Proteomic maps of breast cancer subtypes

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Proteomic maps of breast cancer subtypes. / Tyanova, Stefka; Albrechtsen, Reidar; Kronqvist, Pauliina; Cox, Juergen; Mann, Matthias; Geiger, Tamar.

In: Nature Communications, Vol. 7, 10259, 04.01.2016.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Tyanova, S, Albrechtsen, R, Kronqvist, P, Cox, J, Mann, M & Geiger, T 2016, 'Proteomic maps of breast cancer subtypes', Nature Communications, vol. 7, 10259. https://doi.org/10.1038/ncomms10259

APA

Tyanova, S., Albrechtsen, R., Kronqvist, P., Cox, J., Mann, M., & Geiger, T. (2016). Proteomic maps of breast cancer subtypes. Nature Communications, 7, [10259]. https://doi.org/10.1038/ncomms10259

Vancouver

Tyanova S, Albrechtsen R, Kronqvist P, Cox J, Mann M, Geiger T. Proteomic maps of breast cancer subtypes. Nature Communications. 2016 Jan 4;7. 10259. https://doi.org/10.1038/ncomms10259

Author

Tyanova, Stefka ; Albrechtsen, Reidar ; Kronqvist, Pauliina ; Cox, Juergen ; Mann, Matthias ; Geiger, Tamar. / Proteomic maps of breast cancer subtypes. In: Nature Communications. 2016 ; Vol. 7.

Bibtex

@article{6fa88a7b28714d319056b840bd4e3784,
title = "Proteomic maps of breast cancer subtypes",
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.",
keywords = "Breast Neoplasms, Female, Gene Expression Regulation, Neoplastic, Humans, Proteomics, Transcriptome",
author = "Stefka Tyanova and Reidar Albrechtsen and Pauliina Kronqvist and Juergen Cox and Matthias Mann and Tamar Geiger",
year = "2016",
month = jan,
day = "4",
doi = "10.1038/ncomms10259",
language = "English",
volume = "7",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - Proteomic maps of breast cancer subtypes

AU - Tyanova, Stefka

AU - Albrechtsen, Reidar

AU - Kronqvist, Pauliina

AU - Cox, Juergen

AU - Mann, Matthias

AU - Geiger, Tamar

PY - 2016/1/4

Y1 - 2016/1/4

N2 - 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.

AB - 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.

KW - Breast Neoplasms

KW - Female

KW - Gene Expression Regulation, Neoplastic

KW - Humans

KW - Proteomics

KW - Transcriptome

U2 - 10.1038/ncomms10259

DO - 10.1038/ncomms10259

M3 - Journal article

C2 - 26725330

VL - 7

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 10259

ER -

ID: 167805945