Recent publications from the Linding lab in the University of Copenhagen’s (UCPH) Biotech Resource and Innovation Centre (BRIC) have shown a more extensive disruption of cell signaling pathways by cancer mutations than previously expected. Criexell et al. (2015) used a quantitative mass spectrometric-based phosphoproteomics approach in combination with exome next generation sequencing (NGS) to identify and characterize important mutations involved in cancer development.1 The results obtained and methodologies developed should advance novel therapeutic discovery and improve provision of personalized treatment to patients.
Creixell and co-authors explored the phosphoproteome of ovarian cancer cell lines to define and characterize genetic mutations implicated in oncogenesis. The researchers focused on phosphorylation-based pathways, since kinases are frequently implicated as key players in the development of cancer. Furthermore, the gene mutations associated with malignant phenotype often affect protein kinases.
Using five ovarian cancer cell lines—ES2, OVAS, OVISE, TOV-21 and KOC-7C—the researchers examined the genome and phosphoproteome of each, looking for functional network-attacking mutations, or NAMs, that disrupt cellular signaling. The scientists grew the cells, using SILAC (stable isotope labeling of amino acids in culture) to prepare samples for quantitative proteomic analysis. After cell synchronization and lysis, the researchers digested the proteins using trypsin. They then enriched the phosphoproteins in the peptide digests using TiO2 followed by strong cation exchange (SCX) fractionation. Finally, the team analyzed the enriched fractions by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) using an EASY-n LC 1000 liquid chromatograph coupled with an Q Exactive mass spectrometer (both Thermo Scientific).
Prior to analyzing the spectral data obtained from the quantitative proteomics workflow, Creixell et al. assembled a database of sequence and positional information relating to all known kinase and SH2 domains. In addition, they logged the location of all phosphorylation sites within the human proteome. Combining this information, they created ReKINect, a software program that explores and classifies genomic and proteomic data according to mutations detected. Using ReKINect, the team were able to classify NAMs as follows:
- Activates or deactivates protein kinases
- Affects upstream or downstream signaling interactions
- Generates or destroys phosphorylation sites
Using the algorithm, Creixell et al. classified and explored 9,000 missense mutations and 6,000 unique phosphorylation sites on 2,000 proteins across the five ovarian cancer cell lines, showing signaling network disruption and confirming generation of new phosphorylation sites as proposed.
The team then chose six of these newly identified NAMs for further experimental validation. Using a combination of cellular molecular techniques, including RNAi knockdown, the team verified the in silico predictions, showing that the mutations did indeed disrupt cell signaling pathways.
In conclusion, Creixell et al. are confident that the results show a far greater disruption to cell signaling pathways by cancer mutations than previously expected. Moreover, they expect this new information to be of value in discovering novel therapeutics for precision targeting against oncogenic pathways and developing better personalized treatment strategies. In addition, they suggest that improved knowledge of signaling pathway disruption could benefit patients with less frequently seen cancer mutation, as treatments could be chosen more effectively to target specific networks affected.
1. Creixell, P., et al. (2015) “Kinome-wide decoding of network-attacking mutations rewiring cancer signaling,” Cell,163(1) (pp. 202–17), doi: 10.1016/j.cell.2015.08.056.
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