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Proteogenomic integration reveals therapeutic targets in breast cancer xenografts

Author

Listed:
  • Kuan-lin Huang

    (Washington University in St. Louis
    McDonnell Genome Institute, 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
    McDonnell Genome Institute, 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
    McDonnell Genome Institute, 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
    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
    Washington University in St. Louis
    Washington University in St. Louis)

  • Anna Malovannaya

    (Lester and Sue Smith Breast Center, Baylor College of Medicine
    Baylor College of Medicine)

  • Jason M. Held

    (Washington University in St. Louis
    Siteman Cancer Center, Washington University in St. Louis
    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
    Siteman Cancer Center, Washington University in St. Louis)

  • R. Reid Townsend

    (Washington University in St. Louis
    Siteman Cancer Center, 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
    McDonnell Genome Institute, Washington University in St. Louis
    Washington University in St. Louis
    Siteman Cancer Center, Washington University in St. Louis)

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.

Suggested Citation

  • 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 & Christo, 2017. "Proteogenomic integration reveals therapeutic targets in breast cancer xenografts," Nature Communications, Nature, vol. 8(1), pages 1-17, April.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms14864
    DOI: 10.1038/ncomms14864
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    Cited by:

    1. Karama Asleh & Gian Luca Negri & Sandra E. Spencer Miko & Shane Colborne & Christopher S. Hughes & Xiu Q. Wang & Dongxia Gao & C. Blake Gilks & Stephen K. L. Chia & Torsten O. Nielsen & Gregg B. Morin, 2022. "Proteomic analysis of archival breast cancer clinical specimens identifies biological subtypes with distinct survival outcomes," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    2. Zaili Luo & Dazhuan Xin & Yunfei Liao & Kalen Berry & Sean Ogurek & Feng Zhang & Liguo Zhang & Chuntao Zhao & Rohit Rao & Xinran Dong & Hao Li & Jianzhong Yu & Yifeng Lin & Guoying Huang & Lingli Xu &, 2023. "Loss of phosphatase CTDNEP1 potentiates aggressive medulloblastoma by triggering MYC amplification and genomic instability," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    3. Sam Crowl & Ben T. Jordan & Hamza Ahmed & Cynthia X. Ma & Kristen M. Naegle, 2022. "KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    4. S. Mouron & M. J. Bueno & A. Lluch & L. Manso & I. Calvo & J. Cortes & J. A. Garcia-Saenz & M. Gil-Gil & N. Martinez-Janez & J. V. Apala & E. Caleiras & Pilar Ximénez-Embún & J. Muñoz & L. Gonzalez-Co, 2022. "Phosphoproteomic analysis of neoadjuvant breast cancer suggests that increased sensitivity to paclitaxel is driven by CDK4 and filamin A," Nature Communications, Nature, vol. 13(1), pages 1-18, December.

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