TY - JOUR
T1 - A targeted proteomics-based pipeline for verification of biomarkers in plasma
AU - Whiteaker, Jeffrey R.
AU - Lin, Chenwei
AU - Kennedy, Jacob
AU - Hou, Liming
AU - Trute, Mary
AU - Sokal, Izabela
AU - Yan, Ping
AU - Schoenherr, Regine M.
AU - Zhao, Lei
AU - Voytovich, Uliana J.
AU - Kelly-Spratt, Karen S.
AU - Krasnoselsky, Alexei
AU - Gafken, Philip R.
AU - Hogan, Jason M.
AU - Jones, Lisa A.
AU - Wang, Pei
AU - Amon, Lynn
AU - Chodosh, Lewis A.
AU - Nelson, Peter S.
AU - McIntosh, Martin W.
AU - Kemp, Christopher J.
AU - Paulovich, Amanda G.
N1 - Funding Information:
This work was funded by a grant from The Paul G. Allen Family Foundation and the National Institutes of Health grant U24 CA126476 from the National Cancer Institute Clinical Proteomic Technology Assessment Center (CPTAC). We thank our Biomarker Project advisory board members for advice and input: R. Aebersold, L. Anderson, E. Diamandis, R. Smith, F. Appelbaum, D. Gottschling, M. Groudine, J. Roberts, V. Vasioukhin and N. Urban. We thank all members of the Biomarker Project team for input: L. Hartwell, S.M. Hanash, S.J. Pitteri, H. Wong, K.E. Gurley, D. Liggitt, D.B. Martin, T. Whitmore, A. Peterson, R. Prueitt, M. Fitzgibbon, J.K. Eng, D. May, T. Holzman, Y. Zhang, A. Stimmel, S.L. Zriny, R. Dumpit, I. Coleman and T.D. Lorentzen.
PY - 2011/7
Y1 - 2011/7
N2 - High-throughput technologies can now identify hundreds of candidate protein biomarkers for any disease with relative ease. However, because there are no assays for the majority of proteins and de novo immunoassay development is prohibitively expensive, few candidate biomarkers are tested in clinical studies. We tested whether the analytical performance of a biomarker identification pipeline based on targeted mass spectrometry would be sufficient for data-dependent prioritization of candidate biomarkers, de novo development of assays and multiplexed biomarker verification. We used a data-dependent triage process to prioritize a subset of putative plasma biomarkers from >1,000 candidates previously identified using a mouse model of breast cancer. Eighty-eight novel quantitative assays based on selected reaction monitoring mass spectrometry were developed, multiplexed and evaluated in 80 plasma samples. Thirty-six proteins were verified as being elevated in the plasma of tumor-bearing animals. The analytical performance of this pipeline suggests that it should support the use of an analogous approach with human samples.
AB - High-throughput technologies can now identify hundreds of candidate protein biomarkers for any disease with relative ease. However, because there are no assays for the majority of proteins and de novo immunoassay development is prohibitively expensive, few candidate biomarkers are tested in clinical studies. We tested whether the analytical performance of a biomarker identification pipeline based on targeted mass spectrometry would be sufficient for data-dependent prioritization of candidate biomarkers, de novo development of assays and multiplexed biomarker verification. We used a data-dependent triage process to prioritize a subset of putative plasma biomarkers from >1,000 candidates previously identified using a mouse model of breast cancer. Eighty-eight novel quantitative assays based on selected reaction monitoring mass spectrometry were developed, multiplexed and evaluated in 80 plasma samples. Thirty-six proteins were verified as being elevated in the plasma of tumor-bearing animals. The analytical performance of this pipeline suggests that it should support the use of an analogous approach with human samples.
UR - https://www.scopus.com/pages/publications/79960214321
U2 - 10.1038/nbt.1900
DO - 10.1038/nbt.1900
M3 - Article
C2 - 21685906
AN - SCOPUS:79960214321
SN - 1087-0156
VL - 29
SP - 625
EP - 634
JO - Nature Biotechnology
JF - Nature Biotechnology
IS - 7
ER -