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

Abstract 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

RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib.

Pre-clinical models of tumour biology often rely on propagating human tumour cells in a mouse. In order to gain insight into the alignment of these models to human disease segments or investigate the effects of different therapeutics, approaches such as PCR or array based expression profiling are often employed despite suffering from biased transcript coverage, and a requirement for specialist experimental protocols to separate tumour and host signals. Here, we describe a computational strategy to profile transcript expression in both the tumour and host compartments of pre-clinical xenograft models from the same RNA sample using RNA-Seq. Key to this strategy is a species-specific mapping approach that removes the need for manipulation of the RNA population, customised sequencing protocols, or prior knowledge of the species component ratio. The method demonstrates comparable performance to species-specific RT-qPCR and a standard microarray platform, and allowed us to quantify gene expression changes in both the tumour and host tissue following treatment with cediranib, a potent vascular endothelial growth factor receptor tyrosine kinase inhibitor, including the reduction of multiple murine transcripts associated with endothelium or vessels, and an increase in genes associated with the inflammatory response in response to cediranib. In the human compartment, we observed a robust induction of hypoxia genes and a reduction in cell cycle associated transcripts. In conclusion, the study establishes that RNA-Seq can be applied to pre-clinical models to gain deeper understanding of model characteristics and compound mechanism of action, and to identify both tumour and host biomarkers

A systematic review of the diagnostic accuracy of physical examination for the detection of cirrhosis

BACKGROUND: We conducted a review of the diagnostic accuracy of clinical examination for the diagnosis of cirrhosis. The objectives were: to identify studies assessing the accuracy of clinical examination in the detection of cirrhosis; to summarize the diagnostic accuracy of reported physical examination findings; and to define the effects of study characteristics on estimates of diagnostic accuracy. METHODS: Studies were identified through electronic literature search of MEDLINE (1966 to 2000), search of bibliographic references, and contact with authors. Studies that evaluated indicants from physical examination of patients with known or suspected liver disease undergoing liver biopsy were included. Qualitative data on study characteristics were extracted. Two-by-two tables of presence or absence of physical findings for patients with and without cirrhosis were created from study data. Data for physical findings reported in each study were combined using Summary Receiver Operating Characteristic (SROC) curves or random effects modeling, as appropriate. RESULTS: Twelve studies met inclusion criteria, including a total of 1895 patients, ranging in age from 3 to 90 years. Most studies were conducted in referral populations with elevated aminotransferase levels. Ten physical signs were reported in three or more studies and ten signs in only a single study. Signs for which there was more study data were associated with high specificity (range 75–98%), but low sensitivity (range 15–68%) for histologically-proven cirrhosis. CONCLUSIONS: Physical findings are generally of low sensitivity for the diagnosis of cirrhosis, and signs with higher specificity represent decompensated disease. Most studies have been undertaken in highly selected populations