The hidden genomic landscape of acute myeloid leukemia: subclonal structure revealed by undetected mutations
Menée à partir de données du projet "The Cancer Genome Atlas" portant sur 133 patients atteints d'une leucémie myéloïde aiguë, ainsi que sur une cohorte complémentaire de 20 patients, cette étude met en évidence des différences significatives dans l'identification de mutations somatiques par des algorithmes bioinformatiques couramment utilisés, puis suggère que le paysage des génomes de cette forme de leucémie est plus complexe que celui décrit par des études antérieures
The analyses carried out using two different bioinformatics pipelines (SomaticSniper and MuTect) on the same set of genomic data from 133 Acute Myeloid Leukemia (AML) patients, sequenced inside the Cancer Genome Atlas project, gave discrepant results. We subsequently tested these two variant-calling pipelines on 20 leukemia samples from our series (19 primary AMLs and one secondary AML). By validating many of the predicted somatic variants (variant allele frequencies ranging from 100% to 5%), we observed significantly different calling efficiencies. In particular, despite relatively high specificity, sensitivity was poor in both pipelines resulting in a high rate of false negatives. Our findings raise the possibility that landscapes of AML genomes might be more complex than previously reported and characterized by the presence of hundreds of genes mutated at low variant allele frequency, suggesting that the application of genome sequencing to the clinic requires a careful and critical evaluation. We think that improvements in technology and workflow standardization, through the generation of clear experimental and bioinformatics guidelines, are fundamental to translate the use of Next generation sequencing from research to the clinic and to transform genomic information into better diagnosis and outcomes for the patient.