Samuel H. Payne, Ph.D. - US grants

PROJECT SUMMARY It has become feasible to generate deep quantitative data for many of the molecules that are functional in cells, making it possible to survey a large number of tumors measuring genomic alterations and changes to transcripts, proteins and metabolites. It is, however, not clear what is the best way to integrate these data sets to extract as much information as possible about the biology that drives the cancer and how to best disrupt the tumor growth. Our proposed Proteogenomic Data Analysis Center for Cancer Systems Biology and Clinical Translation will develop new methods for better analyzing and integrating these data sets. In addition to developing statistical and machine learning methods, we also emphasize visual exploration of the data, and we will implement interactive web browser based visualization that will allow researchers to easily explore these vast data sets and gain novel insights by being able to quickly switch between summary information and details of the raw data.

Project Summary ? TR&D 3 The Resource overall has the goal of broadly impacting biomedical research by providing the abilities to: obtain high quality proteomics data from much smaller samples, produce more quantitative and comprehensive measurements, generate improved and more extensive information on low abundance components, distinguish presently problematic peptide isomers, and enable the study of much larger sample sets than presently practical by providing increases in measurement throughput. Advances under TR&Ds 1 and 2 in this renewal will provide large improvements in the sensitivity, breadth, quality, and quantity (i.e. throughput) of proteome data. The efforts of TR&D 3 will enable these capabilities through advanced algorithms for data processing and the integration of multiple proteomics and other data sets to aid the extraction of biomedical insights. TR&D 3 will develop new algorithms for protein identification and quantification that are needed to effectively utilize the unique capabilities of the SLIM ion mobility (IM)-MS platform developed in TR&D 2. Highly accurate and very highly precise and reproducible collision cross section (CCS) values derived from the SLIM ultra-high resolution IM measurements will provide more confident and sensitive identification of peptides/proteins. A key aspect of our approach is the use of large sets of stable isotope labeled peptides for the calibration of SLIM ultra-high resolution IM separations leading to more precise peptide collision cross section information. These same stable isotope labeled peptide sets will also serve as calibrants to enable highly accurate quantification of their unlabeled analogs as well as for broad quantification of all peptides and proteins at somewhat reduced accuracy. Advances under TR&Ds 1 and 2 will also enable a broader measurement of post-translational modifications, which we will use to infer networks and pathways active in the samples. We will continue to develop our collaborative visual analytic tool in conjunction with these networks to facilitate exploration and interpretation of the data. These efforts will build upon previous Resource developments and will be facilitated by key technological developments under TR&D 2. In combination, these efforts will provide a basis for rapid implementation and initial evaluation of new proteomics capabilities providing both larger and richer data sets for challenging biomedical projects, as well as their effective dissemination to the research community.