The rigorous testing of hypotheses on suitable sample cohorts is a major limitation in translational research. This is particularly the case for the validation of protein biomarkers; the lack of accurate, reproducible, and sensitive assays for most proteins has precluded the systematic assessment of hundreds of potential marker proteins described in the literature. Here, we describe a high-throughput method for the development and refinement of selected reaction monitoring (SRM) assays for human proteins. The method was applied to generate such assays for more than 1000 cancer-associated proteins, which are functionally related to candidate cancer driver mutations. We used the assays to determine the detectability of the target proteins in two clinically relevant samples: plasma and urine. One hundred eighty-two proteins were detected in depleted plasma, spanning five orders of magnitude in abundance and reaching below a concentration of 10 ng/ml. The narrower concentration range of proteins in urine allowed the detection of 408 proteins. Moreover, we demonstrate that these SRM assays allow reproducible quantification by monitoring 34 biomarker candidates across 83 patient plasma samples. Through public access to the entire assay library, researchers will be able to target their cancer-associated proteins of interest in any sample type using the detectability information in plasma and urine as a guide. The generated expandable reference map of SRM assays for cancer-associated proteins will be a valuable resource for accelerating and planning biomarker verification studies.
The rigorous testing of hypotheses on suitable sample cohorts is a major limitation in translational research. This is particularly the case for the validation of protein biomarkers; the lack of accurate, reproducible, and sensitive assays for most proteins has precluded the systematic assessment of hundreds of potential marker proteins described in the literature. Here, we describe a high-throughput method for the development and refinement of selected reaction monitoring (SRM) assays for n class="Species">human proteins. The method was applied to generate such assays for more than 1000 cancer-associated proteins, which are functionally related to candidate cancer driver mutations. We used the assays to determine the detectability of the target proteins in two clinically relevant samples: plasma and urine. One hundred eighty-two proteins were detected in depleted plasma, spanning five orders of magnitude in abundance and reaching below a concentration of 10 ng/ml. The narrower concentration range of proteins in urine allowed the detection of 408 proteins. Moreover, we demonstrate that these SRM assays allow reproducible quantification by monitoring 34 biomarker candidates across 83 patient plasma samples. Through public access to the entire assay library, researchers will be able to target their cancer-associated proteins of interest in any sample type using the detectability information in plasma and urine as a guide. The generated expandable reference map of SRM assays for cancer-associated proteins will be a valuable resource for accelerating and planning biomarker verification studies.
Authors: N Leigh Anderson; Norman G Anderson; Terry W Pearson; Christoph H Borchers; Amanda G Paulovich; Scott D Patterson; Michael Gillette; Ruedi Aebersold; Steven A Carr Journal: Mol Cell Proteomics Date: 2009-01-07 Impact factor: 5.911
Authors: Aled M Edwards; Ruth Isserlin; Gary D Bader; Stephen V Frye; Timothy M Willson; Frank H Yu Journal: Nature Date: 2011-02-10 Impact factor: 49.962
Authors: Alex J Rai; Zhen Zhang; Jason Rosenzweig; Ie-Ming Shih; Thang Pham; Eric T Fung; Lori J Sokoll; Daniel W Chan Journal: Arch Pathol Lab Med Date: 2002-12 Impact factor: 5.534
Authors: Rong Wu; Neali Hendrix-Lucas; Rork Kuick; Yali Zhai; Donald R Schwartz; Aytekin Akyol; Samir Hanash; David E Misek; Hidetaka Katabuchi; Bart O Williams; Eric R Fearon; Kathleen R Cho Journal: Cancer Cell Date: 2007-04 Impact factor: 31.743
Authors: Maggie P Y Lam; Vidya Venkatraman; Yi Xing; Edward Lau; Quan Cao; Dominic C M Ng; Andrew I Su; Junbo Ge; Jennifer E Van Eyk; Peipei Ping Journal: J Proteome Res Date: 2016-07-19 Impact factor: 4.466
Authors: Jintang He; Xuefei Sun; Tujin Shi; Athena A Schepmoes; Thomas L Fillmore; Vladislav A Petyuk; Fang Xie; Rui Zhao; Marina A Gritsenko; Feng Yang; Naoki Kitabayashi; Sung-Suk Chae; Mark A Rubin; Javed Siddiqui; John T Wei; Arul M Chinnaiyan; Wei-Jun Qian; Richard D Smith; Jacob Kagan; Sudhir Srivastava; Karin D Rodland; Tao Liu; David G Camp Journal: Mol Oncol Date: 2014-02-21 Impact factor: 6.603
Authors: Olga T Schubert; Ludovic C Gillet; Ben C Collins; Pedro Navarro; George Rosenberger; Witold E Wolski; Henry Lam; Dario Amodei; Parag Mallick; Brendan MacLean; Ruedi Aebersold Journal: Nat Protoc Date: 2015-02-12 Impact factor: 13.491
Authors: Steven L Wood; Margaret A Knowles; Douglas Thompson; Peter J Selby; Rosamonde E Banks Journal: Nat Rev Urol Date: 2013-02-26 Impact factor: 14.432
Authors: Grant M Fujimoto; Matthew E Monroe; Larissa Rodriguez; Chaochao Wu; Brendan MacLean; Richard D Smith; Michael J MacCoss; Samuel H Payne Journal: J Proteome Res Date: 2013-12-16 Impact factor: 4.466
Authors: Bing Zhang; Jeffrey R Whiteaker; Andrew N Hoofnagle; Geoffrey S Baird; Karin D Rodland; Amanda G Paulovich Journal: Nat Rev Clin Oncol Date: 2019-04 Impact factor: 66.675