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Integrated genomic-metabolic classification of acute myeloid leukemia defines a subgroup with NPM1 and cohesin/DNA damage mutations.

Biosciences Laboratory, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy. giorgia.simonetti@irst.emr.it. Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy. giorgia.simonetti@irst.emr.it. Department of Agricultural and Food Sciences, University of Bologna, Cesena, FC, Italy. Department of Physics and Astronomy, University of Bologna, Bologna, Italy. Biosciences Laboratory, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy. antonella.padella@irst.emr.it. Unit of Biostatistics and Clinical Trials, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy. Departamento de Ingeniería Química, Universidad Nacional del Sur, Bahía Blanca, Argentina. IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia "Seràgnoli", Bologna, Italy. Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy. Biosciences Laboratory, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy. Hematology Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy. Giorgio Prodi" Cancer Research Center, University of Bologna, Bologna and Department of Biomedical and Specialty Surgical Sciences, University of Ferrara, Ferrara, Italy. Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA. MLL Munich Leukemia Laboratory, Munich, Germany.

Leukemia. 2021;(10):2813-2826
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Abstract

Although targeting of cell metabolism is a promising therapeutic strategy in acute myeloid leukemia (AML), metabolic dependencies are largely unexplored. We aimed to classify AML patients based on their metabolic landscape and map connections between metabolic and genomic profiles. Combined serum and urine metabolomics improved AML characterization compared with individual biofluid analysis. At intracellular level, AML displayed dysregulated amino acid, nucleotide, lipid, and bioenergetic metabolism. The integration of intracellular and biofluid metabolomics provided a map of alterations in the metabolism of polyamine, purine, keton bodies and polyunsaturated fatty acids and tricarboxylic acid cycle. The intracellular metabolome distinguished three AML clusters, correlating with distinct genomic profiles: NPM1-mutated(mut), chromatin/spliceosome-mut and TP53-mut/aneuploid AML that were confirmed by biofluid analysis. Interestingly, integrated genomic-metabolic profiles defined two subgroups of NPM1-mut AML. One was enriched for mutations in cohesin/DNA damage-related genes (NPM1/cohesin-mut AML) and showed increased serum choline + trimethylamine-N-oxide and leucine, higher mutation load, transcriptomic signatures of reduced inflammatory status and better ex-vivo response to EGFR and MET inhibition. The transcriptional differences of enzyme-encoding genes between NPM1/cohesin-mut and NPM1-mut allowed in silico modeling of intracellular metabolic perturbations. This approach predicted alterations in NAD and purine metabolism in NPM1/cohesin-mut AML that suggest potential vulnerabilities, worthy of being therapeutically explored.