Modeling the DNA copy number aberration patterns in observational high-throughput cancer data.

ABSTRACT

The process of occurrence of genomic aberrations over time in the genetic material of cancer cells reflects the progression of the cancer. Modern technologies like aCGH (array Comparative Genomic Hybridization) and MPS (Massive Parallel Sequencing) provide high-resolution measurements of DNA copy number aberrations, that reveal the full scale of genomic aberrations. A continuous time Markov chain model is proposed to describe the accumulation of aberrations over time. Time however is a latent variable (with the number of aberrations as a proxy). Integrating out time, yields the distribution of the observed DNA copy number data. The model parameters are estimated from high-dimensional DNA copy number data by means of penalized maximum pseudo- and likelihood and method of moments procedures. Having fitted the model, posterior time estimates of the advancement of each sample's cancer are obtained and the most likely locations of a sample's aberrations are predicted. The three estimation methods are compared in a simulation study. The paper closes with an application of the proposed methodology on cancer data.