Classification of acute myeloid leukemia based on multi‐omics and prognosis prediction value

• Published in: Molecular oncology Vol. 19; no. 6; pp. 1836 - 1854
• Main Authors: Song, Yang, Wang, Zhe, Zhang, Guangji, Hou, Jiangxue, Liu, Kaiqi, Wei, Shuning, Li, Yan, Zhou, Chunlin, Lin, Dong, Wang, Min, Wei, Hui, Wang, Jianxiang, Cheng, Tao, Mi, Yingchang
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.06.2025; John Wiley and Sons Inc; Wiley
• Subjects: Acute myeloid leukemia; Adult; Aged; Aged, 80 and over; Angiogenesis; Bioinformatics; Blood Cancer; Acute Myeloid Leukemia Cancer of the Blood, Haematooncology, Leukemia; Blood diseases; CCAAT/enhancer-binding protein; Chromosomes; Classification; Datasets; DNA Methylation; Drug sensitivity testing; Female; Gene expression; Genomes; Hematology; Humans; Hypoxia; Immune status; Leukemia; Leukemia, Myeloid, Acute - classification; Leukemia, Myeloid, Acute - diagnosis; Leukemia, Myeloid, Acute - genetics; Leukemia, Myeloid, Acute - mortality; Lymphocytes; Male; Medical prognosis; Middle Aged; Monocytes; Multiomics; multi‐omics; Mutation; Myc protein; Myelodysplastic syndrome; Predictions; Prognosis; Risk groups; Sensitivity analysis; Stem cell transplantation; Transcriptomics; United States > US; multi‐omics; acute myeloid leukemia; classification; prognosis
• ISSN: 1574-7891; 1878-0261; 1878-0261
• DOI: 10.1002/1878-0261.70000

Acute myeloid leukemia (AML) is a heterogeneous cancer, making outcomes prediction challenging. Several predictive and prognostic models are used but have considerable inaccuracy at individual level. We tried to increase prediction accuracy using a multi‐omics strategy. We interrogated data from 1391 consecutive, newly diagnosed subjects with AML, integrating information on mutation topography, DNA methylation, and transcriptomics. We developed an unsupervised multi‐omics classification system (UAMOCS) with these data. UAMOCS provides a multidimensional understanding of AML heterogeneity and stratifies subjects into three cohorts: (a) UAMOCS1 [high lymphocyte activating 3 (LAG3) expression, chromosome instability, myelodysplasia‐related mutations]; (b) UAMOCS2 (monocytic‐like profile, immune suppression and activated angiogenesis and hypoxia pathways); and (c) UAMOCS3 [CCAAT enhancer binding protein alpha (CEBPA) mutations and MYC pathway activation]. UAMOCS distinguishes overall survival rates across the cohorts (TCGA P = 0.042; GSE71014 P = 0.043; ihCAMs‐AML, GSE102691 and GSE37642 all P < 0.001). The model's C‐statistic is comparable to the 2022 ELN risk classification (0.87 vs 0.82; P = 0.162), but offers a more nuanced distinction between intermediate‐ and high‐risk groups. When combined with high‐throughput drug sensitivity testing, UAMOCS can accurately predict sensitivity to azacitidine (AZA) and venetoclax. The UAMOCS system is available as an R package. The UAMOCS system has the potential to redefine AML subtypes, enhance prognostic predictions, and guide treatment strategies based on patients' immune status and expected responses to therapies. The Unsupervised AML Multi‐Omics Classification System (UAMOCS) integrates genomic, methylation, and transcriptomic data to categorize AML patients into three subtypes (UAMOCS1‐3). This classification reveals clinical relevance, highlighting immune and chromosomal characteristics, prognosis, and therapeutic vulnerabilities. Ex vivo screening of 17 drugs identified potential responses to AZA + Venetoclax treatment in AML patients.