We propose to apply a three-stage biomarker development pipeline that couples candidate discovery In tissues with hypothesis-driven, quantitative qualification and verification studies in plasma. In the first stage of our pipeline, we employ state-of-the-art LC-MS/MS together with iTRAQ stable isotope labeling to deeply characterize with precise relative quantification the proteomes and phospho-proteomes of cancer and normal tissues (provided by TCGA) to provide unprecedented coverage of the functional proteomes of glioblastoma, breast, ovarian, and kidney cancers. The resulting extensive proteomic datasets will be integrated with genomic data provided by TCGA in a """"""""proteo-genomic"""""""" analysis to construct an understanding of cellular pathway activity in these cancers. The results ofthe proteo-genomic analyses will be coupled with additional, publicly available, genomic data containing clinical annotation to nominate viable candidate biomarkers for plasma-based verification studies. In the second stage of our pipeline, accurate inclusion mass screening (AIMS) is used to confirm (qualify) that proteins discovered in tumor tissue are detectable in plasma, thus providing a bridge from unbiased discovery to MS-based targeted assay development.
AIMS i s a targeted, hypothesis-driven mode of MS that achieves higher sensitivity and specificity than untargeted approaches. In the third stage of our pipeline, we build analytically validated assays for measuring candidate biomarkers in patient plasma for verification studies. Our assay technology platform is based on multiple reaction monitoring MS (MRM-MS) coupled with stable isotope dilution (SID) and immuno-enrichment of target peptides by SISCAPA (Stable Isotope Standards with Capture by Anti-Peptide Antibody). We have demonstrated our capability to generate hundreds of highly multiplexed (&30-plex), sensitive (low ng/ml LOQ from 10 ul plasma and low pg/ml LOQ from 1 ml plasma) and precise (CV<20%) analytically validated assays for quantifying cancer biomarker candidates in plasmas for verification studies. Here we will develop SISCAPA assays to 80 peptides from 40 prioritized protein candidates/yr and deploy these assays to measure these analytes in 300 patient plasma samples/yr.

Public Health Relevance

The studies we propose will provide new, protein-level knowledge regarding cellular processes involved in development of cancers, potentially identfying new leads for cancer drug development. The novel assay technologies we have pioneered will enable follow-up testing of unprecedented numbers of protein biomarker candidates, facilitating translation of diagnostic tests for cancer into clinical use.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Resource-Related Research Projects--Cooperative Agreements (U24)
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Special Emphasis Panel (ZCA1-SRLB-R (J1))
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Kinsinger, Christopher
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Broad Institute, Inc.
United States
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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
Whiteaker, Jeffrey R; Zhao, Lei; Ivey, Richard G et al. (2018) Targeted mass spectrometry enables robust quantification of FANCD2 mono-ubiquitination in response to DNA damage. DNA Repair (Amst) 65:47-53
Whiteaker, Jeffrey R; Zhao, Lei; Saul, Rick et al. (2018) A Multiplexed Mass Spectrometry-Based Assay for Robust Quantification of Phosphosignaling in Response to DNA Damage. Radiat Res 189:505-518
Huang, Kuan-Lin; Li, Shunqiang; Mertins, Philipp et al. (2017) Proteogenomic integration reveals therapeutic targets in breast cancer xenografts. Nat Commun 8:14864
Fu, Rong; Wang, Pei; Ma, Weiping et al. (2017) A statistical method for detecting differentially expressed SNVs based on next-generation RNA-seq data. Biometrics 73:42-51
Abelin, Jennifer G; Keskin, Derin B; Sarkizova, Siranush et al. (2017) Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction. Immunity 46:315-326
Zhou, Yan; Wang, Pei; Wang, Xianlong et al. (2017) Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis. Genet Epidemiol 41:70-80
Keshishian, Hasmik; Burgess, Michael W; Specht, Harrison et al. (2017) Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry. Nat Protoc 12:1683-1701
Whiteaker, Jeffrey R; Zhao, Lei; Schoenherr, Regine M et al. (2017) Peptide Immunoaffinity Enrichment with Targeted Mass Spectrometry: Application to Quantification of ATM Kinase Phospho-Signaling. Methods Mol Biol 1599:197-213
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

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