Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment
BACKGROUND: Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consec...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2017-12-01
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| Series: | Neoplasia: An International Journal for Oncology Research |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1476558617302695 |
| _version_ | 1828174113888272384 |
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| author | Carolina Rosswog Rene Schmidt André Oberthuer Dilafruz Juraeva Benedikt Brors Anne Engesser Yvonne Kahlert Ruth Volland Christoph Bartenhagen Thorsten Simon Frank Berthold Barbara Hero Andreas Faldum Matthias Fischer |
| author_facet | Carolina Rosswog Rene Schmidt André Oberthuer Dilafruz Juraeva Benedikt Brors Anne Engesser Yvonne Kahlert Ruth Volland Christoph Bartenhagen Thorsten Simon Frank Berthold Barbara Hero Andreas Faldum Matthias Fischer |
| author_sort | Carolina Rosswog |
| collection | DOAJ |
| description | BACKGROUND: Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consecutive Cox regression models, resulting in a risk score that integrates hazard ratios of prognostic variables. METHODS: A cohort of 695 neuroblastoma patients was divided into a discovery set (n = 75) for multigene predictor generation, a training set (n = 411) for risk score development, and a validation set (n = 209). Relevant prognostic variables were identified by stepwise multivariable L1-penalized least absolute shrinkage and selection operator (LASSO) Cox regression, followed by backward selection in multivariable Cox regression, and then integrated into a novel risk score. RESULTS: The variables stage, age, MYCN status, and two multigene predictors, NB-th24 and NB-th44, were selected as independent prognostic markers by LASSO Cox regression analysis. Following backward selection, only the multigene predictors were retained in the final model. Integration of these classifiers in a risk scoring system distinguished three patient subgroups that differed substantially in their outcome. The scoring system discriminated patients with diverging outcome in the validation cohort (5-year event-free survival, 84.9 ± 3.4 vs 63.6 ± 14.5 vs 31.0 ± 5.4; P < .001), and its prognostic value was validated by multivariable analysis. CONCLUSION: We here propose a translational strategy for developing risk assessment systems based on hazard ratios of relevant prognostic variables. Our final neuroblastoma risk score comprised two multigene predictors only, supporting the notion that molecular properties of the tumor cells strongly impact clinical courses of neuroblastoma patients. |
| first_indexed | 2024-04-12T04:07:51Z |
| format | Article |
| id | doaj.art-c6f0641f7ed243fea6fcc8ab5e4a9724 |
| institution | Directory Open Access Journal |
| issn | 1476-5586 1522-8002 |
| language | English |
| last_indexed | 2024-04-12T04:07:51Z |
| publishDate | 2017-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neoplasia: An International Journal for Oncology Research |
| spelling | doaj.art-c6f0641f7ed243fea6fcc8ab5e4a97242022-12-22T03:48:35ZengElsevierNeoplasia: An International Journal for Oncology Research1476-55861522-80022017-12-01191298299010.1016/j.neo.2017.09.006Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk AssessmentCarolina Rosswog0Rene Schmidt1André Oberthuer2Dilafruz Juraeva3Benedikt Brors4Anne Engesser5Yvonne Kahlert6Ruth Volland7Christoph Bartenhagen8Thorsten Simon9Frank Berthold10Barbara Hero11Andreas Faldum12Matthias Fischer13Department of Experimental Pediatric Oncology, Children's Hospital, University of Cologne, Kerpener Strasse 62, 50937 Cologne, GermanyInstitute of Biostatistics and Clinical Research, University of Muenster, Schmeddingstrasse 56, 48149 Münster, GermanyDepartment of Experimental Pediatric Oncology, Children's Hospital, University of Cologne, Kerpener Strasse 62, 50937 Cologne, GermanyDepartment of Applied Bioinformatics, German Cancer Research Center, Berliner Strasse 41, 69120 Heidelberg, GermanyDepartment of Applied Bioinformatics, German Cancer Research Center, Berliner Strasse 41, 69120 Heidelberg, GermanyDepartment of Experimental Pediatric Oncology, Children's Hospital, University of Cologne, Kerpener Strasse 62, 50937 Cologne, GermanyDepartment of Experimental Pediatric Oncology, Children's Hospital, University of Cologne, Kerpener Strasse 62, 50937 Cologne, GermanyDepartment of Pediatric Oncology and Hematology, Children's Hospital, University of Cologne, Kerpener Strasse 62, 50937 Cologne, GermanyDepartment of Experimental Pediatric Oncology, Children's Hospital, University of Cologne, Kerpener Strasse 62, 50937 Cologne, GermanyDepartment of Pediatric Oncology and Hematology, Children's Hospital, University of Cologne, Kerpener Strasse 62, 50937 Cologne, GermanyDepartment of Pediatric Oncology and Hematology, Children's Hospital, University of Cologne, Kerpener Strasse 62, 50937 Cologne, GermanyDepartment of Pediatric Oncology and Hematology, Children's Hospital, University of Cologne, Kerpener Strasse 62, 50937 Cologne, GermanyInstitute of Biostatistics and Clinical Research, University of Muenster, Schmeddingstrasse 56, 48149 Münster, GermanyDepartment of Experimental Pediatric Oncology, Children's Hospital, University of Cologne, Kerpener Strasse 62, 50937 Cologne, GermanyBACKGROUND: Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consecutive Cox regression models, resulting in a risk score that integrates hazard ratios of prognostic variables. METHODS: A cohort of 695 neuroblastoma patients was divided into a discovery set (n = 75) for multigene predictor generation, a training set (n = 411) for risk score development, and a validation set (n = 209). Relevant prognostic variables were identified by stepwise multivariable L1-penalized least absolute shrinkage and selection operator (LASSO) Cox regression, followed by backward selection in multivariable Cox regression, and then integrated into a novel risk score. RESULTS: The variables stage, age, MYCN status, and two multigene predictors, NB-th24 and NB-th44, were selected as independent prognostic markers by LASSO Cox regression analysis. Following backward selection, only the multigene predictors were retained in the final model. Integration of these classifiers in a risk scoring system distinguished three patient subgroups that differed substantially in their outcome. The scoring system discriminated patients with diverging outcome in the validation cohort (5-year event-free survival, 84.9 ± 3.4 vs 63.6 ± 14.5 vs 31.0 ± 5.4; P < .001), and its prognostic value was validated by multivariable analysis. CONCLUSION: We here propose a translational strategy for developing risk assessment systems based on hazard ratios of relevant prognostic variables. Our final neuroblastoma risk score comprised two multigene predictors only, supporting the notion that molecular properties of the tumor cells strongly impact clinical courses of neuroblastoma patients.http://www.sciencedirect.com/science/article/pii/S1476558617302695 |
| spellingShingle | Carolina Rosswog Rene Schmidt André Oberthuer Dilafruz Juraeva Benedikt Brors Anne Engesser Yvonne Kahlert Ruth Volland Christoph Bartenhagen Thorsten Simon Frank Berthold Barbara Hero Andreas Faldum Matthias Fischer Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment Neoplasia: An International Journal for Oncology Research |
| title | Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment |
| title_full | Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment |
| title_fullStr | Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment |
| title_full_unstemmed | Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment |
| title_short | Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment |
| title_sort | molecular classification substitutes for the prognostic variables stage age and mycn status in neuroblastoma risk assessment |
| url | http://www.sciencedirect.com/science/article/pii/S1476558617302695 |
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