Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome
By David Venet, Jacques E. Dumont, Vincent Detours. 2011
574 citations recorded by the source corpus.
Bridging the gap between animal or in vitro models and human disease is essential in medical research. Researchers often suggest that a biological mechanism is relevant to human cancer from the statistical association of a gene expression marker (a signature) of this mechanism, that was discovered in an experimental system, with disease outcome in humans. We examined this argument for breast cancer. Surprisingly, we found that gene expression signatures—unrelated to cancer—of the effect of postprandial laughter, of mice social defeat and of skin fibroblast localization were all significantly associated with breast cancer outcome. We next compared 47 published breast cancer outcome signatures to signatures made of random genes. Twenty-eight of them (60%) were not significantly better outcome predictors than random signatures of identical size and 11 (23%) were worst predictors than the median random signature. More than 90% of random signatures >100 genes were significant outcome predictors. We next derived a metagene, called meta-PCNA, by selecting the 1% genes most positively correlated with proliferation marker PCNA in a compendium of normal tissues expression. Adjusting breast cancer expression data for meta-PCNA abrogated almost entirely the outcome association of published and random signatures. We also found that, in the absence of adjustment, the hazard ratio of outcome association of a signature strongly correlated with meta-PCNA (R2 = 0.9). This relation also applied to single-gene expression markers. Moreover, >50% of the breast cancer transcriptome was correlated with meta-PCNA. A corollary was that purging cell cycle genes out of a signature failed to rule out the confounding effect of proliferation. Hence, it is questionable to suggest that a mechanism is relevant to human breast cancer from the finding that a gene expression marker for this mechanism predicts human breast cancer outcome, because most markers do. The methods we present help to overcome this problem.
Subfields: Cancer Research, Molecular Biology
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Citations in the corpus
- EgoNet: identification of human disease ego-network modules (2014, background): “However, gene markers based on expression data alone are still not reliable [8].”
- SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis (2013, background): “In breast cancer, for example, more than 40 biomarkers have been proposed containing between 3 and 512 genes and whose prognostic or predictive performance depends on therapy, hormone receptor status, and the number of genes [1,2].”
- Extracting insights from the shape of complex data using topology (2013, background): “Although many molecular sub-groups have been identified, it is often difficult to identify the same sub-group in a broader setting, where data sets are generated on different platforms, on different sets of patients and at a different times, because of the noise and complexity in the data 11,12 .”
- HSF1 drives a transcriptional program distinct from heat shock to support highly malignant human cancers. (2012, unknown): “Next, we considered a recent finding that many published cancer signatures are not significantly better outcome predictors than random signatures of identical size (Venet et al., 2011).”