Abstract
In the current study, the protein expression maps (PEMs) of 26 breast
cancer cell lines and three cell lines derived from normal breast
or benign disease tissue were visualised by high resolution two-dimensional
gel electrophoresis. Analysis of this data was performed with ChiClust
and ChiMap, two analytical bioinformatics tools that are described
here. These tools are designed to facilitate recognition of specific
patterns shared by two or more (a series) PEMs. Both tools use PEMs
that were matched by an image analysis program and locally written
programs to create a match table that is saved in an object relational
database. The ChiClust tool uses clustering and subclustering methods
to extract statistically significant protein expression patterns
from a large series of PEMs. The ChiMap tool calculates a differential
value (either as percentage change or a fold change) and represents
these graphically. All such differentials or just those identified
using ChiClust can be submitted to ChiMap. These methods are not
dependent on any particular commercial image analysis program, and
the whole software package gives an integrated procedure for the
comparison and analysis of a series of PEMs. The ChiClust tool was
used here to order the breast cell lines into groups according to
biological characteristics including morphology in vitro and tumour
forming ability in vivo. ChiMap was then used to highlight eight
major protein feature-changes detected between breast cancer cell
lines that either do or do not proliferate in nude mice. Mass spectrometry
was used to identify the proteins. The possible role of these proteins
in cancer is discussed.
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