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Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets
Esteban Czwan1, Benedikt Brors, David Kipling
1School of Medicine, Cardiff University, Heath Park, Cardiff CF144XN, UK.
BMC Bioinformatics
|January 13, 2010
Summary
Theme-driven cancer survival studies often yield false positives due to incorrect assumptions about gene set p-values. This study introduces a new method to accurately assess gene set prognostic power, identifying novel biological themes linked to patient survival.
Area of Science:
- Bioinformatics
- Cancer Genomics
- Translational Oncology
Background:
- Theme-driven cancer survival studies aim to link gene expression signatures to patient prognosis.
- Current methods often fail to adequately test the biological relevance of gene sets, potentially leading to false conclusions.
- An incorrect assumption of uniform p-value distributions for random gene sets is a key limitation.
Purpose of the Study:
- To develop and validate a robust method for theme-driven cancer survival analysis.
- To address the pitfall of non-uniform p-value distributions in assessing gene set prognostic power.
- To identify novel biological themes and pathways with significant prognostic value in cancer.
Main Methods:
- An automated theme-driven method was developed using a permutation approach to empirically approximate p-value distributions.
- The method assesses predefined biologically-related gene sets for association with patient survival.
- Comparative analysis with existing methods highlighted issues with false positive rates in prior studies.
Main Results:
- Non-uniform p-value distributions were confirmed, indicating a significant problem with false positive rates in previous studies.
- Novel ontological categories with prognostic power were identified in two public cancer datasets.
- Specific findings include associations between "fatty acid metabolism" and breast cancer survival, and "receptor mediated endocytosis", "brain development", "apical plasma membrane", and "MAPK signaling pathway" with lung cancer survival.
Conclusions:
- The assumption of uniform p-values for random gene sets in current survival studies is flawed and can lead to erroneous results.
- The developed approach corrects for this pitfall, offering a more reliable method for identifying prognostic gene sets.
- This work opens new avenues for discovering higher-level biological themes and pathways with prognostic implications in clinical data.
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