Literature DB >> 18199530

Revealing targeted therapy for human cancer by gene module maps.

David J Wong1, Dimitry S A Nuyten, Aviv Regev, Meihong Lin, Adam S Adler, Eran Segal, Marc J van de Vijver, Howard Y Chang.   

Abstract

A major goal of cancer research is to match specific therapies to molecular targets in cancer. Genome-scale expression profiling has identified new subtypes of cancer based on consistent patterns of variation in gene expression, leading to improved prognostic predictions. However, how these new genetic subtypes of cancers should be treated is unknown. Here, we show that a gene module map can guide the prospective identification of targeted therapies for genetic subtypes of cancer. By visualizing genome-scale gene expression in cancer as combinations of activated and deactivated functional modules, gene module maps can reveal specific functional pathways associated with each subtype that might be susceptible to targeted therapies. We show that in human breast cancers, activation of a poor-prognosis "wound signature" is strongly associated with induction of both a mitochondria gene module and a proteasome gene module. We found that 3-bromopyruvic acid, which inhibits glycolysis, selectively killed breast cells expressing the mitochondria and wound signatures. In addition, inhibition of proteasome activity by bortezomib, a drug approved for human use in multiple myeloma, abrogated wound signature expression and selectively killed breast cells expressing the wound signature. Thus, gene module maps may enable rapid translation of complex genomic signatures in human disease to targeted therapeutic strategies.

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Year:  2008        PMID: 18199530     DOI: 10.1158/0008-5472.CAN-07-0382

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  30 in total

1.  Module-based prediction approach for robust inter-study predictions in microarray data.

Authors:  Zhibao Mi; Kui Shen; Nan Song; Chunrong Cheng; Chi Song; Naftali Kaminski; George C Tseng
Journal:  Bioinformatics       Date:  2010-08-17       Impact factor: 6.937

2.  Exploring the within- and between-class correlation distributions for tumor classification.

Authors:  Xuelian Wei; Ker-Chau Li
Journal:  Proc Natl Acad Sci U S A       Date:  2010-03-25       Impact factor: 11.205

Review 3.  Integration and analysis of genome-scale data from gliomas.

Authors:  Gregory Riddick; Howard A Fine
Journal:  Nat Rev Neurol       Date:  2011-07-05       Impact factor: 42.937

4.  Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations.

Authors:  Songjian Lu; Chunhui Cai; Gonghong Yan; Zhuan Zhou; Yong Wan; Vicky Chen; Lujia Chen; Gregory F Cooper; Lina M Obeid; Yusuf A Hannun; Adrian V Lee; Xinghua Lu
Journal:  Cancer Res       Date:  2016-10-10       Impact factor: 12.701

5.  Algorithms for effective querying of compound graph-based pathway databases.

Authors:  Ugur Dogrusoz; Ahmet Cetintas; Emek Demir; Ozgun Babur
Journal:  BMC Bioinformatics       Date:  2009-11-16       Impact factor: 3.169

6.  Gene expression patterns in mismatch repair-deficient colorectal cancers highlight the potential therapeutic role of inhibitors of the phosphatidylinositol 3-kinase-AKT-mammalian target of rapamycin pathway.

Authors:  Eduardo Vilar; Bhramar Mukherjee; Rork Kuick; Leon Raskin; David E Misek; Jeremy M G Taylor; Thomas J Giordano; Samir M Hanash; Eric R Fearon; Gad Rennert; Stephen B Gruber
Journal:  Clin Cancer Res       Date:  2009-04-07       Impact factor: 12.531

7.  Comparative expression pathway analysis of human and canine mammary tumors.

Authors:  Paolo Uva; Luigi Aurisicchio; James Watters; Andrey Loboda; Amit Kulkarni; John Castle; Fabio Palombo; Valentina Viti; Giuseppe Mesiti; Valentina Zappulli; Laura Marconato; Francesca Abramo; Gennaro Ciliberto; Armin Lahm; Nicola La Monica; Emanuele de Rinaldis
Journal:  BMC Genomics       Date:  2009-03-27       Impact factor: 3.969

8.  Detecting coordinated regulation of multi-protein complexes using logic analysis of gene expression.

Authors:  Einat Sprinzak; Shawn J Cokus; Todd O Yeates; David Eisenberg; Matteo Pellegrini
Journal:  BMC Syst Biol       Date:  2009-12-14

Review 9.  Cancer systems biology: a network modeling perspective.

Authors:  Pamela K Kreeger; Douglas A Lauffenburger
Journal:  Carcinogenesis       Date:  2009-10-27       Impact factor: 4.944

10.  Proteasome inhibition represses ERalpha gene expression in ER+ cells: a new link between proteasome activity and estrogen signaling in breast cancer.

Authors:  G L Powers; S J Ellison-Zelski; A J Casa; A V Lee; E T Alarid
Journal:  Oncogene       Date:  2009-11-30       Impact factor: 9.867

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