Aurore Perrot1, Valérie Lauwers-Cances2, Elodie Tournay2, Cyrille Hulin3, Marie-Lorraine Chretien4, Bruno Royer5, Mamoun Dib6, Olivier Decaux7, Arnaud Jaccard8, Karim Belhadj9, Sabine Brechignac10, Jean Fontan11, Laurent Voillat12, Hélène Demarquette13, Philippe Collet14, Philippe Rodon15, Claudine Sohn16, François Lifermann17, Frédérique Orsini-Piocelle18, Valentine Richez19, Mohamad Mohty20, Margaret Macro21, Stéphane Minvielle22, Philippe Moreau22, Xavier Leleu23, Thierry Facon24, Michel Attal25, Hervé Avet-Loiseau25, Jill Corre25. 1. 1 Centre Hospitalier Régional Universitaire Nancy, Nancy, France. 2. 2 Centre Hospitalier Universitaire Toulouse, Toulouse, France. 3. 3 Centre Hospitalier Universitaire Bordeaux, Bordeaux, France. 4. 4 Centre Hospitalier Universitaire Dijon, Dijon, France. 5. 5 Centre Hospitalier Universitaire Amiens, Amiens, France. 6. 6 Centre Hospitalier Universitaire Angers, Angers, France. 7. 7 Centre Hospitalier Universitaire Rennes, Rennes, France. 8. 8 Centre Hospitalier Universitaire Limoges, Limoges, France. 9. 9 Centre Hospitalier Universitaire Créteil, Créteil, France. 10. 10 Centre Hospitalier Universitaire Bobigny, Bobigny, France. 11. 11 Centre Hospitalier Universitaire Besancon, Besançon, France. 12. 12 Centre Hospitalier Chalon sur Saône William Morey, Chalon-sur-Saône, France. 13. 13 Centre Hospitalier de Dunkerque, Dunkirk, France. 14. 14 Centre Hospitalier Universitaire Saint-Étienne, Saint-Étienne, France. 15. 15 Centre Hospitalier Périgueux, Périgueux, France. 16. 16 Centre Hospitalier Toulon, Toulon, France. 17. 17 Centre Hospitalier Dax, Dax, France. 18. 18 Centre Hospitalier Annecy Genevois, Metz-Tessy, France. 19. 19 Centre Hospitalier Universitaire Nice, Nice, France. 20. 20 Centre Hospitalier Universitaire Paris, Paris, France. 21. 21 Centre Hospitalier Universitaire Caen Normandie, Caen, France. 22. 22 Centre Hospitalier Universitaire Nantes, Nantes, France. 23. 23 Centre Hospitalier Universitaire Poitiers, Poitiers, France. 24. 24 Centre Hospitalier Régional Universitaire Lille, Lille, France. 25. 25 Institut Universitaire du Cancer de Toulouse-Oncopole and Centre de Recherches en Cancérologie de Toulouse Institut National de la Santé et de la Recherche Médicale, Toulouse, France.
Abstract
PURPOSE: The wide heterogeneity in multiple myeloma (MM) outcome is driven mainly by cytogenetic abnormalities. The current definition of high-risk profile is restrictive and oversimplified. To adapt MM treatment to risk, we need to better define a cytogenetic risk classification. To address this issue, we simultaneously examined the prognostic impact of del(17p); t(4;14); del(1p32); 1q21 gain; and trisomies 3, 5, and 21 in a cohort of newly diagnosed patients with MM. METHODS: Data were obtained from 1,635 patients enrolled in four trials implemented by the Intergroupe Francophone du Myélome. The oldest collection of data were used for model development and internal validation. For external validation, one of the two independent data sets was used to assess the performance of the model in patients treated with more current regimens. Six cytogenetic abnormalities were identified as clinically relevant, and a prognostic index (PI) that was based on the parameter estimates of the multivariable Cox model was computed for all patients. RESULTS: In all data sets, a higher PI was consistently associated with a poor survival outcome. Dependent on the validation cohorts used, hazard ratios for patients in the high-risk category for death were between six and 15 times higher than those of patients in the low-risk category. Among patients with t(4;14) or del(17p), we observed a worse survival in those classified in the high-risk category than in those in the intermediate-risk category. The PI showed good performance for discriminating between patients who died and those who survived (Harrell's concordance index greater than 70%). CONCLUSION: The cytogenetic PI improves the classification of newly diagnosed patients with MM in the high-risk group compared with current classifications. These findings may facilitate the development of risk-adapted treatment strategies.
RCT Entities:
PURPOSE: The wide heterogeneity in multiple myeloma (MM) outcome is driven mainly by cytogenetic abnormalities. The current definition of high-risk profile is restrictive and oversimplified. To adapt MM treatment to risk, we need to better define a cytogenetic risk classification. To address this issue, we simultaneously examined the prognostic impact of del(17p); t(4;14); del(1p32); 1q21 gain; and trisomies 3, 5, and 21 in a cohort of newly diagnosed patients with MM. METHODS: Data were obtained from 1,635 patients enrolled in four trials implemented by the Intergroupe Francophone du Myélome. The oldest collection of data were used for model development and internal validation. For external validation, one of the two independent data sets was used to assess the performance of the model in patients treated with more current regimens. Six cytogenetic abnormalities were identified as clinically relevant, and a prognostic index (PI) that was based on the parameter estimates of the multivariable Cox model was computed for all patients. RESULTS: In all data sets, a higher PI was consistently associated with a poor survival outcome. Dependent on the validation cohorts used, hazard ratios for patients in the high-risk category for death were between six and 15 times higher than those of patients in the low-risk category. Among patients with t(4;14) or del(17p), we observed a worse survival in those classified in the high-risk category than in those in the intermediate-risk category. The PI showed good performance for discriminating between patients who died and those who survived (Harrell's concordance index greater than 70%). CONCLUSION: The cytogenetic PI improves the classification of newly diagnosed patients with MM in the high-risk group compared with current classifications. These findings may facilitate the development of risk-adapted treatment strategies.
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