Abstract Introduction: African Ancestry (AA) mediates inferior overall survival (OS) in Acute Myeloid Leukemia (AML), a feature highlighted by SEER registries and CALGB-Alliance. We demonstrated that social vulnerability, higher rates of Myelodysplasia- Related (MR) Chromosomic Abnormalities (Abn) e.g., 7q and -7 and DNA repair mutations (mut) e.g., RAD21, FBXW7characterize elderly AA AML Williams et al. BMC Cancer. 2025 in press. The leukemogenic mechanism integrating MR-genomic defects, DNA repair and AA leukemogenesis is unresolved. Leukemia cells express myeloid antigens paralleling healthy hematopoiesis but accumulate specific molecular defects in cells with variable differentiation arrest (e.g., less differentiated cells mostly harboring MR mut). Here, we hypothesize that genomic alterations, differentiation arrest and deregulated transcriptome may inform AA leukemogenic mechanisms. Methods: After IRB approval, 72 and 169 AA and non-AA elderly AML patients age 60 years and older were included in analysis. Descriptive statistics were used to summarize data. Chi-square and Fisher exact tests were used to assess categorical variables. Kaplan Meier method and Cox regression were used to investigate differential OS and variables with independent prognostic effect. One and two sample T-tests prioritized deregulated genes in 12 and 11 AA and non-AA cases of new AML diagnoses. Bonferroni correction was used for one sample T-tests. ToppGene identified canonical deregulated pathways. All analyses were done using SAS version 9.4. Results: Median age was 73 (y) (61-93) and 71 y, (61-90) in AA and non-AA. 65% were male. In AA, 1/60 (1.6%), 18/60 (30%), and 41/60 (68.3%) were favorable, intermediate and adverse ELN 2022, whereas 13/136 (10%), 52/136 (38.2%) and 71/136 (52.2%) were favorable, intermediate and adverse in non-AA AML, p=0.04. ELN 22 HR=0.18, CI 95 0.09-0.42, p=.0001 and age HR=1.02, CI 95 1.01-1.03, p=0.0006 were associated with OS. In AA vs non-AA, favorable and intermediate ELN22 risk disparity was induced by lack of Core Binding Factor (CBF) Abn 0/21 (0%) vs 9/35 (26%), p=0.01, lower NPM1 1/27 (4%) vs 12/49 (24.4%), p=0.02 and lack of FLT3 ITD mut 0/27 (0%) vs 7/47(15%), p=0.03, suggesting that AA AML genome induces less proliferative disease than non-AA. To address this, we examined WBC, marrow blast % and flow cytometry differentiation arrest for cell subtype in 25 and 100 elderly AA and non-AA cases from an external testing cohort. Mean WBC was 18.5 and 52.8 K/uL p=0.001 and marrow blast 37.6% and 57.7% p=0.004 for 25 AA and 100 non-AA. HPC like subtype Hemopoietic stem cell + Common myeloid progenitor + Multipotent progenitor was 15/20 (75%) and maturing-like subtype Monocytic and Granulocytic progenitors 5/20 (25%), p=0.03. However, in non-AA cases, 51/100 (51%) and 49/100 (49%) were HPC and maturing -like. 39/1495 genes demonstrated deregulation based on alpha=0.05 with Bonferroni correction applied. In AA AML, FAS (p=0.0001), CD28 (p=0.006), TNFAIP3 (p=0.01) were downregulated with most significant results. IL13RA2 (p=0.002), BDNF (p=0.004), PRP8F (p=0.006) and ALDH1A1 (p=0.03) demonstrated upregulation with most significant results. T-cell receptor signaling and T-cell activation (FDR=1.32E-3), Nucleotide excision repair (FDR=3.16E-3) and B-cell dysfunction (FDR=3.16E-3) pathways were most impacted by downregulated genes; whereas ligand gated receptor (FDR=2.21E-2) and protein RNA complex assembly (FDR=4.04E-2) pathways were enriched by upregulated genes. Conclusions: Less immunophenotypically differentiated disease with hypoproliferative potential, lack of CBF Abn, low frequency of NPM1/FLT3 ITD mut are observed in elderly AA AML. Adaptive immune T/B cell dysfunction, defects in nucleotide excision repair, compensatory protein RNA complex, ligand gated receptor and stemness-like physiology e.g., upregulated ALDH1A1 expression characterize elderly AA leukemogenesis. Transcriptomic validation with larger number of AA AML cases is needed. However, our data represents proof of concept for immune deregulation involved in AA leukemogenesis. Also, single cell multiomics could deconvolute spatial/cell type interactions to facilitate novel therapies for this vulnerable population.
Andreadakis et al. (2025) studied this question.