title: Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning.
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.
authors.affiliation: Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA. margaret_shipp@dfci.harvard.edu
authors.forename: Margaret A
authors.initials: MA
authors.lastname: Shipp
authors.forename: Ken N
authors.initials: KN
authors.lastname: Ross
authors.forename: Pablo
authors.initials: P
authors.lastname: Tamayo
authors.forename: Andrew P
authors.initials: AP
authors.lastname: Weng
authors.forename: Jeffery L
authors.initials: JL
authors.lastname: Kutok
authors.forename: Ricardo C T
authors.initials: RC
authors.lastname: Aguiar
authors.forename: Michelle
authors.initials: M
authors.lastname: Gaasenbeek
authors.forename: Michael
authors.initials: M
authors.lastname: Angelo
authors.forename: Michael
authors.initials: M
authors.lastname: Reich
authors.forename: Geraldine S
authors.initials: GS
authors.lastname: Pinkus
authors.forename: Tane S
authors.initials: TS
authors.lastname: Ray
authors.forename: Margaret A
authors.initials: MA
authors.lastname: Koval
authors.forename: Kim W
authors.initials: KW
authors.lastname: Last
authors.forename: Andrew
authors.initials: A
authors.lastname: Norton
authors.forename: T Andrew
authors.initials: TA
authors.lastname: Lister
authors.forename: Jill
authors.initials: J
authors.lastname: Mesirov
authors.forename: Donna S
authors.initials: DS
authors.lastname: Neuberg
authors.forename: Eric S
authors.initials: ES
authors.lastname: Lander
authors.forename: Jon C
authors.initials: JC
authors.lastname: Aster
authors.forename: Todd R
authors.initials: TR
authors.lastname: Golub
mesh_terms: D000971:Antineoplastic Combined Chemotherapy Protocols; D001185:Artificial Intelligence; D003520:Cyclophosphamide; D004317:Doxorubicin; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016393:Lymphoma, B-Cell / Q000175:diagnosis* / Q000188:drug therapy / Q000401:mortality; D016403:Lymphoma, Large B-Cell, Diffuse / Q000175:diagnosis* / Q000188:drug therapy / Q000401:mortality; D020411:Oligonucleotide Array Sequence Analysis; D011237:Predictive Value of Tests; D011241:Prednisone; D016896:Treatment Outcome; D014750:Vincristine
chemical_list: D014750:Vincristine; D004317:Doxorubicin; D003520:Cyclophosphamide; D011241:Prednisone