Recent advances in high-throughput and systematic sequencing of cancer genome has enabled the comprehensive characterization of somatic mutations leading to the development of cancers. However, the expression level from transcription to translation is under a complex regulation; several studies suggest that many transcripts are targeted for nonsense-mediated decay, or upon translation and thus are unable to form stable, functional proteins. The expression level of mutated protein encoded from mutation bearing genes is largely unexplored. Mass spectrometry (MS)-based proteomics approach is a powerful tool for reliable identification of peptides/proteins. The integration of proteomics with cancer genomics data with mutations, which is a new horizon called onco-proteogenomics, will help to confirm the translation and identify cancer-specific mutations in protein level. For this purpose, we aim to develop MS-based identification and quantitation strategies for onco-proteogenomics study of tumor-specific mutations on cancer associated oncoproteins. For proof-of-concept of our new methodology, non-small cell lung cancer (NSCLC), the most common type of all lung cancer cases, and the current clinically used targeted therapy target, EGFR mutations, will be used as a model. EGFR mutations were discovered in patients with lung adenocarcinoma to associate with response to EGFR tyrosin kinase inhibitors. Up to now, the EGFR mutation induced alterations in EGFR protein expression and interactome levels to modulate function activity is still unclear. In the first year (2016/08~2016/12) of this MoST project, we have already completed three fundamental blocks including construction of the customized mutated protein sequence database with statistical power evaluation, examination of EGFR mutation status in our previously conducted NSCLC tissue membrane proteomics studies, and development of affinity purification method for purifying total EGFR proteins and two preliminary tests in this proposal. According to the current progress, we aim to achieve four different parts in the following two years: (1) The bioinformatics analysis of 40 EGFR mutation sequences to predict adequate enzymes for generation of detectable and unique peptides which aids comprehensive detection of all the mutation sites in EGFR by LC-MS/MS. (2) The development of MS-based strategies, which integrates EGFR purification, multiple enzymatic digestion, LC-MS/MS analysis and customized database searching using multiple engines in different algorithms, for confident identification of mutated peptides. In addition, a mutated peptide spectra library will be developed using synthetic mutated peptides to serve as a spectra template for confirmation of identified mutated peptides. (3) We will develop a targeted parallel reaction monitoring (PRM)-MS analysis to determine the concentrations of individual mutated and corresponding wild-type EGFR proteins. (4) The new MS methodologies will be applied to measure the abundances of site-specific mutation versus corresponding wild-type EGFR in a panel of NSCLC cell lines with different EGFR mutation status. The mutation induced EGFR protein complex change will also be studied. Finally, through the integration of the expression levels of RNA, site-specific EGFR protein mutations, and protein-protein interaction changes, we will have a bettering understanding towards the molecular impact of EGFR mutations during tumor progression and adaption to TKI treatments. We expect this new MS-based strategy will be a cutting-edge tool for onco-proteogenomics study.
|Effective start/end date||8/1/18 → 7/1/19|
- mass spectrometric analysis
- mutated EGFR protein
- non-small cell lung cancer
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