Pan-cancer quantitation of epithelial-mesenchymal transition dynamics using parallel reaction monitoring-based targeted proteomics approach

• Published in: Journal of translational medicine 2022-02, Vol.20 (1), p.84-84, Article 84
• Main Authors: Jain, Ankit P, Sambath, Janani, Sathe, Gajanan, George, Irene A, Pandey, Akhilesh, Thompson, Erik W, Kumar, Prashant
• Format: Article
• Language: English
• Subjects: Analysis; Cancer; Carcinogenesis; Cell Line, Tumor; Drug development; Epithelial cells; Epithelial-mesenchymal transition; Epithelial-Mesenchymal Transition - genetics; Gene expression; Genomes; Genotype & phenotype; Health aspects; Humans; Mass spectrometry; Mesenchyme; Metastases; Neoplasms - genetics; Ovarian cancer; Pan-cancer targeted proteomics; Parallel reaction monitoring; Peptides; Physiological aspects; Post-translation; Protein expression; Proteins; Proteome; Proteomes; Proteomics; Proteomics - methods; Quantitation; Scientific imaging; Software; Stem cells; Transcriptome; Transcriptomes; Transcriptomics; Tumor cell lines; Tumors
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-021-03227-0

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.