Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia

Lhermitte, Ludovic; Mejstříková, Ester; Sluijs-Gelling, Alita J. van der; Grigore, Georgiana; Sędek, Łukasz; Bras, A.E.; Gaipa, Giuseppe; Costa, Elaine Sobral da; Nováková, Michaela; Sonneveld, Edwin; Buracchi, Chiara; Bacelar, Thiago de Sá; Marvelde, Jeroen G. te; Trinquand, Amélie; Asnafi, Vahid; Szczepański, Tomasz; Matarraz, Sergio; Lopez, Amanda; Vidriales, María‐Belén; Bulsa, Joanna; Hrušák, Ondřej; Kalina, Tomáš; Lécrevisse, Quentin; Ayuso, M Martin; Brüggemann, Monika; Verde, Javier; Fernández, Paula; Burgos, Leire; Paiva, Bruno; Pedreira, Carlos Eduardo; Dongen, Jacques J. M. van; Órfão, Alberto; Velden, Vincent H.J. van der

doi:10.1038/leu.2017.313

Cited by 47 publications

(44 citation statements)

References 13 publications

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“…Different consortia listed below aimed to address diverse levels of reproducibility: (a) the reproducibility of interpretation (all consortia; some argue that more is not necessary for their purpose such as Harmonemia (17), AIEOP-BFM group (3,(18)(19)(20)(21)(22)(23), and ERIC (21-23)), (b) the reproducibility of enumeration (prominently in immunology: The ONE Study (24,25), HIPC (14,26,27), but also in the leukemia residual disease detection: EuroFlow (28)(29)(30), COG group (31), and ERIC (21,22)), (c) the reproducibility of staining pattern is typically achieved when the same panel of reagents is used (The ONE Study (25), HIPC (26), EuroFlow (32,33), and COG (31)), which allows definition of the manual analysis strategy and also automated analysis tools, and (d) the reproducibility of patterns including the staining intensity that allows analysis standardization and database-assisted comparison to previous cases in interlaboratory settings: EuroFlow (34) or The Canadian National Transplant Research Program that analyzed multiple immune cells in multiple centers by automated analysis (35). Different consortia listed below aimed to address diverse levels of reproducibility: (a) the reproducibility of interpretation (all consortia; some argue that more is not necessary for their purpose such as Harmonemia (17), AIEOP-BFM group (3,(18)(19)(20)(21)(22)(23), and ERIC (21-23)), (b) the reproducibility of enumeration (prominently in immunology: The ONE Study (24,25), HIPC (14,2...…”

Section: Existing Efforts To Achieve Reproducibilitymentioning

confidence: 99%

“…Uniform sets of reagents (antibody panels) were assembled and tested together with the staining SOP (33), and software tools were built that enabled the analysis of the standardized data. An analysis of a dataset of 1,438 cases of acute leukemia was published by Lhermitte et al (34) that utilized automated analysis tools applied to standardized data collected in 12 laboratories. Staining intensity-based evaluation of the quality of the locally acquired data file in comparison with the expected variation has been built into a quality assurance scheme (1).…”

Section: Existing Efforts To Achieve Reproducibilitymentioning

confidence: 99%

“…Example of the former approach is the EuroFlow databaseguided diagnosis and classification of acute leukemia (34), while the latter is documented by HIPC (14) and CNTRP (35). Example of the former approach is the EuroFlow databaseguided diagnosis and classification of acute leukemia (34), while the latter is documented by HIPC (14) and CNTRP (35).…”

Section: Existing Efforts To Achieve Reproducibilitymentioning

confidence: 99%

“…Staining intensity-based evaluation of the quality of the locally acquired data file in comparison with the expected variation has been built into a quality assurance scheme (1). An analysis of a dataset of 1,438 cases of acute leukemia was published by Lhermitte et al (34) that utilized automated analysis tools applied to standardized data collected in 12 laboratories. Furthermore, the consortium developed a minimal residual disease detection panel for multiple myeloma (MM) (28) and Bcell precursor leukemia (BCP-ALL) (38) with corresponding data analysis tools that are based on multiparameter evaluations (39).…”

Section: Existing Efforts To Achieve Reproducibilitymentioning

confidence: 99%

“…When approached from the data analysis perspective, the diagnostic use requires reproducible analysis of a singular sample (with the frame of reference of large cohort of control samples), while clinical research studies need to be analyzed as a whole cohort without any bias, but with a possibility to react to unexpected new findings, to accommodate for added markers in further research stages to improve cell definitions. Example of the former approach is the EuroFlow databaseguided diagnosis and classification of acute leukemia (34), while the latter is documented by HIPC (14) and CNTRP (35).…”

Section: Existing Efforts To Achieve Reproducibilitymentioning

confidence: 99%

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Reproducibility of Flow Cytometry Through Standardization: Opportunities and Challenges

