Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation

Data-driven flow cytometry classification of blast differentiation in older patients with acute myeloid leukemia

Catia Simoes et al. Blood Neoplasia. .
No abstract available

PubMed Disclaimer

Conflict of interest statement

Conflict-of-interest disclosure: F.P. reports honoraria and research funding from Oryzon, Janssen, and Bristol Myers Squibb (BMS)-Celgene. R.A. reports membership on an entity´s board of directors advisory committees for Incyte Corporation, and Astellas; and honoraria from Novartis, Celgene, and Incyte. J.F.S.-M. reports consultancy, membership on an entity´s board of directors advisory committees for AbbVie, Amgen, BMS, Celgene, GlaxoSmithKline, Janssen, Karyopharm, Merck Sharpe & Dohme, Novartis, Regeneron, Roche, Sanofi, SecuraBio, and Takeda. P.M. reports consultancy, membership on an entity´s board of directors advisory committees for, research funding from, speaker’s bureau for Celgene, Sanofi, Incyte, Karyopharm, Novartis, Stemline/Menarini, Agios, Astellas Pharma, and Daiichi Sankyo; membership on an entity´s board of directors advisory committees for Pfizer, Teva, and AbbVie; research funding and speakers bureau fees from Janssen; and consultancy for Tolero Pharmaceutical, Forma Therapeutics, and Glycomimetics. B.P. served as a consultant for and received honoraria from Adaptive, Amgen, Becton Dickinson, BMS/Celgene, GSK, Janssen, Roche, Sanofi, and Takeda; and received research support from BMS/Celgene, GSK, Roche, Sanofi, and Takeda. The remaining authors declare no competing financial interests. A complete list of the members of the PETHEMA Cooperative Study Group appears in the supplemental Appendix.

