Recent advances in high-throughput and systematic sequencing of cancer genome has enabled the comprehensive characterization of somatic mutations contributing to the development of cancers. However, the expression level from transcription to translation is under a complex regulation to be fully understood; several studies suggest that many transcripts are targeted for nonsense-mediated decay, or upon translation and thus are unable to form stable, functional protein. In addition, the alteration of post-translation modifications (PTMs) promotes the functional diversity of the proteome and has been linked to human diseases. The expression level and PTMs 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 which heavily rely on the protein sequence database generated from genomic and transcriptomic information. 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 protein mutations. In this proposal, we aim to develop MS-based identification and quantitation strategies for onco-proteogenomics study of tumor-specific mutations and PTMs (focus on phosphorylation and glycosylation) 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, EGFR mutations, will be used as a model. EGFR mutations were discovered in patients with lung adenocarcinoma and were associated with response to EGFR inhibitors. Up to now, the mutation induced alterations in expression level and PTMs of EGFR to modulate function activity is still unclear. In this proposal, several technical developments will be proposed to establish an integrated MS-based platform. (1) Due to the low abundance of endogenous mutated EGFR proteins, affinity purification (AP)-MS will be developed to detect different mutations on EGFR (wild-type, L858R, T790M, G719A, deletion exon 19 and double mutations) as well as glycosylation and phosphorylation change in a panel of NSCLC cell lines. (2) To construct the customized EGFR mutation database, mutated peptide sequence information will be derived from Next-Generation Sequencing and several public databases including UniProt, COSMIC, 1000 Genome, and The Cancer Genome Atlas. (3) A peptide mass spectral library will be constructed by synthetic mutated peptides to serve as a consensus template supporting the identification of mutated peptides based on the correlation between the spectra similarity between library and experimental spectra. (4) For the multiplexed quantification platform, two different MS approaches will be utilized: (i) MS/MS analysis of purified EGFR proteins to identify mutant forms against customized protein sequence database containing the converted EGFR mutations as well as the spectral matching against mutated peptide spectral library; (ii) multiple reaction monitoring (MRM)-MS analysis for targeted detection of wild-type and mutated EGFR for absolute quantitation of EGFR mutated proteins and PTMs. We expect that the developed MS-based strategies can provide a new tool for onco-proteogenomics investigation to study the correlation of somatic mutations and PTMs with cancer progression.
|Effective start/end date||8/1/16 → 7/31/17|
- mass spectrometry
- mutated EGFR protein
- non-small cell lung cancer
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