Integration of Transcriptomic Features to Improve Prognosis Prediction of Pediatric Acute Myeloid Leukemia With KMT2A Rearrangement

Abstract

Lysine methyltransferase 2A-rearranged acute myeloid leukemia (KMT2A-r AML) is a special entity in the 2022 World Health Organization classification of myeloid neoplasms, characterized by high relapse rate and adverse outcomes. Current risk stratification was established on the treatment response and translocation partner of KMT2A. To study the transcriptomic feature and refine the current stratification of pediatric KMT2A-r AML, we analyzed clinical and RNA sequencing data of 351 patients. By implementing least absolute shrinkage and selection operator algorithm, we identified 7 genes (KIAA1522, SKAP2, EGFL7, GAB2, HEBP1, FAM174B, and STARD8) of which the expression levels were strongly associated with outcomes. We then developed a transcriptome-based score, dividing patients into 2 groups with distinct gene expression patterns and prognosis, which was further validated in an independent cohort and outperformed the LSC17 score. We also found cell cycle, oxidative phosphorylation, and metabolism pathways were upregulated in patients with inferior outcomes. By integrating clinical characteristics, we proposed a simple-to-use prognostic scoring system with excellent discriminability, which allowed us to distinguish allogeneic hematopoietic stem cell transplantation candidates more precisely. In conclusion, pediatric KMT2A-r AML is heterogenous on transcriptomic level and the newly proposed scoring system combining clinical characteristics and transcriptomic features can be instructive in clinical routines.

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“…To identify reliable transcriptomic patterns across patient cohorts, much larger data sets are therefore required and this is the approach recently taken by Li et al who used transcriptomic data from 351 patients to try and improve patient stratification of pediatric lysine methyltransferase 2A‐rearranged acute myeloid leukemia (KMT2A‐r AML). 3 Using Least Absolute Shrinkage and Selection Operator modelling, they were able to identify a seven‐gene transcriptional signature which they dubbed a pKMT2A7 score. A high pKMT2A7 score was then shown in a validation cohort to be an independent risk factor for both overall survival and event‐free survival when combined with age and MLL3 mutational status.…”