B Hanfstein1, V Shlyakhto1, M Lauseker2, R Hehlmann1, S Saussele1, C Dietz1, P Erben1, A Fabarius1, U Proetel1, S Schnittger3, S W Krause4, J Schubert5, H Einsele6, M Hänel7, J Dengler8, C Falge9, L Kanz10, A Neubauer11, M Kneba12, F Stegelmann13, M Pfreundschuh14, C F Waller15, K Spiekermann16, G M Baerlocher17, M Pfirrmann2, J Hasford2, W-K Hofmann1, A Hochhaus18, M C Müller1. 1. III. Medizinische Universitätsklinik, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Germany. 2. Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie der Ludwig-Maximilians-Universität München, München, Germany. 3. MLL Münchner Leukämielabor, München, Germany. 4. Medizinische Klinik 5, Universitätsklinikum Erlangen, Erlangen, Germany. 5. Klinik für Hämatologie, Onkologie und Palliativmedizin, Evangelisches Krankenhaus, Hamm, Germany. 6. Medizinischen Klinik und Poliklinik II, Universitätsklinikum Würzburg, Würzburg, Germany. 7. Klinik für Innere Medizin III, Klinikum Chemnitz, Chemnitz, Germany. 8. Medizinische Universitätsklinik, Abteilung Innere Medizin V, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany. 9. Medizinische Klinik 5, Klinikum Nürnberg Nord, Nürnberg, Germany. 10. Medizinische Klinik II, Universitätsklinikum Tübingen, Tübingen, Germany. 11. Klinik für Innere Medizin, Schwerpunkt Hämatologie, Onkologie und Immunologie, Universitätsklinikum Gießen und Marburg, Marburg, Germany. 12. II. Medizinische Klinik und Poliklinik im Städtischen Krankenhaus, Universitätsklinikum Schleswig-Holstein, Kiel, Germany. 13. Klinik für Innere Medizin III, Universitätsklinikum Ulm, Ulm, Germany. 14. Innere Medizin I; Universitätsklinikum des Saarlandes, Homburg, Germany. 15. Klinik für Innere Medizin I, Universitätsklinikum Freiburg, Freiburg, Germany. 16. Medizinische Klinik und Poliklinik III, Ludwig-Maximilians-Universität München, München, Germany. 17. Universitätsklinik für Hämatologie, Inselspital Bern, Bern, Switzerland. 18. Abteilung für Hämatologie und Internistische Onkologie, Klinik für Innere Medizin II, Universitätsklinikum Jena, Jena, Germany.
Abstract
UNLABELLED: Early assessment of response at 3 months of tyrosine kinase inhibitor treatment has become an important tool to predict favorable outcome. We sought to investigate the impact of relative changes of BCR-ABL transcript levels within the initial 3 months of therapy. In order to achieve accurate data for high BCR-ABL levels at diagnosis, beta glucuronidase (GUS) was used as a reference gene. Within the German CML-Study IV, samples of 408 imatinib-treated patients were available in a single laboratory for both times, diagnosis and 3 months on treatment. In total, 301 of these were treatment-naïve at sample collection. RESULTS: (i) with regard to absolute transcript levels at diagnosis, no predictive cutoff could be identified; (ii) at 3 months, an individual reduction of BCR-ABL transcripts to the 0.35-fold of baseline level (0.46-log reduction, that is, roughly half-log) separated best (high risk: 16% of patients, 5-year overall survival (OS) 83% vs 98%, hazard ratio (HR) 6.3, P=0.001); (iii) at 3 months, a 6% BCR-ABL(IS) cutoff derived from BCR-ABL/GUS yielded a good and sensitive discrimination (high risk: 22% of patients, 5-year OS 85% vs 98%, HR 6.1, P=0.002). Patients at risk of disease progression can be identified precisely by the lack of a half-log reduction of BCR-ABL transcripts at 3 months.
RCT Entities:
UNLABELLED: Early assessment of response at 3 months of tyrosine kinase inhibitor treatment has become an important tool to predict favorable outcome. We sought to investigate the impact of relative changes of BCR-ABL transcript levels within the initial 3 months of therapy. In order to achieve accurate data for high BCR-ABL levels at diagnosis, beta glucuronidase (GUS) was used as a reference gene. Within the German CML-Study IV, samples of 408 imatinib-treated patients were available in a single laboratory for both times, diagnosis and 3 months on treatment. In total, 301 of these were treatment-naïve at sample collection. RESULTS: (i) with regard to absolute transcript levels at diagnosis, no predictive cutoff could be identified; (ii) at 3 months, an individual reduction of BCR-ABL transcripts to the 0.35-fold of baseline level (0.46-log reduction, that is, roughly half-log) separated best (high risk: 16% of patients, 5-year overall survival (OS) 83% vs 98%, hazard ratio (HR) 6.3, P=0.001); (iii) at 3 months, a 6% BCR-ABL(IS) cutoff derived from BCR-ABL/GUS yielded a good and sensitive discrimination (high risk: 22% of patients, 5-year OS 85% vs 98%, HR 6.1, P=0.002). Patients at risk of disease progression can be identified precisely by the lack of a half-log reduction of BCR-ABL transcripts at 3 months.
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