Relapsed or refractory pediatric acute myeloid leukemia (AML) is associated with poor outcomes and relapse risk prediction approaches have not changed significantly in decades. To build a robust transcriptional risk prediction model for pediatric AML, we perform RNA-sequencing on 1503 primary diagnostic samples. While a 17 gene leukemia stem cell signature (LSC17) is predictive in our aggregated pediatric study population, LSC17 is no longer predictive within established cytogenetic and molecular (cytomolecular) risk groups. Therefore, we identify distinct LSC signatures on the basis of AML cytomolecular subtypes (LSC47) that were more predictive than LSC17. Based on these findings, we build a robust relapse prediction model within a training cohort and then validate it within independent cohorts. Here, we show that LSC47 increases the predictive power of conventional risk stratification and that applying biomarkers in a manner that is informed by cytomolecular profiling outperforms a uniform biomarker approach.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9485122PMC
http://dx.doi.org/10.1038/s41467-022-33244-6DOI Listing

Publication Analysis

Top Keywords

stem cell
8
cell signature
8
cytomolecular risk
8
pediatric acute
8
acute myeloid
8
myeloid leukemia
8
risk prediction
8
build robust
8
prediction model
8
risk
5

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!