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.

International audienceUNLABELLED: 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

Genotype–phenotype correlations in Down syndrome identified by array CGH in 30 cases of partial trisomy and partial monosomy chromosome 21

Down syndrome (DS) is one of the most frequent congenital birth defects, and the most common genetic cause of mental retardation. In most cases, DS results from the presence of an extra copy of chromosome 21. DS has a complex phenotype, and a major goal of DS research is to identify genotype–phenotype correlations. Cases of partial trisomy 21 and other HSA21 rearrangements associated with DS features could identify genomic regions associated with specific phenotypes. We have developed a BAC array spanning HSA21q and used array comparative genome hybridization (aCGH) to enable high-resolution mapping of pathogenic partial aneuploidies and unbalanced translocations involving HSA21. We report the identification and mapping of 30 pathogenic chromosomal aberrations of HSA21 consisting of 19 partial trisomies and 11 partial monosomies for different segments of HSA21. The breakpoints have been mapped to within ∼85 kb. The majority of the breakpoints (26 of 30) for the partial aneuploidies map within a 10-Mb region. Our data argue against a single DS critical region. We identify susceptibility regions for 25 phenotypes for DS and 27 regions for monosomy 21. However, most of these regions are still broad, and more cases are needed to narrow down the phenotypic maps to a reasonable number of candidate genomic elements per phenotype