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Guarnera L, Fabiani E, Attrotto C, Hajrullaj H, Cristiano A, Latagliata R, Fenu S, Buccisano F, Irno-Consalvo M, Conti C, Voso MT, Maurillo L. Reliability of flow-cytometry in diagnosis and prognostic stratification of myelodysplastic syndromes: correlations with morphology and mutational profile. Ann Hematol 2023; 102:3015-3023. [PMID: 37535147 PMCID: PMC10567902 DOI: 10.1007/s00277-023-05384-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 07/25/2023] [Indexed: 08/04/2023]
Abstract
Diagnosis and prognostic stratification of myelodysplastic syndromes (MDS) have been complemented by new techniques, including flow cytometry and NGS. To analyze the relationship between molecular and cytofluorimetric data, we enrolled in this retrospective study, 145 patients, including 106 diagnosed with MDS and 39 controls. At disease onset, immunophenotypic (IF), cytogenetic tests, and cytomorphological (CM) examination on bone marrow were carried out in all patients, while NGS was performed in 58 cases. Ogata score presented a specificity of 100% and a sensitivity of 59%. The detection of at least two phenotypic aberrancies in Ogata negative patients increased the sensitivity to 83% and specificity to 87%. Correlations were identified between IF aberrancies and mutations, including positive Ogata>2 and mutations in SRSF2 (p=0.035), CD15 and U2AF1 (0.032), CD56 and DNMT3A (p=0.042), and CD38 and TP53 (p=0.026). In multivariate analysis, U2AF1 mutations, associated with del(20q) and/or abnormalities of chromosome 7 (group 4 as defined by the EuroMDS score), significantly correlated with an inferior overall survival (p=0.019). These parameters and Ogata score>2 also showed a significant correlation with inferior event-free survival (p=0.023 and p=0.041, respectively). Both CM and FC features correlated with prognosis and mutational patterns. In an integrated MDS work-up, these tools may guide indications for mutational screening for optimal risk stratification.
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Affiliation(s)
- Luca Guarnera
- Department of Biomedicine and Prevention, Tor Vergata University, Rome, Italy
| | - Emiliano Fabiani
- Department of Biomedicine and Prevention, Tor Vergata University, Rome, Italy
- UniCamillus-Saint Camillus International University of Health Sciences, Rome, Italy
| | - Cristina Attrotto
- Department of Biomedicine and Prevention, Tor Vergata University, Rome, Italy
| | - Hajro Hajrullaj
- Department of Biomedicine and Prevention, Tor Vergata University, Rome, Italy
| | - Antonio Cristiano
- Department of Biomedicine and Prevention, Tor Vergata University, Rome, Italy
| | | | - Susanna Fenu
- Haematology Department, San Giovanni-Addolorata Hospital, Rome, Italy
| | - Francesco Buccisano
- Department of Biomedicine and Prevention, Tor Vergata University, Rome, Italy
| | - Maria Irno-Consalvo
- Department of Biomedicine and Prevention, Tor Vergata University, Rome, Italy
| | - Consuelo Conti
- Department of Biomedicine and Prevention, Tor Vergata University, Rome, Italy
| | - Maria Teresa Voso
- Department of Biomedicine and Prevention, Tor Vergata University, Rome, Italy.
- Neuro-Oncohematology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Santa Lucia, Rome, Italy.
| | - Luca Maurillo
- Department of Biomedicine and Prevention, Tor Vergata University, Rome, Italy
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Goldgof GM, Sun S, Van Cleave J, Wang L, Lucas F, Brown L, Spector JD, Boiocchi L, Baik J, Zhu M, Ardon O, Lu CM, Dogan A, Goldgof DB, Carmichael I, Prakash S, Butte AJ. DeepHeme: A generalizable, bone marrow classifier with hematopathologist-level performance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.20.528987. [PMID: 36865216 PMCID: PMC9979993 DOI: 10.1101/2023.02.20.528987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Morphology-based classification of cells in the bone marrow aspirate (BMA) is a key step in the diagnosis and management of hematologic malignancies. However, it is time-intensive and must be performed by expert hematopathologists and laboratory professionals. We curated a large, high-quality dataset of 41,595 hematopathologist consensus-annotated single-cell images extracted from BMA whole slide images (WSIs) containing 23 morphologic classes from the clinical archives of the University of California, San Francisco. We trained a convolutional neural network, DeepHeme, to classify images in this dataset, achieving a mean area under the curve (AUC) of 0.99. DeepHeme was then externally validated on WSIs from Memorial Sloan Kettering Cancer Center, with a similar AUC of 0.98, demonstrating robust generalization. When compared to individual hematopathologists from three different top academic medical centers, the algorithm outperformed all three. Finally, DeepHeme reliably identified cell states such as mitosis, paving the way for image-based quantification of mitotic index in a cell-specific manner, which may have important clinical applications.
