Literature DB >> 20232575

Positive and negative predictive values of HLA-DR and CD34 in the diagnosis of acute promyelocytic leukemia and other types of acute myeloid leukemia with recurrent chromosomal translocations.

Orathai Promsuwicha1, Chirayu U Auewarakul.   

Abstract

The predictive value of HLA-DR and CD34 in the diagnosis of four distinct genetic entities of acute myeloid leukemia (AML) is presently not established. We evaluated the positive and negative predictive values (PPV and NPV, respectively), sensitivity, specificity, and correlation coefficients of HLA-DR and CD34 in AML patients with t(15;17), t(8;21), inv(16), and abn(11q23). In AML with t(15;17) (n = 64), HLA-DR was expressed in 4.68% and CD34 was expressed in 15.62% and none of the cases expressed both HLA-DR and CD34. In AML with t(8;21) (n = 99), HLA-DR, CD34 or both antigens were expressed in the majority of cases (90.90%, 80.80%, and 79.79%, respectively). AML patients with inv(16) (n = 18) and abn(11q23) (n = 31) also highly expressed HLA-DR and CD34. Eight cases of t(8;21) and 1 case of abn(11q23) did not express either antigen. The highest correlation between CD34 and HLA-DR expression values was observed in cases with t(8;21) (r = 0.72) with the lowest correlation in inv(16) (r = 0.035). The PPV and NPV of HLA-DR-negativity plus CD34-negativity to predict t(15;17) was 85% and 100%, respectively, with 100% sensitivity and 92.74% specificity. The PPV and NPV of other myeloid markers such as CD117, MPO and CD11c to diagnose t(15;17) were much lower than those of HLA-DR and CD34. It was concluded that the absence of double negativity of HLA-DR and CD34 strongly predicts against t(15;17). Rare HLA-DR-positive/CD34-negative cases exist in patients with t(15;17) and 8% of t(8;21) cases expressed neither antigen. Further studies should determine whether HLA-DR-positive t(15;17) and HLA-DR-negative/CD34-negative t(8;21) represent a special entity associated with significant prognostic relevance.

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Year:  2009        PMID: 20232575

Source DB:  PubMed          Journal:  Asian Pac J Allergy Immunol        ISSN: 0125-877X            Impact factor:   2.310


  1 in total

1.  Analysis of flow cytometry data by matrix relevance learning vector quantization.

Authors:  Michael Biehl; Kerstin Bunte; Petra Schneider
Journal:  PLoS One       Date:  2013-03-18       Impact factor: 3.240

  1 in total

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