Gwenaelle Gravis1, Jean-Marie Boher2, Karim Fizazi3, Florence Joly4, Franck Priou5, Patricia Marino6, Igor Latorzeff7, Remy Delva8, Ivan Krakowski9, Brigitte Laguerre10, Jochen Walz11, Fréderic Rolland12, Christine Théodore13, Gael Deplanque14, Jean-Marc Ferrero15, Damien Pouessel16, Loïc Mourey17, Philippe Beuzeboc18, Sylvie Zanetta19, Muriel Habibian20, Jean-François Berdah21, Jerome Dauba22, Marjorie Baciuchka23, Christian Platini24, Claude Linassier25, Jean-Luc Labourey26, Jean Pascal Machiels27, Claude El Kouri28, Alain Ravaud29, Etienne Suc30, Jean-Christophe Eymard31, Ali Hasbini32, Guilhem Bousquet33, Michel Soulie34, Stéphane Oudard35. 1. Medical Oncology Department, Institut Paoli-Calmettes, Marseille, France. Electronic address: gravisg@ipc.unicancer.fr. 2. Biostatistics Department, Institut Paoli-Calmettes, and Aix-Marseille Université, UMR_S 912 (SESSTIM), Marseille, France. 3. Department of Cancer Medicine, Institut Gustave Roussy, University of Paris Sud, Villejuif, France. 4. Medical Oncology Department, Centre François Baclesse-CHU Côte de Nacre, Caen, France. 5. Medical Oncology Department, Centre Hospitalier Les Oudairies, La Roche-sur-Yon, France. 6. Institut Paoli-Calmettes, and Aix-Marseille Université, UMR_S912 (SESSTIM), Marseille, France. 7. Radiotherapy Department, Clinique Pasteur, Toulouse, France. 8. Department of Medical Oncology, Centre Paul Papin, Angers, France. 9. Medical Oncology Department, Centre Alexis Vautrin, Vandoeuvre-les-Nancy, France. 10. Medical Oncology Department, Centre Eugène Marquis, Rennes, France. 11. Surgical Urology Department, Institut Paoli-Calmettes, Marseille, France. 12. Medical Oncology Department, Centre René Gauducheau, Saint-Herblain, France. 13. Medical Oncology Department, Hôpital Foch, Suresnes, France. 14. Medical Oncology Department, Groupe Hospitalier Saint Joseph, Paris, France. 15. Medical Oncology Department, Centre Antoine Lacassagne, Nice, France. 16. Medical Oncology Department, Centre Val d'Aurelle-Paul Lamarque, Montpellier, France. 17. Medical Oncology Department, Institut Claudius Régaud, Toulouse, France. 18. Medical Oncology Department, Institut Curie, Paris, France. 19. Medical Oncology Department, Centre Georges François Leclerc, Dijon, France. 20. R&D UNICANCER, Paris cedex 13, France. 21. Medical Oncology Department, Clinique Sainte Marguerite, Hyeres, France. 22. Medical Oncology Department, Hôpital Layné, Mont de Marsan, France. 23. Medical Oncology Department, Centre Hospitalier La Timone, Marseille, France. 24. Medical Oncology Department, Centre Régional Hospitalier, Metz-Thionville, France. 25. Medical Oncology Department, Hôpital Bretonneau, Tours, France. 26. Medical Oncology Department, Centre Hospitalier Dupuytren, Limoges, France. 27. Medical Oncology Department, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium. 28. Medical Oncology Department, Centre Catherine de Sienne, Nantes, France. 29. Medical Oncology Department, Hôpital Saint-André, Bordeaux, France. 30. Medical Oncology Department, Clinique Saint-Jean Languedoc, Toulouse, France. 31. Medical Oncology Department, Institut Jean Godinot, Reims, France. 32. Medical Oncology Department, Clinique Armoricaine de Radiologie, Saint-Brieux, France. 33. Medical Oncology Department, Hôpital Saint-Louis, Paris, France. 34. Urology Department, Centre Hospitalier Universitaire Rangueil, Toulouse, France. 35. Medical Oncology Department, Georges Pompidou Hospital and Rene Descartes University, Paris, France.
