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Implications for powering biomarker discovery studies.

Sian M Dibben1, Robert J Holt, Timothy S Davison

  • 1Almac Diagnostics, Craigavon, United Kingdom.

The Journal of Molecular Diagnostics : JMD
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

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Tumor gene expression varies between blocks from the same patient, but less than differences in histology. Accounting for this intratumor heterogeneity is crucial for accurate biomarker discovery in FFPE samples.

Area of Science:

  • Molecular Biology
  • Oncology
  • Biostatistics

Background:

  • Tumor heterogeneity, particularly intratumor heterogeneity, poses challenges in biomarker discovery.
  • Formalin-fixed paraffin-embedded (FFPE) tissues are widely used in cancer research but can introduce variability.
  • Understanding gene expression variations within individual tumors is essential for reliable biomarker development.

Purpose of the Study:

  • To quantify gene expression variations within individual tumors using FFPE blocks.
  • To assess the impact of intratumor heterogeneity on biomarker discovery studies.
  • To determine optimal sample sizes for biomarker discovery, considering tumor variability.

Main Methods:

  • Microarray analysis of gene expression in FFPE tumor blocks from individual patients.

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  • Mixed-model analysis of variance to assess statistical significance of variations.
  • Sample size calculations adjusted for observed intratumor heterogeneity.

    • Main Results:

    • Significant gene expression variations were observed between FFPE blocks from the same patient.
    • Intratumor heterogeneity was less pronounced than differences attributed to histological characteristics.
    • A single biopsy sample can be sufficient for biomarker application in non-small-cell lung cancer.

    Conclusions:

    • Intratumor heterogeneity in FFPE samples must be considered to avoid underpowered biomarker studies.
    • Biomarker discovery is feasible with macrodissected tumors or whole FFPE sections when heterogeneity is accounted for.
    • Accurate biomarker development requires careful consideration of sample variability and appropriate statistical methods.