Immunophenotyping of acute leukaemias

Béné, Marie C.

doi:10.1016/j.imlet.2004.10.008

Cited by 38 publications

(12 citation statements)

References 54 publications

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“…Further, the human CD19 antigen gene M28170 and T-cell surface glycoproteins XO4145 and J04132 which are ranked in the top 24 in our study do not appear in the top 50 genes in Ben-Dor [13] or in Thomas [14]. CD19 is known to indicate B-lineage and CD3 to indicate T-lineage [15] This study confirms that significantly different sets of genes are found to be most discriminatory as the sample classes are refined.…”

Section: Discussionsupporting

confidence: 70%

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Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes

2005

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BackgroundIn the clinical context, samples assayed by microarray are often classified by cell line or tumour type and it is of interest to discover a set of genes that can be used as class predictors. The leukemia dataset of Golub et al. [1] and the NCI60 dataset of Ross et al. [2] present multiclass classification problems where three tumour types and nine cell lines respectively must be identified. We apply an evolutionary algorithm to identify the near-optimal set of predictive genes that classify the data. We also examine the initial gene selection step whereby the most informative genes are selected from the genes assayed.ResultsIn the absence of feature selection, classification accuracy on the training data is typically good, but not replicated on the testing data. Gene selection using the RankGene software [3] is shown to significantly improve performance on the testing data. Further, we show that the choice of feature selection criteria can have a significant effect on accuracy. The evolutionary algorithm is shown to perform stably across the space of possible parameter settings – indicating the robustness of the approach. We assess performance using a low variance estimation technique, and present an analysis of the genes most often selected as predictors.ConclusionThe computational methods we have developed perform robustly and accurately, and yield results in accord with clinical knowledge: A Z-score analysis of the genes most frequently selected identifies genes known to discriminate AML and Pre-T ALL leukemia. This study also confirms that significantly different sets of genes are found to be most discriminatory as the sample classes are refined.

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Section: Discussionsupporting

confidence: 70%

“…For example, M23197, which is the human differentiation antigen CD33 and a known indicator of myeloid lineage [15], is ranked first the classification-based scores but is 22nd in terms of differential expression.…”

Section: Discussionmentioning

confidence: 99%

Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes

2005

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“…Pathways relevant to CD10 activity include hematopoietic and B cell-innate immune system development (Maguer-Satta et al, 2011). Interestingly, although CD10 expression has been reported on other cell types, such as B cells and epithelial-stromal cells (Be´ne´, 2005; Mishra et al, 2016), it has never been evaluated on human satellite cells and myoblasts. Here, immunofluorescent staining on human muscle biopsies and flow cytometry data using human primary myoblasts indicated the presence of CD10 on the human muscle progenitors and myoblasts.…”

Section: Discussionmentioning

confidence: 99%

A Myogenic Double-Reporter Human Pluripotent Stem Cell Line Allows Prospective Isolation of Skeletal Muscle Progenitors

Matthias

et al. 2018

Cell Reports

113

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SUMMARY Myogenic differentiation of human pluripotent stem cells (hPSCs) has been done by gene overexpression or directed differentiation. However, viral integration, long-term culture, and the presence of unwanted cells are the main obstacles. By using CRISPR/Cas9n, a double-reporter human embryonic stem cell (hESC) line was generated for PAX7/ MYF5, allowing prospective readout. This strategy allowed pathway screen to define efficient myogenic induction in hPSCs. Next, surface marker screen allowed identification of CD10 and CD24 for purification of myogenic progenitors and exclusion of non-myogenic cells. CD10 expression was also identified on human satellite cells and skeletal muscle progenitors. In vitro and in vivo studies using transgene and/or reporter-free hPSCs further validated myogenic potential of the cells by formation of new fibers expressing human dystrophin as well as donor-derived satellite cells in NSG-mdx4Cv mice. This study provides biological insights for myogenic differentiation of hPSCs using a double-reporter cell resource and defines an improved myogenic differentiation and purification strategy.

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“…CD10, present on pre-B cell, is transiently expressed during different stages in B cell maturation stages and it disappears in mature B cells. This suggests that CD10 might play a role in pre-B cell maturation and differentiation [10]. Apart from pre-B cells, it is expressed by endometrial stromal cells [11], liver [12], and stomach and colon [13] cells.…”

Section: Molecular and Structural Features Of Cd10 (Nep)mentioning

confidence: 99%

Role of B Cell Development Marker CD10 in Cancer Progression and Prognosis

Mishra

Singh

Narayan

2016

Molecular Biology International

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The human CD10 antigen is a single pass, type II transmembrane, 100 kD cell surface glycoprotein belonging to peptidase M13 family. Identified in common acute lymphoblastic leukemia as a cancer specific antigen, CD10 is a cell surface ectoenzyme widely expressed on different types of cells. Earlier, it was used only as a cell surface marker to identify and differentiate between haematological malignancies. Later, reported to be present in various malignancies, it is thought to play significant role in cancer development and progression. Regulated expression of CD10 is necessary for angiogenesis and so forth. However its expression level is found to be deregulated in different cancers. In some cancers, it acts as tumor suppressor and inhibits tumor progression whereas in others it has tumor promoting tendency. However, its role in tumorigenesis remains unclear. This review summarises structural features, functions, and probable role of CD10 in cancer development.

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Immunophenotyping of acute leukaemias

Cited by 38 publications

References 54 publications

Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes

Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes

A Myogenic Double-Reporter Human Pluripotent Stem Cell Line Allows Prospective Isolation of Skeletal Muscle Progenitors

Role of B Cell Development Marker CD10 in Cancer Progression and Prognosis

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