Abstract. Triple negative breast cancer (TNBC) has a poor outcome due to the lack of beneficial therapeutic targets. To clarify the molecular mechanisms involved in the carcinogenesis of TNBC and to identify target molecules for novel anticancer drugs, we analyzed the gene expression profiles of 30 TNBCs as well as 13 normal epithelial ductal cells that were purified by laser-microbeam microdissection. We identified 301 and 321 transcripts that were significantly upregulated and downregulated in TNBC, respectively. In particular, gene expression profile analyses of normal human vital organs allowed us to identify 104 cancer-specific genes, including those involved in breast carcinogenesis such as NEK2, PBK and MELK. Moreover, gene annotation enrichment analysis revealed prominent gene subsets involved in the cell cycle, especially mitosis. Therefore, we focused on cell cycle regulators, asp (abnormal spindle) homolog, microcephaly-associated (Drosophila) (ASPM) and centromere protein K (CENPK) as novel therapeutic targets for TNBC. Small-interfering RNA-mediated knockdown of their expression significantly attenuated TNBC cell viability due to G1 and G2/M cell cycle arrest. Our data will provide a better understanding of the carcinogenesis of TNBC and could contribute to the development of molecular targets as a treatment for TNBC patients.

PurposeTo identify stage I lung adenocarcinoma patients with a poor prognosis who will benefit from adjuvant therapy.Patients and MethodsWhole gene expression profiles were obtained at 19 time points over a 48-hour time course from human primary lung epithelial cells that were stimulated with epidermal growth factor (EGF) in the presence or absence of a clinically used EGF receptor tyrosine kinase (RTK)-specific inhibitor, gefitinib. The data were subjected to a mathematical simulation using the State Space Model (SSM). “Gefitinib-sensitive” genes, the expressional dynamics of which were altered by addition of gefitinib, were identified. A risk scoring model was constructed to classify high- or low-risk patients based on expression signatures of 139 gefitinib-sensitive genes in lung cancer using a training data set of 253 lung adenocarcinomas of North American cohort. The predictive ability of the risk scoring model was examined in independent cohorts of surgical specimens of lung cancer.ResultsThe risk scoring model enabled the identification of high-risk stage IA and IB cases in another North American cohort for overall survival (OS) with a hazard ratio (HR) of 7.16 (P = 0.029) and 3.26 (P = 0.0072), respectively. It also enabled the identification of high-risk stage I cases without bronchioalveolar carcinoma (BAC) histology in a Japanese cohort for OS and recurrence-free survival (RFS) with HRs of 8.79 (P = 0.001) and 3.72 (P = 0.0049), respectively.ConclusionThe set of 139 gefitinib-sensitive genes includes many genes known to be involved in biological aspects of cancer phenotypes, but not known to be involved in EGF signaling. The present result strongly re-emphasizes that EGF signaling status in cancer cells underlies an aggressive phenotype of cancer cells, which is useful for the selection of early-stage lung adenocarcinoma patients with a poor prognosis.Trial RegistrationThe Gene Expression Omnibus (GEO) GSE31210

Using a high-end mass spectrometry, we screened phosphoproteins and phosphopeptides in four types of Alzheimer's disease (AD) mouse models and human AD postmortem brains. We identified commonly changed phosphoproteins in multiple models and also determined phosphoproteins related to initiation of amyloid beta (Aβ) deposition in the mouse brain. After confirming these proteins were also changed in and human AD brains, we put the proteins on experimentally verified protein-protein interaction databases. Surprisingly, most of the core phosphoproteins were directly connected, and they formed a functional network linked to synaptic spine formation. The change of the core network started at a preclinical stage even before histological Aβ deposition. Systems biology analyses suggested that phosphorylation of myristoylated alanine-rich C-kinase substrate (MARCKS) by overactivated kinases including protein kinases C and calmodulin-dependent kinases initiates synapse pathology. Two-photon microscopic observation revealed recovery of abnormal spine formation in the AD model mice by targeting a core protein MARCKS or by inhibiting candidate kinases, supporting our hypothesis formulated based on phosphoproteome analysis.

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Mutations in the progranulin (PGRN) gene cause a tau pathology-negative and TDP43 pathology-positive form of frontotemporal lobar degeneration (FTLD-TDP). We generated a knock-in mouse harboring the R504X mutation (PGRN-KI). Phosphoproteomic analysis of this model revealed activation of signaling pathways connecting PKC and MAPK to tau prior to TDP43 aggregation and cognitive impairments, and identified PKCα as the kinase responsible for the early-stage tau phosphorylation at Ser203. Disinhibition of Gas6 binding to Tyro3 due to PGRN reduction results in activation of PKCα via PLCγ, inducing tau phosphorylation at Ser203, mislocalization of tau to dendritic spines, and spine loss. Administration of a PKC inhibitor, B-Raf inhibitor, or knockdown of molecules in the Gas6-Tyro3-tau axis rescues spine loss and cognitive impairment of PGRN-KI mice. Collectively, these results suggest that targeting of early-stage and aggregation-independent tau signaling represents a promising therapeutic strategy for this disease.

OGT may prevent IND-induced enteropathy by decreasing ADA which results in the elevation of adenosine. Modulation of the adenosine system may be an efficient therapeutic strategy for NSAID-induced enteropathy.

Background: Comprehensive description of the behavior of cellular components in a quantitative manner is essential for systematic understanding of biological events. Recent LC-MS/MS (tandem mass spectrometry coupled with liquid chromatography) technology, in combination with the SILAC (Stable Isotope Labeling by Amino acids in Cell culture) method, has enabled us to make relative quantitation at the proteome level. The recent report by Blagoev et al. (Nat. Biotechnol., 22, 1139-1145 indicated that this method was also applicable for the time-course analysis of cellular signaling events. Relative quatitation can easily be performed by calculating the ratio of peak intensities corresponding to differentially labeled peptides in the MS spectrum. As currently available software requires some GUI applications and is time-consuming, it is not suitable for processing largescale proteome data. Results:To resolve this difficulty, we developed an algorithm that automatically detects the peaks in each spectrum. Using this algorithm, we developed a software tool named AYUMS that automatically identifies the peaks corresponding to differentially labeled peptides, compares these peaks, calculates each of the peak ratios in mixed samples, and integrates them into one data sheet. This software has enabled us to dramatically save time for generation of the final report.Conclusion: AYUMS is a useful software tool for comprehensive quantitation of the proteome data generated by LC-MS/MS analysis. This software was developed using Java and runs on Linux, Windows, and Mac OS X. Please contact ayums@ims.u-tokyo.ac.jp if you are interested in the application. The project web page is http://www.csml.org/ayums/.