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Microarray gene expression profiling:
challenge pancreatic cancers through the
understanding of its biology and improving
diagnosis and treatment.
E. Missiaglia
Dipartimento di Patologia, Sez. Di Anatomia Patologica,
Università di Verona
Although pancreatic cancer is a relatively uncommon disease
(fifth most common cause of cancer-related death in western
countries), its mortality virtually coincides with incidence,
having a five-year survival of less than 5%. This is mainly
due to its silent clinical course, late clinical manifestation,
and its resistance to conventional modes of therapy 1 2 .
An improved understanding of pancreas cancer genetics and
biology are therefore the only means to provide new markers
for earlier diagnosis and to identify potential targets for therapeutic
intervention. Unfortunately, molecular analyses of
pancreatic cancer have been hindered by the low cancer cellularity
of this neoplasm, due to the prominent non-neoplastic
reaction. However, various enrichment techniques such as
propagating neoplatic cells in tissue culture, xenografting,
cryostat-enrichment, and laser capture microdissection of
primary lesions have partially overcome this problem.
Recently, several large-throughput methods have been employed
to analyse the gene expression levels among pancreatic
cancers, pancreatitis and normal tissue. These include RDA,
SAGE and hybridisation on high density spotted nylon filters,
glass DNA microarrays or Affimetrix chips. However, DNA
array/microarray technology is becoming one of the most productive
methods for characterizing physiological and pathological
processes. Arrays are typically positive charged membranes,
usually from 12 to 20 cm 2 , in which different cDNAs
are immobilized in defined positions. Microarrays are normally
glass microscope slides in which an area as small as 1 cm 2
contains thousands of spots, each harboring DNA from a specific
gene or chromosomal area. Microarrays can be constructed
with either cDNA or oligonucleotides, usually prepared
and stored in microplates. Alternatively, oligonucleotides
corresponding to specific genes sequences can be
synthesized directly on the surface of the array using photolithographic
techniques (Affymetrix Chip). The principal
steps in comparing the gene expression profile of different
samples begin with the extraction of mRNA. This represents a
crucial aspect in the analysis that can strongly affect the quality
and the goodness of the results 3 . Once isolated the mRNA
is retrotranscribed into cDNA using inverse transcriptase with
variable efficiency. In this phase the probe can be labelled using
fluorescent dyes that emit light in a specific range after excitation
induced with laser at different wavelengths or radioactive
nucleotides. In addition, it is also possible increase
the probes (particularly when the original sample is available
in limited amount) by generating a double-strand cDNA that
can be amplified linearly (using T7 polymerase) 4 or exponentially
(using canonical DNA polymerase) 5 . While only one
sample is hybridised in the Affymetrix chips or in a membrane
for each reaction, in the cDNA microarray the information obtained
comes always from a comparison between two different
samples, which labelled probes are coohybridised on the
same slide and compete for the target sequence in a stringent
condition.
After the hybridisation the slide is scanned and the images
obtained from the different dyes can be superimposed and
compared. In that respect, several packages have been developed
to explore microarray data from images analysis to the
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final step of the data minding. Unfortunately the scientific
community haven’t found yet consensus around a standard
methods widely applicable to cDNA microarray technology
leaving to the researcher the choice between scores of possibilities
not always standardized.
Transcriptional profiling using DNA arrays has an excellent
potential for the discovery of novel markers for early diagnosis,
prognosis, and potential therapeutic targets. This may
also lead to advances in tumor classification. For example,
Golub et al. 6 showed that the expression pattern provided
new insights into tumor pathology, including cell origin,
stage, grade, and response to the therapy. Alizadeh et al. 7
made the first correlation between gene expression pattern
and disease outcome studying diffuse large B-cell lymphoma,
where progression of disease correlated with a distinct
pattern of gene expression. Such reports are becoming
more common and exemplify the power that the differential
expression profiles generated by cDNA arrays/microarrays.
Nowadays, several reports have emerged regarding the
analysis of common pancreatic cancer using cDNA arrays as
well as Affymetrix chips. Gress et al. 8 generated the first
gene expression profile of panceatic cancers using hybridization
to filters carrying cDNA clones derived from pancreatic
cancer cell lines to restrict the expression profile to genes
more likely derived from the malignant epithelial component
of the tumor. They found that a total of 369 distinct clones
were preferentially expressed in pancreatic cancer and from
those 26% were known genes. Furthermore, in collaboration
with the group of Prof. N.R. Lemoine at the Cancer Research
UK, we studied expression profiles of epithelial cancer cell
and the desmoplastic reaction on cDNA arrays using material
obtained by fine needle aspiration of fresh surgical specimens
9 as well as the expression profiles of 19 pancreatic cancer
cell lines 10 . One of the advantages of using these type of
samples is that pure tumor cells are tested without any or low
contamination from fibroblasts, since, as already mention
above, pancreatic cancers are characterized by a strong stromal
reaction that may represent the 80-90% of tumor mass.
In both studies several genes, some of which already known
to be involved in pancreatic cancer, were found differentially
expressed compared to normal pancreas. In particular, in
the cell line study genes with a wide variety of functions
were identified among the overrepresented transcripts, ranging
from tight junction proteins (claudins 3, 4 and 5), ion
homeostasis regulators (S100P, S100A4, cysteine-rich heart
protein), transcription factors (forkhead box J1, Id2) and extracellular
matrix proteins (MMP2, MMP7, TIMP2, plasminogen
activator). Equally, among the underrepresented
genes there were several putative tumor suppressor genes
(FAT tumor suppressor homologue 2, IGFBP7, S100A2,
TP55) as well as cell cycle-related gene GADD45A, and several
cell adhesion genes (cadherin 3, cysteine-rich angiogenic
inducer 61, plakophilin 1). In addition, employing the
class comparison analysis we were able to isolate a set of
genes that could separate the cell lines on the basis of their
origin.
In the hierarchical clustering on the above figure these genes
are divided in three major subset as consequence of their different
expression behavior in the cell lines deriving from primary
tumors, liver metastasis, ascites and lymph node metastasis.
Among these subsets, one included genes often up-regulated
in cell lines that originated from lymph nodes (namely
FYB, IFITM1, SRGAP2, NID2, RHOBRB2, ABCG1,
SRC1, LIMK2, LMO2, p8). Interestingly, most of those
genes are involved in the cell communication and in the signal
transduction.