Prediction of molecular subtypes in acute myeloid leukemia based on gene expression profiling

Abstract

© F e r r a t a S t o r t i F o u n d a t i o nR.G.W. Verhaak et al. between both AML sample populations. Mutational analyses to detect recurrent mutations in AML were performed as previously described. [13][14][15][16] All supervised class prediction analyses were performed with Prediction Analysis for Microarrays (PAM) software version 1.28 in R version 2.1.0. 17 Clinical, cytogenetic and molecular information as well as the gene expression profiles of all primary AML cases is available at the Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo, accession number GSE6891). Results and DiscussionIn this study of 461 clinically and molecularly well-characterized cases of AML (Table 1), we were able to comprehensively validate the application of GEP to predict therapeutically relevant molecular subtypes in AML.We applied PAM to investigate whether karyotypic and mutational abnormalities with prognostic or therapeutic value in AML were accurately predictable based on GEP. PAM allows the selection of the minimal number of genes required for optimal prediction, which may be beneficial in a diagnostic setting. The AML cohort1 (n=247) was used as training set to derive predictive signatures that were subsequently validated on AML cohort2 (n=214). The deduced expression signatures are available in the Online Supplementary Tables S1-18.The cytogenetic status of all AML patients with favorable risk, i.e. those with t(8;21), t(15;17) or inv(16) abnormalities, was predicted with 100 percent accuracy ( Table 2). In fact, among these predicted AML cases, there were cases with favorable cytogenetics that had previously been missed by routine cytogenetics (4 out of 37 inv(16) and 4 out of 25 t(15;17)). The presence of the translocation-related fusion transcripts in these specific cases was confirmed by real-time quantitative PCR. Thus, GEP is a reliable alternative to discriminate these three AML subtypes, 2,3 which represent approximately 20% of all cases.2,3 Prediction of t(15;17) and inv(16) required only few genes, as seen previously. 8 For the t(8;21) cases, 76 probe sets were needed to correctly classify all samples. However, as few as two probe sets, including one associated with the RUNX1T1 (ETO) gene, were sufficient to accurately classify all but one t(8;21) cases, which is also consistent with earlier studies 8 (Online Supplementary Figure S3). AML cases with mutations in the transcription factor CCAAT/enhancer binding protein α (CEBPA), which are associated with a relatively favorable treatment outcome, were predicted with positive and negative predictive values of 100% and 97% respectively. Six out of 15 CEBPA mutant cases were missed in the validation set (sensitivity 60%; Table 2). Of note, the misclassified cases all carried a single heterozygous CEBPA mutation, whereas samples with biallelic mutations (either homo-or heterozygous) were all correctly recognized (data not shown). In the training cohort, all but two (14/16) samples carried biallelic mutations 14,18 and in cross-validation in the training cohort ...

Gene expression profiling in AML with normal karyotype can predict mutations for molecular markers and allows novel insights into perturbed biological pathways

“…The sensitivity to predict FLT3-ITD positivity was only around B70.0% (Supplementary Tables S14 and S15). However, this result is in agreement with recent data from Verhaak et al, 17 who reported a positive predictive value for FLT3-ITD aberrations of 77%. As many FLT3-ITD cases were intercalating with FLT3 wild-type cases (Supplementary Figure S7), one can speculate that a FLT3-ITD signature might become more robust on further evaluation of the ratio of the mutated allele in comparison to the wild-type allele.…”

CBFB–MYH11/RUNX1 together with a compendium of hematopoietic regulators, chromatin modifiers and basal transcription factors occupies self-renewal genes in inv(16) acute myeloid leukemia

“…Finally, we analyzed the expression status using available microarray expression data 37 of the target genes in patients (n ¼ 33) harboring an inv (16) translocation. This analysis confirmed that CBFb-MYH11 target genes are higher expressed than CBFb/ RUNX1 or RUNX1 only target genes (Figure 5c).…”

Combined gene expression and DNA occupancy profiling identifies potential therapeutic targets of t(8;21) AML

“…To gain insight into the potential clinical relevance of these 214 AE9a direct targets, we analyzed their expression levels in a published large-scale human AML profiling study 37,38 through Oncomine (Compendia Bioscience). Approximately 30% of these genes show t(8;21)-specific up or down-regulation in the same direction as in AE9a leukemia mice compared with AML with other karyotypes, including 19 of 60 AE9a targets that were enriched in both HA and AML1 ChIP-chip assays mentioned in the previous section (supplemental Table 3).…”