Evaluation of Quantification and Normalization Strategies for Phosphoprotein Phosphatase Affinity Proteomics: Application to Breast Cancer Signaling.
Brooke L BrauerKwame WireduScott A GerberArminja N KettenbachPublished in: Journal of proteome research (2022)
Accurate quantification of proteomics data is essential for revealing and understanding biological signaling processes. We have recently developed a chemical proteomic strategy termed phosphatase inhibitor beads and mass spectrometry (PIB-MS) to investigate endogenous phosphoprotein phosphatase (PPP) dephosphorylation signaling. Here, we compare the robustness and reproducibility of status quo quantification methods for optimal performance and ease of implementation. We then apply PIB-MS to an array of breast cancer cell lines to determine differences in PPP signaling between subtypes. Breast cancer, a leading cause of cancer death in women, consists of three main subtypes: estrogen receptor-positive (ER+), human epidermal growth factor receptor two positive (HER2+), and triple-negative (TNBC). Although there are effective treatment strategies for ER+ and HER2+ subtypes, tumors become resistant and progress. Furthermore, TNBC has few targeted therapies. Therefore, there is a need to identify new approaches for treating breast cancers. Using PIB-MS, we distinguished TNBC from non-TNBC based on subtype-specific PPP holoenzyme composition. In addition, we identified an increase in PPP interactions with Hippo pathway proteins in TNBC. These interactions suggest that phosphatases in TNBC play an inhibitory role on the Hippo pathway and correlate with increased expression of YAP/TAZ target genes both in TNBC cell lines and in TNBC patients.
Keyphrases
- mass spectrometry
- estrogen receptor
- epidermal growth factor receptor
- liquid chromatography
- multiple sclerosis
- high resolution
- capillary electrophoresis
- ms ms
- high performance liquid chromatography
- healthcare
- gas chromatography
- end stage renal disease
- type diabetes
- pet imaging
- tyrosine kinase
- endothelial cells
- ejection fraction
- prognostic factors
- newly diagnosed
- poor prognosis
- computed tomography
- transcription factor
- high throughput
- squamous cell carcinoma
- pregnant women
- endoplasmic reticulum
- young adults
- breast cancer risk
- childhood cancer
- peritoneal dialysis
- skeletal muscle
- deep learning
- machine learning
- squamous cell
- lymph node metastasis
- breast cancer cells
- patient reported
- induced pluripotent stem cells