Predicting relapse in patients with medulloblastoma by integrating evidence from clinical and genomic features

Pablo Tamayo, Yoon Jae Cho, Aviad Tsherniak, Heidi Greulich, Lauren Ambrogio, Netteke Schouten Van Meeteren, Tianni Zhou, Allen Buxton, Marcel Kool, Matthew Meyerson, Scott L. Pomeroy, Jill P. Mesirov

Research output: Contribution to journalArticle

49 Scopus citations

Abstract

Purpose Despite significant progress in the molecular understanding of medulloblastoma, stratification of risk in patients remains a challenge. Focus has shifted from clinical parameters to molecular markers, such as expression of specific genes and selected genomic abnormalities, to improve accuracy of treatment outcome prediction. Here, we show how integration of high-level clinical and genomic features or risk factors, including disease subtype, can yield more comprehensive, accurate, and biologically interpretable prediction models for relapse versus no-relapse classification.Wealso introduce a novel Bayesian nomogram indicating the amount of evidence that each feature contributes on a patient-bypatient basis. Patients and Methods A Bayesian cumulative log-odds model of outcome was developed from a training cohort of 96 children treated for medulloblastoma, starting with the evidence provided by clinical features of metastasis and histology (model A) and incrementally adding the evidence from gene-expression- derived features representing disease subtype-independent (model B) and disease subtype- dependent (model C) pathways, and finally high-level copy-number genomic abnormalities (model D). The models were validated on an independent test cohort (n = 78). Results On an independent multi-institutional test data set, models A to D attain an area under receiver operating characteristic (au-ROC) curve of 0.73 (95% CI, 0.60 to 0.84), 0.75 (95% CI, 0.64 to 0.86), 0.80 (95% CI, 0.70 to 0.90), and 0.78 (95% CI, 0.68 to 0.88), respectively, for predicting relapse versus no relapse. Conclusion The proposed models C and D outperform the current clinical classification schema (au-ROC, 0.68), our previously published eight-gene outcome signature (au-ROC, 0.71), and several new schemas recently proposed in the literature for medulloblastoma risk stratification.

Original languageEnglish (US)
Pages (from-to)1415-1423
Number of pages9
JournalJournal of Clinical Oncology
Volume29
Issue number11
DOIs
StatePublished - Apr 10 2011

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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    Tamayo, P., Cho, Y. J., Tsherniak, A., Greulich, H., Ambrogio, L., Meeteren, N. S. V., Zhou, T., Buxton, A., Kool, M., Meyerson, M., Pomeroy, S. L., & Mesirov, J. P. (2011). Predicting relapse in patients with medulloblastoma by integrating evidence from clinical and genomic features. Journal of Clinical Oncology, 29(11), 1415-1423. https://doi.org/10.1200/JCO.2010.28.1675