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Proteomic maps of breast cancer subtypes

Author

Listed:
  • Stefka Tyanova

    (Max Planck Institute of Biochemistry
    Computational Systems Biochemistry, Max Planck Institute of Biochemistry)

  • Reidar Albrechtsen

    (University of Copenhagen)

  • Pauliina Kronqvist

    (University of Turku)

  • Juergen Cox

    (Max Planck Institute of Biochemistry
    Computational Systems Biochemistry, Max Planck Institute of Biochemistry)

  • Matthias Mann

    (Max Planck Institute of Biochemistry)

  • Tamar Geiger

    (Max Planck Institute of Biochemistry
    Sackler Faculty of Medicine, Tel Aviv University)

Abstract

Systems-wide profiling of breast cancer has almost always entailed RNA and DNA analysis by microarray and sequencing techniques. Marked developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analysed 40 oestrogen receptor positive (luminal), Her2 positive and triple negative breast tumours and reached a quantitative depth of >10,000 proteins. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell–cell communication. Furthermore, we derived a signature of 19 proteins, which differ between the breast cancer subtypes, through support vector machine (SVM)-based classification and feature selection. Remarkably, only three proteins of the signature were associated with gene copy number variations and eleven were also reflected on the mRNA level. These breast cancer features revealed by our work provide novel insights that may ultimately translate to development of subtype-specific therapeutics.

Suggested Citation

  • Stefka Tyanova & Reidar Albrechtsen & Pauliina Kronqvist & Juergen Cox & Matthias Mann & Tamar Geiger, 2016. "Proteomic maps of breast cancer subtypes," Nature Communications, Nature, vol. 7(1), pages 1-11, April.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms10259
    DOI: 10.1038/ncomms10259
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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. Yuen Lam Dora Ng & Evelyn Ramberger & Stephan R. Bohl & Anna Dolnik & Christian Steinebach & Theresia Conrad & Sina Müller & Oliver Popp & Miriam Kull & Mohamed Haji & Michael Gütschow & Hartmut Döhne, 2022. "Proteomic profiling reveals CDK6 upregulation as a targetable resistance mechanism for lenalidomide in multiple myeloma," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Rui Chen & Jiasu Xu & Boqian Wang & Yi Ding & Aynur Abdulla & Yiyang Li & Lai Jiang & Xianting Ding, 2024. "SpiDe-Sr: blind super-resolution network for precise cell segmentation and clustering in spatial proteomics imaging," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

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