Global proteomic characterization of microdissected estrogen receptor positive breast tumors
We here describe two proteomic datasets deposited in ProteomeXchange via PRIDE partner repository [1] with dataset identifiers PXD000484 (defined as “training”) and PXD000485 (defined as “test”) that have been used for the development of a tamoxifen outcome predictive signature [2]. Both datasets co...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2015-12-01
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| Series: | Data in Brief |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340915002280 |
| _version_ | 1818309929782476800 |
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| author | Tommaso De Marchi Ning Qing Liu Christoph Sting Marcel Smid Mila Tjoa René B.H. Braakman Theo M. Luider John A. Foekens John W.M. Martens Arzu Umar |
| author_facet | Tommaso De Marchi Ning Qing Liu Christoph Sting Marcel Smid Mila Tjoa René B.H. Braakman Theo M. Luider John A. Foekens John W.M. Martens Arzu Umar |
| author_sort | Tommaso De Marchi |
| collection | DOAJ |
| description | We here describe two proteomic datasets deposited in ProteomeXchange via PRIDE partner repository [1] with dataset identifiers PXD000484 (defined as “training”) and PXD000485 (defined as “test”) that have been used for the development of a tamoxifen outcome predictive signature [2]. Both datasets comprised 56 fresh frozen estrogen receptor (ER) positive primary breast tumor specimens derived from patients who received tamoxifen as first line therapy for recurrent disease. Patient groups were defined based on time to progression (TTP) after start of tamoxifen therapy (6 months cutoff): 32 good and 24 poor treatment outcome patients were comprised in the training set, respectively. The test set included 41 good and 15 poor treatment outcome patients. All specimens were subjected to laser capture microdissection (LCM) to enrich for epithelial tumor cells prior to high resolution mass spectrometric (MS) analysis. Protein identification and label-free quantification (LFQ) were performed with MaxQuant software package [3]. A total of 3109 and 4061 proteins were identified and quantified in the training and test set, respectively. We here present the first public proteomic dataset analyzing ER positive recurrent breast cancer by LCM coupled to high resolution MS. |
| first_indexed | 2024-12-13T07:37:59Z |
| format | Article |
| id | doaj.art-c2865377921c49f1b603beb2360ca2ec |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-12-13T07:37:59Z |
| publishDate | 2015-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-c2865377921c49f1b603beb2360ca2ec2022-12-21T23:55:02ZengElsevierData in Brief2352-34092015-12-015C39940210.1016/j.dib.2015.09.034Global proteomic characterization of microdissected estrogen receptor positive breast tumorsTommaso De Marchi0Ning Qing Liu1Christoph Sting2Marcel Smid3Mila Tjoa4René B.H. Braakman5Theo M. Luider6John A. Foekens7John W.M. Martens8Arzu Umar9Department of Medical Oncology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The NetherlandsDepartment of Medical Oncology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The NetherlandsDepartment of Neurology, Erasmus MC, University Medical Center, Rotterdam, The NetherlandsDepartment of Medical Oncology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The NetherlandsDepartment of Medical Oncology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The NetherlandsDepartment of Medical Oncology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The NetherlandsDepartment of Neurology, Erasmus MC, University Medical Center, Rotterdam, The NetherlandsDepartment of Medical Oncology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The NetherlandsDepartment of Medical Oncology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The NetherlandsDepartment of Medical Oncology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The NetherlandsWe here describe two proteomic datasets deposited in ProteomeXchange via PRIDE partner repository [1] with dataset identifiers PXD000484 (defined as “training”) and PXD000485 (defined as “test”) that have been used for the development of a tamoxifen outcome predictive signature [2]. Both datasets comprised 56 fresh frozen estrogen receptor (ER) positive primary breast tumor specimens derived from patients who received tamoxifen as first line therapy for recurrent disease. Patient groups were defined based on time to progression (TTP) after start of tamoxifen therapy (6 months cutoff): 32 good and 24 poor treatment outcome patients were comprised in the training set, respectively. The test set included 41 good and 15 poor treatment outcome patients. All specimens were subjected to laser capture microdissection (LCM) to enrich for epithelial tumor cells prior to high resolution mass spectrometric (MS) analysis. Protein identification and label-free quantification (LFQ) were performed with MaxQuant software package [3]. A total of 3109 and 4061 proteins were identified and quantified in the training and test set, respectively. We here present the first public proteomic dataset analyzing ER positive recurrent breast cancer by LCM coupled to high resolution MS.http://www.sciencedirect.com/science/article/pii/S2352340915002280 |
| spellingShingle | Tommaso De Marchi Ning Qing Liu Christoph Sting Marcel Smid Mila Tjoa René B.H. Braakman Theo M. Luider John A. Foekens John W.M. Martens Arzu Umar Global proteomic characterization of microdissected estrogen receptor positive breast tumors Data in Brief |
| title | Global proteomic characterization of microdissected estrogen receptor positive breast tumors |
| title_full | Global proteomic characterization of microdissected estrogen receptor positive breast tumors |
| title_fullStr | Global proteomic characterization of microdissected estrogen receptor positive breast tumors |
| title_full_unstemmed | Global proteomic characterization of microdissected estrogen receptor positive breast tumors |
| title_short | Global proteomic characterization of microdissected estrogen receptor positive breast tumors |
| title_sort | global proteomic characterization of microdissected estrogen receptor positive breast tumors |
| url | http://www.sciencedirect.com/science/article/pii/S2352340915002280 |
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