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.

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.

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 β-catenin, play similar roles across animals in maintaining stem cell pluripotency, regulating cell differentiation, and instructing normal embryonic development. Dysregulated Wnt/β-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.

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.

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.

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.

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.

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.

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.

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.