Browsing by Author "Taguchi, Ayumu"

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    Allele-Specific Reprogramming of Cancer Metabolism by the Long Non-coding RNA CCAT2
    (Elsevier, 2016-02-18) Redis, Roxana S.; Vela, Luz E.; Lu, Weiqin; de Oliveira, Juliana Ferreira; Ivan, Cristina; Rodriguez-Aguayo, Cristian; Adamoski, Douglas; Pasculli, Barbara; Taguchi, Ayumu; Chen, Yunyun; Fernandez, Agustin F.; Valledor, Luis; Van Roosbroeck, Katrien; Chang, Samuel; Shah, Maitri; Kinnebrew, Garrett; Han, Leng; Atlasi, Yaser; Cheung, Lawrence H.; Huang, Gilbert Yuanjay; Monroig, Paloma; Ramirez, Marc S.; Ivkovic, Tina Catela; Van, Long; Ling, Hui; Gafà, Roberta; Kapitanovic, Sanja; Lanza, Giovanni; Bankson, James A.; Huang, Peng; Lai, Stephan Y.; Bast, Robert C.; Rosenblum, Michael G.; Radovich, Milan; Ivan, Mircea; Bartholomeusz, Geoffrey; Liang, Han; Fraga, Mario F.; Widger, William R.; Hanash, Samir; Berindan-Neagoe, Ioana; Lopez-Berestein, Gabriel; Ambrosio, Andre L.B.; Dias, Sandra M Gomes; Calin, George A.; Department of Surgery, IU School of Medicine
    Altered energy metabolism is a cancer hallmark as malignant cells tailor their metabolic pathways to meet their energy requirements. Glucose and glutamine are the major nutrients that fuel cellular metabolism, and the pathways utilizing these nutrients are often altered in cancer. Here, we show that the long ncRNA CCAT2, located at the 8q24 amplicon on cancer risk-associated rs6983267 SNP, regulates cancer metabolism in vitro and in vivo in an allele-specific manner by binding the Cleavage Factor I (CFIm) complex with distinct affinities for the two subunits (CFIm25 and CFIm68). The CCAT2 interaction with the CFIm complex fine-tunes the alternative splicing of Glutaminase (GLS) by selecting the poly(A) site in intron 14 of the precursor mRNA. These findings uncover a complex, allele-specific regulatory mechanism of cancer metabolism orchestrated by the two alleles of a long ncRNA.
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    A Plasma-Derived Protein-Metabolite Multiplexed Panel for Early-Stage Pancreatic Cancer
    (Oxford University Press, 2019-04-01) Fahrmann, Johannes F.; Bantis, Leonidas E.; Capello, Michela; Scelo, Ghislaine; Dennison, Jennifer B.; Patel, Nikul; Murage, Eunice; Vykoukal, Jody; Kundnani, Deepali L.; Foretova, Lenka; Fabianova, Eleonora; Holcatova, Ivana; Janout, Vladimir; Feng, Ziding; Yip-Schneider, Michele; Zhang, Jianjun; Brand, Randall; Taguchi, Ayumu; Maitra, Anirban; Brennan, Paul; Max Schmidt, C.; Hanash, Samir; Surgery, School of Medicine
    BACKGROUND: We applied a training and testing approach to develop and validate a plasma metabolite panel for the detection of early-stage pancreatic ductal adenocarcinoma (PDAC) alone and in combination with a previously validated protein panel for early-stage PDAC. METHODS: A comprehensive metabolomics platform was initially applied to plasmas collected from 20 PDAC cases and 80 controls. Candidate markers were filtered based on a second independent cohort that included nine invasive intraductal papillary mucinous neoplasm cases and 51 benign pancreatic cysts. Blinded validation of the resulting metabolite panel was performed in an independent test cohort consisting of 39 resectable PDAC cases and 82 matched healthy controls. The additive value of combining the metabolite panel with a previously validated protein panel was evaluated. RESULTS: Five metabolites (acetylspermidine, diacetylspermine, an indole-derivative, and two lysophosphatidylcholines) were selected as a panel based on filtering criteria. A combination rule was developed for distinguishing between PDAC and healthy controls using the Training Set. In the blinded validation study with early-stage PDAC samples and controls, the five metabolites yielded areas under the curve (AUCs) ranging from 0.726 to 0.842, and the combined metabolite model yielded an AUC of 0.892 (95% confidence interval [CI] = 0.828 to 0.956). Performance was further statistically significantly improved by combining the metabolite panel with a previously validated protein marker panel consisting of CA 19-9, LRG1, and TIMP1 (AUC = 0.924, 95% CI = 0.864 to 0.983, comparison DeLong test one-sided P= .02). CONCLUSIONS: A metabolite panel in combination with CA19-9, TIMP1, and LRG1 exhibited substantially improved performance in the detection of early-stage PDAC compared with a protein panel alone.