This application proposes a Vanderbilt Biomarker Developmental Laboratory (Vanderbilt BDL), which will serve as a component of the NCI Early Detection Research Network (EDRN). The Vanderbilt BDL will be established within the Jim Ayers Institute for Precancer Detection and Diagnosis in the Vanderbilt-lngram Cancer Center which provides outstanding resources, instrumentation and staff dedicated to the problem of biomarker development. The Vanderbilt BDL will employ standardized liquid chromatography-tandem mass spectrometry (LC-MS/MS) and liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM-MS) analysis platforms for unbiased discovery and targeted quantitation of protein biomarkers. These analysis platforms are coupled to extensive informatics support infrastructure and ongoing biospecimen collections of the Ayers Institute and the Thoracic Oncology Program in the Vanderbilt-lngram Cancer Center. The Vanderbilt BDL will address the goals of the EDRN BDL program through three specific aims, 1) Develop and apply new informatics tools and approaches to identify mutant, variant and modified proteins as candidate cancer biomarkers. This work will emphasize application of new sequence tagging and signal analysis algorithms and software to identify novel protein forms that distinguish cancer phenotypes, 2) Discover and verify protein biomarker candidates for plasma-based detection of colorectal and lung cancers. This work will apply standardized shotgun proteomics analyses for biomarker candidate discovery in tissues and a standardized LC- MRM-MS platform for quantitation of biomarker candidates in plasma. The Vanderbilt BDL will interact closely with a proposed EDRN Biomarker Reference Laboratory (William Pao, PI) and a proposed EDRN Clinical Validation Center (Pierre Massion, PI) at Vanderbilt as well as with EDRN network partners to broadly deploy standardized proteomic technologies for biomarker development.
The early detection of cancer offers one of the most effective ways to reduce the impact of this disease. Better diagnostic tests for cancer will require the development of new cancer biomarkers, which indicate the presence of disease. This application proposes to employ standardized, refined proteomic technologies to more reliably identify biomarker proteins.
|Holman, Jerry D; Tabb, David L; Mallick, Parag (2014) Employing ProteoWizard to Convert Raw Mass Spectrometry Data. Curr Protoc Bioinformatics 46:13.24.1-9|
|Liebler, Daniel C; Zimmerman, Lisa J (2013) Targeted quantitation of proteins by mass spectrometry. Biochemistry 52:3797-806|
|Chen, Yao-Yi; Chambers, Matthew C; Li, Ming et al. (2013) IDPQuantify: combining precursor intensity with spectral counts for protein and peptide quantification. J Proteome Res 12:4111-21|
|Demory Beckler, Michelle; Higginbotham, James N; Franklin, Jeffrey L et al. (2013) Proteomic analysis of exosomes from mutant KRAS colon cancer cells identifies intercellular transfer of mutant KRAS. Mol Cell Proteomics 12:343-55|
|Chen, Yao-Yi; Dasari, Surendra; Ma, Ze-Qiang et al. (2012) Refining comparative proteomics by spectral counting to account for shared peptides and multiple search engines. Anal Bioanal Chem 404:1115-25|
|Martinez, Melissa N; Emfinger, Christopher H; Overton, Matthew et al. (2012) Obesity and altered glucose metabolism impact HDL composition in CETP transgenic mice: a role for ovarian hormones. J Lipid Res 53:379-89|
|Baycin-Hizal, Deniz; Tabb, David L; Chaerkady, Raghothama et al. (2012) Proteomic analysis of Chinese hamster ovary cells. J Proteome Res 11:5265-76|
|Halvey, Patrick J; Ferrone, Cristina R; Liebler, Daniel C (2012) GeLC-MRM quantitation of mutant KRAS oncoprotein in complex biological samples. J Proteome Res 11:3908-13|