Immunophenotypic signatures of benign and dysplastic granulopoiesis by cytomic profiling
dc.contributor.author | Finn, William G. | en_US |
dc.contributor.author | Harrington, Alexandra M. | en_US |
dc.contributor.author | Carter, Kevin Michael | en_US |
dc.contributor.author | Raich, Raviv | en_US |
dc.contributor.author | Kroft, Steven H. | en_US |
dc.contributor.author | Hero, Alfred O. III | en_US |
dc.date.accessioned | 2011-11-10T15:37:10Z | |
dc.date.available | 2012-11-02T18:56:47Z | en_US |
dc.date.issued | 2011-09 | en_US |
dc.identifier.citation | Finn, William G.; Harrington, Alexandra M.; Carter, Kevin M.; Raich, Raviv; Kroft, Steven H.; Hero, Alfred O. (2011). "Immunophenotypic signatures of benign and dysplastic granulopoiesis by cytomic profiling ." Cytometry Part B: Clinical Cytometry 80B(5): 282-290. <http://hdl.handle.net/2027.42/87051> | en_US |
dc.identifier.issn | 1552-4949 | en_US |
dc.identifier.issn | 1552-4957 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/87051 | |
dc.description.abstract | Background: The role of flow cytometry (FCM) in diagnosing myelodysplastic syndromes (MDS) remains controversial, because analysis of myeloid maturation may involve subjective interpretation of sometimes subtle patterns on multiparameter FCM. Methods: Using six‐parameter marker combinations known to be useful in evaluating the myeloid compartment in MDS, we measured objective immunophenotypic differences between non‐neoplastic ( n = 25) and dysplastic ( n = 17) granulopoiesis using a novel method, called Fisher information nonparametric embedding (FINE), that measures information distances among FCM datasets modeled as individual high‐dimensional probability density functions, rather than as sets of two‐dimensional histograms. Information‐preserving component analysis (IPCA) was used to create information‐optimized “rotated” two‐dimensional histograms for visualizing myelopoietic immunophenotypes for each individual sample. Results: There was a consistent trend of segregation of higher‐grade MDS (RAEB and RCMD) from benign by FINE analysis. This difference was accentuated in cases with morphologic dysgranulopoiesis and in cases with clonal cytogenetic abnormalities. However, lower grades of MDS or cases that lacked morphologic dysgranulopoiesis showed much greater overlap with non‐neoplastic cases. Two cases of reactive left shift were consistently embedded within the higher‐grade MDS group. IPCA yielded two‐dimensional histogram projections for each individual case by relative weighting of measured cellular characteristics, optimized for preserving information distances derived through FINE. Conclusions: Objective analysis by information geometry supports the conclusions of previous studies that there are immunophenotypic differences in the maturation patterns of benign granulopoiesis and high grade MDS, but also reinforces the known pitfalls of overlap between low‐grade MDS and benign granulopoiesis and overlap between reactive granulocytic left shifts and dysplastic granulopoiesis. © 2011 International Clinical Cytometry Society | en_US |
dc.publisher | Wiley Subscription Services, Inc., A Wiley Company | en_US |
dc.subject.other | Myelodysplastic Syndromes | en_US |
dc.subject.other | Flow Cytometry | en_US |
dc.subject.other | Cytomics | en_US |
dc.subject.other | Machine Learning | en_US |
dc.title | Immunophenotypic signatures of benign and dysplastic granulopoiesis by cytomic profiling | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Medicine (General) | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Pathology, University of Michigan, Ann Arbor, Michigan | en_US |
dc.contributor.affiliationum | Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan | en_US |
dc.contributor.affiliationum | University of Michigan, Department of Pathology, Room M5242 Medical Science I, 1301 Catherine Road, Ann Arbor, MI 48109‐5602, USA | en_US |
dc.contributor.affiliationother | Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin | en_US |
dc.contributor.affiliationother | Lincoln Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts | en_US |
dc.contributor.affiliationother | Department of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/87051/1/20592_ftp.pdf | |
dc.identifier.doi | 10.1002/cyto.b.20592 | en_US |
dc.identifier.source | Cytometry Part B: Clinical Cytometry | en_US |
dc.identifier.citedreference | Brunning RD, Orazi A, Germing U, Le Beau MM, Porwit A, Baumann I, Vardiman JW, Hellstrom‐Lindberg E. Myelodysplastic syndromes/neoplasms, overview. In: Swerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H, Thiele J, Vardiman JW, editors. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. Lyon: International Agency for Research on Cancer; 2008. pp 87 – 107. | en_US |
dc.identifier.citedreference | Kussick SJ, Fromm JR, Rossini A, Li Y, Chang A, Norwood TH, Wood BL. Four‐color flow cytometry shows strong concordance with bone marrow morphology and cytogenetics in the evaluation for myelodysplasia. Am J Clin Pathol 2005; 124: 170 – 181. | en_US |
