Systematic Bias in Genomic Classification Due to Contaminating Non-neoplastic Tissue in Breast Tumor Samples

BMC Medical Genomics

Melissa A Troester²  Keith D Amos⁵  Margaret L Gulley³  Joel S Parker¹  Yan Li⁴  Zhiyuan Hu⁴  Fathi Elloumi⁴ 

[1] Curriculum in Molecular Biology and Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

关键词: bias;    normal tissue;    breast cancer;    genomic assays;    biomarker validation;   

Others  :  1137947
DOI  :  10.1186/1755-8794-4-54

 received in 2011-02-22, accepted in 2011-06-30,  发布年份 2011

【 摘 要 】

Background

Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification.

Methods

To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors.

Results

Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability.

Conclusions

Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor.

【 授权许可】

   
2011 Elloumi et al; licensee BioMed Central Ltd.

【 图 表 】

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