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.

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BACKGROUND: Tyrosine kinase inhibitor (TKI) resistance is the most relevant event during the treatment of chronic myeloid leukemia (CML), which correlates with high risk of treatment failure, disease progression and death, explaining half of treatment failed CML patients. However, the remaining half with TKI resistance does not show any ABL1 tyrosine kinase domain (TKD) mutation indicating the presence of alternative pathogenic pathways behind TKI resistance. Thus we hypothesized that the novel mutation besides ABL1-TKD mutation occurs during the development of TKI resistance. Using whole exome sequencing, we screened 13 pairs of CML cases with TKI resistance, but without ABL1-TKD mutation. The present study attempts to: 1) explore novel mutation(s) developing TKI resistance to CML treatment and 2) validate the somatic variants in an independent cohort of CML patients (n=100). METHODS: Thirteen CML cases with TKI resistance but not having ABL1 TKD mutation were included prospectively. Reason for TKI resistance includes Loss of MCyR (n=7) with (n=2) or without additional cytogenetic abnormality (ACA; n=5), progression to blastic crisis (n=3), development of ACA (n=1), development of clonal evolution in Ph neg clone (n=1), primary cytogenetic resistance (n=1). TKI resistance were demonstrated to imatinib (n=12), dasatinib (n=5), nilotinib (n=4) or ponatinib (n=2). The latest treatment includes ponatinib (n=3), dasatinib (n=8) alone (n=4), with smoothen inhibitor (n=2), or with after systemic chemotherapy (n=2), omacetaxine (n=1), and nilotinib (n=1). Disease stage at the time of exome sequencing was chronic phase (n=10) or blastic crisis (n=3). Germline and tumor samples at the time of TKI resistance were compared using whole exome sequencing (Illumina TruSeq kit, HiSeq 2000). Targeted sequencing for selected variants was performed to validate the result. All patients were confirmed the absence of ABL1-TKD mutations using Sanger sequencing. RESULTS: 1) Exome sequencing (Illumina Truseq kit) was performed as per the manufacturer's protocol using an Illumina HiSeq 2000 sequencer. DNA from buccal mucosa was used as a control for variant calling. Exome sequencing reads processing includes mapping to human genome hg19, marking PCR duplicates, realignment of indels, fixing mate information, and discard the reads with more than 2 mismatches to increase the true positive rate. In the end, we have on-target-coverage of 57x. Lastly, 72% of target positions are mapped more than 30x. 2) One hundred nineteen somatic variants were identified in 13 patients in 108 genes. Among them 5 genes have variants in multiple patients including DNMT3A (n=3), ASXL1 (n=2), NPIPB5 (n=2), ATXN3 (n=2) and EFEMP1 (n=2) . We also found at least 1 mutation in well-known driver genes in 6 patients (6/13 = 46%). 3) Three out of 4 patients with ACA carry variants at least one of DNMT3A (n=2), ASXL1 (n=2), and SETBP1 (n=1). Also, 2 out of 3 cases progressed to blastic crisis demonstrate variants in DNMT3A (n=1) and IDH1 (n=1). 4) Interestingly, in one patient, exome sequencing reveals ABL1-TKD mutation (T315I), which was not detected at the initial screening by Sanger sequencing. 5) The result of targeted sequencing in an independent cohort of CML patients (n=100) will be presented in the annual meeting of American Society of Hematology in Dec 2015. CONCLUSION: Our study suggest that DNMT3A and ASXL1 mutations seem to be the driver mutations involved in the development of TKI resistance/progression, independent of ABL1-TKD mutation. Also, exome sequencing can detect ABL1-TKD mutations including T315I prior to be detected by initial Sanger sequencing. Disclosures Lipton: Ariad: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Teva: Consultancy, Research Funding; Novartis Pharmaceuticals: Consultancy, Research Funding. Kim:Bristol-Myers Squibb: Consultancy, Research Funding; Novartis Pharmaceuticals: Consultancy, Research Funding.

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.

Introduction: The discoveries of JAK2-V617F as well as MPL and CARL mutations have greatly clarified the underlying genetics of Philadelphia negative myeloproliferative disease (MPN). Mutation status on these three genes, especially JAK2-V617F, can characterize over 90% of MPN patients. However, the heterogeneity of MPN in terms of its AML transformation and treatment response remains unclear. To assess the difference in mutational status between MPN patients who progress to secondary AML and those who do not, we aim to examine longitudinal samples taken from multiple time points using next generation sequencing. Patients and Methods: Bone-marrow (BM) samples were collected from 19 MPNpatientsfrom2003 to 2012atChonnamNational UniversityHwasunHospital.The diagnosis of MPN was established according to the revised criteria of the World Health Organization.Longitudinal samples were taken at the time of diagnosis and at a follow-up as well as its T-cell fractions (CD+3) isolated from the peripheral blood using MAC separation column.Targeted sequencing was performed using an Agilent custom probe set of a panel of 84 myeloid genes. We multiplexed and sequenced the samples using an IlluminaHiseq2000. Results: The mean on-target coverage for the 57 sequenced samples was 861.4x. We detected a total of 48 somatic mutations in 25 genes in 17 patients (89%) throughout the course of the disease. Five of the 25 genes were recurrently mutated (JAK2, IDH2, ASXL1, SRSF2 and, TP53). As expected, JAK2-V617F was the most commonly observed (15/17 patients). One of the patients without JAK2-V617F carried a MPL mutation and another was triple negative.At the time of follow-up, 12 patients had chronic MPN (11 stable disease and 1 spleen response) withRuxolitinib treatment for a median duration of 373 days (range 255 - 729). Among 7 progressed patients, 5 patients had additional mutation at diagnosis of MPN other than JAK2 or MPL: MPN-13 (DNMT3A and ASXL1), MPN-14 (SRSF2 and IDH2), MPN-15 (IDH2), MPN16 (U2AF1), MPN-17 (IDH2 and SRSF2) as shown in figures. On the other hand, 4/12 non-progressed patients carried additional mutations: MPN-01 (ZRSR2), MPN-05 (ASXL1, CBL, FGR, KMT2D and TET2), MPN-10 (ASXL1, CDH13, EED, EZH2, MN1, NF1 and TRRAP), MPN-11 (FOXP1). In summary, only ASXL1 and JAK2-V617F were recurrently mutated among the non-progressed group. At the time of leukemic transformation, 6 out of 7 patients acquired new mutations: MPN-13 (SETBP1 and TP53), MPN-14 (RUNX1, ASXL1, and IDH1), MPN-15 (TP53 and CASP8), MPN-16 (TP53), MPN-18 (CEBPA), and MPN-20 (ASXL1). In the remaining case (MPN-17), increase of JAK2-V617F VAF was also observed. In summary, TP53 mutation was the most common mutation to acquire by the time of leukemic transformation (n=3). Other mutations acquired by this stage were in SETBP1, RUNX1, IDH1, CEBPA, and ASXL1. We did not observe any significant difference in the allelic burden increase of JAK2-V617F from diagnosis to follow-up between the non-progressed and progressed groups (Figure A). Conclusion: There is no significant difference in mutation burden increase of JAK2-V617F between patients who progressed to secondary AML and patients that did not. Acquisition of mutations other than JAK2-V617F at both diagnosis and at follow-up is associated with the risk of transformation to secondary AML. Mutation profiling using a myeloid gene panel at timed follow-up after MPN diagnosis can be more helpful than monitoring JAK2-V617F status in these patients. Figure Figure. Disclosures No relevant conflicts of interest to declare.