Proteogenomic integration reveals therapeutic targets in breast cancer xenografts

Kuan-lin Huang, Shunqiang Li, Philipp Mertins, Song Cao, Harsha P. Gunawardena, Kelly V. Ruggles, D. R. Mani, Karl R. Clauser, Maki Tanioka, Jerry Usary, Shyam M. Kavuri, Ling Xie, Christopher Yoon, Jana W Qiao, John Wrobel, Matthew A. Wyczalkowski, Petra Erdmann-Gilmore, Jacqueline E. Snider, Jeremy Hoog, Purba Singh, Beifang Niu, Zhanfang Guo, Sam Qiancheng Sun, Souzan Sanati, Emily Kawaler, Xuya Wang, Adam Scott, Kai Ye, Michael D. McLellan, Michael C. Wendl, Anna Malovannaya, Jason M. Held, Michael A. Gillette, David Fenyö, Christopher R. Kinsinger, Mehdi Mesri, Henry Rodriguez, Sherri R. Davies, Charles M. Perou, Cynthia Ma, R. Reid Townsend, Xian Chen, Steven A. Carr (), Matthew J. Ellis () and Li Ding ()
Additional contact information
Kuan-lin Huang: Washington University in St. Louis
Shunqiang Li: Washington University in St. Louis
Philipp Mertins: The Broad Institute of MIT and Harvard
Song Cao: McDonnell Genome Institute, Washington University in St. Louis
Harsha P. Gunawardena: University of North Carolina
Kelly V. Ruggles: Center for Health Informatics and Bioinformatics, New York University School of Medicine
D. R. Mani: The Broad Institute of MIT and Harvard
Karl R. Clauser: The Broad Institute of MIT and Harvard
Maki Tanioka: Lineberger Comprehensive Cancer Center, University of North Carolina
Jerry Usary: Lineberger Comprehensive Cancer Center, University of North Carolina
Shyam M. Kavuri: Lester and Sue Smith Breast Center, Baylor College of Medicine
Ling Xie: University of North Carolina
Christopher Yoon: Washington University in St. Louis
Jana W Qiao: The Broad Institute of MIT and Harvard
John Wrobel: University of North Carolina
Matthew A. Wyczalkowski: McDonnell Genome Institute, Washington University in St. Louis
Petra Erdmann-Gilmore: Washington University in St. Louis
Jacqueline E. Snider: Washington University in St. Louis
Jeremy Hoog: Washington University in St. Louis
Purba Singh: Lester and Sue Smith Breast Center, Baylor College of Medicine
Beifang Niu: McDonnell Genome Institute, Washington University in St. Louis
Zhanfang Guo: Washington University in St. Louis
Sam Qiancheng Sun: Washington University in St. Louis
Souzan Sanati: Washington University in St. Louis
Emily Kawaler: Center for Health Informatics and Bioinformatics, New York University School of Medicine
Xuya Wang: Center for Health Informatics and Bioinformatics, New York University School of Medicine
Adam Scott: McDonnell Genome Institute, Washington University in St. Louis
Kai Ye: McDonnell Genome Institute, Washington University in St. Louis
Michael D. McLellan: McDonnell Genome Institute, Washington University in St. Louis
Michael C. Wendl: McDonnell Genome Institute, Washington University in St. Louis
Anna Malovannaya: Lester and Sue Smith Breast Center, Baylor College of Medicine
Jason M. Held: Washington University in St. Louis
Michael A. Gillette: The Broad Institute of MIT and Harvard
David Fenyö: Center for Health Informatics and Bioinformatics, New York University School of Medicine
Christopher R. Kinsinger: National Cancer Institute, National Institutes of Health
Mehdi Mesri: National Cancer Institute, National Institutes of Health
Henry Rodriguez: National Cancer Institute, National Institutes of Health
Sherri R. Davies: Washington University in St. Louis
Charles M. Perou: Lineberger Comprehensive Cancer Center, University of North Carolina
Cynthia Ma: Washington University in St. Louis
R. Reid Townsend: Washington University in St. Louis
Xian Chen: University of North Carolina
Steven A. Carr: The Broad Institute of MIT and Harvard
Matthew J. Ellis: Lester and Sue Smith Breast Center, Baylor College of Medicine
Li Ding: Washington University in St. Louis

Nature Communications, 2017, vol. 8, issue 1, 1-17

Abstract: Abstract Recent advances in mass spectrometry (MS) have enabled extensive analysis of cancer proteomes. Here, we employed quantitative proteomics to profile protein expression across 24 breast cancer patient-derived xenograft (PDX) models. Integrated proteogenomic analysis shows positive correlation between expression measurements from transcriptomic and proteomic analyses; further, gene expression-based intrinsic subtypes are largely re-capitulated using non-stromal protein markers. Proteogenomic analysis also validates a number of predicted genomic targets in multiple receptor tyrosine kinases. However, several protein/phosphoprotein events such as overexpression of AKT proteins and ARAF, BRAF, HSP90AB1 phosphosites are not readily explainable by genomic analysis, suggesting that druggable translational and/or post-translational regulatory events may be uniquely diagnosed by MS. Drug treatment experiments targeting HER2 and components of the PI3K pathway supported proteogenomic response predictions in seven xenograft models. Our study demonstrates that MS-based proteomics can identify therapeutic targets and highlights the potential of PDX drug response evaluation to annotate MS-based pathway activities.

Date: 2017
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DOI: 10.1038/ncomms14864

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