The future of hospital laboratories. Position statement from the Royal Belgian Society of Clinical Chemistry (RBSCC)

2009 ◽  
Vol 47 (10) ◽  
Author(s):  
Michel R. Langlois ◽  
Pierre Wallemacq
Keyword(s):  
Clinical Chemistry ◽  
Position Statement ◽  
Belgian Society ◽  
The Future

A Gene Expression Signature that Predicts the Future Onset of Drug-Induced Renal Tubular Toxicity

2005 ◽  
Vol 33 (6) ◽  
pp. 675-683 ◽  
Author(s):  
Mark R. Fielden ◽  
Barrett P. Eynon ◽  
Georges Natsoulis ◽  
Kurt Jarnagin ◽  
Deborah Banas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Future Development ◽  
Clinical Chemistry ◽  
Expression Profiles ◽  
Gene Expression Signature ◽  
Small Data ◽  
Traditional Methods ◽  
The Future ◽  
Renal Tubular ◽  
Traditional Approaches

One application of genomics in drug safety assessment is the identification of biomarkers to predict compound toxicity before it is detected using traditional approaches, such as histopathology. However, many genomic approaches have failed to demonstrate superiority to traditional methods, have not been appropriately validated on external samples, or have been derived using small data sets, thus raising concerns of their general applicability. Using kidney gene expression profiles from male SD rats treated with 64 nephrotoxic or non-nephrotoxic compound treatments, a gene signature consisting of only 35 genes was derived to predict the future development of renal tubular degeneration weeks before it appears histologically following short-term test compound administration. By comparison, histopathology or clinical chemistry fails to predict the future development of tubular degeneration, thus demonstrating the enhanced sensitivity of gene expression relative to traditional approaches. In addition, the performance of the signature was validated on 21 independent compound treatments structurally distinct from the training set. The signature correctly predicted the ability of test compounds to induce tubular degeneration 76% of the time, far better than traditional approaches. This study demonstrates that genomic data can be more sensitive than traditional methods for the early prediction of compound-induced pathology in the kidney.

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