Literature DB >> 25837819

Improving the quality of biomarker discovery research: the right samples and enough of them.

Margaret S Pepe1, Christopher I Li2, Ziding Feng3.   

Abstract

BACKGROUND: Biomarker discovery research has yielded few biomarkers that validate for clinical use. A contributing factor may be poor study designs.
METHODS: The goal in discovery research is to identify a subset of potentially useful markers from a large set of candidates assayed on case and control samples. We recommend the PRoBE design for selecting samples. We propose sample size calculations that require specifying: (i) a definition for biomarker performance; (ii) the proportion of useful markers the study should identify (Discovery Power); and (iii) the tolerable number of useless markers amongst those identified (False Leads Expected, FLE).
RESULTS: We apply the methodology to a study of 9,000 candidate biomarkers for risk of colon cancer recurrence where a useful biomarker has positive predictive value ≥ 30%. We find that 40 patients with recurrence and 160 without recurrence suffice to filter out 98% of useless markers (2% FLE) while identifying 95% of useful biomarkers (95% Discovery Power). Alternative methods for sample size calculation required more assumptions.
CONCLUSIONS: Biomarker discovery research should utilize quality biospecimen repositories and include sample sizes that enable markers meeting prespecified performance characteristics for well-defined clinical applications to be identified. IMPACT: The scientific rigor of discovery research should be improved. ©2015 American Association for Cancer Research.

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Year:  2015        PMID: 25837819      PMCID: PMC4452419          DOI: 10.1158/1055-9965.EPI-14-1227

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.254


  15 in total

Review 1.  Phases of biomarker development for early detection of cancer.

Authors:  M S Pepe; R Etzioni; Z Feng; J D Potter; M L Thompson; M Thornquist; M Winget; Y Yasui
Journal:  J Natl Cancer Inst       Date:  2001-07-18       Impact factor: 13.506

2.  Selecting differentially expressed genes from microarray experiments.

Authors:  Margaret Sullivan Pepe; Gary Longton; Garnet L Anderson; Michel Schummer
Journal:  Biometrics       Date:  2003-03       Impact factor: 2.571

3.  Sample size determination in microarray experiments for class comparison and prognostic classification.

Authors:  Kevin Dobbin; Richard Simon
Journal:  Biostatistics       Date:  2005-01       Impact factor: 5.899

4.  FDR-controlling testing procedures and sample size determination for microarrays.

Authors:  Shuying S Li; Jeannette Bigler; Johanna W Lampe; John D Potter; Ziding Feng
Journal:  Stat Med       Date:  2005-08-15       Impact factor: 2.373

5.  Sample size planning for developing classifiers using high-dimensional DNA microarray data.

Authors:  Kevin K Dobbin; Richard M Simon
Journal:  Biostatistics       Date:  2006-04-13       Impact factor: 5.899

6.  Use of archived specimens in evaluation of prognostic and predictive biomarkers.

Authors:  Richard M Simon; Soonmyung Paik; Daniel F Hayes
Journal:  J Natl Cancer Inst       Date:  2009-10-08       Impact factor: 13.506

7.  Classification versus association models: should the same methods apply?

Authors:  Ziding Feng
Journal:  Scand J Clin Lab Invest Suppl       Date:  2010

8.  Improving the biomarker pipeline to develop and evaluate cancer screening tests.

Authors:  Stuart G Baker
Journal:  J Natl Cancer Inst       Date:  2009-07-02       Impact factor: 13.506

9.  Biomarker discovery for heterogeneous diseases.

Authors:  Garrick Wallstrom; Karen S Anderson; Joshua LaBaer
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2013-03-05       Impact factor: 4.254

10.  Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design.

Authors:  Margaret S Pepe; Ziding Feng; Holly Janes; Patrick M Bossuyt; John D Potter
Journal:  J Natl Cancer Inst       Date:  2008-10-07       Impact factor: 13.506

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  15 in total

Review 1.  The search for drug-targetable diagnostic, prognostic and predictive biomarkers in chronic graft-versus-host disease.

Authors:  Hong-Gang Ren; Djamilatou Adom; Sophie Paczesny
Journal:  Expert Rev Clin Immunol       Date:  2018-04-19       Impact factor: 4.473

Review 2.  Extracellular vesicles as cancer liquid biopsies: from discovery, validation, to clinical application.

