Towards proteome standards: the use of absolute quantitation in high-throughput biomarker discovery.
The use of proteomics to profile biological fluids and identify therein biomarkers for cancer and other diseases was initially received with considerable excitement. However, results have fallen short of the expectations. Traditionally, protein biomarkers have been identified by measurement of relative expression changes between case and control samples from which differentially expressed proteins are then considered to represent biomarker candidates. We argue that current individual proteomics-based biomarker discovery studies lack the statistical strength for the identification of high-confidence biomarkers. Instead, multi-group efforts are necessary to facilitate the generation of sufficient sample sizes. This is contingent on the ability to collate and cross-compare data from different studies, which will require the use of a common metric or standards. Though profound, the technical challenges for absolute protein quantification can be overcome. The use of matrix specific, shared standards for absolute quantitation presents an opportunity to facilitate the much needed, but currently impossible, comparisons of different studies. In addition to community-wide approaches to standardize pre-analytical biomarker research studies, it is also important to establish means to integrate experimental data from different studies in order to assess the usefulness of proposed biomarkers with sufficient statistical certainty.
Chao
Tzu-Chiao
TC
Hansmeier
Nicole
N
Halden
Rolf U
RU
D015415:Biomarkers / Q000032:analysis* / Q000378:metabolism; D006801:Humans; D020543:Proteome / Q000592:standards*; D040901:Proteomics / Q000379:methods / Q000592:standards*; D054928:Validation Studies as Topic
D015415:Biomarkers; D020543:Proteome
Nat Biotechnol. 2006 Mar;24(3):333-8
16525410
BMC Med Genomics. 2008 Sep 15;1:41
18793429
J Proteome Res. 2007 Sep;6(9):3371-6
17655344
BMC Bioinformatics. 2003 Jun 9;4:24
12795817
N Engl J Med. 2004 May 27;350(22):2239-46
15163773
Clin Chem. 2008 Nov;54(11):1749-52
18957555
J Natl Cancer Inst. 2007 Jan 17;99(2):147-57
17227998
Biomark Insights. 2007 Feb 07;1:1-48
19690635
Urology. 2009 May;73(5 Suppl):S11-20
19375622
J Proteome Res. 2004 Mar-Apr;3(2):235-44
15113099
Bioinformatics. 2005 Aug 1;21(15):3301-7
15905277
Proteomics Clin Appl. 2007 Feb;1(2):148-56
21136664
Proteomics Clin Appl. 2008 Oct 1;2(10-11):1386-1402
20976028
Mol Cell Proteomics. 2007 Dec;6(12):2212-29
17939991
Cancer J. 2001 Jan-Feb;7(1):69-78
11269650
Mol Cell Proteomics. 2009 May;8(5):883-6
19131327
Anal Biochem. 2007 Mar 1;362(1):44-54
17241609
J Chromatogr B Biomed Sci Appl. 1999 Feb 5;722(1-2):191-202
10068141
Proteomics. 2001 Oct;1(10):1264-70
11721637
Mol Cell Proteomics. 2004 Apr;3(4):345-54
14749446
Bioinformatics. 2005 Jan 15;21(2):171-8
15308542
Bioinformatics. 2004 Mar 22;20(5):777-85
14751995
Cancer. 1994 Sep 1;74(5):1615-20
7520352
Nucleic Acids Res. 2009 Jan;37(Database issue):D907-12
18978022
Proteomics. 2009 Feb;9(4):861-81
19212957
Mol Cell Proteomics. 2007 Aug;6(8):1416-27
17510050
Mol Cell Proteomics. 2007 Sep;6(9):1666-7
17766694
Proteomics. 2001 Oct;1(10):1205-15
11721633
Nat Biotechnol. 2006 Aug;24(8):971-83
16900146
J Natl Cancer Inst. 2005 Feb 16;97(4):315-9
15713968
Toxicol Lett. 2001 Mar 31;120(1-3):379-84
11323197
Proteomics. 2005 Aug;5(13):3262-77
16052621
Nat Biotechnol. 2008 Aug;26(8):860-1
18688232
Nat Methods. 2009 Jun;6(6):423-30
19448641
Adv Genet. 2008;60:293-308
18358325
Nat Rev Cancer. 2006 Jul;6(7):565-71
16794639
Proteomics Clin Appl. 2009 Feb;3(2):173-84
26238617
J Cell Biochem. 2003 Aug 1;89(5):868-86
12874822
J Proteome Res. 2009 Feb;8(2):787-97
19105742