Mechanisms underlying synergy between DNA topoisomerase I-targeted drugs and mTOR kinase inhibitors in NF1-associated malignant peripheral nerve sheath tumors.

Malignant peripheral nerve sheath tumors (MPNSTs) are soft-tissue sarcomas that frequently arise in patients with neurofibromatosis type 1 (NF1). Most of these tumors are unresectable at diagnosis and minimally responsive to conventional treatment, lending urgency to the identification of new pathway dependencies and drugs with potent antitumor activities. We therefore examined a series of candidate agents for their ability to induce apoptosis in MPNST cells arising in nf1/tp53-deficient zebrafish. In this study, we found that DNA topoisomerase I-targeted drugs and mTOR kinase inhibitors were the most effective single agents in eliminating MPNST cells without prohibitive toxicity. In addition, three members of these classes of drugs, either AZD2014 or INK128 in combination with irinotecan, acted synergistically to induce apoptosis both in vitro and in vivo. In mechanistic studies, irinotecan not only induces apoptosis by eliciting a DNA damage response, but also acts synergistically with AZD2014 to potentiate the hypophosphorylation of 4E-BP1, a downstream target of mTORC1. Profound hypophosphorylation of 4E-BP1 induced by this drug combination causes an arrest of protein synthesis, which potently induces tumor cell apoptosis. Our findings provide a compelling rationale for further in vivo evaluation of the combination of DNA topoisomerase I-targeted drugs and mTOR kinase inhibitors against these aggressive nerve sheath tumors.

ABCB1 2677G>T/A variant enhances chemosensitivity to anti-cancer agents acting on microtubule dynamics through LAMP1 inhibition.

Overexpression of ABCB1 associated with single nucleotide variants in cancers was reported to encode a protein responsible for drug resistance. We studied chemosensitivity-related genes associated with ABCB1 2677G>T/A variant. The associated genes were identified based on the results of the significance analysis of microarray, and then prediction accuracy was evaluated using the prediction analysis of microarray. Functional assay of the selected gene was performed by using siRNA and drug accumulation study. A higher frequency of chemoresistance to microtubule-modulating agents was found in cell lines with wild-type ABCB1 compared to cell lines with 2677G>T/A ABCB1 variant. Based on the pharmacogenetic association study with 2677 variant, we identified seven genes that could predict chemosensitivity to microtubule dynamics modulators. The classification accuracy with these seven genes was 90.0%, and the predicted probability was 0.73. LAMP1 was the only gene that was commonly related to chemosensitivity. LAMP1 expression levels were relatively higher in chemoresistant ABCB1 wild-type compared to chemosensitive polymorphic cells. But, there was no difference in ABCB1 expression levels between the two groups. Following LAMP1 siRNA, chemosensitivity was restored due to increased intracellular drug accumulation in wild type cell line. In conclusion, ABCB1 2677G>T/A variant enhances chemosensitivity on microtubule dynamics through LAMP1 inhibition.

Gtpbp2 is a positive regulator of Wnt signaling and maintains low levels of the Wnt negative regulator Axin.

BACKGROUND: Canonical Wnt signals, transduced by stabilized ß-catenin, play similar roles across animals in maintaining stem cell pluripotency, regulating cell differentiation, and instructing normal embryonic development. Dysregulated Wnt/ß-catenin signaling causes diseases and birth defects, and a variety of regulatory processes control this pathway to ensure its proper function and integration with other signaling systems. We previously identified GTP-binding protein 2 (Gtpbp2) as a novel regulator of BMP signaling, however further exploration revealed that Gtpbp2 can also affect Wnt signaling, which is a novel finding reported here. RESULTS: Knockdown of Gtpbp2 in Xenopus embryos causes severe axial defects and reduces expression of Spemann-Mangold organizer genes. Gtpbp2 knockdown blocks responses to ectopic Wnt8 ligand, such as organizer gene induction in ectodermal tissue explants and induction of secondary axes in whole embryos. However, organizer gene induction by ectopic Nodal2 is unaffected by Gtpbp2 knockdown. Epistasis tests, conducted by activating Wnt signal transduction at sequential points in the canonical pathway, demonstrate that Gtpbp2 is required downstream of Dishevelled and Gsk3ß but upstream of ß-catenin, which is similar to the previously reported effects of Axin1 overexpression in Xenopus embryos. Focusing on Axin in Xenopus embryos, we find that knockdown of Gtpbp2 elevates endogenous or exogenous Axin protein levels. Furthermore, Gtpbp2 fusion proteins co-localize with Dishevelled and co-immunoprecipitate with Axin and Gsk3b. CONCLUSIONS: We conclude that Gtpbp2 is required for canonical Wnt/ß-catenin signaling in Xenopus embryos. Our data suggest a model in which Gtpbp2 suppresses the accumulation of Axin protein, a rate-limiting component of the ß-catenin destruction complex, such that Axin protein levels negatively correlate with Gtpbp2 levels. This model is supported by the similarity of our Gtpbp2-Wnt epistasis results and previously reported effects of Axin overexpression, the physical interactions of Gtpbp2 with Axin, and the correlation between elevated Axin protein levels and lost Wnt responsiveness upon Gtpbp2 knockdown. A wide variety of cancer-causing Wnt pathway mutations require low Axin levels, so development of Gtpbp2 inhibitors may provide a new therapeutic strategy to elevate Axin and suppress aberrant ß-catenin signaling in cancer and other Wnt-related diseases.

