Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning

Margaret A. Shipp, Ken N. Ross, Pablo Tamayo, Andrew P. Weng, Ricardo C.T. Aguiar, Michelle Gaasenbeek, Michael Angelo, Michael Reich, Geraldine S. Pinkus, Tane S. Ray, Margaret A. Koval, Kim W. Last, Andrew Norton, T. Andrew Lister, Jill Mesirov, Donna S. Neuberg, Eric S. Lander, Jon C. Aster, Todd R. Golub

Research output: Contribution to journalArticlepeer-review

1869 Scopus citations

Abstract

Diffuse large B-cell lymphoma (DLBCL), the most common lymphoid malignancy in adults, is curable in less than 50% of patients. Prognostic models based on pre-treatment characteristics, such as the International Prognostic Index (IPI), are currently used to predict outcome in DLBCL. However, clinical outcome models identify neither the molecular basis of clinical heterogeneity, nor specific therapeutic targets. We analyzed the expression of 6,817 genes in diagnostic tumor specimens from DLBCL patients who received cyclophosphamide, adriamycin, vincristine and prednisone (CHOP)-based chemotherapy, and applied a supervised learning prediction method to identify cured versus fatal or refractory disease. The algorithm classified two categories of patients with very different five-year overall survival rates (70% versus 12%). The model also effectively delineated patients within specific IPI risk categories who were likely to be cured or to die of their disease. Genes implicated in DLBCL outcome included some that regulate responses to B-cell-receptor signaling, critical serine/threonine phosphorylation pathways and apoptosis. Our data indicate that supervised learning classification techniques can predict outcome in DLBCL and identify rational targets for intervention.

Original languageEnglish (US)
Pages (from-to)68-74
Number of pages7
JournalNature Medicine
Volume8
Issue number1
DOIs
StatePublished - 2002

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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