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Abstract

In cancer research, prediction of time to death or relapse is important for a meaningful tumor classification and selecting appropriate therapies. The accumulation of genetic alterations during tumor progression can be used for the assessment of the genetic status of the tumor. ArrayCGH technology is used to measure genomic amplifications and deletions, with a high resolution that allows the detection of down to single genes copy number changes. \\\\We propose an automated method for analysis of cancer mutations accumulation based on statistical analysis of arrayCGH data. The method consists of the four steps: arrayCGH smoothing, aberrations detection, consensus analysis and oncogenetic tree models estimation. For the second and third steps, we propose new algorithmic solutions. First, we use the adaptive weights smoothing-based algorithm GLAD for identifying regions of constant copy number. Then, in order to select regions of gain and loss, we fit robust normals to the smoothed Log$_2$Ratios of each CGH array and choose appropriate significance cutoffs. The consensus analysis step consists of an automated selection of recurrent aberrant regions when multiple CGH experiments on the same tumor type are available. We propose to associate $p$-values to each measured genomic position and to select the regions where the $p$-value is sufficiently small. \\\\The aberrant regions computed by our method can be further used to estimate evolutionary trees, which model the dependencies between genetic mutations and can help to predict tumor progression stages and survival times. \\\\We applied our method to two arrayCGH data sets obtained from prostate cancer and glioblastoma patients, respectively. The results confirm previous knowledge on the genetic mutations specific to these types of cancer, but also bring out new regions, often reducing to single genes, due to the high resolution of arrayCGH measurements. An oncogenetic tree mixture model fitted to the Prostate Cancer data set shows two distinct evolutionary patterns discriminating between two different cell lines. Moreover, when used as clustering features, the genetic mutations our algorithm outputs separate well arrays representing 4 different cell lines, proving that we extract meaningful information. }