Dataset: Common and overlapping oncogenic pathways contribute to the evolution of acute myeloid leukemias

Recently, it has been demonstrated that transcriptionally active leukemia-associated fusion oncogenes alter self-renewal in and generate acute myeloid leukemia (AML) from committed progenitors, linking transformation and self-renewal pathways. AML is a heterogeneous disease, both genetically and biologically, and it is not known whether transformation is mediated by common or overlapping genetic programs downstream of multiple mutations or through the engagement of unique programs downstream of individual mutations. This distinction is important, as the demonstration of common pathways may identify common molecular targets for the treatment of AML. Here we demonstrate that the ability to alter self-renewal in vitro and in vivo is a more generalized property of leukemia-associated oncogenes. We further demonstrate that disparate leukemia-associated oncogenes initiate early common and overlapping transformation and self-renewal gene expression programs to mediate these effects. Furthermore, elements of these programs can be detected in established leukemia stem cells from an animal model and across a large cohort of patients with differing acute myeloid leukemia (AML) subtypes, where they strongly predict for disease biology. Finally, individual genes from the programs are demonstrated to partially phenocopy the leukemia-associated oncogenes and themselves alter self-renewal in committed murine progenitors and generate AML when expressed in murine bone marrow. The dataset comprises granulocyte monocyte precursors (GMP) transduced with either GFP alone or the fusion oncogenes MOZ-TIF2 or NUP98-HOXA9 or AML1-ETO, as labelled. Early gene expression changes (36 hours after transduction) were analysed between these replicate samples in two separate ways; either in a single two-class comparison of all oncogene transduced GMP with empty-vector transduced GMP or in three separate two-class comparisons comparing each oncogene individually with empty-vector transduced GMP. In the first analysis we identified 1082 genes/1119 probesets whose expression levels significantly differed (FDR level 0.05). There is a loss of self-renewal potential in the transition from the LSK to GMP population and to further enrich for potential self-renewal genes we compared the expression of these genes/probes in sorted normal LSK versus GMP populations. Genes which were not significantly (p<0.05) and similarly differentially expressed between LSK and GMP were excluded, allowing us to prioritize an immediate “leukemia initiation” program of 167 genes (182 probesets). Our second analysis demonstrated differential expression of 5750, 1161, and 5109 genes, when MOZ-TIF2, NUP98-HOXA9 and AML1-ETO transduced GMP were respectively compared with empty-vector transduced GMP (p<0.05). When our “leukemia initiation” signature was compared to the single comparisons, 127/167 (76%) genes overlapped. To assess the evolutionary nature of transcriptional programs critical for leukemia induction and maintenance, the gene expression profiles of replicate functionally validated leukemic stem cells from a MOZ-TIF2 model of leukemia (L-GMP) were then compared to their normal phenotypic counterpart (normal GMP). 2715 genes were differentially expressed between normal and leukemic GMP (p<0.05). Similarities were seen between our immediate pre-leukemic signatures and the gene expression profiles in the overt leukemias. The overlap with the leukemia initiation and leukemia self-renewal signatures was highly significant at 59/167 genes (35%) and 29/91 genes (32%), respectively (both p<0.0001). Gene expression levels were measured using Affymetrix (Santa Clara, CA) GeneChip Mouse Genome 430A 2.0 arrays (22,690 probesets) with hybridisation and washes as per the manufacturers specifications. The starting point for all analyses was the ''.CEL'' files from the MAS5 software. Data was analyzed using the R statistical package bioconductor18. Data quality was assessed using functions in the affy and affyPLM packages and outlier arrays were removed from subsequent analysis. The GCRMA algorithm (ver. 2.4.1) was used to obtain normalized expression estimates. Genes were selected for further analysis on the following basis: genes that had probesets for which the expression value was greater than 60 (which in our study constitute the average background reading of all probesets) and that had a present flag call in at least two of three samples. To detect significant changes in the expression levels, two-sample Welch t-tests (parametric; assuming unequal variances; Benjamini and Hochberg step-up multiple testing correction at a False Discovery Rate <0.05) was applied to the resulting genes.

Species:

mouse

Samples:

24

Source:

E-GEOD-24797

PubMed:

21505102

Updated:

Dec.12, 2014

Registered:

Nov.24, 2014