Synergy between loss of NF1 and overexpression of MYCN in neuroblastoma is mediated by the GAP-related domain.

Earlier reports showed that hyperplasia of sympathoadrenal cell precursors during embryogenesis in Nf1-deficient mice is independent of Nf1's role in down-modulating RAS-MAPK signaling. We demonstrate in zebrafish that nf1 loss leads to aberrant activation of RAS signaling in MYCN-induced neuroblastomas that arise in these precursors, and that the GTPase-activating protein (GAP)-related domain (GRD) is sufficient to suppress the acceleration of neuroblastoma in nf1-deficient fish, but not the hypertrophy of sympathoadrenal cells in nf1 mutant embryos. Thus, even though neuroblastoma is a classical "developmental tumor", NF1 relies on a very different mechanism to suppress malignant transformation than it does to modulate normal neural crest cell growth. We also show marked synergy in tumor cell killing between MEK inhibitors (trametinib) and retinoids (isotretinoin) in primary nf1a-/- zebrafish neuroblastomas. Thus, our model system has considerable translational potential for investigating new strategies to improve the treatment of very high-risk neuroblastomas with aberrant RAS-MAPK activation.

Malignant Peripheral Nerve Sheath Tumors.

Malignant peripheral nerve sheath tumors (MPNST) are tumors derived from Schwann cells or Schwann cell precursors. Although rare overall, the incidence of MPNST has increased with improved clinical management of patients with the neurofibromatosis type 1 (NF1) tumor predisposition syndrome. Unfortunately, current treatment modalities for MPNST are limited, with no targeted therapies available and poor efficacy of conventional radiation and chemotherapeutic regimens. Many murine and zebrafish models of MPNST have been developed, which have helped to elucidate the genes and pathways that are dysregulated in MPNST tumorigenesis, including the p53, and the RB1, PI3K-Akt-mTOR, RAS-ERK and Wnt signaling pathways. Preclinical results have suggested that new therapies, including mTOR and ERK inhibitors, may synergize with conventional chemotherapy in human tumors. The discovery of new genome editing technologies, like CRISPR-cas9, and their successful application to the zebrafish model will enable rapid progress in the faithful modeling of MPNST molecular pathogenesis. The zebrafish model is especially suited for high throughput screening of new targeted therapeutics as well as drugs approved for other purposes, which may help to bring enhanced treatment modalities into human clinical trials for this devastating disease.

Identification of novel gastric cancer-associated CNVs by integrated analysis of microarray.

BACKGROUND: Microarray-CGH facilitates analysis of cancer-associated genomic differences between normal and tumor tissues and provides a genome-wide assessment of copy number variations (CNVs). METHODS: To identify CNVs and their clinical significance in gastric cancer, Microarray-CGH was performed to identify CNVs with genomic DNA (gDNA) from normal placenta tissue, peripheral blood mononuclear cells (PBMCs), and normal gastric tissue. RESULTS: A total of 20 CNVs, including 8 novel CNVs, were identified by Microarray-CGH. Among the 20 CNVs, 5 showed an aberration frequency of over 50%. In addition, mRNA expression of W72437 (TFIIH), AI968311 (GAGE10), AI352361, and AA169807 (PTCH1) in normal tissues and AA485362 (GPX1), AI201652, and AI968311 (GAGE10) in cancer tissues was associated with DNA change. As a whole, incidences of oncogene-like, suppressor-like, and innocent CNVs were 13.8%, 13.2%, and 73.0%, respectively (gain 11.4%, loss 11.8%). AA936795 (C19orf61) appeared as an oncogene-like CNV (9/30, 30%), A1352361 (13/30, 43%), and AA281797 (LOC728340, 10/30, 33%) appeared as tumor suppressor-related CNVs. CONCLUSIONS: This study identified gastric cancer-associated and innocent CNVs in gDNA isolated from placenta tissue and PBMC, which are generally used as reference samples in Microarray-CGH. These novel CNVs may be used for gastric cancer-specific gene selection in comparative analysis of genomics.

Improving the prediction accuracy in classification using the combined data sets by ranks of gene expressions.

