Abstract 6206: Molecular stratification of AML: Integrative genomic and immune profiling points to high risk subgroup and targetable molecular drivers
Authors: Karagoz Kubra , Brady Lauren , Higgs Brandon , Si Han 
Published: March 2024

Background: Acute Myeloid Leukemia (AML) is a complex hematological malignancy characterized by clinical diversity. The ability to accurately subtype patients can inform prognosis and guide therapeutic approaches. With the integration of multi-omics data, it is possible to elucidate the molecular mechanisms underpinning unique AML subtypes, with intent to identify potential drug targeting pathways in a precision medicine strategy.

Method: RNAseq datasets from Beat-AML (discovery, N=462), TCGA-AML (validation, N=173), and Leucegene (validation, N=452) were analyzed through non-negative matrix factorization to delineate distinct transcriptomic-driven molecular subtypes within AML. A curated set of the top 1,000 most variable genes was utilized to minimize analytical noise. GSVA was used for pathway analyses across Hallmark, KEGG, Reactome, and the immune microenvironment was characterized via xCell. Recurring DNA variants were examined in each subtype. Clinical outcomes of each subtype were analyzed using the Kaplan-Meier method and assessed by a log-rank test. These insights were subsequently validated in two independent cohorts - TCGA AML and Leucegene. The most influential components of immunological effector mechanisms, which characterize poor prognostic subpopulation, were assessed using bootstrap forest partitioning as a feature selection technique.

Results: Our Beat-AML analysis identified 5 distinct clusters. Cluster 3 consisted of 20% of AML cases (n=86), and displayed a worse overall survival with a HR (95% CI) of 1.41 (1.03, 1.92, P=0.03). This cluster was characterized by three co-mutations: FLT3, NPM1, and DNMT3A. Pathway analysis highlighted biological processes key to antibody therapy intervention, specifically complement pathways, NK cell cytotoxicity, and FCGR-mediated phagocytosis as enriched within this subpopulation. Concurrently, oncogenic (PI3K/AKT/MTOR, TGFB, NOTCH) and metabolic pathways (fatty acid and oxidative phosphorylation) were activated. Immune profiling indicated increased levels of M2 macrophages. Using bootstrap forest partitioning, C2 and ITGAM were identified as key genes defining cluster 3. These observations were confirmed in TCGA AML and Leucegene cohorts.

Conclusion: Our comprehensive analysis identified a distinct high-risk AML subgroup characterized by unique genomic, transcriptomic, and immunological patterns. The combined presence of NPM1/FLT3-ITD and DNMT3A mutations in cluster 3—each previously attributed with varied prognostic outcomes—warrants in-depth biological investigation. With these consistent AML subgroup results identified across three independent cohorts (total of 1,087 patients), we highlight the complexities within the molecular landscape of AML and emphasize the need for tailored therapeutic approaches - a transformative shift in AML treatment.

Citation Format: Kubra Karagoz, Lauren Brady, Brandon Higgs, Han Si. Molecular stratification of AML: Integrative genomic and immune profiling points to high risk subgroup and targetable molecular drivers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6206.