Multi-modal and data-driven assessment of myeloid neoplasms refines classification across disease states

Lachowiez, Curtis A., Asimomitis, Georgios, Bernard, Elsa, Devlin, Sean M., Tazi, Yanis, Creignou, Maria, Germing, Ulrich, Gattermann, Norbert, Gilkes, Amanda, Thomas, Ian, Bullinger, Lars, Döhner, Konstanze, Malcovati, Luca, Othman, Jad, Dillon, Richard, Eisfeld, Ann-Kathrin, Nicolet, Deedra, Issa, Ghayas C., Daver, Naval, Kadia, Tapan M., DiNardo, Courtney D., Ravandi, Farhad, Garcia-Manero, Guillermo, Montalban-Bravo, Guillermo, Russell, Nigel, Cazzola, Mario, Döhner, Hartmut, Huntly, Brian JP., Hasserjian, Robert P., Hellström-Lindberg, Eva, Papaemmanuil, Elli and Loghavi, Sanam 2025. Multi-modal and data-driven assessment of myeloid neoplasms refines classification across disease states. Blood Cancer Discovery 10.1158/2643-3230.bcd-25-0047

Full text not available from this repository.

Abstract

The World Health Organization (WHO) 5th edition and International Consensus Classification (ICC) for myeloid neoplasms both incorporate empirical numerical thresholds to morphologic and molecular features defining certain disease entities. However, the clinical implications of these thresholds remain unclear. We analyzed a large cohort (N=6,976) of patients with myeloid neoplasms to evaluate the impact of proposed, yet different numerical thresholds for variant allele frequency of genetic mutations or hematologic parameters set forth by WHO 5th and ICC for classification of SF3B1-mutated (SF3B1m) myelodysplastic neoplasms (MDS), NPM1m acute myeloid leukemia (AML), and oligomonocytic-chronic myelomonocytic leukemia (O-CMML). Our analysis demonstrated the clonal burden of SF3B1m in MDS informs biological classification and prognosis, supported the notion that NPM1 mutation should be AML-defining regardless of blast percentage, highlighted the prognostic impact of the cumulative number of myelodysplasia-related mutations in NPM1-mutated AML, and provided evidence that integrating specific molecular signatures could improve accuracy of O-CMML classification.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Medicine
Publisher:	American Association for Cancer Research
ISSN:	2643-3230
Date of Acceptance:	1 July 2025
Last Modified:	15 Jul 2025 14:00
URI:	https://orca.cardiff.ac.uk/id/eprint/179848

Actions (repository staff only)

Edit Item