Open Access Research article

Routine use of microarray-based gene expression profiling to identify patients with low cytogenetic risk acute myeloid leukemia: accurate results can be obtained even with suboptimal samples

Diane Raingeard de la Blétière12, Odile Blanchet23, Pascale Cornillet-Lefèbvre4, Anne Coutolleau1, Laurence Baranger5, Franck Geneviève3, Isabelle Luquet4, Mathilde Hunault-Berger6, Annaelle Beucher3, Aline Schmidt-Tanguy26, Marc Zandecki3, Yves Delneste2, Norbert Ifrah26 and Philippe Guardiola126*

Author Affiliations

1 Plateforme SNP, Transcriptome & Epigénomique, Centre Hospitalier Universitaire, Angers, France

2 Institut National de la Santé et de la Recherche Médicale, Unité 892, Centre de Recherche sur le Cancer Nantes Angers et UMR_S 892, Université d'Angers, Angers, France

3 Laboratoire d'Hématologie, Centre Hospitalier Universitaire, Angers, France

4 Laboratoire d'Hématologie, Centre Hospitalier Universitaire, Reims, France

5 Laboratoire de Génétique, Centre Hospitalier Universitaire, Angers, France

6 Service des Maladies du Sang, Centre Hospitalier Universitaire, Angers, France

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BMC Medical Genomics 2012, 5:6  doi:10.1186/1755-8794-5-6

Published: 30 January 2012

Abstract

Background

Gene expression profiling has shown its ability to identify with high accuracy low cytogenetic risk acute myeloid leukemia such as acute promyelocytic leukemia and leukemias with t(8;21) or inv(16). The aim of this gene expression profiling study was to evaluate to what extent suboptimal samples with low leukemic blast load (range, 2-59%) and/or poor quality control criteria could also be correctly identified.

Methods

Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n = 101) and/or poor quality control criteria (n = 10) (test set).

Results

With 10-marker classifiers, all training set samples as well as 97 of the 101 test samples with a low blast load, and all 10 samples with poor quality control criteria were correctly classified. Regarding test set samples, the overall error rate of the class prediction was below 4 percent, even though the leukemic blast load was as low as 2%. Sensitivity, specificity, negative and positive predictive values of the class assignments ranged from 91% to 100%. Of note, for acute promyelocytic leukemia and leukemias with t(8;21) or inv(16), the confidence level of the class assignment was influenced by the leukemic blast load.

Conclusion

Gene expression profiling and a supervised method requiring 10-marker classifiers enable the identification of favorable cytogenetic risk acute myeloid leukemia even when samples contain low leukemic blast loads or display poor quality control criterion.