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 SinghBeifung 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, Li Ding

Research output: Contribution to journalArticlepeer-review

102 Scopus citations


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.

Original languageEnglish
Article number14864
JournalNature communications
StatePublished - Mar 28 2017


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