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Related Experiment Video

Updated: May 13, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Biomarker discovery for heterogeneous diseases.

Garrick Wallstrom1, Karen S Anderson, Joshua LaBaer

  • 1Center for Personalized Diagnostics, Biodesign Institute, ASU, 1001 S. McAllister Ave, Tempe, AZ 85287, USA. garrick.wallstrom@asu.edu

Cancer Epidemiology, Biomarkers & Prevention : a Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology
|March 7, 2013
PubMed
Summary

Disease subtypes require unique biomarkers. This study shows heterogeneous diseases need different selection methods and larger sample sizes for effective biomarker discovery compared to homogeneous diseases.

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Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
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Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

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Last Updated: May 13, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

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Published on: October 11, 2018

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

Area of Science:

  • Genomics and Proteomics
  • Biomarker Discovery
  • Disease Subtyping

Background:

  • Modern studies show diseases are often heterogeneous, with multiple subtypes.
  • Each subtype may have unique biomarkers, challenging traditional discovery approaches.
  • Rethinking biomarker prediction from a single marker to subtype-specific markers is crucial.

Purpose of the Study:

  • To compare biomarker selection methods for homogeneous and heterogeneous diseases.
  • To evaluate the impact of disease heterogeneity on study design and sample size.
  • To provide guidance for designing and analyzing biomarker discovery studies.

Main Methods:

  • Utilized Monte Carlo simulations to assess eight selection methods.
  • Compared single-stage and two-stage study designs.
  • Applied methods to a proteomic biomarker screening study in breast cancer.

Main Results:

  • Optimal selection methods differed between homogeneous and heterogeneous diseases.
  • Heterogeneous diseases required over twofold larger sample sizes.
  • Two-stage designs offered similar statistical power to single-stage designs at lower cost for large studies.

Conclusions:

  • Disease heterogeneity significantly impacts biomarker performance.
  • Specific statistical methods and larger sample sizes are essential for heterogeneous diseases.
  • Findings offer a methodological framework for biomarker discovery in complex diseases.