title: Microarray-based classifiers and prognosis models identify subgroups with distinct clinical outcomes and high risk of AML transformation of myelodysplastic syndrome.
abstract: The diagnosis of myelodysplastic syndrome (MDS) currently relies primarily on the morphologic assessment of the patient's bone marrow and peripheral blood cells. Moreover, prognostic scoring systems rely on observer-dependent assessments of blast percentage and dysplasia. Gene expression profiling could enhance current diagnostic and prognostic systems by providing a set of standardized, objective gene signatures. Within the Microarray Innovations in LEukemia study, a diagnostic classification model was investigated to distinguish the distinct subclasses of pediatric and adult leukemia, as well as MDS. Overall, the accuracy of the diagnostic classification model for subtyping leukemia was approximately 93%, but this was not reflected for the MDS samples giving only approximately 50% accuracy. Discordant samples of MDS were classified either into acute myeloid leukemia (AML) or "none-of-the-targets" (neither leukemia nor MDS) categories. To clarify the discordant results, all submitted 174 MDS samples were externally reviewed, although this did not improve the molecular classification results. However, a significant correlation was noted between the AML and "none-of-the-targets" categories and prognosis, leading to a prognostic classification model to predict for time-dependent probability of leukemic transformation. The prognostic classification model accurately discriminated patients with a rapid transformation to AML within 18 months from those with more indolent disease.
authors.affiliation: Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, BT9 7BL, United Kingdom. k.mills@qub.ac.uk
authors.forename: Ken I
authors.initials: KI
authors.lastname: Mills
authors.forename: Alexander
authors.initials: A
authors.lastname: Kohlmann
authors.forename: P Mickey
authors.initials: PM
authors.lastname: Williams
authors.forename: Lothar
authors.initials: L
authors.lastname: Wieczorek
authors.forename: Wei-min
authors.initials: WM
authors.lastname: Liu
authors.forename: Rachel
authors.initials: R
authors.lastname: Li
authors.forename: Wen
authors.initials: W
authors.lastname: Wei
authors.forename: David T
authors.initials: DT
authors.lastname: Bowen
authors.forename: Helmut
authors.initials: H
authors.lastname: Loeffler
authors.forename: Jesus M
authors.initials: JM
authors.lastname: Hernandez
authors.forename: Wolf-Karsten
authors.initials: WK
authors.lastname: Hofmann
authors.forename: Torsten
authors.initials: T
authors.lastname: Haferlach
mesh_terms: D000208:Acute Disease; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D018450:Disease Progression; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D007951:Leukemia, Myeloid / Q000175:diagnosis / Q000453:epidemiology* / Q000209:etiology; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009190:Myelodysplastic Syndromes / Q000145:classification* / Q000453:epidemiology / Q000235:genetics / Q000473:pathology; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D012333:RNA, Messenger / Q000235:genetics; D018570:Risk Assessment / Q000379:methods*; D012720:Severity of Illness Index; D016037:Single-Blind Method; D016896:Treatment Outcome
chemical_list: D012333:RNA, Messenger