Identification of a Relapse Signature in t(8;21) Pediatric AML

Pediatric acute myeloid leukemia (AML) is a malignancy of the blood in which myeloid progenitor cells are interrupted in their normal course of development and proliferate out of control. Recent studies such as the TARGET initiative, have revealed pediatric AML to be driven by relatively few molecular abnormalities when compared to adult AML. Most frequently occurring in genes directly involved in or regulating transcription. These molecular aberrations are drivers of disease, inform patient risk stratification and are transcriptomically unique from one another. Of these events, RUNX1-RUNX1T1 is the most common, evident in approximately 15% of pediatric AML cases. While RUNX1-RUNX1T1 is a positive outcome prognosticator, 20% of these patients go on to relapse with significantly poorer outcomes. Our objective was to identify a relapse signature that could be identified at the time of diagnosis so that alternative therapeutic strategies can be employed. Here we report the investigation of RNAseq data from 3,022 pediatric AML patient samples and identification of a transcriptomic relapse-signature (TRS) via differential expression analysis, supervised clustering and machine learning modelling. We performed differential gene expression analysis comparing diagnostic t(8;21) patient samples for those who relapsed (R) (n=38)vs. those who did not relapse (NR) (n=111). Analysis was performed using DESeq2 in R 4.3.0. P-values were adjusted with Benjamini-Hochberg correction to control for false discovery rate a significance threshold for padj of < 0.05 was applied. A list of 462 differentially genes was returned. Hierarchical clustering of the 462 differentially expressed genes revealed 4 patient clusters, each with a distinct transcriptomic signature ( Fig 1A). K-means (n=4) also returned similar results to the hierarchical clustering. Kaplan-Meier analysis of the 4 clusters showed a significant difference in patient outcomes both in terms of overall survival (P < 0.001) and event-free survival (P < 0.05) ( Fig 1B). In marked contrast to clusters 1-3, cluster 4 was highly enriched in patients who relapsed at a rate of 86%. Clusters 1, 2 and 3 had relapse rates of 36, 29 and 38 percent respectively. To identify the most relevant genes that could serve as a TRS, we trained a random forest (RF) model using transcript per million (TPM) transformed and length normalized counts for each of the 462 differentially expressed genes at the time of diagnosis between the R and