Microarray Based Diagnosis Profits from Better Documentation of Gene Expression Signatures

Microarray gene expression signatures hold great promise to improve diagnosis and prognosis of disease. However, current documentation standards of such signatures do not allow for an unambiguous application to study-external patients. This hinders independent evaluation, effectively delaying the use of signatures in clinical practice. Data from eight publicly available clinical microarray studies were analyzed and the consistency of study-internal with study-external diagnoses was evaluated. Study-external classifications were based on documented information only. Documenting a signature is conceptually different from reporting a list of genes. We show that even the exact quantitative specification of a classification rule alone does not define a signature unambiguously. We found that discrepancy between study-internal and study-external diagnoses can be as frequent as 30% (worst case) and 18% (median). By using the proposed documentation by value strategy, which documents quantitative preprocessing information, the median discrepancy was reduced to 1%. The process of evaluating microarray gene expression diagnostic signatures and bringing them to clinical practice can be substantially improved and made more reliable by better documentation of the signatures.: It has been shown that microarray based gene expression signatures have the potential to be powerful tools for patient stratification, diagnosis of disease, prognosis of survival, assessment of risk group, and selection of treatment. However, documentation standards in current publications do not allow for a signature's unambiguous application to study-external patients. This hinders independent evaluation, effectively delaying the use of signatures in clinical practice. Based on eight clinical microarray studies, we show that common documentation standards have the following shortcoming: when using the documented information only, the same patient might receive a diagnosis different from the one he would have received in the original study. To address the problem, we derive a documentation protocol that reduces the ambiguity of diagnoses to a minimum. The resulting gain in consistency of study-internal versus study-external diagnosis is validated by statistical resampling analysis: using the proposed documentation by value strategy, the median inconsistency dropped from 18% to 1%. Software implementing the proposed method, as well as practical guidelines for using it, are provided. We conclude that the process of evaluating microarray gene expression diagnostic signatures and bringing them to clinical practice can be substantially improved and made more reliable by better documentation.