From infancy to young adulthood: Exploring the divergent genomic mechanisms that drive AML.

6544 Background: Pediatric & young adult Acute Myeloid Leukemias (pAML & YA AML) are an uncommon, heterogenous, & clinically challenging group of malignancies, as age ranges widely & proper therapy selection (i.e. pediatric vs. adult regimens, etc.) can be unclear. The genomics of these disorders can help understand distinct disease biology, better risk stratification, & improve outcomes. Methods: Bone marrow, peripheral blood, or FFPE samples from 870 suspected AML patients were sequenced using a 302 gene panel to detect SNVs/indels. 466 underwent RNA fusion detection of 184 genes & DNA analysis for CNV detection of 24 genes. Only pathogenic/likely pathogenic variants were included in the analysis. AML patients were split into 4 groups based on age: Infant (≤3, n=30), Childhood (4-14, n=51), Adolescent (15-19, n=51), YA (20-35, n=738). 4750 DNA/RNA sequenced adult AML patients (>35) were also included in the analysis. Statistics were performed using Fisher’s exact test. Results: pAML patients (infant, childhood, & adolescent) had a high prevalence of FLT3 & NRAS variants (17.4% & 15.2%) & fusions (26.2%) while YA AML had a high prevalence of fusions (29.2%), FLT3 (20%), NRAS (12.5%), & WT1 (12.6%) variants. Adult AML had a high prevalence of TET2 (18.4%), DNTM3A (18.3%), ASXL1 (16.8%), SRSF2 (14.8%), & TP53 (14.2%) variants, highlighting an increased prevalence of mutations associated with clonal hematopoiesis or prior chronic myeloid neoplasms. Infant AML had a higher number of GATA1 variants (33.3% vs. 0% - 2%, p<0.00001) vs. childhood, adolescent, & YA AML; trisomy 21 was present in 90% of patients by CNV detection. Fusions were only found in 5% of infant AML (p=0.02) vs. 29.2% - 34.5% in other groups. FLT3 variants were lower in infant (10%) and childhood AML (13.7%) vs. adolescent (25.5%) & YA (20%) while NRAS variants were higher in adolescent AML (21.6% vs. 6.7% - 13.7%). Childhood AML had a higher prevalence of RUNX1 fusions (16.1% vs. 0 – 5.4%, p=0.03) & Adolescent AML had a higher prevalence of PML::RARA fusions (13.8% vs. 0% – 6.5%) vs. other groups. YA AML had a higher prevalence of WT1 variants (12.6% vs. 0% - 7.8%, p=0.02). CNV loss in IKZF1 (7p12) were more prevalent in adolescent AML (10.3% vs. 0-0.5%, p=0.002); EZH2 CNV loss (7q36) were more prevalent in YA & childhood AML (4.7% & 3.4% vs. 0%). Upon aggregating genes by their molecular function, infant AML had a lower prevalence of variants in epigenetic genes (6.7% vs. 15.57-24.8%, p=0.02), RAS (10% vs. 23.2%-25.5%), & signaling genes (16.7% vs. 27.5% – 41.2%). Variants in DNA repair genes were more frequent in adolescent AML (13.7% vs. 3.9% - 6.7%, p=0.01). Conclusions: Infant, childhood, adolescent, & YA AML harbor unique genetic profiles that distinguish themselves from each other and reflect divergent and evolutionarily favored mechanisms behind leukemogenesis. Understanding these genetic profiles can help predict prognosis & help tailor more effective treatments.

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Journal
Journal of Clinical Oncology
Published
2026-05-27
DOI
https://doi.org/10.1200/jco.2026.44.16_suppl.6544
Primary Topic
Acute Myeloid Leukemia Research
Type
article
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article

From infancy to young adulthood: Exploring the divergent genomic mechanisms that drive AML.

