Flow cytometric analysis of erythroid precursors and mutational signatures of lower risk myelodysplastic syndromes identify responders to erythroid stimulating agents

Marco G Raddi^{1

2}, Sara Bencini³, Benedetta Peruzzi³, Giorgio Mattiuz¹, Sven De Pourcq^{1

2}, Michele Tanturli⁴, Nicolas Chapuis⁵, Angela Consagra¹, Luca Rigodanza^{1

6}, Cristina Amato¹, Alessandro Sanna⁶, Elena Tofacchi^{1

2}, Enrico Attardi¹, Sophie Park⁷, Olivier Kosmider⁵, Francesco Annunziato^{3

8}, Michaela Fontenay⁵, Valeria Santini^{9

10}

Affiliations

¹ MDS Unit, DMSC, AOU Careggi, University of Florence, Florence, Italy.
² Department of Medical Biotechnologies, University of Siena, Siena, Italy.
³ Flow Cytometry and Immunotherapy Diagnostic Center, AOU Careggi, Florence, Italy.
⁴ Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy.
⁵ Université Paris Cité and Assistance Publique-Hôpitaux de Paris. Hôpital Cochin, Laboratory of Hematology, Paris, France.
⁶ AOU Careggi, Hematology department, Florence, Italy.
⁷ Grenoble Alpes Hospital, Hematology Department, University Grenoble Alpes, Grenoble, France.
⁸ Department of Experimental and Clinical Medicine and DENOTHE Center, University of Florence, Florence, Italy.
⁹ MDS Unit, DMSC, AOU Careggi, University of Florence, Florence, Italy. valeria.santini@unifi.it.
¹⁰ AOU Careggi, Hematology department, Florence, Italy. valeria.santini@unifi.it.

PMID: 39112451
PMCID: PMC11306215
DOI: 10.1038/s41408-024-01112-9

Flow cytometric analysis of erythroid precursors and mutational signatures of lower risk myelodysplastic syndromes identify responders to erythroid stimulating agents

Marco G Raddi et al. Blood Cancer J. 2024.

. 2024 Aug 7;14(1):127.

doi: 10.1038/s41408-024-01112-9.

Authors

Affiliations

¹ MDS Unit, DMSC, AOU Careggi, University of Florence, Florence, Italy.
² Department of Medical Biotechnologies, University of Siena, Siena, Italy.
³ Flow Cytometry and Immunotherapy Diagnostic Center, AOU Careggi, Florence, Italy.
⁴ Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy.
⁵ Université Paris Cité and Assistance Publique-Hôpitaux de Paris. Hôpital Cochin, Laboratory of Hematology, Paris, France.
⁶ AOU Careggi, Hematology department, Florence, Italy.
⁷ Grenoble Alpes Hospital, Hematology Department, University Grenoble Alpes, Grenoble, France.
⁸ Department of Experimental and Clinical Medicine and DENOTHE Center, University of Florence, Florence, Italy.
⁹ MDS Unit, DMSC, AOU Careggi, University of Florence, Florence, Italy. valeria.santini@unifi.it.
¹⁰ AOU Careggi, Hematology department, Florence, Italy. valeria.santini@unifi.it.

PMID: 39112451
PMCID: PMC11306215
DOI: 10.1038/s41408-024-01112-9

No abstract available

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Conflict of interest statement

The authors declare no competing interests.

Figures

**Fig. 1. Erythroid precursors composition and mutational pattern identify luspatercept responders.**
A Repartition of CD34⁺ and CD117⁺ erythroid precursors in LR-MDS (n = 97) according to ESA response duration and compared with age-matched healthy BM controls (n = 6) from the learning cohort. Ery-HPC/CD34⁺ are CD34⁺ erythroid committed precursors among total CD34⁺ BM cells, erythroid CD117⁺ cells (ery-CD117⁺) are expressed as fraction of total CD117⁺ myeloid progenitors (ery-117⁺/CD117⁺) and total erythroid cells (ery-117⁺/ery). Long-r = long responders (>24 months), short-r = short responders (<24 months) and non-r = non-responders, ctrl = healthy controls. B Percentages of ery-HPC/CD34⁺, ery-CD117⁺/ CD117⁺ and total erythroid cells among total nucleated BM cells (ery/TOT.) across WHO 2022 categories (n = 83) and in age-matched healthy BM controls (n = 6) from the learning cohort. MDS-LB RS = MDS with low blast count and ring sideroblasts (presence of >15% RS without SF3B1 mutation and BM blasts < 5%). C Percentages of immature erythroid cells in MDS non-SF3B1 cases (n = 50) according to ESA response duration and compared with age-matched healthy BM controls (n = 6) from the learning cohort. D ROC analysis to identify thresholds of IPSS-M score (left) and CD117⁺ erythroid cells (right) discriminating ESA responders from non-responders. The graphics report the thresholds with the best combination of sensitivity and specificity (in brackets). AUC = area under the curve. Binomial multivariate analysis based on these thresholds is reported in the bottom part. OR = odd ratio, 95%CI = 95% confidence interval. E Genetic clusters for MDS non-SF3B1 (A, n = 50) and MDS-SF3B1 (B, n = 38) from the learning cohort. In both groups, clustering is based on the type of mutated gene (35 genes) and corresponding VAF expressed in log₂ scale within each type of ESA response (LR = long response, SR = short response, NR = non-response). In the upper part of the graphic, clinical information is also provided: sex (M = male, F = female), sEPO ranges (U/L), Transferrin saturation ranges (Tf, %), Ferritin ranges (ng/mL) and transfusion dependence (TD, yes/no). The legend on the right reports the threshold and colors used for each variable. *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001.

See this image and copyright information in PMC

References

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Flow cytometric analysis of erythroid precursors and mutational signatures of lower risk myelodysplastic syndromes identify responders to erythroid stimulating agents

Affiliations

Flow cytometric analysis of erythroid precursors and mutational signatures of lower risk myelodysplastic syndromes identify responders to erythroid stimulating agents

Authors

Affiliations

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources

Medical