Estimating cancer survival and clinical outcome based on genetic tumor progression scores

• Published in: Bioinformatics 2005-05, Vol.21 (10), p.2438-2446
• Main Authors: Rahnenführer, Jörg, Beerenwinkel, Niko, Schulz, Wolfgang A., Hartmann, Christian, von Deimling, Andreas, Wullich, Bernd, Lengauer, Thomas
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Biomarkers, Tumor - analysis; Chromosome Aberrations; Clinical outcomes; Diagnosis, Computer-Assisted - methods; Disease Progression; Fundamental and applied biological sciences. Psychology; General aspects; Genetic Markers - genetics; Genetic Predisposition to Disease - genetics; Genetic Testing - methods; Genetics, Population - methods; Humans; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Neoplasms - diagnosis; Neoplasms - genetics; Neoplasms - mortality; Neoplasms - therapy; Prognosis; Proportional Hazards Models; Risk Assessment - methods; Risk Factors; Survival Analysis; Survival Rate; Treatment Outcome
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/bti312

Motivation: In cancer research, prediction of time to death or relapse is important for a meaningful tumor classification and selecting appropriate therapies. Survival prognosis is typically based on clinical and histological parameters. There is increasing interest in identifying genetic markers that better capture the status of a tumor in order to improve on existing predictions. The accumulation of genetic alterations during tumor progression can be used for the assessment of the genetic status of the tumor. For modeling dependences between the genetic events, evolutionary tree models have been applied. Results: Mixture models of oncogenetic trees provide a probabilistic framework for the estimation of typical pathogenetic routes. From these models we derive a genetic progression score (GPS) that estimates the genetic status of a tumor. GPS is calculated for glioblastoma patients from loss of heterozygosity measurements and for prostate cancer patients from comparative genomic hybridization measurements. Cox proportional hazard models are then fitted to observed survival times of glioblastoma patients and to times until PSA relapse following radical prostatectomy of prostate cancer patients. It turns out that the genetically defined GPS is predictive even after adjustment for classical clinical markers and thus can be considered a medically relevant prognostic factor. Availability: Mtreemix, a software package for estimating tree mixture models, is freely available for non-commercial users at http://mtreemix.bioinf.mpi-sb.mpg.de. The raw cancer datasets and R code for the analysis with Cox models are available upon request from the corresponding author. Contact: [e-mail]