Integrated stem cell signature and cytomolecular risk determination in pediatric acute myeloid leukemia.

Abstract

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.

• Authors: Benjamin J Huang, Jenny L Smith, Jason E Farrar, Yi-Cheng Wang, Masayuki Umeda, Rhonda E Ries, Amanda R Leonti, Erin Crowgey, Scott N Furlan, Katherine Tarlock, Marcos Armendariz, Yanling Liu, Timothy I Shaw, Lisa Wei, Robert B Gerbing, Todd M Cooper, Alan S Gamis, Richard Aplenc, E Anders Kolb, Jeffrey Rubnitz, Jing Ma, Jeffery M Klco, Xiaotu Ma, Todd A Alonzo, Timothy Triche Jr, Soheil Meshinchi
• Journal: Nature communications (Nat Commun) Vol. 13 Issue 1 Pg. 5487 (09 19 2022) ISSN: 2041-1723 [Electronic] England
• PMID: 36123353 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural)
• Copyright: © 2022. The Author(s).

Chemical References

• Biomarkers
• RNA

Topics

• Biomarkers
• Child
• Gene Expression Profiling
• Humans
• Leukemia, Myeloid, Acute (diagnosis, genetics)
• Neoplastic Stem Cells
• RNA
• Recurrence