Kalina

2019

Cytometry Pt A

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There is an agreement in the field that interlaboratory reproducibility of flow cytometry measurements as well as the whole studies might be improved by a consensual use of methodological approach. Typically, a consensus is made on a crucial markers needed in the immunostaining panel, sometimes on the particular fluorochrome conjugates and rarely on a complete set of methods for sample preparation. The term "standardization" is used to describe the complete set of methodical steps, while "harmonization" is used for partial agreement on the method. Standardization can provide a platform for improved reproducibility of cytometry results over prolonged periods of time, across different sites and across different instruments. For the purpose of structured discussion, several desired aims are described: common interpretation of the immunophenotype definition of a target subset, accurate quantification, reproducible pattern of a multicolor immunophenotype, and reproducible intensity of all measured parameters. An overview of how standardization was approached by several large consortia is provided: EuroFlow, The ONE Study, Human Immunology Project Consortium (HIPC), and several other groups. Their particular aims and the tools adopted to reach those aims are noted. How those standardization efforts were adopted in the field and how the resulting outcome was evaluated is reviewed. Multiple challenges in the instrument hardware design, instrument setup tools, reagent design, and quality features need to be addressed to achieve optimal standardization. Furthermore, the aims of different studies vary, and thus, the reasonable requirements for standardization differ. A framework of reference for the reasonable outcomes of different approaches is offered. Finally, it is argued that complete standardization is important not only for the reproducibility of measurements but also for education, for quality assessment and for algorithmic data analysis. The different standardized approaches can and in fact should serve as benchmarking reference tools for the development of future flow cytometry studies.

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Section: Existing Efforts To Achieve Reproducibilitymentioning

confidence: 99%

Section: Existing Efforts To Achieve Reproducibilitymentioning

confidence: 99%

Section: Existing Efforts To Achieve Reproducibilitymentioning

confidence: 99%

Section: Existing Efforts To Achieve Reproducibilitymentioning

confidence: 99%

Section: Existing Efforts To Achieve Reproducibilitymentioning

confidence: 99%

See 3 more Smart Citations

Reproducibility of Flow Cytometry Through Standardization: Opportunities and Challenges

Kalina

2019

Cytometry Pt A

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Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B‐precursor acute lymphoblastic leukaemia

et al. 2021

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Relapsed acute lymphoblastic leukaemia (ALL) remains a prevalent paediatric cancer and one of the most common causes of mortality from malignancy in children. Tailoring the intensity of therapy according to early stratification is a promising strategy but remains a major challenge due to heterogeneity and subtyping difficulty. In this study, we subgroup B‐precursor ALL patients by gene expression profiles, using non‐negative matrix factorization and minimum description length which unsupervisedly determines the number of subgroups. Within each of the four subgroups, logistic and Cox regression with elastic net regularization are used to build models predicting minimal residual disease (MRD) and relapse‐free survival (RFS) respectively. Measured by area under the receiver operating characteristic curve (AUC), subgrouping improves prediction of MRD in one subgroup which mostly overlaps with subtype TCF3‐PBX1 (AUC = 0·986 in the training set and 1·0 in the test set), compared to a global model published previously. The models predicting RFS displayed acceptable concordance in training set and discriminate high‐relapse‐risk patients in three subgroups of the test set (Wilcoxon test p = 0·048, 0·036, and 0·016). Genes playing roles in the models are specific to different subgroups. The improvement of subgrouped MRD prediction and the differences of genes in prediction models of subgroups suggest that the heterogeneity of B‐precursor ALL can be handled by subgrouping according to gene expression profiles to improve the prediction accuracy.

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Current trends in flow cytometry automated data analysis software

et al. 2021

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Automated flow cytometry (FC) data analysis tools for cell population identification and characterization are increasingly being used in academic, biotechnology, pharmaceutical, and clinical laboratories. The development of these computational methods is designed to overcome reproducibility and process bottleneck issues in manual gating, however, the take‐up of these tools remains (anecdotally) low. Here, we performed a comprehensive literature survey of state‐of‐the‐art computational tools typically published by research, clinical, and biomanufacturing laboratories for automated FC data analysis and identified popular tools based on literature citation counts. Dimensionality reduction methods ranked highly, such as generic t‐distributed stochastic neighbor embedding (t‐SNE) and its initial Matlab‐based implementation for cytometry data viSNE. Software with graphical user interfaces also ranked highly, including PhenoGraph, SPADE1, FlowSOM, and Citrus, with unsupervised learning methods outnumbering supervised learning methods, and algorithm type popularity spread across K‐Means, hierarchical, density‐based, model‐based, and other classes of clustering algorithms. Additionally, to illustrate the actual use typically within clinical spaces alongside frequent citations, a survey issued by UK NEQAS Leucocyte Immunophenotyping to identify software usage trends among clinical laboratories was completed. The survey revealed 53% of laboratories have not yet taken up automated cell population identification methods, though among those that have, Infinicyt software is the most frequently identified. Survey respondents considered data output quality to be the most important factor when using automated FC data analysis software, followed by software speed and level of technical support. This review found differences in software usage between biomedical institutions, with tools for discovery, data exploration, and visualization more popular in academia, whereas automated tools for specialized targeted analysis that apply supervised learning methods were more used in clinical settings.

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Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia

Cited by 47 publications

References 13 publications

Reproducibility of Flow Cytometry Through Standardization: Opportunities and Challenges

Reproducibility of Flow Cytometry Through Standardization: Opportunities and Challenges

Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B‐precursor acute lymphoblastic leukaemia

Current trends in flow cytometry automated data analysis software

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