Key Points Mutation clearance in CML does not directly result in successful treatment in CML. Clinical implications of patterns of mutation acquisition, persistence, and clearance in CML should be interpreted with caution.

Key Points Higher allelic burden at day 21 of post-HCT is associated with higher risk of relapse and mortality. Longitudinal tracking of AML patients receiving HCT is feasible and provides clinically relevant information.

The genetics behind the progression of myelodysplasia to secondary acute myeloid leukemia (sAML) is poorly understood. In this study, we profiled somatic mutations and their dynamics using next generation sequencing on serial samples from a total of 124 patients, consisting of a 31 patient discovery cohort and 93 patients from two validation cohorts. Whole-exome analysis on the discovery cohort revealed that 29 of 31 patients carry mutations related to at least one of eight commonly mutated pathways in AML. Mutations in genes related to DNA methylation and splicing machinery were found in T-cell samples, which expand at the initial diagnosis of the myelodysplasia, suggesting their importance as early disease events. On the other hand, somatic variants associated with signaling pathways arise or their allelic burdens expand significantly during progression. Our results indicate a strong association between mutations in activated signaling pathways and sAML progression. Overall, we demonstrate that distinct categories of genetic lesions play roles at different stages of sAML in a generally fixed order.

Peptide recognition domains and transcription factors play crucial roles in cellular signaling. They bind linear stretches of amino acids or nucleotides, respectively, with high specificity. Experimental techniques that assess the binding specificity of these domains, such as microarrays or phage display, can retrieve thousands of distinct ligands, providing detailed insight into binding specificity. In particular, the advent of next-generation sequencing has recently increased the throughput of such methods by several orders of magnitude. These advances have helped reveal the presence of distinct binding specificity classes that co-exist within a set of ligands interacting with the same target. Here, we introduce a software system called MUSI that can rapidly analyze very large data sets of binding sequences to determine the relevant binding specificity patterns. Our pipeline provides two major advances. First, it can detect previously unrecognized multiple specificity patterns in any data set. Second, it offers integrated processing of very large data sets from next-generation sequencing machines. The results are visualized as multiple sequence logos describing the different binding preferences of the protein under investigation. We demonstrate the performance of MUSI by analyzing recent phage display data for human SH3 domains as well as microarray data for mouse transcription factors.