The hidden genomic landscape of acute myeloid leukemia: subclonal structure revealed by undetected mutations.

Bodini M1, Ronchini C1, Giacò L2, Russo A2, Melloni GE1, Luzi L2, Sardella D3, Volorio S4, Hasan SK5, Ottone T6, Lavorgna S7, Lo-Coco F6, Candoni A8,Fanin R8, Toffoletti E8, Iacobucci I9, Martinelli G9, Cignetti A10, Tarella C10, Bernard L2, Pelicci PG11, Riva L12.

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Abstract

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.