Integration of pan-cancer transcriptomics with RPPA proteomics reveals mechanisms of epithelial-mesenchymal transition.
Simon KoplevKatie LinAnders B DohlmanAvi Ma'ayanPublished in: PLoS computational biology (2018)
Integrating data from multiple regulatory layers across cancer types could elucidate additional mechanisms of oncogenesis. Using antibody-based protein profiling of 736 cancer cell lines, along with matching transcriptomic data, we show that pan-cancer bimodality in the amounts of mRNA, protein, and protein phosphorylation reveals mechanisms related to the epithelial-mesenchymal transition (EMT). Based on the bimodal expression of E-cadherin, we define an EMT signature consisting of 239 genes, many of which were not previously associated with EMT. By querying gene expression signatures collected from cancer cell lines after small-molecule perturbations, we identify enrichment for histone deacetylase (HDAC) inhibitors as inducers of EMT, and kinase inhibitors as mesenchymal-to-epithelial transition (MET) promoters. Causal modeling of protein-based signaling identifies putative drivers of EMT. In conclusion, integrative analysis of pan-cancer proteomic and transcriptomic data reveals key regulatory mechanisms of oncogenic transformation.
Keyphrases
- epithelial mesenchymal transition
- papillary thyroid
- gene expression
- small molecule
- squamous cell
- single cell
- electronic health record
- protein protein
- transforming growth factor
- lymph node metastasis
- binding protein
- poor prognosis
- childhood cancer
- squamous cell carcinoma
- big data
- bone marrow
- rna seq
- deep learning
- amino acid
- artificial intelligence
- data analysis