Abstract
Cancer genome sequencing studies have revealed a number of variants in coding regions of several genes. Some of these coding variants play an important role in activating specific pathways that drive proliferation. Coding variants present on cancer cell surfaces by the major histocompatibility complex serve as neo-antigens and result in immune activation. The success of immune therapy in patients is attributed to neo-antigen load on cancer cell surfaces. However, which coding variants are expressed at the protein level can't be predicted based on genomic data. Complementing genomic data with proteomic data can potentially reveal coding variants that are expressed at the protein level. However, identification of variant peptides using mass spectrometry data is still a challenging task due to the lack of an appropriate tool that integrates genomic and proteomic data analysis pipelines. To overcome this problem, and for the ease of the biologists, we have developed a graphical user interface (GUI)-based tool called CusVarDB. We integrated variant calling pipeline to generate sample-specific variant protein database from next-generation sequencing datasets. We validated the tool with triple negative breast cancer cell line datasets and identified 423, 408, 386 and 361 variant peptides from BT474, MDMAB157, MFM223 and HCC38 datasets, respectively.
Highlights
Cancer genome sequencing studies have revealed a number of variants in coding regions of several genes
Amino acid substitution of arginine to leucine at position 858 (L858R) in the epidermal growth factor receptor (EGFR) gene has been observed in 17% of pulmonary adenocarcinoma patients (Morgensztern et al, 2015)
Mass spectrometry-based raw files were searched against their respective custom variant protein database, which resulted in identification of 423, 408, 386 and 361 variant peptides from BT474, MDMAB157, MFM223 and HCC38, cell line datasets (Figure 2B)
Summary
2. Richard Kumaran Kandasamy, Norwegian University of Science and Technology, Trondheim, Norway. 3. Jorge Duitama , Universidad de Los Andes, Bogotá, Colombia Daniel Mahecha, Universidad de Los Andes, Bogotá, Colombia. Any reports and responses or comments on the article can be found at the end of the article. The new modifications for this article are as follows: 1. We removed some sentences in articles to avoid exceeding the word limit. 2. As per the reviewer 1 and 2 comments, we modified the contents in the Introduction and Use case section
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