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Abstract
We show that variability in general levels of drug sensitivity in pre-clinical cancer models confounds biomarker discovery. However, using a very large panel of cell lines, each treated with many drugs, we could estimate a general level of sensitivity to all drugs in each cell line. By conditioning on this variable, biomarkers were identified that were more likely to be effective in clinical trials than those identified using a conventional uncorrected approach. We find that differences in general levels of drug sensitivity are driven by biologically relevant processes. We developed a gene expression based method that can be used to correct for this confounder in future studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1050-9) contains supplementary material, which is available to authorized users.
Highlights
Personalized cancer medicine promised the ability to improve cancer treatment using molecular marker(s) obtained from the patient’s tumor
Variability in general levels of drug sensitivity (GLDS) is evident in cancer cell lines To assess whether GLDS varies in pre-clinical models, we used cell line data from the Cancer Genome Project (CGP)
This pattern was even stronger in other large pharmacogenomics cell line screening studies; in Cell Line Encyclopedia (CCLE) 274 of 276 pairwise correlations reached an false discovery rate (FDR) < 0.05 and 100 % of these correlations were in a positive direction (Additional file 1: Table S2 and Additional file 2: Figure S2)
Summary
Personalized cancer medicine promised the ability to improve cancer treatment using molecular marker(s) (e.g. genome sequence, gene expression) obtained from the patient’s tumor. There have been some notable successes, for example, tyrosine kinase inhibitors in BCRABL1 positive chronic myeloid leukemia (CML) [1]. Many other compounds/targets have proved ineffective in clinical testing, resulting in financial and human cost. Many studies have proposed biomarkers aimed at repurposing or improving the efficacy of existing drugs, but there have been countless failures when predictions from pre-clinical data have been applied in the clinic. The number of clinically applied biomarkers has been described as “staggeringly small” compared to the number proposed in the literature [2]. There is an urgent need to improve biomarker discovery strategies
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