Target Discovery and Validation Reviews and Protocols

94 Sørlie
are based on such a platform. We have routinely used a reference strategy in
which transcripts (or genomic DNA for copy number detection) extracted from
the tumor sample were labeled with one fluorescent dye (Cy5), whereas transcripts
from a standard reference (3) were labeled with a different dye (Cy3).
Relative levels of the two transcripts were calculated by log transforming the
ratio between the two fluorescent intensities (Cy5/Cy3; red/green) (Fig. 1).
Appropriate treatment and handling of surgical specimens is crucial to obtain
high-quality, intact RNA, which is a fundamental requirement for a successful
and reliable microarray experiment. Immediate snap-freezing of clinical samples
after surgery or storage in appropriate buffers that permeate the cells and
stabilize the RNA reduce the chances of jeopardizing both the quality and the
quantity of RNA available for analysis.
The advantage of microarray experiments when applied to the study of cancer
is most clear when a large number of samples is analyzed similarly and combined
so that variation in gene expression patterns across a large number of tumors can
be investigated. For the analysis and interpretation of microarray data, several
sophisticated computational tools are available (4,5), and constantly being developed.
In general, they can be divided into unsupervised approaches that entail
searching for patterns in the data with no prior assumptions, and supervised
approaches in which predictions are generated based on existing knowledge of the
data. As more primary data are compiled on specific tissues and tumor types, integrative
analyses that explore the data in the context of other data sources may aid
in a deeper biological understanding (6). In our analyses of breast tumors, hierarchical
clustering algorithms have been applied to organize both genes and samples
into meaningful groups based on similarity in their overall expression patterns (7).
In addition, different supervised methods to analyze the gene expression profiles
in relation to existing knowledge of the tumor samples have been successfully used
(8–11). Another great challenge of microarray experiments is the storage of the
massive amounts of data that are generated in these experiments. Common standards
and ontologies for the management and sharing of data are essential and
have been put forward by the microarray community (12). At present, the challenges
of microarray technology include the use of different platforms, issues relating
to consistent reproducibility, sample variability, and high cost. Despite these
issues, more data comparing different data sets and various microarray platforms
illustrate that the underlying biology is still the largest contributing factor (13,13a).
3. Molecular Portraits of Breast Tumors
3.1. Whole-Genome Profiling
The phenotypic diversity of tumors is accompanied by a corresponding
diversity in gene expression patterns that can be captured by DNA microarrays.