Samir B. Amin

Elevated IL-17 produced by Th17 cells promotes myeloma cell growth and inhibits immune function in multiple myeloma

Elevated cytokines in bone marrow (BM) micro-environment (interleukin-6 [IL-6], transforming growth factor-beta [TGF-beta], and IL-1beta) may play an important role in observed immune dysfunction in multiple myeloma (MM). As IL-6 and TGF-beta are important for the generation of T-helper 17 (T(H)17) cells, we evaluated and observed a significantly elevated baseline and induced frequency of T(h)17 cells in peripheral blood mononuclear cells (PBMCs) and BM mononuclear cells (BMMCs) from MM patients compared with healthy donors. We observed significant increase in levels of serum IL-17, IL-21, IL-22, and IL-23 in blood and BM in MM compared with healthy donors. We also observed that myeloma PBMCs after T(H)17 polarization significantly induced IL-1alpha, IL-13, IL-17, and IL-23 production compared with healthy donor PBMCs. We next observed that IL-17 promotes myeloma cell growth and colony formation via IL-17 receptor, adhesion to bone marrow stromal cells (BMSCs) as well as increased growth in vivo in murine xenograft model of human MM. Additionally, we have observed that combination of IL-17 and IL-22 significantly inhibited the production of T(H)1-mediated cytokines, including interferon-gamma (IFN-gamma), by healthy donor PBMCs. In conclusion, IL-17-producing T(h)17 cells play an important role in MM pathobiology and may be an important therapeutic target for anti-MM activity and to improve immune function.

Integrative analysis of gene and miRNA expression profiles with transcription factor–miRNA feed-forward loops identifies regulators in human cancers

We describe here a novel method for integrating gene and miRNA expression profiles in cancer using feed-forward loops (FFLs) consisting of transcription factors (TFs), miRNAs and their common target genes. The dChip-GemiNI (Gene and miRNA Network-based Integration) method statistically ranks computationally predicted FFLs by their explanatory power to account for differential gene and miRNA expression between two biological conditions such as normal and cancer. GemiNI integrates not only gene and miRNA expression data but also computationally derived information about TF–target gene and miRNA–mRNA interactions. Literature validation shows that the integrated modeling of expression data and FFLs better identifies cancer-related TFs and miRNAs compared to existing approaches. We have utilized GemiNI for analyzing six data sets of solid cancers (liver, kidney, prostate, lung and germ cell) and found that top-ranked FFLs account for ∼20% of transcriptome changes between normal and cancer. We have identified common FFL regulators across multiple cancer types, such as known FFLs consisting of MYC and miR-15/miR-17 families, and novel FFLs consisting of ARNT, CREB1 and their miRNA partners. The results and analysis web server are available at http://www.canevolve.org/dChip-GemiNi.

Investigational agent MLN9708/2238 targets tumor-suppressor miR33b in MM cells

miRs play a critical role in tumor pathogenesis as either oncogenes or tumor-suppressor genes. However, the role of miRs and their regulation in response to proteasome inhibitors in multiple myeloma (MM) is unclear. In the current study, miR profiling in proteasome inhibitor MLN2238-treated MM.1S MM cells shows up-regulation of miR33b. Mechanistic studies indicate that the induction of miR33b is predominantly via transcriptional regulation. Examination of miR33b in patient MM cells showed a constitutively low expression. Overexpression of miR33b decreased MM cell viability, migration, colony formation, and increased apoptosis and sensitivity of MM cells to MLN2238 treatment. In addition, overexpression of miR33b or MLN2238 exposure negatively regulated oncogene PIM-1 and blocked PIM-1 wild-type, but not PIM-1 mutant, luciferase activity. Moreover, PIM-1 overexpression led to significant abrogation of miR33b- or MLN2238-induced cell death. SGI-1776, a biochemical inhibitor of PIM-1, triggered apoptosis in MM. Finally, overexpression of miR33b inhibited tumor growth and prolonged survival in both subcutaneous and disseminated human MM xenograft models. Our results show that miR33b is a tumor suppressor that plays a role during MLN2238-induced apoptotic signaling in MM cells, and these data provide the basis for novel therapeutic strategies targeting miR33b in MM.