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Affiliation(s)
- Gregory M. Goldgof
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Shenghuan Sun
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Jacob Van Cleave
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Linlin Wang
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Fabienne Lucas
- Department of Pathology, Brigham and Women’s Hospital/Harvard Medical School, Boston, MA, USA
| | - Laura Brown
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Jacob D. Spector
- Department of Laboratory Medicine, Boston Children’s Hospital/Harvard Medical School, Boston, MA, USA
| | - Leonardo Boiocchi
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jeeyeon Baik
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Menglei Zhu
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Orly Ardon
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Chuanyi M. Lu
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
- Department of Laboratory Medicine, Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Ahmet Dogan
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dmitry B. Goldgof
- Department of Computer Science, University of South Florida, Tampa, FL, USA
| | - Iain Carmichael
- Department of Statistics, University of California, Berkeley, CA, USA
| | - Sonam Prakash
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Atul J. Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
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Mestrum SGC, Vanblarcum RBY, Drent RJM, Boonen BT, van Hemert WLW, Ramaekers FCS, Hopman AHN, Leers MPG. Proliferative and anti‐apoptotic fractions in maturing hematopoietic cell lineages and their role in homeostasis of normal bone marrow. Cytometry A 2022; 101:552-563. [PMID: 35429122 PMCID: PMC9540078 DOI: 10.1002/cyto.a.24558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/04/2022] [Accepted: 04/06/2022] [Indexed: 11/17/2022]
Abstract
Recent developments in clinical flow cytometry allow the simultaneous assessment of proliferative and anti‐apoptotic activity in the different hematopoietic cell lineages and during their maturation process. This can further advance the flow cytometric diagnosis of myeloid malignancies. In this study we established indicative reference values for the Ki‐67 proliferation index and Bcl‐2 anti‐apoptotic index in blast cells, as well as maturing erythroid, myeloid, and monocytic cells from normal bone marrow (BM). Furthermore, the cell fractions co‐expressing both proliferation and anti‐apoptotic markers were quantified. Fifty BM aspirates from femoral heads of patients undergoing hip replacement were included in this study. Ten‐color/twelve‐parameter flow cytometry in combination with a software‐based maturation tool was used for immunophenotypic analysis of Ki‐67 and Bcl‐2 positive fractions during the erythro‐, myelo‐, and monopoiesis. Indicative reference values for the Ki‐67 and Bcl‐2 positive fractions were established for different relevant hematopoietic cell populations in healthy BM. Ki‐67 and Bcl‐2 were equally expressed in the total CD34 positive blast cell compartment and 30% of Ki‐67 positive blast cells also showed Bcl‐2 positivity. The Ki‐67 and Bcl‐2 positive fractions were highest in the more immature erythroid, myeloid and monocytic cells. Both fractions then gradually declined during the subsequent maturation phases of these cell lineages. We present a novel application of an earlier developed assay that allows the simultaneous determination of the Ki‐67 proliferative and Bcl‐2 anti‐apoptotic indices in maturing hematopoietic cell populations of the BM. Their differential expression levels during the maturation process were in accordance with the demand and lifespan of these cell populations. The indicative reference values established in this study can act as a baseline for further cell biological and biomedical studies involving hematological malignancies.
