Proteomic profiling across breast cancer cell lines and models
Abstract We performed quantitative proteomics on 60 human-derived breast cancer cell line models to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and s...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02355-0 |
| _version_ | 1827635654188597248 |
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| author | Marian Kalocsay Matthew J. Berberich Robert A. Everley Maulik K. Nariya Mirra Chung Benjamin Gaudio Chiara Victor Gary A. Bradshaw Robyn J. Eisert Marc Hafner Peter K. Sorger Caitlin E. Mills Kartik Subramanian |
| author_facet | Marian Kalocsay Matthew J. Berberich Robert A. Everley Maulik K. Nariya Mirra Chung Benjamin Gaudio Chiara Victor Gary A. Bradshaw Robyn J. Eisert Marc Hafner Peter K. Sorger Caitlin E. Mills Kartik Subramanian |
| author_sort | Marian Kalocsay |
| collection | DOAJ |
| description | Abstract We performed quantitative proteomics on 60 human-derived breast cancer cell line models to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics. |
| first_indexed | 2024-03-09T15:30:36Z |
| format | Article |
| id | doaj.art-d36f69bd2a254d5687bc8e7f98e95fc1 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-09T15:30:36Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-d36f69bd2a254d5687bc8e7f98e95fc12023-11-26T12:17:50ZengNature PortfolioScientific Data2052-44632023-08-011011910.1038/s41597-023-02355-0Proteomic profiling across breast cancer cell lines and modelsMarian Kalocsay0Matthew J. Berberich1Robert A. Everley2Maulik K. Nariya3Mirra Chung4Benjamin Gaudio5Chiara Victor6Gary A. Bradshaw7Robyn J. Eisert8Marc Hafner9Peter K. Sorger10Caitlin E. Mills11Kartik Subramanian12Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical SchoolLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical SchoolLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical SchoolLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical SchoolLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical SchoolLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical SchoolLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical SchoolLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical SchoolLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical SchoolLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical SchoolLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical SchoolLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical SchoolLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical SchoolAbstract We performed quantitative proteomics on 60 human-derived breast cancer cell line models to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.https://doi.org/10.1038/s41597-023-02355-0 |
| spellingShingle | Marian Kalocsay Matthew J. Berberich Robert A. Everley Maulik K. Nariya Mirra Chung Benjamin Gaudio Chiara Victor Gary A. Bradshaw Robyn J. Eisert Marc Hafner Peter K. Sorger Caitlin E. Mills Kartik Subramanian Proteomic profiling across breast cancer cell lines and models Scientific Data |
| title | Proteomic profiling across breast cancer cell lines and models |
| title_full | Proteomic profiling across breast cancer cell lines and models |
| title_fullStr | Proteomic profiling across breast cancer cell lines and models |
| title_full_unstemmed | Proteomic profiling across breast cancer cell lines and models |
| title_short | Proteomic profiling across breast cancer cell lines and models |
| title_sort | proteomic profiling across breast cancer cell lines and models |
| url | https://doi.org/10.1038/s41597-023-02355-0 |
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