A substantial percentage of microarray-based studies in oncology contain critical flaws in analysis or in their conclusions, reports a study in the January 17 issue of the Journal of the National Cancer Institute. The study's authors provide a checklist and a set of guidelines for performing and reporting such studies.
Microarrays are a tool used to study gene expression. Researchers can study thousands of genes at a time, all on a single glass slide. In oncology, scientists have used microarrays to study unique gene expression patterns of specific tumor types, to discover new drug targets, and to categorize unique characteristics of a particular tumor to help doctors tailor treatments to an individual patient. However, such studies produce volumes of data that is easily misinterpreted. It has been difficult to replicate such studies, which is considered the best way to validate scientific findings.
To study the statistical methods used in cancer-focused microarray studies, Alain Dupuy, M.D., and Richard M. Simon, D.Sc., of the National Cancer Institute in Bethesda, Md., reviewed 90 studies published through the end of 2004 that related microarray expression profiling to clinical outcome. The most common cancers in those studies were hematologic malignancies (24 studies), lung cancer (12 studies), and breast cancer (12 studies). The studies fell into three general categories: an outcome-related gene finding, such as searching for specific genes that are expressed differently in people who have a good versus bad prognosis; a class discovery, where researchers cluster together tumors with similar gene expression profiles; and supervised prediction, in which the gene expression profiles are used to generate an algorithm or set of rules that will predict clinical outcomes for patients based on their individual gene expression profiles.
The authors closely scrutinized the statistical methods and reporting in 42 studies published in
Contact: Andrea Widener
Journal of the National Cancer Institute