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Gillette, Michael

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Gillette

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Michael

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Gillette, Michael

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2016 - 20172

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Author's Publications: search.filters.isAuthorOfPublication.0899ba60-9fa6-4780-a747-ec3baf1d263a

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    Proteogenomics connects somatic mutations to signaling in breast cancer
    (2016) Mertins, Philipp; Mani, D. R.; Ruggles, Kelly V.; Gillette, Michael; Clauser, Karl R.; Wang, Pei; Wang, Xianlong; Qiao, Jana W.; Cao, Song; Petralia, Francesca; Kawaler, Emily; Mundt, Filip; Krug, Karsten; Tu, Zhidong; Lei, Jonathan T.; Gatza, Michael L.; Wilkerson, Matthew; Perou, Charles M.; Yellapantula, Venkata; Huang, Kuan-lin; Lin, Chenwei; McLellan, Michael D.; Yan, Ping; Davies, Sherri R.; Townsend, R. Reid; Skates, Steven; Wang, Jing; Zhang, Bing; Kinsinger, Christopher R.; Mesri, Mehdi; Rodriguez, Henry; Ding, Li; Paulovich, Amanda G.; Fenyo, David; Ellis, Matthew J.; Carr, Steven A.
    Summary Somatic mutations have been extensively characterized in breast cancer, but the effects of these genetic alterations on the proteomic landscape remain poorly understood. We describe quantitative mass spectrometry-based proteomic and phosphoproteomic analyses of 105 genomically annotated breast cancers of which 77 provided high-quality data. Integrated analyses allowed insights into the somatic cancer genome including the consequences of chromosomal loss, such as the 5q deletion characteristic of basal-like breast cancer. The 5q trans effects were interrogated against the Library of Integrated Network-based Cellular Signatures, thereby connecting CETN3 and SKP1 loss to elevated expression of EGFR, and SKP1 loss also to increased SRC. Global proteomic data confirmed a stromal-enriched group in addition to basal and luminal clusters and pathway analysis of the phosphoproteome identified a G Protein-coupled receptor cluster that was not readily identified at the mRNA level. Besides ERBB2, other amplicon-associated, highly phosphorylated kinases were identified, including CDK12, PAK1, PTK2, RIPK2 and TLK2. We demonstrate that proteogenomic analysis of breast cancer elucidates functional consequences of somatic mutations, narrows candidate nominations for driver genes within large deletions and amplified regions, and identifies therapeutic targets.
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    Proteogenomic integration reveals therapeutic targets in breast cancer xenografts
    (Nature Publishing Group, 2017) Huang, Kuan-lin; Li, Shunqiang; Mertins, Philipp; Cao, Song; Gunawardena, Harsha P.; Ruggles, Kelly V.; Mani, D. R.; Clauser, Karl R.; Tanioka, Maki; Usary, Jerry; Kavuri, Shyam M.; Xie, Ling; Yoon, Christopher; Qiao, Jana W; Wrobel, John; Wyczalkowski, Matthew A.; Erdmann-Gilmore, Petra; Snider, Jacqueline E.; Hoog, Jeremy; Singh, Purba; Niu, Beifung; Guo, Zhanfang; Sun, Sam Qiancheng; Sanati, Souzan; Kawaler, Emily; Wang, Xuya; Scott, Adam; Ye, Kai; McLellan, Michael D.; Wendl, Michael C.; Malovannaya, Anna; Held, Jason M.; Gillette, Michael; Fenyö, David; Kinsinger, Christopher R.; Mesri, Mehdi; Rodriguez, Henry; Davies, Sherri R.; Perou, Charles M.; Ma, Cynthia; Reid Townsend, R.; Chen, Xian; Carr, Steven A.; Ellis, Matthew J.; Ding, Li
    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.