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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. }
