MOTIVATION: In recent years, microarray technology has revealed many tumor-expressed genes prognostic of clinical outcomes in early-stage breast cancer patients. However, in the presence of cured patients, evaluating gene effect on time to relapse is quite complex since it may affect either the probability of never experiencing a relapse (cure effect) or the time to relapse among the uncured patients (disease progression effect) or both. In this context, we propose a simple and an efficient method for identifying gene expression changes that characterize early and late recurrence for uncured patients. RESULTS: Simulation results show the good performance of the proposed statistic for detecting a disease progression effect. In a study of early-stage breast cancer, our results show that the proposed statistic provides a more powerful basis for gene selection than the classical Cox model-based statistic. From a biological perspective, many of the genes identified here as associated with the speed of disease recurrence have known roles in tumorigenesis.

Microarray technology is a high-throughput platform for the comprehensive analysis of gene expression on a whole genome scale. Most approaches use a solid phase surface to geographically fix specific gene probes and quantification is based on radiometric, fluorometric, or electrochemical detection of hybridized biomolecules such as RNA, DNA, or protein. The technique produces two-dimensional "images" of gene expression and has been discussed in the context of in vitro "molecular imaging". The major challenge in using this technology is the analysis of its massive "image" data output which requires computational means for interpretation, a heightened need for quality data, and precise experimental design. We will discuss the current technologies focused on nucleic-acid-based microarray platforms. This technology has been extended to the detection of DNA binding complexes, single nucleotide polymorphisms, and in DNA copy number assessment with reasonable success. The future focus will be on improving the probe density, array flexibility, and the dynamic range of detection of the technical platform. On a larger scale, data integration and visualization, and on the intelligent utilization of prior biologic information will be the computational challenge.

Abstract One of the challenges to the effective utilization of cDNA microarray analysis in mouse models of oncogenesis is the choice of a critical set of probes that are informative for human disease. Given the thousands of genes with a potential role in human oncogenesis and the hundreds of thousands of mouse sequences available for use as probes, selection of an informative set of mouse probes can be an overwhelming task. We have developed a web based sequence mining tool using DataBase Independent (DBI) Perl to annotate publicly available sequences. The Mouse Oncochip Design Tool uses the Mouse Genome Database (MGD) developed and maintained by the Jackson Laboratories for mouse DNA sequences. There are over 380 000 sequences in their database. The output list has been ordered to present the genes more likely to be informative in a mouse model of human cancer using a candidate set of oncogenes to order the list. Mouse sequences that represent genes that are homologous with a member of a human oncogene set are listed first. In addition it provides a set of links for information on clone source gene function. Contact: http://nciarray.nci.nih.gov/cgi-bin/me/mouse_design.cgi * To whom correspondence should be addressed. 5 Current address: Department of Pathology and Biomedical Informatics, Vanderbilt University Medical Center, MCN C-3321, Nashville TN 37232, USA. 6 Current address: Department of Pharmacology, School of Medicine, University of Colorado Health Sciences Center, 4200 E Ninth Avenue, Denver CO 80262, USA. 7 Current address: Genome Institute of Singapore, 1 Research Link, IMA Building #04-01, National University of Singapore, Singapore 117604.