BACKGROUND: The information from different data sets experimented under different conditions may be inconsistent even though they are performed with the same research objectives. More than that, even when the data sets were generated from the same platform, the data agreement may be affected by the technical variation among the laboratories. In this case, it is necessary to use the combined data set after adjusting the differences between such data sets, for detecting the more reliable information. RESULTS: The proposed method combines data sets posterior to the discretization of data sets based on the ranks of the gene expression ratios, and the statistical method is applied to the combined data set for predictive gene selection. The efficiency of the proposed method was evaluated using five colon cancer related data sets, which were experimented using cDNA microarrays with different RNA sources, and one experiment utilized oligonucleotide arrays. NCI-60 cell lines data sets were used, which were performed with two different platforms of cDNA microarrays and Affymetrix HU6800 oligonucleotide arrays. The combined data set by the proposed method predicted the test data sets more accurately than the separated data sets did. The biological significant genes were detected from the combined data set, which were missed on the separated data sets. CONCLUSION: By transforming gene expressions using ranks, the proposed method is not influenced by systematic bias among chips and normalization method. The method may be especially more useful to find predictive genes from data sets which have different scale in gene expressions.

Whole genome analysis for liver metastasis gene signatures in colorectal cancer.

Liver metastasis is one of the major causes of death in colorectal cancer (CRC) patients. To understand this process, we investigated whether the gene expression profiling of matched colorectal carcinomas and liver metastases could reveal key molecular events involved in tumor progression and metastasis. We performed experiments using a cDNA microarray containing 17,104 genes with the following tissue samples: paired tissues of 25 normal colorectal mucosa, 27 primary colorectal tumors, 13 normal liver and 27 liver metastasis, and 20 primary colorectal tumors without liver metastasis. To remove the effect of normal cell contamination, we selected 4,583 organ-specific genes with a false discovery rate (FDR) of 0.0067% by comparing normal colon and liver tissues using significant analysis of microarray, and these genes were excluded from further analysis. We then identified and validated 46 liver metastasis-specific genes with an accuracy of 83.3% by comparing the expression of paired primary colorectal tumors and liver metastases using prediction analysis of microarray. The 46 selected genes contained several known oncogenes and 2 ESTs. To confirm that the results correlated with the microarray expression patterns, we performed RT-PCR with WNT5A and carbonic anhydrase II. Additionally, we observed that 21 of the 46 genes were differentially expressed (FDR=2.27%) in primary tumors with synchronous liver metastasis compared with primary tumors without liver metastasis. We scanned the human genome using a cDNA microarray and identified 46 genes that may play an important role in the progression of liver metastasis in CRC.

Novel and simple transformation algorithm for combining microarray data sets.

BACKGROUND: With microarray technology, variability in experimental environments such as RNA sources, microarray production, or the use of different platforms, can cause bias. Such systematic differences present a substantial obstacle to the analysis of microarray data, resulting in inconsistent and unreliable information. Therefore, one of the most pressing challenges in the field of microarray technology is how to integrate results from different microarray experiments or combine data sets prior to the specific analysis. RESULTS: Two microarray data sets based on a 17k cDNA microarray system were used, consisting of 82 normal colon mucosa and 72 colorectal cancer tissues. Each data set was prepared from either total RNA or amplified mRNA, and the difference of RNA source between these two data sets was detected by ANOVA (Analysis of variance) model. A simple integration method was introduced which was based on the distributions of gene expression ratios among different microarray data sets. The method transformed gene expression ratios into the form of a reference data set on a gene by gene basis. Hierarchical clustering analysis, density and box plots, and mixture scores with correlation coefficients revealed that the two data sets were well intermingled, indicating that the proposed method minimized the experimental bias. In addition, any RNA source effect was not detected by the proposed transformation method. In the mixed data set, two previously identified subgroups of normal and tumor were well separated, and the efficiency of integration was more prominent in tumor groups than normal groups. The transformation method was slightly more effective when a data set with strong homogeneity in the same experimental group was used as a reference data set. CONCLUSION: Proposed method is simple but useful to combine several data sets from different experimental conditions. With this method, biologically useful information can be detectable by applying various analytic methods to the combined data set with increased sample size.

An attempt for combining microarray data sets by adjusting gene expressions.

PURPOSE: The diverse experimental environments in microarray technology, such as the different platforms or different RNA sources, can cause biases in the analysis of multiple microarrays. These systematic effects present a substantial obstacle for the analysis of microarray data, and the resulting information may be inconsistent and unreliable. Therefore, we introduced a simple integration method for combining microarray data sets that are derived from different experimental conditions, and we expected that more reliable information can be detected from the combined data set rather than from the separated data sets. MATERIALS AND METHODS: This method is based on the distributions of the gene expression ratios among the different microarray data sets and it transforms, gene by gene, the gene expression ratios into the form of the reference data set. The efficiency of the proposed integration method was evaluated using two microarray data sets, which were derived from different RNA sources, and a newly defined measure, the mixture score. RESULTS: The proposed integration method intermixed the two data sets that were obtained from different RNA sources, which in turn reduced the experimental bias between the two data sets, and the mixture score increased by 24.2%. A data set combined by the proposed method preserved the inter-group relationship of the separated data sets. CONCLUSION: The proposed method worked well in adjusting systematic biases, including the source effect. The ability to use an effectively integrated microarray data set yields more reliable results due to the larger sample size and this also decreases the chance of false negatives.