Significant Biological Role of Sp1 Transactivation in Multiple Myeloma

Purpose: The transcription factor specificity protein 1 (Sp1) controls number of cellular processes by regulating the expression of critical cell cycle, differentiation, and apoptosis-related genes containing proximal GC/GT-rich promoter elements. We here provide experimental and clinical evidence that Sp1 plays an important regulatory role in multiple myeloma (MM) cell growth and survival. Experimental Design: We have investigated the functional Sp1 activity in MM cells using a plasmid with Firefly luciferase reporter gene driven by Sp1-responsive promoter. We have also used both siRNA- and short hairpin RNA–mediated Sp1 knockdown to investigate the growth and survival effects of Sp1 on MM cells and further investigated the anti-MM activity of terameprocol (TMP), a small molecule that specifically competes with Sp1-DNA binding in vitro and in vivo. Results: We have confirmed high Sp1 activity in MM cells that is further induced by adhesion to bone marrow stromal cells (BMSC). Sp1 knockdown decreases MM cell proliferation and induces apoptosis. Sp1-DNA binding inhibition by TMP inhibits MM cell growth both in vitro and in vivo, inducing caspase-9–dependent apoptosis and overcoming the protective effects of BMSCs. Conclusions: Our results show Sp1 as an important transcription factor in myeloma that can be therapeutically targeted for clinical application by TMP. Clin Cancer Res; 17(20); 6500–9. ©2011 AACR.

Identification of novel antigens with induced immune response in monoclonal gammopathy of undetermined significance

The transformation from monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM) is thought to be associated with changes in immune processes. We have therefore used serologic analysis of recombinant cDNA expression library to screen the sera of MGUS patients to identify tumor-associated antigens. A total of 10 antigens were identified, with specific antibody responses in MGUS. Responses appeared to be directed against intracellular proteins involved in cellular functions, such as apoptosis (SON, IFT57/HIPPI), DNA and RNA binding (ZNF292, GPATCH4), signal transduction regulators (AKAP11), transcriptional corepressor (IRF2BP2), developmental proteins (OFD1), and proteins of the ubiquitin-proteasome pathway (PSMC1). Importantly, the gene responsible for the oral-facial-digital type I syndrome (OFD1) had response in 6 of 29 (20.6%) MGUS patients but 0 of 11 newly diagnosed MM patients. Interestingly, 3 of 11 (27.2%) MM patients after autologous stem cell transplantations showed responses to OFD1. We have confirmed T-cell responses against OFD1 in MGUS and observed down-regulation of GLI1/PTCH1 and p-beta-catenin after OFD1 knock-down with specific siRNA, suggesting its functional role in the regulation of Hh and Wnt pathways. These findings demonstrate OFD1 as an important immune target and highlight its possible role in signal transduction and tumorigenesis in MGUS and MM.

Transcription factor-pathway co-expression analysis reveals cooperation between SP1 and ESR1 on dysregulating cell cycle arrest in non-hyperdiploid multiple myeloma

Multiple myeloma is a hematological cancer of plasma B cells and remains incurable. Two major subtypes of myeloma, hyperdiploid MM (HMM) and non-hyperdiploid MM (NHMM), have distinct chromosomal alterations and different survival outcomes. Transcription factors (TrFs) have been implicated in myeloma oncogenesis, but their dysregulation in myeloma subtypes are less studied. Here, we developed a TrF-pathway coexpression analysis to identify altered coexpression between two sample types. We apply the method to the two myeloma subtypes and the cell cycle arrest pathway, which is significantly differentially expressed between the two subtypes. We find that TrFs MYC, nuclear factor-κB and HOXA9 have significantly lower coexpression with cell cycle arrest in HMM, co-occurring with their overactivation in HMM. In contrast, TrFs ESR1 (estrogen receptor 1), SP1 and E2F1 have significantly lower coexpression with cell cycle arrest in NHMM. SP1 chromatin immunoprecipitation targets are enriched by cell cycle arrest genes. These results motivate a cooperation model of ESR1 and SP1 in regulating cell cycle arrest, and a hypothesis that their overactivation in NHMM disrupts proper regulation of cell cycle arrest. Cotargeting ESR1 and SP1 shows a synergistic effect on inhibiting myeloma proliferation in NHMM cell lines. Therefore, studying TrF-pathway coexpression dysregulation in human cancers facilitates forming novel hypotheses toward clinical utility.

