvist, Carl; et al.
Abstract: Background: Predict Breast (www.predict.nhs.uk ) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2). Method: The prognostic effect of PR status was based on the analysis of data from 45, 088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11, 365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance. Results: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours ( p = 0.023) and from 0.898 to 0.902 for patients with ER-positive tumours ( p = 2.3 × 10 −6 ) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted. Conclusion: The inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predictions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration. Highlights: PR) status is associated with a better prognosis after adjusting for other factors. PREDICT with the inclusion of PR was assessed by calibration and discrimination. addition of PR status improved model performance in terms of discrimination. Calibration in an external dataset showed over-estimation of breast cancer mortality.