Authors:  Zhen Zhao; Jia Fan; Yen-Michael S Hsu; Christopher J Lyon; Bo Ning; Tony Y Hu
Journal:  Lab Chip       Date:  2019-03-27       Impact factor: 6.799

3.  Recommendation to use exact P-values in biomarker discovery research in place of approximate P-values.

Authors:  Matthew F Buas; Christopher I Li; Garnet L Anderson; Margaret S Pepe
Journal:  Cancer Epidemiol       Date:  2018-08-10       Impact factor: 2.984

4.  Standard Operating Procedures for Biospecimen Collection, Processing, and Storage: From the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer.

Authors:  William E Fisher; Zobeida Cruz-Monserrate; Amy L McElhany; Gregory B Lesinski; Phil A Hart; Ria Ghosh; George Van Buren; Douglas S Fishman; Jo Ann S Rinaudo; Jose Serrano; Sudhir Srivastava; Thomas Mace; Mark Topazian; Ziding Feng; Dhiraj Yadav; Stephen J Pandol; Steven J Hughes; Robert Y Liu; Emily Lu; Robert Orr; David C Whitcomb; Amer S Abouhamze; Hanno Steen; Zachary M Sellers; David M Troendle; Aliye Uc; Mark E Lowe; Darwin L Conwell
Journal:  Pancreas       Date:  2018 Nov/Dec       Impact factor: 3.327

5.  Study Design Considerations for Cancer Biomarker Discoveries.

Authors:  Yingye Zheng
Journal:  J Appl Lab Med       Date:  2018-09

6.  Statistical inference for net benefit measures in biomarker validation studies.

Authors:  Tracey L Marsh; Holly Janes; Margaret S Pepe
Journal:  Biometrics       Date:  2019-11-28       Impact factor: 2.571

7.  A Prospective Evaluation of Early Detection Biomarkers for Ovarian Cancer in the European EPIC Cohort.

Authors:  Kathryn L Terry; Helena Schock; Renée T Fortner; Anika Hüsing; Raina N Fichorova; Hidemi S Yamamoto; Allison F Vitonis; Theron Johnson; Kim Overvad; Anne Tjønneland; Marie-Christine Boutron-Ruault; Sylvie Mesrine; Gianluca Severi; Laure Dossus; Sabina Rinaldi; Heiner Boeing; Vassiliki Benetou; Pagona Lagiou; Antonia Trichopoulou; Vittorio Krogh; Elisabetta Kuhn; Salvatore Panico; H Bas Bueno-de-Mesquita; N Charlotte Onland-Moret; Petra H Peeters; Inger Torhild Gram; Elisabete Weiderpass; Eric J Duell; Maria-Jose Sanchez; Eva Ardanaz; Nerea Etxezarreta; Carmen Navarro; Annika Idahl; Eva Lundin; Karin Jirström; Jonas Manjer; Nicholas J Wareham; Kay-Tee Khaw; Karl Smith Byrne; Ruth C Travis; Marc J Gunter; Melissa A Merritt; Elio Riboli; Daniel W Cramer; Rudolf Kaaks
Journal:  Clin Cancer Res       Date:  2016-04-08       Impact factor: 12.531

8.  Early-Phase Studies of Biomarkers: What Target Sensitivity and Specificity Values Might Confer Clinical Utility?

Authors:  Margaret S Pepe; Holly Janes; Christopher I Li; Patrick M Bossuyt; Ziding Feng; Jørgen Hilden
Journal:  Clin Chem       Date:  2016-03-21       Impact factor: 8.327

9.  Biomarkers of Chronic Pancreatitis: A systematic literature review.

Authors:  Zobeida Cruz-Monserrate; Kristyn Gumpper; Valentina Pita; Phil A Hart; Christopher Forsmark; David C Whitcomb; Dhiraj Yadav; Richard T Waldron; Stephen Pandol; Hanno Steen; Vincent Anani; Natasha Kanwar; Santhi Swaroop Vege; Savi Appana; Liang Li; Jose Serrano; Jo Ann S Rinaudo; Mark Topazian; Darwin L Conwell
Journal:  Pancreatology       Date:  2021-01-22       Impact factor: 3.996

10.  Biomarkers for Early Detection of Colorectal Cancer: The Early Detection Research Network, a Framework for Clinical Translation.

Authors:  Robert S Bresalier; William M Grady; Sanford D Markowitz; Hans Jørgen Nielsen; Surinder K Batra; Paul D Lampe
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2020-04-16       Impact factor: 4.254

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