Literature DB >> 21431104

Quantitative proteomic analysis in breast cancer.

A Tabchy1, B T Hennessy, A M Gonzalez-Angulo, F M Bernstam, Y Lu, G B Mills.   

Abstract

Much progress has recently been made in the genomic and transcriptional characterization of tumors. However, historically the characterization of cells at the protein level has suffered limitations in reproducibility, scalability and robustness. Recent technological advances have made it possible to accurately and reproducibly portray the global levels and active states of cellular proteins. Protein microarrays examine the native post-translational conformations of proteins including activated phosphorylated states, in a comprehensive high-throughput mode, and can map activated pathways and networks of proteins inside the cells. The reverse-phase protein microarray (RPPA) offers a unique opportunity to study signal transduction networks in small biological samples such as human biopsy material and can provide critical information for therapeutic decision-making and the monitoring of patients for targeted molecular medicine. By providing the key missing link to the story generated from genomic and gene expression characterization efforts, functional proteomics offer the promise of a comprehensive understanding of cancer. Several initial successes in breast cancer are showing that such information is clinically relevant. Copyright 2011 Prous Science, S.A.U. or its licensors. All rights reserved.

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Year:  2011        PMID: 21431104     DOI: 10.1358/dot.2011.47.2.1576695

Source DB:  PubMed          Journal:  Drugs Today (Barc)        ISSN: 1699-3993            Impact factor:   2.245


  10 in total

1.  Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts.

Authors:  Shunqiang Li; Dong Shen; Jieya Shao; Robert Crowder; Wenbin Liu; Aleix Prat; Xiaping He; Shuying Liu; Jeremy Hoog; Charles Lu; Li Ding; Obi L Griffith; Christopher Miller; Dave Larson; Robert S Fulton; Michelle Harrison; Tom Mooney; Joshua F McMichael; Jingqin Luo; Yu Tao; Rodrigo Goncalves; Christopher Schlosberg; Jeffrey F Hiken; Laila Saied; Cesar Sanchez; Therese Giuntoli; Caroline Bumb; Crystal Cooper; Robert T Kitchens; Austin Lin; Chanpheng Phommaly; Sherri R Davies; Jin Zhang; Megha Shyam Kavuri; Donna McEachern; Yi Yu Dong; Cynthia Ma; Timothy Pluard; Michael Naughton; Ron Bose; Rama Suresh; Reida McDowell; Loren Michel; Rebecca Aft; William Gillanders; Katherine DeSchryver; Richard K Wilson; Shaomeng Wang; Gordon B Mills; Ana Gonzalez-Angulo; John R Edwards; Christopher Maher; Charles M Perou; Elaine R Mardis; Matthew J Ellis
Journal:  Cell Rep       Date:  2013-09-19       Impact factor: 9.423

2.  HSP70 Inhibition Limits FAK-Dependent Invasion and Enhances the Response to Melanoma Treatment with BRAF Inhibitors.

Authors:  Anna Budina-Kolomets; Marie R Webster; Julia I-Ju Leu; Matthew Jennis; Clemens Krepler; Anastasia Guerrini; Andrew V Kossenkov; Wei Xu; Giorgos Karakousis; Lynn Schuchter; Ravi K Amaravadi; Hong Wu; Xiangfan Yin; Qin Liu; Yiling Lu; Gordon B Mills; Xiaowei Xu; Donna L George; Ashani T Weeraratna; Maureen E Murphy
Journal:  Cancer Res       Date:  2016-03-16       Impact factor: 12.701

3.  α-Tubulin acetylation elevated in metastatic and basal-like breast cancer cells promotes microtentacle formation, adhesion, and invasive migration.

Authors:  Amanda E Boggs; Michele I Vitolo; Rebecca A Whipple; Monica S Charpentier; Olga G Goloubeva; Olga B Ioffe; Kimberly C Tuttle; Jana Slovic; Yiling Lu; Gordon B Mills; Stuart S Martin
Journal:  Cancer Res       Date:  2014-12-12       Impact factor: 12.701

Review 4.  Using reverse-phase protein arrays as pharmacodynamic assays for functional proteomics, biomarker discovery, and drug development in cancer.

Authors:  Yiling Lu; Shiyun Ling; Apurva M Hegde; Lauren A Byers; Kevin Coombes; Gordon B Mills; Rehan Akbani
Journal:  Semin Oncol       Date:  2016-06-15       Impact factor: 4.929

5.  Protein Markers Predict Survival in Glioma Patients.

Authors:  Lindsay C Stetson; Jean-Eudes Dazard; Jill S Barnholtz-Sloan
Journal:  Mol Cell Proteomics       Date:  2016-05-03       Impact factor: 5.911

6.  BAYESIAN SPARSE GRAPHICAL MODELS FOR CLASSIFICATION WITH APPLICATION TO PROTEIN EXPRESSION DATA.

Authors:  Veerabhadran Baladandayuthapani; Rajesh Talluri; Yuan Ji; Kevin R Coombes; Yiling Lu; Bryan T Hennessy; Michael A Davies; Bani K Mallick
Journal:  Ann Appl Stat       Date:  2014       Impact factor: 2.083

7.  Predicting time to ovarian carcinoma recurrence using protein markers.

Authors:  Ji-Yeon Yang; Kosuke Yoshihara; Kenichi Tanaka; Masayuki Hatae; Hideaki Masuzaki; Hiroaki Itamochi; Masashi Takano; Kimio Ushijima; Janos L Tanyi; George Coukos; Yiling Lu; Gordon B Mills; Roel G W Verhaak
Journal:  J Clin Invest       Date:  2013-08-15       Impact factor: 14.808

Review 8.  New concepts in breast cancer genomics and genetics.

Authors:  Rodrigo Goncalves; Wayne A Warner; Jingqin Luo; Matthew J Ellis
Journal:  Breast Cancer Res       Date:  2014       Impact factor: 6.466

9.  Predicting high-risk endometrioid carcinomas using proteins.

Authors:  Di Du; Wencai Ma; Melinda S Yates; Tao Chen; Karen H Lu; Yiling Lu; John N Weinstein; Russell R Broaddus; Gordon B Mills; Yuexin Liu
Journal:  Oncotarget       Date:  2018-04-13

Review 10.  Integrated Approaches for the Use of Large Datasets to Identify Rational Therapies for the Treatment of Lung Cancers.

Authors:  Robert J Cardnell; Lauren Averett Byers; Jing Wang
Journal:  Cancers (Basel)       Date:  2019-02-19       Impact factor: 6.639

  10 in total

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