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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2002-01

AUTHORS

Margaret A. Shipp, Ken N. Ross, Pablo Tamayo, Andrew P. Weng, Jeffery L. Kutok, 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

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. More... »

PAGES

68-74

Journal

TITLE

Nature Medicine

ISSUE

1

VOLUME

8

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/nm0102-68

    DOI

    http://dx.doi.org/10.1038/nm0102-68

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1042502685

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/11786909


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