RNA and phosphoprotein profiles of TP53- and PTEN-knockouts in MCF10A at baseline and responding to DNA damage

• Published in: Scientific data Vol. 11; no. 1; pp. 27 - 12
• Main Authors: Lin, ChenWei, Schoenherr, Regine M., Voytovich, Uliana J., Ivey, Richard G., Kennedy, Jacob J., Whiteaker, Jeffrey R., Wang, Pei, Paulovich, Amanda G.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04.01.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1347; 692/699/67/1347; Amino acids; Breast cancer; Cell culture; Cholera; Chromatography; Data Descriptor; DNA Damage; Epidermal growth factor; Epithelial Cells; Humanities and Social Sciences; Humans; Insulin; Isotopes; Laboratories; Light; Mass spectroscopy; Methyl methanesulfonate; multidisciplinary; Mutation; Neoplasms; p53 Protein; Peptides; Phosphoproteomes; PTEN Phosphohydrolase - genetics; PTEN protein; RNA; Science; Science (multidisciplinary); Tumor suppressor genes; Tumor Suppressor Protein p53 - genetics; Tumors
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-023-02829-1

A wealth of proteogenomic data has been generated using cancer samples to deepen our understanding of the mechanisms of cancer and how biological networks are altered in association with somatic mutation of tumor suppressor genes, such as TP53 and PTEN. To generate functional signatures of TP53 or PTEN loss, we profiled the RNA and phosphoproteomes of the MCF10A epithelial cell line, along with its congenic TP53- or PTEN-knockout derivatives, upon perturbation with the monofunctional DNA alkylating agent methyl methanesulfonate (MMS) vs. mock treatment. To enable quantitative and reproducible mass spectrometry data generation, the cell lines were SILAC-labeled (stable isotope labeling with amino acids in cell culture), and the experimental design included label swapping and biological replicates. All data are publicly available and may be used to advance our understanding of the TP53 and PTEN tumor suppressor genes and to provide functional signatures for bioinformatic analyses of proteogenomic datasets.