Dr Dennis KAPPEI Cancer Science Institute Singapore Link to biosketch

Abstract

While next-generation sequencing technologies have revolutionized the field of precision medicine by providing information about changes at both the DNA and RNA level, the analysis of protein expression landscapes lacks behind. This is unfortunate since RNA levels often primarily serve as a proxy for the functionally relevant protein expression levels due to widespread accessibility of next-generation sequencing technologies although RNA levels only correlate with protein expression data with a correlation coefficient of ~0.5 globally.

With proteins as the ultimate actionable molecules, we here explored proteomics profiling as the next level for diagnosis and identification of putative novel therapeutic targets. In a proof-of-concept example, we performed label-free quantitative mass spectrometry analysis to generate deep expression proteomes for both a panel of gastric cancer cell lines and primary tumor samples. Through this data set, we exemplify the potential of coordinated discovery of putative biomarkers/drug targets based on association with clinical data while simultaneously identifying matched cellular models.