Figures

Figure 1.
Figure 1.
Data-driven phenotypic subclassification of older patients with AML. (Ai) Seven cell types were identified using the first five 8-color combinations of the EuroFlow panel for the characterization of AML, in a total of 15 healthy donors (HD). Each cell type was identified using the backbone markers present in all 8-color combinations (ie, CD34, CD45, CD117, and HLADR). (Aii) Expression percentage of 19 different markers (CD22, CD34, CD45, CD117, CD105, CD71, CD36, CD38, HLADR, CD13, CD15, CD33, CD64, CD35, CD14, CD300e, CD11b, CD16, and CD10). Markers were analyzed per healthy donor to create and define normal hematopoiesis. (Aiii) After obtained the reference of the normal hematopoiesis, principal component (PC) analysis was used to calculate the centroid for each cell type was calculated considering the mean values of PC1 and PC2. (Aiv) The percentage of expression for each marker was obtained in blasts of 252 patients with AML enrolled in the PETHEMA/FLUGAZA clinical trial (NCT02319135). (Av) After projecting each case onto the matrix generated from healthy adults, the distance between blasts and normal cell types was calculated, and the shortest distance identified the corresponding differentiation stage. (B) Pie chart showing the number of patients and relative distribution according to the 7 differentiation stages defined in healthy adults. Because of low numbers, patients were regrouped according into HPC-like, as well as granulocytic-, monocytic-, and erythroid-like, AML. (C) Concordance between the phenotypic and the FAB subclassification of patients with AML according to the differentiation stage of blasts. (D) OS of patients with HPC-like vs maturing AML subtypes treated with azacitidine. P values were determined by the 2-sided log-rank test. (E) Multivariate analysis of OS in the FLUGAZA cohort, including the data-driven phenotypic classification (HPC- vs maturing-like) and the Medical Research Council (MRC) risk stratification (favorable/intermediate vs adverse). (F) OS of patients from the TUH external validation cohort with HPC-like vs maturing AML subtypes treated with azacitidine. P values were determined by the 2-sided log-rank test. (G) Multivariate analysis of OS in the TUH external validation cohort, including the data-driven phenotypic classification (HPC- vs maturing-like), performance status (PS), age (>80 vs ≤80 years), concentration of white blood cells (>50 × 109/L or ≤50 × 109/L), and the MRC risk stratification (favorable/intermediate vs adverse). FAB, French-American-British; HR, hazard ratio; MRC, Medical Research Council; NRBC, nucleated red blood cell; TUH, Toulouse University Hospital; WBC, white blood count.
Figure 1.
Figure 1.
Data-driven phenotypic subclassification of older patients with AML. (Ai) Seven cell types were identified using the first five 8-color combinations of the EuroFlow panel for the characterization of AML, in a total of 15 healthy donors (HD). Each cell type was identified using the backbone markers present in all 8-color combinations (ie, CD34, CD45, CD117, and HLADR). (Aii) Expression percentage of 19 different markers (CD22, CD34, CD45, CD117, CD105, CD71, CD36, CD38, HLADR, CD13, CD15, CD33, CD64, CD35, CD14, CD300e, CD11b, CD16, and CD10). Markers were analyzed per healthy donor to create and define normal hematopoiesis. (Aiii) After obtained the reference of the normal hematopoiesis, principal component (PC) analysis was used to calculate the centroid for each cell type was calculated considering the mean values of PC1 and PC2. (Aiv) The percentage of expression for each marker was obtained in blasts of 252 patients with AML enrolled in the PETHEMA/FLUGAZA clinical trial (NCT02319135). (Av) After projecting each case onto the matrix generated from healthy adults, the distance between blasts and normal cell types was calculated, and the shortest distance identified the corresponding differentiation stage. (B) Pie chart showing the number of patients and relative distribution according to the 7 differentiation stages defined in healthy adults. Because of low numbers, patients were regrouped according into HPC-like, as well as granulocytic-, monocytic-, and erythroid-like, AML. (C) Concordance between the phenotypic and the FAB subclassification of patients with AML according to the differentiation stage of blasts. (D) OS of patients with HPC-like vs maturing AML subtypes treated with azacitidine. P values were determined by the 2-sided log-rank test. (E) Multivariate analysis of OS in the FLUGAZA cohort, including the data-driven phenotypic classification (HPC- vs maturing-like) and the Medical Research Council (MRC) risk stratification (favorable/intermediate vs adverse). (F) OS of patients from the TUH external validation cohort with HPC-like vs maturing AML subtypes treated with azacitidine. P values were determined by the 2-sided log-rank test. (G) Multivariate analysis of OS in the TUH external validation cohort, including the data-driven phenotypic classification (HPC- vs maturing-like), performance status (PS), age (>80 vs ≤80 years), concentration of white blood cells (>50 × 109/L or ≤50 × 109/L), and the MRC risk stratification (favorable/intermediate vs adverse). FAB, French-American-British; HR, hazard ratio; MRC, Medical Research Council; NRBC, nucleated red blood cell; TUH, Toulouse University Hospital; WBC, white blood count.
Figure 2.
Figure 2.
Underlying mutations and gene deregulation in patients with AML defined by the phenotypic stage of blast arrest. (A) Percentage of patients with HPC- and maturing-like AML carrying somatic mutations in each gene. (B) Top 10 more frequent mutated genes among HPC- and maturing-like AML cohorts. (C) Volcano plot showing differentially expressed genes between HPC- vs maturing-like AML subtypes. (D) Violin plots showing upregulated genes in patients with HPC-like AML. Outliers were removed for improved visualization. (E) Violin plots showing upregulated genes in patients with maturing-like AML. Outliers were removed for improved visualization.
Figure 2.
Figure 2.
Underlying mutations and gene deregulation in patients with AML defined by the phenotypic stage of blast arrest. (A) Percentage of patients with HPC- and maturing-like AML carrying somatic mutations in each gene. (B) Top 10 more frequent mutated genes among HPC- and maturing-like AML cohorts. (C) Volcano plot showing differentially expressed genes between HPC- vs maturing-like AML subtypes. (D) Violin plots showing upregulated genes in patients with HPC-like AML. Outliers were removed for improved visualization. (E) Violin plots showing upregulated genes in patients with maturing-like AML. Outliers were removed for improved visualization.
See this image and copyright information in PMC

References

    1. Döhner H, Estey E, Grimwade D, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017;129(4):424–447. - PMC - PubMed
    1. Khoury JD, Solary E, Abla O, et al. The 5th edition of the World Health Organization classification of haematolymphoid tumours: myeloid and histiocytic/dendritic neoplasms. Leukemia. 2022;36(7):1703–1719. - PMC - PubMed
    1. Arber DA, Orazi A, Hasserjian RP, et al. International Consensus classification of myeloid neoplasms and acute leukemias: integrating morphologic, clinical, and genomic data. Blood. 2022;140(11):1200–1228. - PMC - PubMed
    1. Döhner H, Wei AH, Appelbaum FR, et al. Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood. 2022;140(12):1345–1377. - PubMed
    1. Xiao W, Chan A, Waarts MR, et al. Plasmacytoid dendritic cell expansion defines a distinct subset of RUNX1-mutated acute myeloid leukemia. Blood. 2021;137(10):1377–1391. - PMC - PubMed

LinkOut - more resources