Dataset Information

Phenotypically-defined stages of leukemia arrest predict main driver mutations subgroups, and outcome in acute myeloid leukemia.

ABSTRACT: Classifications of acute myeloid leukemia (AML) patients rely on morphologic, cytogenetic, and molecular features. Here we have established a novel flow cytometry-based immunophenotypic stratification showing that AML blasts are blocked at specific stages of differentiation where features of normal myelopoiesis are preserved. Six stages of leukemia differentiation-arrest categories based on CD34, CD117, CD13, CD33, MPO, and HLA-DR expression were identified in two independent cohorts of 2087 and 1209 AML patients. Hematopoietic stem cell/multipotent progenitor-like AMLs display low proliferation rate, inv(3) or RUNX1 mutations, and high leukemic stem cell frequency as well as poor outcome, whereas granulocyte-monocyte progenitor-like AMLs have CEBPA mutations, RUNX1-RUNX1T1 or CBFB-MYH11 translocations, lower leukemic stem cell frequency, higher chemosensitivity, and better outcome. NPM1 mutations correlate with most mature stages of leukemia arrest together with TET2 or IDH mutations in granulocyte progenitors-like AML or with DNMT3A mutations in monocyte progenitors-like AML. Overall, we demonstrate that AML is arrested at specific stages of myeloid differentiation (SLA classification) that significantly correlate with AML genetic lesions, clinical presentation, stem cell properties, chemosensitivity, response to therapy, and outcome.

SUBMITTER: Vergez F

PROVIDER: S-EPMC9381519 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Phenotypically-defined stages of leukemia arrest predict main driver mutations subgroups, and outcome in acute myeloid leukemia.

Vergez François F Largeaud Laetitia L Bertoli Sarah S Nicolau Marie-Laure ML Rieu Jean-Baptiste JB Vergnolle Inès I Saland Estelle E Sarry Audrey A Tavitian Suzanne S Huguet Françoise F Picard Muriel M Vial Jean-Philippe JP Lechevalier Nicolas N Bidet Audrey A Dumas Pierre-Yves PY Pigneux Arnaud A Luquet Isabelle I Mansat-De Mas Véronique V Delabesse Eric E Carroll Martin M Danet-Desnoyers Gwenn G Sarry Jean-Emmanuel JE Récher Christian C

Blood cancer journal 20220816 8

Classifications of acute myeloid leukemia (AML) patients rely on morphologic, cytogenetic, and molecular features. Here we have established a novel flow cytometry-based immunophenotypic stratification showing that AML blasts are blocked at specific stages of differentiation where features of normal myelopoiesis are preserved. Six stages of leukemia differentiation-arrest categories based on CD34, CD117, CD13, CD33, MPO, and HLA-DR expression were identified in two independent cohorts of 2087 and ...[more]

PMID: 35973983

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Project description:ObjectiveComputing patients' similarity is of great interest in precision oncology since it supports clustering and subgroup identification, eventually leading to tailored therapies. The availability of large amounts of biomedical data, characterized by large feature sets and sparse content, motivates the development of new methods to compute patient similarities able to fuse heterogeneous data sources with the available knowledge.Materials and methodsIn this work, we developed a data integration approach based on matrix trifactorization to compute patient similarities by integrating several sources of data and knowledge. We assess the accuracy of the proposed method: (1) on several synthetic data sets which similarity structures are affected by increasing levels of noise and data sparsity, and (2) on a real data set coming from an acute myeloid leukemia (AML) study. The results obtained are finally compared with the ones of traditional similarity calculation methods.ResultsIn the analysis of the synthetic data set, where the ground truth is known, we measured the capability of reconstructing the correct clusters, while in the AML study we evaluated the Kaplan-Meier curves obtained with the different clusters and measured their statistical difference by means of the log-rank test. In presence of noise and sparse data, our data integration method outperform other techniques, both in the synthetic and in the AML data.DiscussionIn case of multiple heterogeneous data sources, a matrix trifactorization technique can successfully fuse all the information in a joint model. We demonstrated how this approach can be efficiently applied to discover meaningful patient similarities and therefore may be considered a reliable data driven strategy for the definition of new research hypothesis for precision oncology.ConclusionThe better performance of the proposed approach presents an advantage over previous methods to provide accurate patient similarities supporting precision medicine.

Dataset Information

Phenotypically-defined stages of leukemia arrest predict main driver mutations subgroups, and outcome in acute myeloid leukemia.

Publications

Phenotypically-defined stages of leukemia arrest predict main driver mutations subgroups, and outcome in acute myeloid leukemia.

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