Frank J. Scarpa, Madhuri Paul, Nathan Montgomery, John Michael Furgason
Journal of Clinical Oncology
Acute Myeloid Leukemia Research
article

From infancy to young adulthood: Exploring the divergent genomic mechanisms that drive AML.

Frank J. Scarpa, Madhuri Paul, Nathan Montgomery, John Michael Furgason
article en

Abstract

6544 Background: Pediatric & young adult Acute Myeloid Leukemias (pAML & YA AML) are an uncommon, heterogenous, & clinically challenging group of malignancies, as age ranges widely & proper therapy selection (i.e. pediatric vs. adult regimens, etc.) can be unclear. The genomics of these disorders can help understand distinct disease biology, better risk stratification, & improve outcomes. Methods: Bone marrow, peripheral blood, or FFPE samples from 870 suspected AML patients were sequenced using a 302 gene panel to detect SNVs/indels. 466 underwent RNA fusion detection of 184 genes & DNA analysis for CNV detection of 24 genes. Only pathogenic/likely pathogenic variants were included in the analysis. AML patients were split into 4 groups based on age: Infant (≤3, n=30), Childhood (4-14, n=51), Adolescent (15-19, n=51), YA (20-35, n=738). 4750 DNA/RNA sequenced adult AML patients (>35) were also included in the analysis. Statistics were performed using Fisher’s exact test. Results: pAML patients (infant, childhood, & adolescent) had a high prevalence of FLT3 & NRAS variants (17.4% & 15.2%) & fusions (26.2%) while YA AML had a high prevalence of fusions (29.2%), FLT3 (20%), NRAS (12.5%), & WT1 (12.6%) variants. Adult AML had a high prevalence of TET2 (18.4%), DNTM3A (18.3%), ASXL1 (16.8%), SRSF2 (14.8%), & TP53 (14.2%) variants, highlighting an increased prevalence of mutations associated with clonal hematopoiesis or prior chronic myeloid neoplasms. Infant AML had a higher number of GATA1 variants (33.3% vs. 0% - 2%, p<0.00001) vs. childhood, adolescent, & YA AML; trisomy 21 was present in 90% of patients by CNV detection. Fusions were only found in 5% of infant AML (p=0.02) vs. 29.2% - 34.5% in other groups. FLT3 variants were lower in infant (10%) and childhood AML (13.7%) vs. adolescent (25.5%) & YA (20%) while NRAS variants were higher in adolescent AML (21.6% vs. 6.7% - 13.7%). Childhood AML had a higher prevalence of RUNX1 fusions (16.1% vs. 0 – 5.4%, p=0.03) & Adolescent AML had a higher prevalence of PML::RARA fusions (13.8% vs. 0% – 6.5%) vs. other groups. YA AML had a higher prevalence of WT1 variants (12.6% vs. 0% - 7.8%, p=0.02). CNV loss in IKZF1 (7p12) were more prevalent in adolescent AML (10.3% vs. 0-0.5%, p=0.002); EZH2 CNV loss (7q36) were more prevalent in YA & childhood AML (4.7% & 3.4% vs. 0%). Upon aggregating genes by their molecular function, infant AML had a lower prevalence of variants in epigenetic genes (6.7% vs. 15.57-24.8%, p=0.02), RAS (10% vs. 23.2%-25.5%), & signaling genes (16.7% vs. 27.5% – 41.2%). Variants in DNA repair genes were more frequent in adolescent AML (13.7% vs. 3.9% - 6.7%, p=0.01). Conclusions: Infant, childhood, adolescent, & YA AML harbor unique genetic profiles that distinguish themselves from each other and reflect divergent and evolutionarily favored mechanisms behind leukemogenesis. Understanding these genetic profiles can help predict prognosis & help tailor more effective treatments.

Journal of Clinical OncologyVol. 44(16_suppl)
NeoGenomics (United States) (US)
Good health and well-being
Openalex Percentile: Top 8%
Acute Myeloid Leukemia Research
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