Incorporating progesterone receptor expression into the PREDICT breast prognostic model
Isabelle Grootes, Renske Keeman, Fiona M. Blows, Roger L. Milne, Graham G. Giles, Anthony J. Swerdlow, Peter A. Fasching, Mustapha Abubakar, Irene L. Andrulis, Hoda Anton‐Culver, Matthias W. Beckmann, Carl Blomqvist, Stig E. Bojesen, Manjeet K. Bolla, Bernardo Bonanni, Ignacio Briceño, Barbara Burwinkel, Nicola J. Camp, Jose E. Castelao, Ji‐Yeob Choi, Christine L. Clarke, Fergus J. Couch, Angela Cox, Simon S. Cross, Kamila Czene, Peter Devilee, Thilo Dörk, Alison M. Dunning, Miriam Dwek, Douglas F. Easton, Diana M. Eccles, Mikael Eriksson, Kristina Ernst, D. Gareth Evans, Jonine D. Figueroa, Visnja Fink, Giuseppe Floris, Stephen B. Fox, Marike Gabrielson, Manuela Gago-Domínguez, José Á. García-Sáenz, Anna González‐Neira, Lothar Haeberle, Christopher A. Haiman, Per Hall, Ute Hamann, Elaine F. Harkness, Mikael Hartman, Alexander Hein, Maartje J. Hooning, Ming‐Feng Hou, Sacha J. Howell, Hidemi Ito, Anna Jakubowska, Wolfgang Janni, Esther M. John, Audrey Jung, Daehee Kang, Vessela N. Kristensen, Ava Kwong, Diether Lambrechts, Jingmei Li, Jan Lubiński, Mehdi Manoochehri, Sara Margolin, Keitaro Matsuo, Nur Aishah Mohd Taib, Anna Marie Mulligan, Heli Nevanlinna, William G. Newman, Kenneth Offit, Ana Osório, Sue K. Park, Tjoung‐Won Park‐Simon, Alpa V. Patel, Nadège Presneau, Katri Pylkäs, Brigitte Rack, Paolo Radice, Gad Rennert, Atocha Romero, Emmanouil Saloustros, Elinor J. Sawyer, Andreas Schneeweiß, Fabienne Schochter, Minouk J. Schoemaker, Chen‐Yang Shen, Rana Shibli, Hans‐Peter Sinn, William Tapper, Essa Tawfiq, Soo‐Hwang Teo, Lauren R. Teras, Diana Torres, Celine M. Vachon, Carolien H. M. van Deurzen, Camilla Wendt, Justin A. Williams, Robert Winqvist, Mark Elwood
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: ) 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.