Addressing the Challenges of High‐Throughput Cancer Tissue Proteomics for Clinical Application: ProCan. Issue 21 (24th September 2019)

• Record Type: Journal Article
• Authors: Tully, Brett; Balleine, Rosemary L.; Hains, Peter G.; Zhong, Qing; Reddel, Roger R.; Robinson, Phillip J.
• Other Names: Hondermarck Hubert guestEditor.
• Is Part Of: Proteomics. Volume 19:Issue 21/22(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-09-24
• Subjects: cancer -- data analysis -- data‐independent acquisition -- proteomics -- sequential window acquisition of all theoretical mass spectra–mass spectrometry; Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201900109
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract: The cancer tissue proteome has enormous potential as a source of novel predictive biomarkers in oncology. Progress in the development of mass spectrometry (MS)‐based tissue proteomics now presents an opportunity to exploit this by applying the strategies of comprehensive molecular profiling and big‐data analytics that are refined in other fields of 'omics research. ProCan (ProCan is a registered trademark) is a program aiming to generate high‐quality tissue proteomic data across a broad spectrum of cancer types. It is based on data‐independent acquisition–MS proteomic analysis of annotated tissue samples sourced through collaboration with expert clinical and cancer research groups. The practical requirements of a high‐throughput translational research program have shaped the approach that ProCan is taking to address challenges in study design, sample preparation, raw data acquisition, and data analysis. The ultimate goal is to establish a large proteomics knowledge‐base that, in combination with other cancer 'omics data, will accelerate cancer research.