Partial least squares proportional hazard regression for application to DNA microarray survival data

• Published in: Bioinformatics (Oxford, England) Vol. 18; no. 12; pp. 1625 - 1632
• Main Authors: NGUYEN, Danh V, ROCKE, David M
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.12.2002
• Subjects: Biological and medical sciences; Breast Neoplasms - classification; Breast Neoplasms - genetics; Breast Neoplasms - mortality; Fundamental and applied biological sciences. Psychology; Gene Expression - genetics; Gene Expression Regulation, Neoplastic - genetics; General aspects; Humans; Least-Squares Analysis; Lymphoma, B-Cell - classification; Lymphoma, B-Cell - genetics; Lymphoma, B-Cell - mortality; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Genetic; Neoplasms - genetics; Neoplasms - mortality; Oligonucleotide Array Sequence Analysis - methods; Proportional Hazards Models; Regression Analysis; Reproducibility of Results; Sensitivity and Specificity; Survival Analysis; Breast disease; Complementary DNA; Transcription; Prediction; DNA chip; Malignant hemopathy; B-Lymphocyte; Models; Gene expression; Web site; Bioinformatics
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/18.12.1625

Microarrays are increasingly used in cancer research. When gene transcription data from microarray experiments also contains patient survival information, it is often of interest to predict the survival times based on the gene expression. In this paper we consider the well-known proportional hazard (PH) regression model for survival analysis. Ordinarily, the PH model is used with a few covariates and many observations (subjects). We consider here the case that the number of covariates, p, exceeds the number of samples, N, a setting typical of gene expression data from DNA microarrays. For a given vector of response values which are survival times and p gene expressions (covariates) we examine the problem of how to predict the survival probabilities, when N << p. The approach taken to cope with the high dimensionality is to reduce the dimension using partial least squares with the response variable as the vector of survival times. After dimension reduction, the extracted PLS gene components are then used as covariates in a PH regression to predict the survival probabilities. We demonstrate the use of the methodology on two cDNA gene expression data sets, both containing survival data. The first data set contains 40 diffuse large B-cell lymphoma (DLBCL) tissue samples and the second data set contains 49 tissue samples from patients with locally advanced breast cancer in a prospective study.