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. - Archive ouverte HAL Access content directly
Journal Articles BMC Medical Genomics Year : 2012

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.

(1, 2) , (2, 3) , (4) , (1) , (5) , (3) , (4) , (6) , (3) , (2, 6) , (3) , (2) , (2, 6) , (1, 2, 6)
1
2
3
4
5
6
Laurence Baranger
  • Function : Author
  • PersonId : 921241
Franck Geneviève
  • Function : Author
  • PersonId : 921242
Isabelle Luquet
  • Function : Author
  • PersonId : 921243
Annaelle Beucher
  • Function : Author
  • PersonId : 921244
Marc Zandecki
  • Function : Author
  • PersonId : 920770

Abstract

UNLABELLED: 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.
Fichier principal
Vignette du fichier
1755-8794-5-6.pdf (264.33 Ko) Télécharger le fichier
Vignette du fichier
1755-8794-5-6-S1.DOC (330.5 Ko) Télécharger le fichier
Vignette du fichier
1755-8794-5-6.xml (166.39 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive
Format : Other
Format : Other
Loading...

Dates and versions

inserm-00673125 , version 1 (22-02-2012)

Identifiers

Cite

Diane Raingeard de La Blétière, Odile Blanchet, Pascale Cornillet-Lefèbvre, Anne Coutolleau, Laurence Baranger, et al.. 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.. BMC Medical Genomics, 2012, 5 (1), pp.6. ⟨10.1186/1755-8794-5-6⟩. ⟨inserm-00673125⟩
143 View
271 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More