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Literature DB >> 29128077

Automated Analysis of Clinical Flow Cytometry Data: A Chronic Lymphocytic Leukemia Illustration.

Richard H Scheuermann¹, Jack Bui², Huan-You Wang³, Yu Qian⁴.

Abstract

Flow cytometry is used in cell-based diagnostic evaluation for blood-borne malignancies including leukemia and lymphoma. The current practice for cytometry data analysis relies on manual gating to identify cell subsets in complex mixtures, which is subjective, labor-intensive, and poorly reproducible. This article reviews recent efforts to develop, validate, and disseminate automated computational methods and pipelines for cytometry data analysis that could help overcome the limitations of manual analysis and provide for efficient and data-driven diagnostic applications. It demonstrates the performance of an optimized computational pipeline in a pilot study of chronic lymphocytic leukemia data from the authors' clinical diagnostic laboratory.

Entities: Chemical Disease Gene Species

Keywords: Automated gating; Cell-based diagnostics; Chronic lymphocytic leukemia; Cluster analysis; FLOCK; Flow cytometry; Minimal residual disease

Mesh：

Year: 2017 PMID： 29128077 PMCID： PMC5766345 DOI： 10.1016/j.cll.2017.07.011

Source DB: PubMed Journal: Clin Lab Med ISSN： 0272-2712 Impact factor: 1.935

15 in total

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Authors: David R Parks; Mario Roederer; Wayne A Moore
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Journal: Immunity Date: 2015-04-21 Impact factor: 31.745

3. FLOCK cluster analysis of plasma cell flow cytometry data predicts bone marrow involvement by plasma cell neoplasia.

Authors: David M Dorfman; Charlotte D LaPlante; Betty Li
Journal: Leuk Res Date: 2016-07-19 Impact factor: 3.156

4. Automated analysis of multidimensional flow cytometry data improves diagnostic accuracy between mantle cell lymphoma and small lymphocytic lymphoma.

Authors: Habil Zare; Ali Bashashati; Robert Kridel; Nima Aghaeepour; Gholamreza Haffari; Joseph M Connors; Randy D Gascoyne; Arvind Gupta; Ryan R Brinkman; Andrew P Weng
Journal: Am J Clin Pathol Date: 2012-01 Impact factor: 2.493

5. Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.

Authors: Jacob H Levine; Erin F Simonds; Sean C Bendall; Kara L Davis; El-ad D Amir; Michelle D Tadmor; Oren Litvin; Harris G Fienberg; Astraea Jager; Eli R Zunder; Rachel Finck; Amanda L Gedman; Ina Radtke; James R Downing; Dana Pe'er; Garry P Nolan
Journal: Cell Date: 2015-06-18 Impact factor: 41.582

6. B cells with high side scatter parameter by flow cytometry correlate with inferior survival in diffuse large B-cell lymphoma.

Authors: Ali Bashashati; Nathalie A Johnson; Alireza Hadj Khodabakhshi; Matthew D Whiteside; Habil Zare; David W Scott; Kenneth Lo; Raphael Gottardo; Fiona S L Brinkman; Joseph M Connors; Graham W Slack; Randy D Gascoyne; Andrew P Weng; Ryan R Brinkman
Journal: Am J Clin Pathol Date: 2012-05 Impact factor: 2.493

7. 2006 Bethesda International Consensus recommendations on the flow cytometric immunophenotypic analysis of hematolymphoid neoplasia: medical indications.

Authors: B H Davis; J T Holden; M C Bene; M J Borowitz; R C Braylan; D Cornfield; W Gorczyca; R Lee; R Maiese; A Orfao; D Wells; B L Wood; M Stetler-Stevenson
Journal: Cytometry B Clin Cytom Date: 2007 Impact factor: 3.058

Automated Analysis of Clinical Flow Cytometry Data: A Chronic Lymphocytic Leukemia Illustration.

1. A new "Logicle" display method avoids deceptive effects of logarithmic scaling for low signals and compensated data.

2. Thinking outside the gate: single-cell assessments in multiple dimensions.

3. FLOCK cluster analysis of plasma cell flow cytometry data predicts bone marrow involvement by plasma cell neoplasia.

4. Automated analysis of multidimensional flow cytometry data improves diagnostic accuracy between mantle cell lymphoma and small lymphocytic lymphoma.

5. Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.

6. B cells with high side scatter parameter by flow cytometry correlate with inferior survival in diffuse large B-cell lymphoma.

7. 2006 Bethesda International Consensus recommendations on the flow cytometric immunophenotypic analysis of hematolymphoid neoplasia: medical indications.

8. ACMG clinical laboratory standards for next-generation sequencing.

9. Flow cytometry bioinformatics.

10. Critical assessment of automated flow cytometry data analysis techniques.

1. Machine Learning of Discriminative Gate Locations for Clinical Diagnosis.