Prospective assessment of NGS-detectable mutations in CML patients with nonoptimal response: the NEXT-in-CML study.

In chronic myeloid leukemia (CML) patients, tyrosine kinase inhibitors (TKIs) may select for drug-resistant BCR-ABL1 kinase domain (KD) mutants. Although Sanger sequencing (SS) is considered the gold standard for BCR-ABL1 KD mutation screening, next-generation sequencing (NGS) has recently been assessed in retrospective studies. We conducted a prospective, multicenter study (NEXT-in-CML) to assess the frequency and clinical relevance of low-level mutations and the feasibility, cost, and turnaround times of NGS-based BCR-ABL1 mutation screening in a routine setting. A series of 236 consecutive CML patients with failure (n = 124) or warning (n = 112) response to TKI therapy were analyzed in parallel by SS and NGS in 1 of 4 reference laboratories. Fifty-one patients (22 failure, 29 warning) who were negative for mutations by SS had low-level mutations detectable by NGS. Moreover, 29 (27 failure, 2 warning) of 60 patients who were positive for mutations by SS showed additional low-level mutations. Thus, mutations undetectable by SS were identified in 80 out of 236 patients (34%), of whom 42 (18% of the total) had low-level mutations somehow relevant for clinical decision making. Prospective monitoring of mutation kinetics demonstrated that TKI-resistant low-level mutations are invariably selected if the patients are not switched to another TKI or if they are switched to a inappropriate TKI or TKI dose. The NEXT-in-CML study provides for the first time robust demonstration of the clinical relevance of low-level mutations, supporting the incorporation of NGS-based BCR-ABL1 KD mutation screening results in the clinical decision algorithms.

Statistical assessment of discriminative features for protein-coding and non coding cross-species conserved sequence elements.

BACKGROUND: The identification of protein coding elements in sets of mammalian conserved elements is one of the major challenges in the current molecular biology research. Many features have been proposed for automatically distinguishing coding and non coding conserved sequences, making so necessary a systematic statistical assessment of their differences. A comprehensive study should be composed of an association study, i.e. a comparison of the distributions of the features in the two classes, and a prediction study in which the prediction accuracies of classifiers trained on single and groups of features are analyzed, conditionally to the compared species and to the sequence lengths. RESULTS: In this paper we compared distributions of a set of comparative and non comparative features and evaluated the prediction accuracy of classifiers trained for discriminating sequence elements conserved among human, mouse and rat species. The association study showed that the analyzed features are statistically different in the two classes. In order to study the influence of the sequence lengths on the feature performances, a predictive study was performed on different data sets composed of coding and non coding alignments in equal number and equally long with an ascending average length. We found that the most discriminant feature was a comparative measure indicating the proportion of synonymous nucleotide substitutions per synonymous sites. Moreover, linear discriminant classifiers trained by using comparative features in general outperformed classifiers based on intrinsic ones. Finally, the prediction accuracy of classifiers trained on comparative features increased significantly by adding intrinsic features to the set of input variables, independently on sequence length (Kolmogorov-Smirnov P-value