Proteogenomic strategies for identification of aberrant cancer peptides using large‐scale next‐generation sequencing data. Issue 23 (17th November 2014)

• Record Type: Journal Article
• Authors: Woo, Sunghee; Cha, Seong Won; Na, Seungjin; Guest, Clark; Liu, Tao; Smith, Richard D.; Rodland, Karin D.; Payne, Samuel; Bafna, Vineet; Pandey, Akhilesh; Pevzner, Pavel A.
• Is Part Of: Proteomics. Volume 14:Issue 23/24(2014)
• Page Start: 2719
• Page End: 2730
• Publication Date: 2014-11-17
• Subjects: Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201400206
• Languages: English
• ISSNs: 1615-9853

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Cancer is driven by the acquisition of somatic DNA lesions. Distinguishing the early driver mutations from subsequent passenger mutations is key to molecular subtyping of cancers, understanding cancer progression, and the discovery of novel biomarkers. The advances of genomics technologies (whole‐genome exome, and transcript sequencing, collectively referred to as NGS (next‐generation sequencing)) have fueled recent studies on somatic mutation discovery. However, the vision is challenged by the complexity, redundancy, and errors in genomic data, and the difficulty of investigating the proteome translated portion of aberrant genes using only genomic approaches. Combination of proteomic and genomic technologies are increasingly being employed. Various strategies have been employed to allow the usage of large‐scale NGS data for conventional MS/MS searches. This paper provides a discussion of applying different strategies relating to large database search, and FDR (false discovery rate) ‐based error control, and their implication to cancer proteogenomics. Moreover, it extends and develops the idea of a unified genomic variant database that can be searched by any MS sample. A total of 879 BAM files downloaded from TCGA repository were used to create a 4.34 GB unified FASTA database that contained <inline-formula><alternatives><inline-graphic mimetype="image" xlink:href="ark:/27927/pgh2jc84txf" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink" /><mml:math display="inline" altimg="urn:x-wiley:16159853:media:pmic7918:pmic7918-math-0001" overflow="scroll" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>2</mml:mn><mml:mspace width="0.16em" /><mml:mn>787</mml:mn><mml:mspace width="0.16em" /><mml:mn>062</mml:mn></mml:mrow></mml:math></alternatives></inline-formula> novel splice junctions, 38 464 deletions, 1 105 insertions, and 182 302 substitutions. Proteomic data from a single ovarian carcinoma sample (439 858 spectra) was searched against the database. By applying the most conservative FDR measure, we have identified 524 novel peptides and 65 578 known peptides at 1% FDR threshold. The novel peptides include interesting examples of doubly mutated peptides, frame‐shifts, and nonsample‐recruited mutations, which emphasize the strength of our approach.</p> </abstract>