Ze Tian

PURPOSE: The success of bortezomib therapy for treatment of multiple myeloma (MM) led to the development of structurally and pharmacologically distinct novel proteasome inhibitors. In the present study, we evaluated the efficacy of one such novel orally bioactive proteasome inhibitor MLN9708/MLN2238 in MM using well-established in vitro and in vivo models. EXPERIMENTAL DESIGN: MM cell lines, primary patient cells, and the human MM xenograft animal model were used to study the antitumor activity of MN2238. RESULTS: Treatment of MM cells with MLN2238 predominantly inhibits chymotrypsin-like activity of the proteasome and induces accumulation of ubiquitinated proteins. MLN2238 inhibits growth and induces apoptosis in MM cells resistant to conventional and bortezomib therapies without affecting the viability of normal cells. In animal tumor model studies, MLN2238 is well tolerated and inhibits tumor growth with significantly reduced tumor recurrence. A head-to-head analysis of MLN2238 versus bortezomib showed a significantly longer survival time in mice treated with MLN2238 than mice receiving bortezomib. Immununostaining of MM tumors from MLN2238-treated mice showed growth inhibition, apoptosis, and a decrease in associated angiogenesis. Mechanistic studies showed that MLN2238-triggered apoptosis is associated with activation of caspase-3, caspase-8, and caspase-9; increase in p53, p21, NOXA, PUMA, and E2F; induction of endoplasmic reticulum (ER) stress response proteins Bip, phospho-eIF2-α, and CHOP; and inhibition of nuclear factor kappa B. Finally, combining MLN2238 with lenalidomide, histone deacetylase inhibitor suberoylanilide hydroxamic acid, or dexamethasone triggers synergistic anti-MM activity. CONCLUSION: Our preclinical study supports clinical evaluation of MLN9708, alone or in combination, as a potential MM therapy.

Proteasome inhibitors have demonstrated that targeting protein degradation is effective therapy in multiple myeloma (MM). Here we show that deubiquitylating enzymes (DUBs) USP14 and UCHL5 are more highly expressed in MM cells than in normal plasma cells. USP14 and UCHL5 short interfering RNA knockdown decreases MM cell viability. A novel 19S regulatory particle inhibitor b-AP15 selectively blocks deubiquitylating activity of USP14 and UCHL5 without inhibiting proteasome activity. b-AP15 decreases viability in MM cell lines and patient MM cells, inhibits proliferation of MM cells even in the presence of bone marrow stroma cells, and overcomes bortezomib resistance. Anti-MM activity of b-AP15 is associated with growth arrest via downregulation of CDC25C, CDC2, and cyclin B1 as well as induction of caspase-dependent apoptosis and activation of unfolded protein response. In vivo studies using distinct human MM xenograft models show that b-AP15 is well tolerated, inhibits tumor growth, and prolongs survival. Combining b-AP15 with suberoylanilide hydroxamic acid, lenalidomide, or dexamethasone induces synergistic anti-MM activity. Our preclinical data showing efficacy of b-AP15 in MM disease models validates targeting DUBs in the ubiquitin proteasomal cascade to overcome proteasome inhibitor resistance and provides the framework for clinical evaluation of USP14/UCHL5 inhibitors to improve patient outcome in MM.

OBJECTIVES: To investigate a B-cell-depleting strategy to reverse diabetes in naïve NOD mice. RESEARCH DESIGN AND METHODS: We targeted the CD22 receptor on B-cells of naïve NOD mice to deplete and reprogram B-cells to effectively reverse autoimmune diabetes. RESULTS: Anti-CD22/cal monoclonal antibody (mAb) therapy resulted in early and prolonged B-cell depletion and delayed disease in pre-diabetic mice. Importantly, when new-onset hyperglycemic mice were treated with the anti-CD22/cal mAb, 100% of B-cell-depleted mice became normoglycemic by 2 days, and 70% of them maintained a state of long-term normoglycemia. Early therapy after onset of hyperglycemia and complete B-cell depletion are essential for optimal efficacy. Treated mice showed an increase in percentage of regulatory T-cells in islets and pancreatic lymph nodes and a diminished immune response to islet peptides in vitro. Transcriptome analysis of reemerging B-cells showed significant changes of a set of proinflammatory genes. Functionally, reemerging B-cells failed to present autoantigen and prevented diabetes when cotransferred with autoreactive CD4(+) T-cells into NOD.SCID hosts. CONCLUSIONS: Targeting CD22 depletes and reprograms B-cells and reverses autoimmune diabetes, thereby providing a blueprint for development of novel therapies to cure autoimmune diabetes.

MOTIVATION: Incorporating biological prior knowledge into predictive models is a challenging data integration problem in analyzing high-dimensional genomic data. We introduce a hypergraph-based semi-supervised learning algorithm called HyperPrior to classify gene expression and array-based comparative genomic hybridization (arrayCGH) data using biological knowledge as constraints on graph-based learning. HyperPrior is a robust two-step iterative method that alternatively finds the optimal labeling of the samples and the optimal weighting of the features, guided by constraints encoding prior knowledge. The prior knowledge for analyzing gene expression data is that cancer-related genes tend to interact with each other in a protein-protein interaction network. Similarly, the prior knowledge for analyzing arrayCGH data is that probes that are spatially nearby in their layout along the chromosomes tend to be involved in the same amplification or deletion event. Based on the prior knowledge, HyperPrior imposes a consistent weighting of the correlated genomic features in graph-based learning. RESULTS: We applied HyperPrior to test two arrayCGH datasets and two gene expression datasets for both cancer classification and biomarker identification. On all the datasets, HyperPrior achieved competitive classification performance, compared with SVMs and the other baselines utilizing the same prior knowledge. HyperPrior also identified several discriminative regions on chromosomes and discriminative subnetworks in the PPI, both of which contain cancer-related genomic elements. Our results suggest that HyperPrior is promising in utilizing biological prior knowledge to achieve better classification performance and more biologically interpretable findings in gene expression and arrayCGH data. AVAILABILITY: http://compbio.cs.umn.edu/HyperPrior CONTACT: kuang@cs.umn.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at bioinformatics online.