Biomarkers are unique, detectable signatures of cancer that are vital to early diagnosis and treatment. The causes of many cancers are somatic mutations in critical pathways that serve organ growth and metabolism. For over a decade there have been intense proteomic efforts to find biomarkers with clinical utility. There are thousands of published studies and thousands of candidate biomarkers of unknown value for the management of cancer. With the advent of whole genome sequencing, the challenge and immense potential value of characterizing the cancer proteome the readout of the genome-is unfolding. A new paradigm is therefore emerging -""""""""genome-out"""""""" directed proteomics. In this application, we propose a comprehensive, blood-based, protein biomarker discovery and verification pipeline that addresses biomarker discovery by starting where the cancer biology begins: with the driving somatic mutations. In the discovery phase, we will use information about recurrent genomic mutations in cancer (i.e. those that occur with a greater than 5% incidence in any given cancer) that are identified by ongoing whole-genome sequencing efforts to focus our collection and analyses of high-throughput, quantitative proteomic data on samples provided by the NCI CPTC in concert with unique resources such as comprehensively sequenced """"""""human in mouse"""""""" breast cancer xenografts. Proteomic analyses will include a multiplicity of high-resolution, current and advanced proteomics methods that can characterize intact proteins, massively complex peptide mixtures and protein modifications to elucidate the proteomic facade of cellular pathways and networks. Bioinformatic tools with rigorous statistical models will be applied to meet the challenges of querying the genome directly with proteomic data (proteogenomics). This will provide the orthogonality that cancer genomics requires to biologically validate copy number alterations, point mutations, splice variants, and the complex biological effects from loss of function mutations and epigenetic changes and translate these findings into actionable clinical information. With this melding of proteomic and genomic knowledge, clinically and biologically informed decisions will be made to select candidate biomarkers. The properties of each candidate will be verified by demonstrating an ability of the biomarker assay to reliably distinguish between blood samples taken from healthy individuals from those accrued from patients with cancer.
While early diagnosis can lead to treatments to eliminate a malignancy before the lethal phase, there are presently few clinically viable diagnostics that can be used as a reliable marker for the need for early intervention. Therefore, the goal of this project is to assist the CPTC in discovering new biomarkers, verifying their clinical applicability, and ultimately, helping translate selected biomarkers into clinical practice to reduce mortality from cancer.
|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|
|Matsunuma, Ryoichi; Chan, Doug W; Kim, Beom-Jun et al. (2018) DPYSL3 modulates mitosis, migration, and epithelial-to-mesenchymal transition in claudin-low breast cancer. Proc Natl Acad Sci U S A 115:E11978-E11987|
|Mertins, Philipp; Tang, Lauren C; Krug, Karsten et al. (2018) Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography-mass spectrometry. Nat Protoc 13:1632-1661|
|Menschaert, Gerben; Fenyö, David (2017) Proteogenomics from a bioinformatics angle: A growing field. Mass Spectrom Rev 36:584-599|
|Huang, Kuan-Lin; Li, Shunqiang; Mertins, Philipp et al. (2017) Proteogenomic integration reveals therapeutic targets in breast cancer xenografts. Nat Commun 8:14864|
|Wang, Xuya; Mooradian, Arshag D; Erdmann-Gilmore, Petra et al. (2017) Breast tumors educate the proteome of stromal tissue in an individualized but coordinated manner. Sci Signal 10:|
|Wang, Jing; Ma, Zihao; Carr, Steven A et al. (2017) Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction. Mol Cell Proteomics 16:121-134|
|Huang, Kuan-Lin; Li, Shunqiang; Mertins, Philipp et al. (2017) Corrigendum: Proteogenomic integration reveals therapeutic targets in breast cancer xenografts. Nat Commun 8:15479|
|Ruggles, Kelly V; Krug, Karsten; Wang, Xiaojing et al. (2017) Methods, Tools and Current Perspectives in Proteogenomics. Mol Cell Proteomics 16:959-981|
|Ray, Bisakha; Liu, Wenke; Fenyö, David (2017) Adaptive Multiview Nonnegative Matrix Factorization Algorithm for Integration of Multimodal Biomedical Data. Cancer Inform 16:1176935117725727|
Showing the most recent 10 out of 48 publications