Grading breast cancer tissues using molecular portraits.

Research output: Contribution to journalArticle

Abstract

Tumor progression and prognosis of breast cancer patients is difficult to assess using current clinical and laboratory parameters, where a pathological grading is indicative of tumor aggressiveness. This grading is based on assessment of nuclear grade, tubule formation, and mitotic rate. We report here the first protein signatures associated with histological grades of breast cancer, using a novel affinity proteomics approach. We profiled 52 breast cancer tissue samples, by combining nine antibodies and label-free LC-MS/MS, which generated detailed quantified proteomic maps representing 1,388 proteins. The results showed that we could define in-depth molecular portraits of histologically graded breast cancer tumors. Consequently, a 49-plex candidate tissue protein signature was defined that discriminated between histological grade 1, 2, and 3 of breast cancer tumors with high accuracy. Highly biologically relevant proteins were identified, and the differentially expressed proteins indicated further support for the current hypothesis regarding remodeling of tumor microenvironment during tumor progression. The protein signature was corroborated using meta-analysis of transcriptional profiling data from an independent patient cohort. In addition, the potential for using the markers to estimate the risk of distant metastasis free survival was also indicated. Taken together, these molecular portraits could pave the way for improved classification and prognostication of breast cancer.

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Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Cancer and Oncology
  • Surgery
Original languageEnglish
Pages (from-to)3612-3623
JournalMolecular & Cellular Proteomics
Volume12
Issue number12
Publication statusPublished - 2013
Publication categoryResearch
Peer-reviewedYes

Related research output

Skoog, P., 2017 Jun 9, 1 ed. Lund: Department of Immunotechnology, Lund University. 74 p.

Research output: ThesisDoctoral Thesis (compilation)

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