Biomedical Informatics Insights

David E. Axelrod¹, Naomi Miller² and Judith-Anne W. Chapman³

¹Department of Genetics and Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, 604 Allison Road, Piscataway, NJ 08854-8082 U.S.A. ²Department of Pathology, University Health Network, 200 Elizabeth St., Toronto, Ontario, Canada, M5G 2C4. ³NCIC Clinical Trials Group, Queen’s University, Kingston, Ontario, Canada, K7L 3N6.

Abstract

Information about tumors is usually obtained from a single assessment of a tumor sample, performed at some point in the course of the development and progression of the tumor, with patient characteristics being surrogates for natural history context. Differences between cells within individual tumors (intratumor heterogeneity) and between tumors of different patients (intertumor heterogeneity) may mean that a small sample is not representative of the tumor as a whole, particularly for solid tumors which are the focus of this paper. This issue is of increasing importance as high-throughput technologies generate large multi-feature data sets in the areas of genomics, proteomics, and image analysis. Three potential pitfalls in statistical analysis are discussed (sampling, cut-points, and validation) and suggestions are made about how to avoid these pitfalls.