Getting the Details Right: Gene Signatures for Cancer Therapy

By any account, we have now entered the second decade of the “-omics revolution.” High-throughput measurement techniques capable of surveying large segments of the genome or proteome were expected to rapidly expand the pool of diagnostic biomarkers for many disease states. To date, however, this anticipated explosion in diagnostics has not occurred. The lack of progress—despite significant private and public investment—has led to a certain degree of introspection. With so many excellent discovery experiments, where are the promised biomarkers? A number of important answers to this question have been offered, including such factors as the expense of translational validation, regulatory challenges for multiplex biomarkers, and the need to better use the expertise of clinical laboratorians in identifying preanalytical and analytical hurdles. A recent report by Baggerly and Coombes (1), however, suggests another possibility: namely that common errors in data management can compromise the bioinformatics analysis of valuable -omics data sets.Baggerly and Coombes are at the forefront of the emerging discipline of “forensic bioinformatics,” an effort to reconstruct and validate analytical results that have been reported in the literature. Although one might think that this exercise would be straightforward, given that the published methods have passed peer review, it frequently requires a laborious reconstruction of unreported parameters, methodologies, and transformations. The authors argue that without a clear understanding and documentation of analytical methods, one can easily miss important errors that compromise experimental interpretation and, by extension, may impair the treatment of patients.In this recent work, Baggerly and Coombes have reanalyzed a set of experiments aimed at predicting the response of a tumor to chemotherapy. In brief, a number of reports from 2006 onward (2–4) explored the appealing concept of combining cell line drug-sensitivity data with microarray profiles to predict the therapeutic response of a given tumor. If …