Literature DB >> 20739680

Recommendations for biomarker identification and qualification in clinical proteomics.

Harald Mischak1, Günter Allmaier, Rolf Apweiler, Teresa Attwood, Marc Baumann, Ariela Benigni, Samuel E Bennett, Rainer Bischoff, Erik Bongcam-Rudloff, Giovambattista Capasso, Joshua J Coon, Patrick D'Haese, Anna F Dominiczak, Mohammed Dakna, Hassan Dihazi, Jochen H Ehrich, Patricia Fernandez-Llama, Danilo Fliser, Jorgen Frokiaer, Jerome Garin, Mark Girolami, William S Hancock, Marion Haubitz, Denis Hochstrasser, Rury R Holman, John P A Ioannidis, Joachim Jankowski, Bruce A Julian, Jon B Klein, Walter Kolch, Theo Luider, Ziad Massy, William B Mattes, Franck Molina, Bernard Monsarrat, Jan Novak, Karlheinz Peter, Peter Rossing, Marta Sánchez-Carbayo, Joost P Schanstra, O John Semmes, Goce Spasovski, Dan Theodorescu, Visith Thongboonkerd, Raymond Vanholder, Timothy D Veenstra, Eva Weissinger, Tadashi Yamamoto, Antonia Vlahou.   

Abstract

Clinical proteomics has yielded some early positive results-the identification of potential disease biomarkers-indicating the promise for this analytical approach to improve the current state of the art in clinical practice. However, the inability to verify some candidate molecules in subsequent studies has led to skepticism among many clinicians and regulatory bodies, and it has become evident that commonly encountered shortcomings in fundamental aspects of experimental design mainly during biomarker discovery must be addressed in order to provide robust data. In this Perspective, we assert that successful studies generally use suitable statistical approaches for biomarker definition and confirm results in independent test sets; in addition, we describe a brief set of practical and feasible recommendations that we have developed for investigators to properly identify and qualify proteomic biomarkers, which could also be used as reporting requirements. Such recommendations should help put proteomic biomarker discovery on the solid ground needed for turning the old promise into a new reality.

Mesh:

Substances:

Year:  2010        PMID: 20739680     DOI: 10.1126/scitranslmed.3001249

Source DB:  PubMed          Journal:  Sci Transl Med        ISSN: 1946-6234            Impact factor:   17.956


  96 in total

1.  Improving validation practices in "omics" research.

Authors:  John P A Ioannidis; Muin J Khoury
Journal:  Science       Date:  2011-12-02       Impact factor: 47.728

Review 2.  Biomarkers in IgA nephropathy: relationship to pathogenetic hits.

Authors:  Margaret Colleen Hastings; Zina Moldoveanu; Hitoshi Suzuki; Francois Berthoux; Bruce A Julian; John T Sanders; Matthew B Renfrow; Jan Novak; Robert J Wyatt
Journal:  Expert Opin Med Diagn       Date:  2013-11

Review 3.  Proteomic biomarkers in kidney disease: issues in development and implementation.

Authors:  Harald Mischak; Christian Delles; Antonia Vlahou; Raymond Vanholder
Journal:  Nat Rev Nephrol       Date:  2015-02-03       Impact factor: 28.314

4.  Teaming up for biomarker future. Many problems still hinder the use of biomarkers in clinical practice, but new public-private partnerships could improve the situation.

Authors:  Andrea Rinaldi
Journal:  EMBO Rep       Date:  2011-06       Impact factor: 8.807

Review 5.  Urinary biomarkers for renal tract malformations.

Authors:  Pedro Magalhães; Joost P Schanstra; Emma Carrick; Harald Mischak; Petra Zürbig
Journal:  Expert Rev Proteomics       Date:  2016-11-15       Impact factor: 3.940

Review 6.  Developing proteomic biomarkers for bladder cancer: towards clinical application.

Authors:  Maria Frantzi; Agnieszka Latosinska; Leif Flühe; Marie C Hupe; Elena Critselis; Mario W Kramer; Axel S Merseburger; Harald Mischak; Antonia Vlahou
Journal:  Nat Rev Urol       Date:  2015-05-26       Impact factor: 14.432

Review 7.  Next-generation proteomics: towards an integrative view of proteome dynamics.

Authors:  A F Maarten Altelaar; Javier Munoz; Albert J R Heck
Journal:  Nat Rev Genet       Date:  2012-12-04       Impact factor: 53.242

8.  Perspective: Limiting Dependence on Nonrandomized Studies and Improving Randomized Trials in Human Nutrition Research: Why and How.

Authors:  John F Trepanowski; John P A Ioannidis
Journal:  Adv Nutr       Date:  2018-07-01       Impact factor: 8.701

9.  Multicentre prospective validation of a urinary peptidome-based classifier for the diagnosis of type 2 diabetic nephropathy.

Authors:  Justyna Siwy; Joost P Schanstra; Angel Argiles; Stephan J L Bakker; Joachim Beige; Petr Boucek; Korbinian Brand; Christian Delles; Flore Duranton; Beatriz Fernandez-Fernandez; Marie-Luise Jankowski; Mohammad Al Khatib; Thomas Kunt; Maria Lajer; Ralf Lichtinghagen; Morten Lindhardt; David M Maahs; Harald Mischak; William Mullen; Gerjan Navis; Marina Noutsou; Alberto Ortiz; Frederik Persson; John R Petrie; Johannes M Roob; Peter Rossing; Piero Ruggenenti; Ivan Rychlik; Andreas L Serra; Janet Snell-Bergeon; Goce Spasovski; Olivera Stojceva-Taneva; Matias Trillini; Heiko von der Leyen; Brigitte M Winklhofer-Roob; Petra Zürbig; Joachim Jankowski
Journal:  Nephrol Dial Transplant       Date:  2014-03-02       Impact factor: 5.992

10.  Urinary proteome analysis enables assessment of renoprotective treatment in type 2 diabetic patients with microalbuminuria.

Authors:  Sten Andersen; Harald Mischak; Petra Zürbig; Hans-Henrik Parving; Peter Rossing
Journal:  BMC Nephrol       Date:  2010-11-01       Impact factor: 2.388

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.