Evaluation of gene expression signatures predictive of cytogenetic and molecular subtypes of pediatric acute myeloid leukemia

Abstract

The online version of this article has a Supplementary Appendix. BackgroundPediatric acute myeloid leukemia is a heterogeneous disease characterized by non-random genetic aberrations related to outcome. The genetic subtype is currently detected by different diagnostic procedures which differ in success rate and/or specificity. Design and MethodsWe examined the potential of gene expression profiles to classify pediatric acute myeloid leukemia. Gene expression microarray data of 237 children with acute myeloid leukemia were collected and a double-loop cross validation approach was used to generate a subtype-predictive gene expression profile in the discovery cohort (n=157) which was then tested for its true predictive value in the independent validation cohort (n=80). The classifier consisted of 75 probe sets, representing the top 15 discriminating probe sets for MLL-rearranged, t(8;21)(q22;q22), inv(16)(p13q22), t(15;17)(q21;q22) and t(7;12)(q36;p13)-positive acute myeloid leukemia. ResultsThese cytogenetic subtypes represent approximately 40% of cases of pediatric acute myeloid leukemia and were predicted with 92% and 99% accuracy in the discovery and independent validation cohort, respectively. However, for NPM1, CEBPA, MLL(-PTD), FLT3 (-ITD), KIT, PTPN11 and N/K-RAS gene expression signatures had limited predictive value. This may be caused by a limited frequency of these mutations and by underlying cytogenetics. This latter is exemplified by the fact that different gene expression signatures were discovered for FLT3-ITD in patients with normal cytogenetics and in those with t(15;17)(q21;q22)-positive acute myeloid leukemia, which pointed to HOXB-upregulation being specific for FLT3-ITD + cytogenetically normal acute myeloid leukemia. ConclusionsIn conclusion, gene expression profiling correctly predicted the most prevalent cytogenetic subtypes of pediatric acute myeloid leukemia with high accuracy. In clinical practice, this gene expression signature may replace multiple diagnostic tests for approximately 40% of pediatric acute myeloid leukemia cases whereas only for the remaining cases (predicted as 'acute myeloid leukemia-other') are additional tests indicated. Moreover, the discriminative genes reveal new insights into the biology of acute myeloid leukemia subtypes that warrants followup as potential targets for new therapies.

The heterogeneity of pediatric MLL-rearranged acute myeloid leukemia

“…However, GEP has illustrated that current diagnostic techniques do not guarantee 100% sensitivity for detection of MLL rearrangements. 12 For three cases that clustered together with those with an MLL rearrangement, long-distance inverse-PCR showed that an MLL rearrangement was present, which had not been detected by split-signal FISH. This also shows the advantage of long-distance inverse-PCR, which can detect all translocation partners of MLL using only a little amount of DNA, Table 2 Outcome of MLL-rearranged AML per study group compared with outcome in the total group of pediatric AML Abbreviations: AML, acute myeloid leukemia; EFS, event-free survival; MLL, mixed-lineage leukemia; OS, overall survival; 5y-pEFS, probability of EFS at 5 years from diagnosis; 5y-pOS, probability of OS at 5 years from diagnosis; y, years.…”

“…11 In pediatric AML, MLL-rearranged AML presents with a distinct, unique, expression profile as compared with MLL-PTD. 12,13 Therefore, MLL-rearranged AML and MLL-PTD are considered two different entities.…”

Pediatric Acute Myeloid Leukemia

“…(Ross, et al 2004) This was confirmed by Balgobind et al in an independent study of 237 children with pediatric AML (specificity and sensitivity for discovery of the indicated cytogenetic subclasses was 92% and 99%, respectively). (Balgobind, et al 2011b) However, in the latter study no general predictive gene expression signatures were found for the molecular genetic aberrations NPM1, CEBPA,or KIT. This may have been caused either by a low frequency of certain mutations, but also by underlying cytogenetics or cell line of origin.…”

Insights into cell ontogeny, age, and acute myeloid leukemia

“…In an exclusively paediatric cohort (n=130) gene expression profiling study distinct clusters were identified including t(15;17), t(8;21), inv(16), MLL chimeric genes and FAB M7 that could be used with 93% accuracy to predict clusters in both a validated paediatric and adult cohort [76]. Improved predictions with 99% accuracy could be made from gene expression signatures from one paediatric cohort (n=237) on exclusively paediatric validation sets [77]. This higher accuracy may reflect age related differences to be discovered when 11 comparing adult and paediatric datasets.…”