RNA sequencing as an alternative tool for detecting measurable residual disease in core-binding factor acute myeloid leukemia.

DNA sequencing-based measurable residual disease (MRD) detection has shown to be clinically relevant in AML. However, the same methodology cannot be applied to fusion gene-driven subtypes of AML such as core-binding factor AML (CBF-AML). Here in this study, we evaluated the effectiveness of using DNA and RNA sequencing in MRD detection and in tracking clonal dynamics in CBF-AML. Using RNA-seq, we were able to quantify expression levels of RUNX1-RUNX1T1 and CBFB-MYH11 at diagnosis and their levels of reduction during remission (P < 6.3e-05 and P < 2.2e-13). The level of reduction of RUNX1-RUNX1T1 as measured by RNA-seq and qPCR were highly correlated (R2 = 0.74, P < 5.4e-05). A decision tree analysis, based on 3-log reduction of RUNX1-RUNX1T1 and cKIT-D816mut at diagnosis, stratified RUNX1-RUNX1T1 AML patients into three subgroups. These three subgroups had 2-year overall survival rates at 87%, 74%, and 33% (P < 0.08) and 2-year relapse incidence rates at 13%, 42%, and 67% (P < 0.05). On the other hand, although low residual allelic burden was common, it was not associated with long-term outcome, indicating that mutation clearance alone cannot be interpreted as MRD-negative. Overall, our study demonstrates that the clinical utility of RNA sequencing as a potential tool for MRD monitoring in fusion gene-driven AML such as RUNX1-RUNX1T1 AML.

Next-generation sequencing-based posttransplant monitoring of acute myeloid leukemia identifies patients at high risk of relapse.

Next-generation sequencing (NGS) has been applied to define clinically relevant somatic mutations and classify subtypes in acute myeloid leukemia (AML). Persistent allelic burden after chemotherapy is associated with higher relapse incidence, but presence of allelic burden in AML patients after receiving allogeneic hematopoietic cell transplantation (HCT) has not been examined longitudinally. As such, we aimed to assess the feasibility of NGS in monitoring AML patients receiving HCT. Using a targeted gene panel, we performed NGS in 104 AML patients receiving HCT using samples collected at diagnosis, pre-HCT, and post-HCT at day 21 (post-HCTD21). NGS detected 256 mutations in 90 of 104 patients at diagnosis, which showed stepwise clearances after chemotherapy and HCT. In a subset of patients, mutations were still detectable pre-HCT and post-HCT. Most post-HCT mutations originate from mutations initially detected at diagnosis. Post-HCTD21 allelic burdens in relapsed patients were higher than in nonrelapsed patients. Post-HCTD21 mutations in relapsed patients all expanded at relapse. Assessment of variant allele frequency (VAF) revealed that overall VAF post-HCTD21 (VAF0.2%-post-HCTD21) is associated with an increased risk of relapse (56.2% vs 16.0% at 3 years; P < .001) and worse overall survival (OS; 36.5% vs 67.0% at 3 years; P = .006). Multivariate analyses confirmed that VAF0.2%-post-HCTD21 is an adverse prognostic factor for OS (hazard ratio [HR], 3.07; P = .003) and relapse incidence (HR, 4.75; P < .001), independent of the revised European LeukemiaNet risk groups. Overall, current study demonstrates that NGS-based posttransplant monitoring in AML patients is feasible and can distinguish high-risk patients for relapse.

Exome sequencing reveals DNMT3A and ASXL1 variants associate with progression of chronic myeloid leukemia after tyrosine kinase inhibitor therapy.

OBJECTIVE: The development of tyrosine kinase inhibitors (TKIs) has significantly improved the treatment of chronic myeloid leukemia (CML). However, approximately one third of patients are resistant to TKI and/or progress to advanced disease stages. TKI therapy failure has a well-known association with ABL1 kinase domain (KD) mutations, but only around half of TKI non-responders have detectable ABL1 KD mutations. METHOD: We attempt to identify genetic markers associated with TKI therapy failure in 13 patients (5 resistant, 8 progressed) without ABL1 KD mutations using whole-exome sequencing. RESULTS: In 6 patients, we detected mutations in 6 genes commonly mutated in other myeloid neoplasms: ABL1, ASXL1, DNMT3A, IDH1, SETBP1, and TP63. We then used targeted deep sequencing to validate our finding in an independent cohort consisting of 100 CML patients with varying drug responses (74 responsive, 18 resistant, and 8 progressed patients). Mutations in genes associated with epigenetic regulations such as DNMT3A and ASXL1 seem to play an important role in the pathogenesis of CML progression and TKI-resistance independent of ABL1 KD mutations. CONCLUSION: This study suggests the involvement of other somatic mutations in the development of TKI resistant progression to advanced disease stages in CML, particularly in patients lacking ABL1 KD mutations.

Spectrum of somatic mutation dynamics in chronic myeloid leukemia following tyrosine kinase inhibitor therapy.

Somatic mutations commonly detected in a variety of myeloid neoplasms have not been systematically investigated in chronic myeloid leukemia (CML). We performed targeted deep sequencing on a total of 300 serial samples from 100 CML patients; 37 patients carried mutations. Sixteen of these had evidence of mutations originating from preleukemic clones. Using unsupervised hierarchical clustering, we identified 5 distinct patterns of mutation dynamics arising following tyrosine kinase inhibitor (TKI) therapy. This study demonstrates that patterns of mutation acquisition, persistence, and clearance vary but have a number of interesting correlations with clinical outcomes. Mutation burden often persisted despite successful TKI response (pattern 1), providing indirect evidence that these mutations also originated from preleukemic mutations, whereas patients exhibiting mutation clearance (pattern 3) showed mixed clinical outcomes. Unsurprisingly, patients acquiring new mutations during treatment failed TKI therapy (pattern 2). These patterns show that CML mutation dynamics following TKI therapy are markedly distinct from other myeloid neoplasms. In summary, clinical implications of mutation profiles and dynamics in CML should be interpreted with caution.

MUSI: an integrated system for identifying multiple specificity from very large peptide or nucleic acid data sets.

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.