Abstract P27: Proteogenomic and metabolomic analysis of acute myeloid leukemia reveals molecular and functional underpinnings of cellular and clinical phenotypes

Abstract

Acute myeloid leukemia (AML) is a blood malignancy of poor prognosis with marked heterogeneity. To elucidate the underlying mechanisms that drive AML as part of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) effort, we performed large scale comprehensive genomics, transcriptomics, proteomics including multiple post-translational modifications (phosphorylation, acetylation, and glycosylation), metabolomics, and lipidomics characterization of 173 treatment-naïve AML patients. Applying the similarity network fusion method on both transcriptomics and proteomics data, we identified 8 proteogenomic clusters. These clusters recapitulate specific recurrent mutations, fusions, structural variants, and established clinical subtypes available within the cohort, as well as reveal new cluster-specific phenotypes within other multi-omic datasets. We used single-cell RNAseq data as a reference to perform immune component analysis for collected bulk samples. The result reveals that our proteogenomic clustering also captures the variations of AML differentiation hierarchies including CD14+ monocyte-like and GMP-like AML. To assess the complex disease nature of AML, we performed functional analysis for each cluster to reveal interplay between multiple genomic aberrations such as NPM1, FLT3-ITD, DNMT3A mutations, complex chromosomal alterations, and the leukemia cell differentiation. Additionally, the multi-omics analysis performed not only connects previously identified molecular drivers and cell differentiation variations within AML, but also links them with observed cancer metabolomic reprogramming alongside differences in MTOR signaling, MYC activities, mitochondrial activities, and drug responses. Moreover, our study also identified site-specific post-translational modifications previously not known in AML, highlighting the valuable insights and clinical relevance of these newly identified clusters. Citation Format: Shih-Chun Alec Chu, Yi Hsiao, Yamei Deng, Chenwei Wang, Jennifer Kyle, Yongchao Dou, James Pino, Camilo Posso, Leanne Henry, Ginny Li, Li Ding, Lijun Chen, Mamie Lih, Yifat Geffen, Gilbert Omenn, Chandan Kumar, Saravana Dhanasekaran, Fengchao Yu, Elie Traer, Jeffrey W. Tyner, Hui Zhang, Tao Liu, Sara Gosline, Bing Zhang, Arul Chinnaiyan, Alexey I Nesvizhskii, Marcin Cieslik. Proteogenomic and metabolomic analysis of acute myeloid leukemia reveals molecular and functional underpinnings of cellular and clinical phenotypes [abstract]. In: Proceedings of the Blood Cancer Discovery Symposium; 2024 Mar 4-6; Boston, MA. Philadelphia (PA): AACR; Blood Cancer Discov 2024;5(2_Suppl):Abstract nr P27.