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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
5U24CA160034-02
Application #
8325033
Study Section
Special Emphasis Panel (ZCA1-SRLB-R (J1))
Program Officer
Kinsinger, Christopher
Project Start
2011-08-26
Project End
2014-07-31
Budget Start
2012-08-01
Budget End
2013-07-31
Support Year
2
Fiscal Year
2012
Total Cost
$3,122,514
Indirect Cost
$485,469
Name
Broad Institute, Inc.
Department
Type
DUNS #
623544785
City
Cambridge
State
MA
Country
United States
Zip Code
02142
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
Huang, Kuan-Lin; Li, Shunqiang; Mertins, Philipp et al. (2017) Proteogenomic integration reveals therapeutic targets in breast cancer xenografts. Nat Commun 8:14864
Jung, Sunhee; Whiteaker, Jeffrey R; Zhao, Lei et al. (2017) Quantification of ATP7B Protein in Dried Blood Spots by Peptide Immuno-SRM as a Potential Screen for Wilson's Disease. J Proteome Res 16:862-871
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
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
Ruggles, Kelly V; Krug, Karsten; Wang, Xiaojing et al. (2017) Methods, Tools and Current Perspectives in Proteogenomics. Mol Cell Proteomics 16:959-981
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
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
Schoenherr, Regine M; Zhao, Lei; Ivey, Richard G et al. (2016) Commercially available antibodies can be applied in quantitative multiplexed peptide immunoaffinity enrichment targeted mass spectrometry assays. Proteomics 16:2141-5
Abelin, Jennifer G; Patel, Jinal; Lu, Xiaodong et al. (2016) Reduced-representation Phosphosignatures Measured by Quantitative Targeted MS Capture Cellular States and Enable Large-scale Comparison of Drug-induced Phenotypes. Mol Cell Proteomics 15:1622-41

Showing the most recent 10 out of 68 publications