Abstract 3217: Identification of mutated peptides/proteins driving cancer phenotype utilizing improved shotgun proteomics and data analysis approaches.

Background: Mutations in cancer can either abolish or alter protein function. In the latter case, mutated proteins may rewire regulatory networks to promote tumor growth or metastasis. Identifying and understanding the role of potential mutated oncogenes is important for developing effective therapies for individual patients. Unfortunately, detecting mutated proteins in tumors is challenging because the level of mutated proteins is typically low and identification of potential mutations is challenging due to limited database search engines. Utilizing analytical methods incorporating modified protein search engines can facilitate detection of protein variants. Methods: Bottom up proteomics was performed on colorectal cancer cell line HCT-116, whereby five million cells were harvested and cell lysates/protein fraction was fractionated ten-fold using a gel-based approach. Unbiased discovery-mode LC-MS analysis was performed in LTQ Velos-Orbitrap Mass Spectrometer for all the fractions using nano LC-MS/MS. A novel computational approach integrated into the MassMatrix search engine has been developed to analyze the pooled tandem MS dataset and identify significant variant peptides/proteins. Peptide and protein level FDR was carefully controlled to yield variant candidates. Selected peptides with mutations of interest were then synthesized and analyzed in the same instrument for validation purposes. Results: Analysis of the HCT-116 cell line data returned 14000 peptides overall, indicating satisfactory proteome mining. By utilizing the new variant detection pipeline, we have identified variant proteins in HCT-116 compared to the reference sequence database, including 43 SNPs and 12 mutations. Based on close manual evaluation of the quality of the tandem MS spectra, six candidates were selected for validation. These variant peptide sequences were confirmed by comparing them to the synthesized peptide MS/MS patterns. The specific mutations in all of these proteins, including PTPN11, FYN, DUSP23, PRPF8, FUBP1 and RRM2B, have been reported in earlier gene sequencing studies. We have also examined public HCT-116 RNAseq data to further validate expression of these mutated sequences. Conclusions: We have created a novel proto-genomic variant detection pipeline developed on top of existing protein search engines. This method can be married with popular search engines such as MassMatrix, and shows great potential for variant peptide detection. The identified mutated proteins in this study represent targets of immense biological interest for rewiring of the HCT-116 regulatory landscape. Functional studies to understand the molecular basis of the phenotype are ongoing, specifically we are testing the potential gain or loss of function of these proteins due to mutations, and we are probing the likely roles they play in maintaining the tumor phenotype. Citation Format: Gurkan Bebek, Giridharan Gokulrangan, Hua Xu, Mark R. Chance. Identification of mutated peptides/proteins driving cancer phenotype utilizing improved shotgun proteomics and data analysis approaches. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3217. doi:10.1158/1538-7445.AM2013-3217 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.