WHOLE GENOMIC APPROACH IN BREAST CANCER

current methods of classification of breast cancer are based
on clinical stage, histopathological characteristics and few
immunohistochemical markers. These traditional methods
are often subjective, inconsistent and do not have the ability to differentiate subtle differences that may be of importance
for developing a better understanding of the tumor and
advancing therapeutic strategies for treatment. For example,
to identify breast cancer patients who need to be
administered adjuvant systemic therapy, the criteria
followed are a combination of age, tumor size, axillary node
status, and histopathological grade and hormonal status of
the tumor. However, these criteria do not predict disease
progression and clinical outcome precisely. This uncertainty
sometimes results in patients who need adjuvant therapy
not receiving it, while some are treated unnecessarily,
thereby being exposed to the risk of side effects. Expression
profiling of thousands of genes simultaneously using DNA
microarrays has a great potential to sub-classify tumors,
which are indistinguishable with current criteria, to different
groups so that the outcome to therapy can be predicted
more precisely [39••]. Although individual gene variations
may exert major effects on drug response, this response is
often a complex trait with several polymorphic genes and
other environmental factors contributing to different
degrees to overall treatment outcome. A number of studies
have reported the gene expression profiling of breast cancer,
and those of particular relevance to pharmacogenomics are
discussed below [40••,41-46,47••,48,49••,50••,51••].
Expression profiling can be used to define subtypes of breast
cancer more precisely. Approximately 60% of breast cancer
cases are ER positive (ER+), which is a well recognized
prognostic and predictive factor in early breast cancer.
Women with ER+ status respond better to hormonal or anti-
estrogen (such as tamoxifen) therapy. However, the
immunohistochemical method for determining ER status
often produces false results. For example, alterations in the
genes involved in ER signaling pathways leading to
defective ER pathways cannot be differentiated by the above
method. Gruvberger et al have identified the 100 most
important ER discriminator genes to demonstrate that ER+
and ER negative (ER-) tumors display remarkably different
phenotypes, which they attributed to their evolution from
distinct cell lineages [40••]. Unexpectedly, only a few of the
ER discriminator genes appear to be part of the ER signaling
pathway, suggesting that the difference in gene expression
profile between ER+ and ER- tumors can only partly be
explained by the activity of a functional ER pathway in ER+
tumors. Another independent study by West et al identified
a specific expression of a set of genes, which included not
only ER and ER pathway genes, but also genes that encode
proteins that synergize with ER, such as HNF3α and the
androgen receptor, to identify the ER status [51••]. This
study also identified a gene expression pattern to categorize
lymph node status. A study by Sørlie et al [47••] refined the
previously defined subtypes of breast tumors [44,46]. A total
of 115 malignant breast tumors were analyzed by
hierarchical clustering based on patterns of expression of 534
'intrinsic' genes and were subdivided into five subgroups,
some of which were previously known and some of which
are new entities.
Microarray-based expression profiling can also identify
expression patterns of a small set of markers that can predict
the outcome in a patient with a tumor or response of a
patient to a specific therapy. Landmark studies by van de vijvar et al revealed two genetic signatures, one correlated
with a good prognosis and the other with a poor prognosis,
based on the overall survival and development of distant
metastasis [50••,49••]. In their first study, oligonucleotide
microarrays containing 25,000 genes were used to study the
expression patterns of 98 primary tumors from young
patients under the age of 55 years with lymph node-negative
type cancer [49••]. Supervised clustering using information
about the clinical outcome in these patients identified a set
of 70 genes with an expression pattern that allowed highly
accurate classification of the patients into those with a poor
prognosis and those with good prognosis. A limitation of
this study was that patients with a known outcome were
used. To provide a more accurate estimate of the risks of
metastasis associated with the two gene expression patterns,
and to substantiate that the gene expression profile of breast
cancer is a clinically meaningful tool, van de Vijver et al
studied a cohort of 295 patients with either lymph node-
negative or lymph node-positive breast cancer [50••].
Examination of the expression levels of the previously
identified 70 predictor genes allowed the classification of 115
patients to the good-prognosis category and 180 patients to
the poor-prognosis category. Interestingly, the prognosis
profile did not appear to depend on lymph node status, as
patients with node-negative and node-positive status were
uniformly present in both categories. However, the
molecular profiling-based classification correlated well with
the age of the patient, histological grade and ER status of the
tumor. The prognosis profile predicted both the survival
and the risk of distant metastases. The overall ten-year
survival rate was 94.5% in the good-prognosis group and
54.6% in the poor-prognosis group. The probability of
remaining free of distant metastases at ten years was 85.2%
in the good-prognosis group and 50.6% in the poor-
prognosis group. A major implication of this study is that
molecular signature-based prediction of clinical outcome is
better than any of the currently used criteria