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Numrering	Referens	Omslagsbild	Hitta
1.	Olsson, Niclas, et al. (författare) Grading breast cancer tissues using molecular portraits. 2013 Ingår i: Molecular & Cellular Proteomics. - 1535-9484. ; 12:12, s. 3612-3623 Tidskriftsartikel (refereegranskat)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.

Olsson, Niclas, et al. (författare)
Grading breast cancer tissues using molecular portraits.
2013
Ingår i: Molecular & Cellular Proteomics. - 1535-9484. ; 12:12, s. 3612-3623
Tidskriftsartikel (refereegranskat)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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