The Novel Tubulin-Targeting Agent Pyrrolo-1,5-Benzoxazepine-15 Induces Apoptosis in Poor Prognostic Subgroups of Chronic Lymphocytic Leukemia

Pyrrolo-1,5-benzoxazepine-15 (PBOX-15) is a novel microtubule depolymerization agent that induces cell cycle arrest and subsequent apoptosis in a number of cancer cell lines. Chronic lymphocytic leukemia (CLL) is characterized by clonal expansion of predominately nonproliferating mature B cells. Here, we present data suggesting PBOX-15 is a potential therapeutic agent for CLL. We show activity of PBOX-15 in samples taken from a cohort of CLL patients (n = 55) representing both high-risk and low-risk disease. PBOX-15 exhibited cytotoxicity in CLL cells (n = 19) in a dose-dependent manner, with mean IC(50) of 0.55 mu mol/L. PBOX-15 significantly induced apoptosis in CLL cells (n = 46) including cells with poor prognostic markers: unmutated IgV(II) genes, CD38 and zeta-associated protein 70 (ZAP-70) expression, and fludarabine-resistant cells with chromosomal deletions in 17p. In addition, PBOX-15 was more potent than fludarabine in inducing apoptosis in fludarabine-sensitive cells. Pharmacologic inhibition and small interfering RNA knockdown of caspase-8 significantly inhibited PBOX-15-induced apoptosis. Pharmacologic inhibition of c-jun NH(2)-terminal kinase inhibited PBOX-15-induced apoptosis in mutated IgV(II) and ZAP-70(-) CLL cells but not in unmutated IgV(II) and ZAP-70(+) cells. PBOX-15 exhibited selective cytotoxicity in CLL cells compared with normal hematopoietic cells. Our data suggest that PBOX-15 represents a novel class of agents that are toxic toward both high-risk and low-risk CLL cells. The need for novel treatments is acute in CLL, especially for the subgroup of patients with poor clinical outcome and drug-resistant disease. This study identifies a novel agent with significant clinical potential.

Distinct poor prognostic subgroups of acute myeloid leukaemia, FLT3-ITD and P-glycoprotein-positive, have contrasting levels of FOXO1

Regulation of ABCB1 (P-glycoprotein/Pgp) in AML was investigated. In a historical cohort with Pgp and transcriptional regulator expression profiling data available (n=141), FOXO1 correlated with Pgp protein expression. This was confirmed in an independent cohort (n=204). Down-regulation (siRNA) or hyperactivation (nicotinamide) of FOXO1 led to corresponding changes in Pgp. Low FOXO1 expression correlated with FLT3-ITDs (p

Ideals of A(G) and bimodules over maximal abelian selfadjoint algebras

This paper is concerned with weak⁎ closed masa-bimodules generated by A(G)-invariant subspaces of VN(G). An annihilator formula is established, which is used to characterise the weak⁎ closed subspaces of B(L2(G)) which are invariant under both Schur multipliers and a canonical action of M(G) on B(L2(G)) via completely bounded maps. We study the special cases of extremal ideals with a given null set and, for a large class of groups, we establish a link between relative spectral synthesis and relative operator synthesis.

DNA methylation subgroups and the CpG island methylator phenotype in gastric cancer: a comprehensive profiling approach

BACKGROUND: Methylation-induced silencing of promoter CpG islands in tumor suppressor genes plays an important role in human carcinogenesis. In colorectal cancer, the CpG island methylator phenotype (CIMP) is defined as widespread and elevated levels of DNA methylation and CIMP+ tumors have distinctive clinicopathological and molecular features. In contrast, the existence of a comparable CIMP subtype in gastric cancer (GC) has not been clearly established. To further investigate this issue, in the present study we performed comprehensive DNA methylation profiling of a well-characterised series of primary GC.

METHODS: The methylation status of 1,421 autosomal CpG sites located within 768 cancer-related genes was investigated using the Illumina GoldenGate Methylation Panel I assay on DNA extracted from 60 gastric tumors and matched tumor-adjacent gastric tissue pairs. Methylation data was analysed using a recursively partitioned mixture model and investigated for associations with clinicopathological and molecular features including age, Helicobacter pylori status, tumor site, patient survival, microsatellite instability and BRAF and KRAS mutations.

RESULTS: A total of 147 genes were differentially methylated between tumor and matched tumor-adjacent gastric tissue, with HOXA5 and hedgehog signalling being the top-ranked gene and signalling pathway, respectively. Unsupervised clustering of methylation data revealed the existence of 6 subgroups under two main clusters, referred to as L (low methylation; 28% of cases) and H (high methylation; 72%). Female patients were over-represented in the H tumor group compared to L group (36% vs 6%; P = 0.024), however no other significant differences in clinicopathological or molecular features were apparent. CpG sites that were hypermethylated in group H were more frequently located in CpG islands and marked for polycomb occupancy.

CONCLUSIONS: High-throughput methylation analysis implicates genes involved in embryonic development and hedgehog signaling in gastric tumorigenesis. GC is comprised of two major methylation subtypes, with the highly methylated group showing some features consistent with a CpG island methylator phenotype.

Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices

In the study of complex genetic diseases, the identification of subgroups of patients sharing similar genetic characteristics represents a challenging task, for example, to improve treatment decision. One type of genetic lesion, frequently investigated in such disorders, is the change of the DNA copy number (CN) at specific genomic traits. Non-negative Matrix Factorization (NMF) is a standard technique to reduce the dimensionality of a data set and to cluster data samples, while keeping its most relevant information in meaningful components. Thus, it can be used to discover subgroups of patients from CN profiles. It is however computationally impractical for very high dimensional data, such as CN microarray data. Deciding the most suitable number of subgroups is also a challenging problem. The aim of this work is to derive a procedure to compact high dimensional data, in order to improve NMF applicability without compromising the quality of the clustering. This is particularly important for analyzing high-resolution microarray data. Many commonly used quality measures, as well as our own measures, are employed to decide the number of subgroups and to assess the quality of the results. Our measures are based on the idea of identifying robust subgroups, inspired by biologically/clinically relevance instead of simply aiming at well-separated clusters. We evaluate our procedure using four real independent data sets. In these data sets, our method was able to find accurate subgroups with individual molecular and clinical features and outperformed the standard NMF in terms of accuracy in the factorization fitness function. Hence, it can be useful for the discovery of subgroups of patients with similar CN profiles in the study of heterogeneous diseases.