dc.identifier.citedreference | Loken MR, van de Loosdrecht A, Ogata K, Orfao A, Wells DA. Flow cytometry in myelodysplastic syndromes: Report from a working conference. Leuk Res 2008; 32: 5 – 17. | en_US |
dc.identifier.citedreference | Stetler‐Stevenson M, Arthur DC, Jabbour N, Xie XY, Molldrem J, Barrett AJ, Venzon D, Rick ME. Diagnostic utility of flow cytometric immunophenotyping in myelodysplastic syndrome. Blood 2001; 98: 979 – 987. | en_US |
dc.identifier.citedreference | Wells DA, Benesch M, Loken MR, Vallejo C, Myerson D, Leisenring WM, Deeg HJ. Myeloid and monocytic dyspoiesis as determined by flow cytometric scoring in myelodysplastic syndrome correlates with the IPSS and with outcome after hematopoietic stem cell transplantation. Blood 2003; 102: 394 – 403. | en_US |
dc.identifier.citedreference | Wells DA, Ogata K. On flow cytometry in myelodysplastic syndromes, with caveats. Leuk Res 2008; 32: 209 – 210. | en_US |
dc.identifier.citedreference | Carter KM, Raich R, Finn WG, Hero AO III. FINE: Fisher information nonparametric embedding. IEEE Trans Pattern Anal Mach Intell 2009; 31: 2093 – 2098. | en_US |
dc.identifier.citedreference | Finn WG, Carter KM, Raich R, Stoolman LM, Hero AO. Analysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: Treating flow cytometry data as high‐dimensional objects. Cytometry B Clin Cytom B 2009; 76B: 1 – 7. | en_US |
dc.identifier.citedreference | Carter KM, Raich R, Finn WG, Hero AO. Information preserving component analysis: Data projections for flow cytometry analysis. IEEE J Select Topics Signal Process 2009; 3: 148 – 158. | en_US |
dc.identifier.citedreference | Miller DT, Stelzer GT. Contributions of flow cytometry to the analysis of the myelodysplastic syndrome. Clin Lab Med 2001; 21: 811 – 828. | en_US |
dc.identifier.citedreference | Truong F, Smith BR, Stachurski D, Cerny J, Medeiros LJ, Woda BA, Wang SA. The utility of flow cytometric immunophenotyping in cytopenic patients with a non‐diagnostic bone marrow: A prospective study. Leuk Res 2009; 33: 1039 – 1046. | en_US |
dc.identifier.citedreference | Bowen KL, Davis BH. Abnormal patterns of expression of CD16(FcR‐III) and CD11b(CRIII) antigens by developing neutrophils in the bone marrow of patients with myelodysplastic syndrome. Lab Hematol 1997; 3: 292 – 298. | en_US |
dc.identifier.citedreference | Loken MR, Wells DA. The role of flow cytometry in myelodysplastic syndromes. J Natl Compr Canc Netw 2008; 6: 935 – 941. | en_US |
dc.identifier.citedreference | Maynadie M, Picard F, Husson B, Chatelain B, Cornet Y, Le Roux G, Campos L, Dromelet A, Lepelley P, Jouault H, et al. Immunophenotypic clustering of myelodysplastic syndromes. Blood 2002; 100: 2349 – 2356. | en_US |
dc.identifier.citedreference | Stachurski D, Smith BR, Pozdnyakova O, Andersen M, Xiao Z, Raza A, Woda BA, Wang SA. Flow cytometric analysis of myelomonocytic cells by a pattern recognition approach is sensitive and specific in diagnosing myelodysplastic syndrome and related marrow diseases: Emphasis on a global evaluation and recognition of diagnostic pitfalls. Leuk Res 2008; 32: 215 – 224. | en_US |
dc.identifier.citedreference | Fujimoto H, Sakata T, Hamaguchi Y, Shiga S, Tohyama K, Ichiyama S, Wang FS, Houwen B. Flow cytometric method for enumeration and classification of reactive immature granulocyte populations. Cytometry 2000; 42: 371 – 378. | en_US |
dc.identifier.citedreference | van Lochem EG, van der Velden VH, Wind HK, te Marvelde JG, Westerdaal NA, van Dongen JJ Immunophenotypic differentiation patterns of normal hematopoiesis in human bone marrow: Reference patterns for age‐related changes and disease‐induced shifts. Cytometry B Clin Cytom B 2004; 60B: 1 – 13. | en_US |
dc.identifier.citedreference | Roederer M, Hardy RR. Frequency difference gating: A multivariate method for identifying subsets that differ between samples. Cytometry 2001; 45: 56 – 64. | en_US |
dc.identifier.citedreference | Roederer M, Moore W, Treister A, Hardy RR, Herzenberg LA. Probability binning comparison: A metric for quantitating multivariate distribution differences. Cytometry 2001; 45: 47 – 55. | en_US |
dc.identifier.citedreference | Zamir E, Geiger B, Cohen N, Kam Z, Katz BZ. Resolving and classifying haematopoietic bone‐marrow cell populations by multi‐dimensional analysis of flow‐cytometry data. Br J Haematol 2005; 129: 420 – 431. | en_US |
dc.identifier.citedreference | Zeng QT, Pratt JP, Pak J, Ravnic D, Huss H, Mentzer SJ. Feature‐guided clustering of multi‐dimensional flow cytometry datasets. J Biomed Inform 2007; 40: 325 – 331. | en_US |
dc.identifier.citedreference | Amari S, Nagaoka H. Differential‐Geometrical Methods in Statistics. Berlin: Springer‐Verlage; 1990. | en_US |
dc.identifier.citedreference | Della Porta MG, Malcovati L, Invernizzi R, Travaglino E, Pascutto C, Maffioli M, Galli A, Boggi S, Pietra D, Vanelli L, et al. Flow cytometry evaluation of erythroid dysplasia in patients with myelodysplastic syndrome. Leukemia 2006; 20: 549 – 555. | en_US |
dc.identifier.citedreference | Wood B. 9‐color and 10‐color flow cytometry in the clinical laboratory. Arch Pathol Lab Med 2006; 130: 680 – 690. | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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