Selective FLT3 inhibition of FLT3-ITD+ acute myeloid leukaemia resulting in secondary D835Y mutation: a model for emerging clinical resistance patterns.

Acquired resistance to selective FLT3 inhibitors is an emerging clinical problem in the treatment of FLT3-ITD(+) acute myeloid leukaemia (AML). The paucity of valid pre-clinical models has restricted investigations to determine the mechanism of acquired therapeutic resistance, thereby limiting the development of effective treatments. We generated selective FLT3 inhibitor-resistant cells by treating the FLT3-ITD(+) human AML cell line MOLM-13 in vitro with the FLT3-selective inhibitor MLN518, and validated the resistant phenotype in vivo and in vitro. The resistant cells, MOLM-13-RES, harboured a new D835Y tyrosine kinase domain (TKD) mutation on the FLT3-ITD(+) allele. Acquired TKD mutations, including D835Y, have recently been identified in FLT3-ITD(+) patients relapsing after treatment with the novel FLT3 inhibitor, AC220. Consistent with this clinical pattern of resistance, MOLM-13-RES cells displayed high relative resistance to AC220 and Sorafenib. Furthermore, treatment of MOLM-13-RES cells with AC220 lead to loss of the FLT3 wild-type allele and the duplication of the FLT3-ITD-D835Y allele. Our FLT3-Aurora kinase inhibitor, CCT137690, successfully inhibited growth of FLT3-ITD-D835Y cells in vitro and in vivo, suggesting that dual FLT3-Aurora inhibition may overcome selective FLT3 inhibitor resistance, in part due to inhibition of Aurora kinase, and may benefit patients with FLT3-mutated AML.

Characterization of IGH locus breakpoints in multiple myeloma indicates a subset of translocations appear to occur in pregerminal center B cells.

Translocations in myeloma are thought to occur solely in mature B cells in the germinal center through class switch recombination (CSR). We used a targeted captured technique followed by massively parallel sequencing to determine the exact breakpoints in both the immunoglobulin heavy chain (IGH) locus and the partner chromosome in 61 presentation multiple myeloma samples. The majority of samples (62%) have a breakpoint within the switch regions upstream of the IGH constant genes and are generated through CSR in a mature B cell. However, the proportion of CSR translocations is not consistent between cytogenetic subgroups. We find that 100% of t(4;14) are CSR-mediated; however, 21% of t(11;14) and 25% of t(14;20) are generated through DH-JH recombination activation gene-mediated mechanisms, indicating they occur earlier in B-cell development at the pro-B-cell stage in the bone marrow. These 2 groups also generate translocations through receptor revision, as determined by the breakpoints and mutation status of the segments used in 10% and 50% of t(11;14) and t(14;20) samples, respectively. The study indicates that in a significant number of cases the translocation-based etiological events underlying myeloma may arise at the pro-B-cell hematological progenitor cell level, much earlier in B-cell development than was previously thought.

Incomplete DJH rearrangements as a novel tumor target for minimal residual disease quantitation in multiple myeloma using real-time PCR.

The hypervariable regions of immunoglobulin heavy-chain (IgH) rearrangements provide a specific tumor marker in multiple myeloma (MM). Recently, real-time PCR assays have been developed in order to quantify the number of tumor cells after treatment. However, these strategies are hampered by the presence of somatic hypermutation (SH) in VDJH rearrangements from multiple myeloma (MM) patients, which causes mismatches between primers and/or probes and the target, leading to a nonaccurate quantification of tumor cells. Our group has recently described a 60% incidence of incomplete DJH rearrangements in MM patients, with no or very low rates of SH. In this study, we compare the efficiency of a real-time PCR approach for the analysis of both complete and incomplete IgH rearrangements in eight MM patients using only three JH consensus probes. We were able to design an allele-specific oligonucleotide for both the complete and incomplete rearrangement in all patients. DJH rearrangements fulfilled the criteria of effectiveness for real-time PCR in all samples (ie no unspecific amplification, detection of less than 10 tumor cells within 10(5) polyclonal background and correlation coefficients of standard curves higher than 0.98). By contrast, only three out of eight VDJH rearrangements fulfilled these criteria. Further analyses showed that the remaining five VDJH rearrangements carried three or more somatic mutations in the probe and primer sites, leading to a dramatic decrease in the melting temperature. These results support the use of incomplete DJH rearrangements instead of complete somatically mutated VDJH rearrangements for investigation of minimal residual disease in multiple myeloma.

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.