Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression

Svea Stratmann, Sara A. Yones, Mateusz Garbulowski, Jitong Sun, Aron Skaftason, Markus Mayrhofer, Nina Norgren, Morten Krogh Herlin, Christer Sundstrom, Anna Eriksson, Martin Hoglund, Josefine Palle, Jonas Abrahamsson, Kirsi Jahnukainen, Monica Cheng Munthe-Kaas, Bernward Zeller, Katja Pokrovskaja Tamm, Lucia Cavelier, Jan Komorowski, Linda Holmfeldt

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Sammanfattning

Numerous studies have been performed over the last decade to exploit the complexity of genomic and transcriptomic lesions driving the initiation of acute myeloid leukemia (AML). These studies have helped improve risk classification and treatment options. Detailed molecular characterization of longitudinal AML samples is sparse, however; meanwhile, relapse and therapy resistance represent the main challenges in AML care. To this end, we performed transcriptome-wide RNA sequencing of longitudinal diagnosis, relapse, and/or primary resistant samples from 47 adult and 23 pediatric AML patients with known mutational background. Gene expression analysis revealed the association of short event-free survival with overexpression of GLI2 and IL1R1, as well as downregulation of ST18. Moreover, CR1 downregulation and DPEP1 upregulation were associated with AML relapse both in adults and children. Finally, machine learning–based and network-based analysis identified overexpressed CD6 and downregulated INSR as highly copredictive genes depicting important relapse-associated characteristics among adult patients with AML. Our findings highlight the importance of a tumor-promoting inflammatory environment in leukemia progression, as indicated by several of the herein identified differentially expressed genes. Together, this knowledge provides the foundation for novel personalized drug targets and has the potential to maximize the benefit of current treatments to improve cure rates in AML.

Key Points
Progression of AML is associated with pro-inflammatory mediators through altered expression levels of CR1, DPEP1, IL1R1, and ST18.

Upregulated CD6 and downregulated INSR are nodes in gene expression networks linked to AML relapse, according to machine learning analysis.
Originalspråkengelska
TidskriftBlood advances
Volym6
Utgåva1
Sidor (från-till)152-164
Antal sidor13
ISSN2473-9529
DOI
StatusPublicerad - 11 jan. 2022
MoE-publikationstypA1 Tidskriftsartikel-refererad

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