Abstract
BACKGROUND: The Glass model developed in 2003 uses prognostic factors for noncastrate metastatic prostate cancer (NCMPC) to define subgroups with good, intermediate, and poor prognosis. OBJECTIVE: To validate NCMPC risk groups in a more recently diagnosed population and to develop a more sensitive prognostic model. DESIGN, SETTING, AND PARTICIPANTS: NCMPC patients were randomized to receive continuous androgen deprivation therapy (ADT) with or without docetaxel in the GETUG-15 phase 3 trial. Potential prognostic factors were recorded: age, performance status, Gleason score, hemoglobin (Hb), prostate-specific antigen, alkaline phosphatase (ALP), lactate dehydrogenase (LDH), metastatic localization, body mass index, and pain. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: These factors were used to develop a new prognostic model using a recursive partitioning method. Before analysis, the data were split into learning and validation sets. The outcome was overall survival (OS). RESULTS AND LIMITATIONS: For the 385 patients included, those with good (49%), intermediate (29%), and poor (22%) prognosis had median OS of 69.0, 46.5 and 36.6 mo (p=0.001), and 5-yr survival estimates of 60.7%, 39.4%, and 32.1%, respectively (p=0.001). The most discriminatory variables in univariate analysis were ALP, pain intensity, Hb, LDH, and bone metastases. ALP was the strongest prognostic factor in discriminating patients with good or poor prognosis. In the learning set, median OS in patients with normal and abnormal ALP was 69.1 and 33.6 mo, and 5-yr survival estimates were 62.1% and 23.2%, respectively. The hazard ratio for ALP was 3.11 and 3.13 in the learning and validation sets, respectively. The discriminatory ability of ALP (concordance [C] index 0.64, 95% confidence interval [CI] 0.58-0.71) was superior to that of the Glass risk model (C-index 0.59, 95% CI 0.52-0.66). The study limitations include the limited number of patients and low values for the C-index. CONCLUSION: A new and simple prognostic model was developed for patients with NCMPC, underlying the role of normal or abnormal ALP. PATIENT SUMMARY: We analyzed clinical and biological factors that could affect overall survival in noncastrate metastatic prostate cancer. We showed that normal or abnormal alkaline phosphatase at baseline might be useful in predicting survival.
RCT Entities:
BACKGROUND: The Glass model developed in 2003 uses prognostic factors for noncastrate metastatic prostate cancer (NCMPC) to define subgroups with good, intermediate, and poor prognosis. OBJECTIVE: To validate NCMPC risk groups in a more recently diagnosed population and to develop a more sensitive prognostic model. DESIGN, SETTING, AND PARTICIPANTS: NCMPC patients were randomized to receive continuous androgen deprivation therapy (ADT) with or without docetaxel in the GETUG-15 phase 3 trial. Potential prognostic factors were recorded: age, performance status, Gleason score, hemoglobin (Hb), prostate-specific antigen, alkaline phosphatase (ALP), lactate dehydrogenase (LDH), metastatic localization, body mass index, and pain. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: These factors were used to develop a new prognostic model using a recursive partitioning method. Before analysis, the data were split into learning and validation sets. The outcome was overall survival (OS). RESULTS AND LIMITATIONS: For the 385 patients included, those with good (49%), intermediate (29%), and poor (22%) prognosis had median OS of 69.0, 46.5 and 36.6 mo (p=0.001), and 5-yr survival estimates of 60.7%, 39.4%, and 32.1%, respectively (p=0.001). The most discriminatory variables in univariate analysis were ALP, pain intensity, Hb, LDH, and bone metastases. ALP was the strongest prognostic factor in discriminating patients with good or poor prognosis. In the learning set, median OS in patients with normal and abnormal ALP was 69.1 and 33.6 mo, and 5-yr survival estimates were 62.1% and 23.2%, respectively. The hazard ratio for ALP was 3.11 and 3.13 in the learning and validation sets, respectively. The discriminatory ability of ALP (concordance [C] index 0.64, 95% confidence interval [CI] 0.58-0.71) was superior to that of the Glass risk model (C-index 0.59, 95% CI 0.52-0.66). The study limitations include the limited number of patients and low values for the C-index. CONCLUSION: A new and simple prognostic model was developed for patients with NCMPC, underlying the role of normal or abnormal ALP. PATIENT SUMMARY: We analyzed clinical and biological factors that could affect overall survival in noncastrate metastatic prostate cancer. We showed that normal or abnormal alkaline phosphatase at baseline might be useful in predicting survival.
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