Literature DB >> 30828695

Study Design Considerations for Cancer Biomarker Discoveries.

Yingye Zheng1.   

Abstract

Background: Biomarker discovery studies have generated an array of omic data, however few novel biomarkers have reached clinical use. Guidelines for rigorous study designs are needed. Content: Biases frequently occur in sample selection, outcome ascertainment, or unblinded sample handling and assaying process. The principles of a prospective-specimen collection and retrospective-blinded-evaluation (PRoBE) design can be adapted to mitigate various sources of biases in discovery. We recommend establishing quality biospecimen repositories using matched two-phase designs to minimize biases and maximize efficiency. We also highlight the importance of taking the clinical context into consideration in both sample selection and power calculation for discovery studies. Summary: Biomarker discovery research should follow rigorous design principles in sample se- lection to avoid biases. Consideration of clinical application and the corresponding biomarker performance characteristics in study designs will lead to a more fruitful discovery study. Impact: Appropriate study designs will improve the quality and clinical rigor of biomarker discovery studies.

Entities:  

Keywords:  bias; case-cohort study; discovery study; nested case-control study; sample size; specimen repository; two-phase designs

Mesh:

Substances:

Year:  2018        PMID: 30828695      PMCID: PMC6391721          DOI: 10.1373/jalm.2017.025809

Source DB:  PubMed          Journal:  J Appl Lab Med        ISSN: 2475-7241


  23 in total

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

Authors:  Margaret S Pepe; Christopher I Li; Ziding Feng
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2015-04-02       Impact factor: 4.254

Review 2.  Bias as a threat to the validity of cancer molecular-marker research.

Authors:  David F Ransohoff
Journal:  Nat Rev Cancer       Date:  2005-02       Impact factor: 60.716

3.  Matching in studies of classification accuracy: implications for analysis, efficiency, and assessment of incremental value.

Authors:  Holly Janes; Margaret S Pepe
Journal:  Biometrics       Date:  2007-05-14       Impact factor: 2.571

4.  Screening for prostate cancer--the controversy that refuses to die.

Authors:  Michael J Barry
Journal:  N Engl J Med       Date:  2009-03-18       Impact factor: 91.245

5.  Adopting nested case-control quota sampling designs for the evaluation of risk markers.

Authors:  Yingye Zheng; Tianxi Cai; Margaret S Pepe
Journal:  Lifetime Data Anal       Date:  2013-06-27       Impact factor: 1.588

6.  IMPROVING EFFICIENCY IN BIOMARKER INCREMENTAL VALUE EVALUATION UNDER TWO-PHASE DESIGNS.

Authors:  Yingye Zheng; Marshall Brown; Anna Lok; Tianxi Cai
Journal:  Ann Appl Stat       Date:  2017-07-20       Impact factor: 2.083

Review 7.  Better cancer biomarker discovery through better study design.

Authors:  Andrew Rundle; Habibul Ahsan; Paolo Vineis
Journal:  Eur J Clin Invest       Date:  2012-09-23       Impact factor: 4.686

8.  Analytical validation of serum proteomic profiling for diagnosis of prostate cancer: sources of sample bias.

Authors:  Dale McLerran; William E Grizzle; Ziding Feng; William L Bigbee; Lionel L Banez; Lisa H Cazares; Daniel W Chan; Jose Diaz; Elzbieta Izbicka; Jacob Kagan; David E Malehorn; Gunjan Malik; Denise Oelschlager; Alan Partin; Timothy Randolph; Nicole Rosenzweig; Shiv Srivastava; Sudhir Srivastava; Ian M Thompson; Mark Thornquist; Dean Troyer; Yutaka Yasui; Zhen Zhang; Liu Zhu; O John Semmes
Journal:  Clin Chem       Date:  2007-11-02       Impact factor: 8.327

9.  Evaluating the predictive value of biomarkers with stratified case-cohort design.

Authors:  Dandan Liu; Tianxi Cai; Yingye Zheng
Journal:  Biometrics       Date:  2012-11-22       Impact factor: 2.571

10.  Study design and data analysis considerations for the discovery of prognostic molecular biomarkers: a case study of progression free survival in advanced serous ovarian cancer.

Authors:  Li-Xuan Qin; Douglas A Levine
Journal:  BMC Med Genomics       Date:  2016-06-10       Impact factor: 3.063

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  3 in total

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Authors:  T H Hui; X Shao; D W Au; W C Cho; Y Lin
Journal:  RSC Adv       Date:  2020-08-14       Impact factor: 3.361

Review 2.  Current challenges and best practices for cell-free long RNA biomarker discovery.

Authors:  Lluc Cabús; Julien Lagarde; Joao Curado; Esther Lizano; Jennifer Pérez-Boza
Journal:  Biomark Res       Date:  2022-08-18

3.  Plasma Extracellular Vesicle miRNAs Can Identify Lung Cancer, Current Smoking Status, and Stable COPD.

Authors:  Hannah E O'Farrell; Rayleen V Bowman; Kwun M Fong; Ian A Yang
Journal:  Int J Mol Sci       Date:  2021-05-28       Impact factor: 5.923

  3 in total

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