Discussion
With a CGH experiment chromosomal imbalances in entire genomes
can be detected quantitatively. To do this requires dealing with
a lot of complex issues. The program described in this paper is
based on an existing program for karyotyping. For the evaluation
steps it is advantageous if the chromosomes were organized in
a karyotyp.
Speed, easy usability and accuracy are the main performance criteria
for the CGH program. The generation of a CGH karyogram (including
correction of optical shift, segmentation and classification)
by an experienced user takes about 10 to 15 minutes.
The segmentation of the chromosomes is performed by manually
setting a single global gray value threshold for the entire DAPI
image. The interactive thresholding method is preferred to an
automatic histogram based thresholding method because its superiority
has been proved in many investigations.8,9 The limitations
of histogram methods are usually connected with the fact that
the histogram structure and image contents have little in common
(since histograms do not include any information about local relations
in the image). It should be investigated if segmentation methods
which include global information and apriori knowledge can be
used for automatic chromosome segmentation.1,5,10
The representation of the chromosomes in form of a CGH karyogram
facilitates the evaluation of the CGH experiment. In the evaluation
process of a CGH experiment the user can add as many metaphases
he or she wants. Averaging the pictures and profiles from several
homologous chromosomes improves the statistical fidelity of the
detected genetic alterations. This averaging will stop systematic
mistakes that could lead to a wrong interpretation of the results.
It leads to smooth local variations caused by random noise and
reduces the influence of errors that were caused from incorrect
lighting or mistakes in the recording or in the analysis of the
image. However, it might decrease the resolution capability of
a CGH experiment. The resolution of DNA gains and losses in the
test genome is highly influenced by the quality of the fluorescence
images and the algorithms used for chromosome averaging.
The result in form of a mean CGH karyogram together with the averaged
ratio profiles is shown at all times. The view of the CGH karyogram
can be switched between DAPI, FITC, TRITC and FITC/TRITC ratio.
At each step the program allows the user to visually inspect all
chromosomes which are included in the averaging process. Chromosomes
can be then excluded from the evaluation if they are incorrectly
segmented, are incorrectly stretched or are unsuitable for the
evaluation due to other causes.
The program allows not only for the calculation of mean chromosomes
and the standard deviation, but also for statistical tests to
be carried out in order to test the significance of an alteration.
Through statistical tests it is possible to detect obvious outliers,
that, for example, indicate faulty digital image processing or
misclassification, and exclude them from the calculation.
As mentioned above about ten good quality metaphases should be
evaluated to get stable results for a CGH experiment. Nevertheless,
the optimal number of metaphases that are needed to be evaluated
for reliable CGH analysis depends on the variability of the data.
The higher the variability the more metaphases must be evaluated.
This is true only to a certain extent, from where a CGH analysis
makes no more sense. The variability critically depends on the
quality of the hybridization and the digital images and the image
processing . In our opinion it should be possible to use a statistical
test based on the measured data to determine the number of metaphases
needed to be evaluated for an actual case.
Up to now only a visual inspection of the results of a CGH experiment
is possible using the final ratio image and the ratio profiles.
We are working on an automation of data interpretation that is
statistically validated. This is desirable for reasons of reproducibility
that is the basis for comparing results. Especially for interpreting
a so called super karyogram, a statistically validated interpretation
is necessary.
The overall result of a CGH experiment depends on the performance
of each step. Every step in the preparation and the image analysis
must be optimized in order to obtain reproducible results. With
a greater exactness, the resolution capability of the CGH is raised.
That means that it would be possible for changes with lower local
extensions and lower strengths to be detected.
In the same way that it is possible for a case to be ascertained
over several metaphases, it is also possible for a super karyogram
to be generated through the combination of several cases of a
tumor entity.(summary of a series of CGH experiments of a particular
tumor entity).
Through the comparison of individual cases with super karyograms
of different tumor classes it should be possible to carry out
a classification of the individual case. It is important here
to once again emphasize that this can only function if a successful
complete standardization of all the steps of a CGH experiment
can be realized including an automated statistically validated
interpretation of the results.