A Survey of Classification Techniques for Microarray Data Analysis

With the recent advance of biomedical technology, a lot of ‘OMIC’ data from genomic, transcriptomic, and proteomic domain can now be collected quickly and cheaply. One such technology is the microarray technology which allows researchers to gather information on expressions of thousands of genes all at the same time. With the large amount of data, a new problem surfaces – how to extract useful information from them. Data mining and machine learning techniques have been applied in many computer applications for some time. It would be natural to use some of these techniques to assist in drawing inference from the volume of information gathered through microarray experiments. This chapter is a survey of common classification techniques and related methods to increase their accuracies for microarray analysis based on data mining methodology. Publicly available datasets are used to evaluate their performance.

The dChip survival analysis module for microarray data.

BackgroundGenome-wide expression signatures are emerging as potential marker for overall survival and disease recurrence risk as evidenced by recent commercialization of gene expression based biomarkers in breast cancer. Similar predictions have recently been carried out using genome-wide copy number alterations and microRNAs. Existing software packages for microarray data analysis provide functions to define expression-based survival gene signatures. However, there is no software that can perform survival analysis using SNP array data or draw survival curves interactively for expression-based sample clusters.ResultsWe have developed the survival analysis module in the dChip software that performs survival analysis across the genome for gene expression and copy number microarray data. Built on the current dChip softwares microarray analysis functions such as chromosome display and clustering, the new survival functions include interactive exploring of Kaplan-Meier (K-M) plots using expression or copy number data, computing survival p-values from the log-rank test and Cox models, and using permutation to identify significant chromosome regions associated with survival.ConclusionsThe dChip survival module provides user-friendly way to perform survival analysis and visualize the results in the context of genes and cytobands. It requires no coding expertise and only minimal learning curve for thousands of existing dChip users. The implementation in Visual C++ also enables fast computation. The software and demonstration data are freely available at http://dchip-surv.chenglilab.org.

Abstract 1978: The poly-SUMO protein specific E3 ubiquitin ligase RNF4 is induced in multiple myeloma and reduces bortezomib-induced cell killing

Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC Multiple Myeloma (MM) is a fatal neoplasm of B-cell origin characterized by the clonal proliferation and accumulation of malignant plasma cells in the bone marrow. While recent advances in mechanistic understanding and treatment modalities have extended median survival to >6 years and 10% of patients survive >10 years, the vast majority of MM remains incurable with conventional, high dose therapy or stem cell transplantation. Bortezomib is a selective, reversible inhibitor of the 26S proteasome that inhibits protein degradation and is now FDA-approved for the treatment of newly diagnosed, relapsed and refractory MM. Though the catabolism of ubiquitinated substrates has been targeted therapeutically with significantly improved prognosis, patient response to bortezomib remains highly variable and cannot be predicted accurately. E3 ligases confer specificity on target selection for Ub+proteasome degradation. We therefore analyzed the expression of individual E3s using a microarray dataset obtained from MM patient tumor samples and found a striking variability in the expression level of individual E3 ligases between normal plasma cells and patients MM cells. RNF4, an E3 specific for poly-sumoylated proteins, was induced in MM patients and correlated with decreased patient response to the proteasome inhibitor bortezomib. Expression profiling of pretreatment tumor samples obtained from MM patients in independent clinical trials were used to generate a signature that correlated expression of SUMO+Ub+Proteasome pathway components with clinical outcome to predict patient response to bortezomib. Experimental validation by overexpression of RNF4-wt rendered myeloma cell lines relatively resistant to bortezomib while RNF4 depletion by shRNA enhanced sensitivity to bortezomib. Transfection of HA-tagged SUMO followed by bortezomib exposure led to the accumulation of HA-SUMO∼conjugates that were immunoreactive with Ub and proteasome components to further link the pathways. In summary, RNF4 an E3 ligase specific for SUMO-conjugates and induced in myeloma, modulates the cellular response to proteasome-based therapy and promotes bortezomib resistance. Our results support regulators of the sumoylation pathway as biomarkers to predict clinical response to bortezomib and provide evidence for targeting SUMO pathway to improve therapeutic outcome in myeloma in general and bortezomib specifically. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1978.