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    Incorporating progesterone receptor expression into the PREDICT breast prognostic model

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    Authors
    Grootes, I.
    Keeman, R.
    Blows, F. M.
    Milne, R. L.
    Giles, G. G.
    Swerdlow, A. J.
    Fasching, P. A.
    Abubakar, M.
    Andrulis, I. L.
    Anton-Culver, H.
    Beckmann, M. W.
    Blomqvist, C.
    Bojesen, S. E.
    Bolla, M. K.
    Bonanni, B.
    Briceno, I.
    Burwinkel, B.
    Camp, N. J.
    Castelao, J. E.
    Choi, J. Y.
    Clarke, C. L.
    Couch, F. J.
    Cox, A.
    Cross, S. S.
    Czene, K.
    Devilee, P.
    Dörk, T.
    Dunning, A. M.
    Dwek, M.
    Easton, D. F.
    Eccles, D. M.
    Eriksson, M.
    Ernst, K.
    Evans, D Gareth R
    Figueroa, J. D.
    Fink, V.
    Floris, G.
    Fox, S.
    Gabrielson, M.
    Gago-Dominguez, M.
    García-Sáenz, J. A.
    González-Neira, A.
    Haeberle, L.
    Haiman, C. A.
    Hall, P.
    Hamann, U.
    Harkness, E. F.
    Hartman, M.
    Hein, A.
    Hooning, M. J.
    Hou, M. F.
    Howell, Sacha J
    Ito, H.
    Jakubowska, A.
    Janni, W.
    John, E. M.
    Jung, A.
    Kang, D.
    Kristensen, V. N.
    Kwong, A.
    Lambrechts, D.
    Li, J.
    Lubiński, J.
    Manoochehri, M.
    Margolin, S.
    Matsuo, K.
    Taib, N. A. M.
    Mulligan, A. M.
    Nevanlinna, H.
    Newman, W. G.
    Offit, K.
    Osorio, A.
    Park, S. K.
    Park-Simon, T. W.
    Patel, A. V.
    Presneau, N.
    Pylkäs, K.
    Rack, B.
    Radice, P.
    Rennert, G.
    Romero, A.
    Saloustros, E.
    Sawyer, E. J.
    Schneeweiss, A.
    Schochter, F.
    Schoemaker, M. J.
    Shen, C. Y.
    Shibli, R.
    Sinn, P.
    Tapper, W. J.
    Tawfiq, E.
    Teo, S. H.
    Teras, L. R.
    Torres, D.
    Vachon, C. M.
    van Deurzen, C. H. M.
    Wendt, C.
    Williams, J. A.
    Winqvist, R.
    Elwood, M.
    Schmidt, M. K.
    García-Closas, M.
    Pharoah, P. D. P.
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    Affiliation
    University of Cambridge, Centre for Cancer Genetic Epidemiology, Department of Oncology, Cambridge, CB1 8RN, UK.
    Issue Date
    2022
    
    Metadata
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    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.
    Citation
    Grootes I, Keeman R, Blows FM, Milne RL, Giles GG, Swerdlow AJ, et al. Incorporating progesterone receptor expression into the PREDICT breast prognostic model. European journal of cancer (Oxford, England : 1990). 2022 Aug 4;173:178-93. PubMed PMID: 35933885. Epub 2022/08/08. eng.
    Journal
    European Journal of Cancer
    URI
    http://hdl.handle.net/10541/625554
    DOI
    10.1016/j.ejca.2022.06.011
    PubMed ID
    35933885
    Additional Links
    https://dx.doi.org/10.1016/j.ejca.2022.06.011
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.ejca.2022.06.011
    Scopus Count
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