Inferring the location and effect of tumor suppressor genes by instability-selection modeling of allelic-loss data.
Cancerous tumor growth creates cells with abnormal DNA. Allelic-loss experiments identify genomic deletions in cancer cells, but sources of variation and intrinsic dependencies complicate inference about the location and effect of suppressor genes; such genes are the target of these experiments and are thought to be involved in tumor development. We investigate properties of an instability-selection model of allelic-loss data, including likelihood-based parameter estimation and hypothesis testing. By considering a special complete-data case, we derive an approximate calibration method for hypothesis tests of sporadic deletion. Parametric bootstrap and Bayesian computations are also developed. Data from three allelic-loss studies are reanalyzed to illustrate the methods.
Newton
M A
MA
Department of Statistics, University of Wisconsin-Madison, 1210 West Dayton Street, Madison, Wisconsin 53706-1685, USA. newton@stat.wisc.edu
Lee
Y
Y
D000230:Adenocarcinoma / Q000235:genetics; D018263:Adenocarcinoma, Follicular / Q000235:genetics; D000483:Alleles; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D002872:Chromosome Deletion; D017353:Gene Deletion; D016147:Genes, Tumor Suppressor; D006801:Humans; D016013:Likelihood Functions; D008126:Lod Score; D019656:Loss of Heterozygosity; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology; D011471:Prostatic Neoplasms / Q000235:genetics; D013964:Thyroid Neoplasms / Q000235:genetics