Age and the genomic landscape of AML in precision oncology: An analysis using ASH HematOmics Program.

6543 Background: Age is a major determinant of prognosis and treatment selection in acute myeloid leukemia (AML), yet the extent to which chronological age reflects underlying genomic and transcriptomic features remains incompletely characterized. The American Society of Hematology HematOmics Program (ASHOP) provides a centralized, clinically annotated genomic resource enabling systematic evaluation of age-associated AML biology across molecular subtypes. Methods: We conducted a cross-sectional analysis of adults with AML using ASHOP data. Patients were categorized by age (<40, 40-59, 60-74, and ≥75 years). AML molecular subtypes were defined based on recurrent cytogenetic and genomic alterations (NPM1, PML::RARA, CBFB::MYH11, MECOM, CEBPA, AML-MR, KMT2A, DEK::NUP214, TP53, and other recurrent translocations), aligned with WHO classification. Adverse genomic risk was defined by AML-MR, MECOM, and TP53. Gene expression levels for epigenetic regulators (DNMT3A, TET2, IDH1, IDH2, FLT3) and stemness-associated genes (HOXA9, MEIS1, HLF, GATA2, PROM1) were standardized as z-scores, and composite scores generated. Associations were assessed using chi-square tests, linear regression, and multivariable models adjusted for sex. Results: Among 302 patients (mean age 71.1 ± 17.3 years; 47.7% aged ≥75; 54.3% male), the most common subtypes were NPM1 (20.5%), PML::RARA (16.6%), CBFB::MYH11 (15.6%), MECOM (14.2%), CEBPA (10.9%), AML-MR (9.6%), and KMT2A (7.6%). Subtype distribution varied modestly across age groups (p=0.053) but not by sex (p=0.858). Adverse genomic risk increased significantly with age (p=0.016). In multivariable logistic regression adjusted for sex, patients aged ≥75 had significantly higher odds of adverse genomic features (OR 2.78, 95% CI 1.26, 6.12; p=0.011), while sex was not independently associated (p=0.325). In transcriptomic analyses (n=265), age was not associated with individual epigenetic or stemness genes, with age and sex explaining minimal variance in composite scores (epigenetic score R²=0.013, p=0.506; stemness score R²=0.003, p=0.957). In contrast, molecular subtype showed strong associations. Subtype along with age and sex explained 16.4% of epigenetic score variance (R²=0.164; p<0.001) and 38.0% of the stemness score variance (R²=0.380; p<0.001). Key stemness genes were highly subtype-dependent (HOXA9 R²=0.709; MEIS1 R²=0.599; both p<0.001), driven primarily by NPM1 and KMT2A subtypes. Conclusions: Advanced age in AML is associated with adverse genomic risk accumulation but explains minimal variance in stemness or epigenetic transcriptional programs. Instead, these programs are largely determined by molecular subtype. Hence, stemness and epigenetic programs are intrinsic properties of AML molecular subtypes rather than age, supporting subtype-driven risk stratification and therapeutic decision-making in the precision oncology era.