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.
The mission of the proposed data analysis center is to leverage high dimensional large-scale data from tumor samples to identify new avenues for the development of clinical prognostics and therapeutics for cancer. This mission will be realized through analysis, integration and visualization of multi-omic datasets including genomic, transcriptomic, and proteomic data collected from patient samples to develop predictive models, and during drug treatment of patient derived xenografts and cell lines to validate mechanisms.
|Mundt, Filip; Rajput, Sandeep; Li, Shunqiang et al. (2018) Mass Spectrometry-Based Proteomics Reveals Potential Roles of NEK9 and MAP2K4 in Resistance to PI3K Inhibition in Triple-Negative Breast Cancers. Cancer Res 78:2732-2746|
|Jayasinghe, Reyka G; Cao, Song; Gao, Qingsong et al. (2018) Systematic Analysis of Splice-Site-Creating Mutations in Cancer. Cell Rep 23:270-281.e3|
|Menschaert, Gerben; Wang, Xiaojing; Jones, Andrew R et al. (2018) The proBAM and proBed standard formats: enabling a seamless integration of genomics and proteomics data. Genome Biol 19:12|
|Mostovenko, Ekaterina; Végvári, Ákos; Rezeli, Melinda et al. (2018) Large Scale Identification of Variant Proteins in Glioma Stem Cells. ACS Chem Neurosci 9:73-79|
|Sengupta, Sohini; Sun, Sam Q; Huang, Kuan-Lin et al. (2018) Integrative omics analyses broaden treatment targets in human cancer. Genome Med 10:60|
|Lee, Joon-Yong; Fujimoto, Grant M; Wilson, Ryan et al. (2018) Blazing Signature Filter: a library for fast pairwise similarity comparisons. BMC Bioinformatics 19:221|
|Huang, Kuan-Lin; Mashl, R Jay; Wu, Yige et al. (2018) Pathogenic Germline Variants in 10,389 Adult Cancers. Cell 173:355-370.e14|
|Ruggles, Kelly V; Krug, Karsten; Wang, Xiaojing et al. (2017) Methods, Tools and Current Perspectives in Proteogenomics. Mol Cell Proteomics 16:959-981|
|Wyczalkowski, Matthew A; Wylie, Kristine M; Cao, Song et al. (2017) BreakPoint Surveyor: a pipeline for structural variant visualization. Bioinformatics 33:3121-3122|
|Menschaert, Gerben; Fenyö, David (2017) Proteogenomics from a bioinformatics angle: A growing field. Mass Spectrom Rev 36:584-599|
Showing the most recent 10 out of 13 publications