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Affiliation(s)
- Stefan G. C. Mestrum
- Department of Molecular Cell Biology, GROW‐School for Oncology and Developmental Biology Maastricht University Medical Center Maastricht The Netherlands
- Department of Clinical Chemistry & Hematology Zuyderland Medical Center Sittard‐Geleen The Netherlands
| | - Roanalis B. Y. Vanblarcum
- Department of Clinical Chemistry & Hematology Zuyderland Medical Center Sittard‐Geleen The Netherlands
| | - Roosmarie J. M. Drent
- Department of Clinical Chemistry & Hematology Zuyderland Medical Center Sittard‐Geleen The Netherlands
| | - Bert T. Boonen
- Department of Orthopedic Surgery Zuyderland Medical Center Heerlen The Netherlands
| | | | - Frans C. S. Ramaekers
- Department of Molecular Cell Biology, GROW‐School for Oncology and Developmental Biology Maastricht University Medical Center Maastricht The Netherlands
- Nordic‐MUbio, Susteren The Netherlands
| | - Anton H. N. Hopman
- Department of Molecular Cell Biology, GROW‐School for Oncology and Developmental Biology Maastricht University Medical Center Maastricht The Netherlands
| | - Math P. G. Leers
- Department of Clinical Chemistry & Hematology Zuyderland Medical Center Sittard‐Geleen The Netherlands
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Mestrum SG, Cremers EM, de Wit NC, Drent RJ, Ramaekers FC, Hopman AH, Leers MP. Optimized gating strategy and supporting flow cytometry data for the determination of the Ki-67 proliferation index in the diagnosis of myelodysplastic syndrome. Data Brief 2022; 41:107976. [PMID: 35252494 PMCID: PMC8891968 DOI: 10.1016/j.dib.2022.107976] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 02/11/2022] [Accepted: 02/15/2022] [Indexed: 11/23/2022] Open
Abstract
This Data in Brief article presents a novel flow cytometric assay used to acquire and process the data presented and discussed in the research paper by Mestrum et al., co-submitted to Leukemia Research, entitled: “Integration of the Ki-67 proliferation index into the Ogata score improves its diagnostic sensitivity for low-grade myelodysplastic syndromes.” [1]. The dataset includes the gated fractions of the different myeloid populations in bone marrow (BM) aspirates (total BM cells, CD34 positive blast cells, erythroid cells, granulocytes and monocytes. The raw data is hosted in FlowRepository, while the analyzed data of 1) the fractions of the different myeloid cell populations and 2) the Ki-67 proliferation indices of these myeloid cell populations are provided in tabular form to allow comparison and reproduction of the data when such analyses are performed in a different setting. BM cells from aspirates of 50 myelodysplastic syndrome (MDS) patients and 20 non-clonal cytopenic controls were stained using specific antibody panels and proper fixation and permeabilization to determine the Ki-67 proliferation indices of the different myeloid cell populations. Data was acquired with the three laser, 10-color Navios™ Flow cytometer (Beckman Coulter, Marseille, France) with a blue diode Argon laser (488 nm, 22 mW), red diode Helium/Neon laser (638 nm, 25 mW) and violet air-cooled solid-state diode laser laser (405 nm, 50 mW). A minimum of 100,000 relevant events were acquired per sample, while we aimed at acquiring 500,000 events per sample. Gating was performed with the Infinicyt v2.0 software package (Cytognos SL, Salamanca, Spain). These data may guide the development and standardization of the flow cytometric analysis of the Ki-67 proliferation index (and other markers for cell behavior) for differentiation between non-clonal cytopenic patients and MDS patients. In addition, this assay may be used in myeloid malignancies for research and clinical purposes in other laboratories. This data can be used to encourage future research regarding stem-/progenitor cell resistance against anti-cancer therapies for myeloid malignancies, diagnostics of myeloid malignancies and prognosis of myeloid malignancies. Therefore, these data are of relevance to internist-hematologists, clinical chemists with sub-specialization of hematology and hemato-oncology oriented researchers.
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Affiliation(s)
- Stefan G.C. Mestrum
- Department of Molecular Cell Biology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Clinical Chemistry and Hematology, Zuyderland Medical Center, Sittard-Geleen, the Netherlands
- Corresponding author at: Department of Molecular Cell Biology, Maastricht University Medical Center, PO Box 616, 6200 MD Maastricht, the Netherlands.
| | - Eline M.P. Cremers
- Department of Internal Medicine, Division of Hematology, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Hematology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Norbert C.J. de Wit
- Central Diagnostic Laboratory (CDL), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Roosmarie J.M. Drent
- Department of Clinical Chemistry and Hematology, Zuyderland Medical Center, Sittard-Geleen, the Netherlands
| | - Frans C.S. Ramaekers
- Department of Molecular Cell Biology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands
- Nordic-MUbio, Susteren, the Netherlands
| | - Anton H.N. Hopman
- Department of Molecular Cell Biology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Math P.G. Leers
- Department of Clinical Chemistry and Hematology, Zuyderland Medical Center, Sittard-Geleen, the Netherlands
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