Pan-cancer quantitation of epithelial-mesenchymal transition dynamics using parallel reaction monitoring-based targeted proteomics approach.
Ankit P JainJanani SambathGajanan SatheIrene A GeorgeAkhilesh PandeyErik W ThompsonPrashant KumarPublished in: Journal of translational medicine (2022)
Epithelial-mesenchymal transition (EMT) is a dynamic and complex cellular process that is known to be hijacked by cancer cells to facilitate invasion, metastasis and therapeutic resistance. Several quantitative measures to assess the interplay between EMT and cancer progression are available, based on large scale genome and transcriptome data. However, these large scale multi-omics studies have repeatedly illustrated a lack of correlation in mRNA and protein abundances that may be influenced by diverse post-translational regulation. Hence, it is imperative to understand how changes in the EMT proteome are associated with the process of oncogenic transformation. To this effect, we developed a parallel reaction monitoring-based targeted proteomics method for quantifying abundances of EMT-associated proteins across cancer cell lines. Our study revealed that quantitative measurement of EMT proteome which enabled a more accurate assessment than transcriptomics data and revealed specific discrepancies against a backdrop of generally strong concordance between proteomic and transcriptomic data. We further demonstrated that changes in our EMT proteome panel might play a role in tumor transformation across cancer types. In future, this EMT panel assay has the potential to be used for clinical samples to guide treatment choices and to congregate functional information for the development and advancing novel therapeutics.
Keyphrases
- epithelial mesenchymal transition
- papillary thyroid
- transforming growth factor
- single cell
- signaling pathway
- squamous cell
- mass spectrometry
- rna seq
- high resolution
- big data
- healthcare
- gene expression
- high throughput
- lymph node metastasis
- small molecule
- dna methylation
- amino acid
- social media
- deep learning
- transcription factor
- squamous cell carcinoma
- label free
- childhood cancer
- current status
- cell migration
- liquid chromatography
- binding protein
- protein protein