Target Discovery and Validation Reviews and Protocols

Molecular Profiling of Breast Cancer 103
4.2. Prediction
Despite reduced mortality because of earlier diagnosis and implementation of
adjuvant chemo- and hormone therapies, breast cancer is still the most common
cause of cancer death in women worldwide (39). The patients selected for adjuvant
therapy (based on current criteria) experience a reduction is death hazard
ratio only in the range of 35–30% (40). Even with moderate improvement in outcome
with use of novel drug combinations (41), we are left with the truth that
adjuvant therapy may cure only a minority of the patients. To select patients for
adjuvant treatment, both prognostic and predictive factors are used. In brief, a
prognostic factor defines outcome with respect to survival or relapse-free survival
in a group of patients (ideally) not exposed to systemic therapy. In contrast, a predictive
factor should define sensitivity of a tumor to a distinct therapeutic agent.
With the exception of expression of ER-α and progesterone receptor for
endocrine therapy and ERBB2+ amplification for the response to anti-ERBB2
treatment (Trastuzumab ® ), no predictive factor has unequivocally been accepted.
Thus, the selection of patients for adjuvant therapy is mainly based on prognosis
rather than sensitivity to treatment. Very few microarray studies have addressed
the question of prediction of treatment response by using gene expression patterns
(42–45). However, the predictive values reported in these studies were not robust
enough for use in clinical practice. A key problem when characterizing “biological
systems” is the high degree of genetic redundancy. For example, manipulating
TP53 in TP53-defective cell lines may result in altered expression of several
hundred genes (46,47). It is most likely that only a few of these genes are associated
with the execution of growth arrest or apoptosis. Similarly, the observation
that prognosis or response to drug therapy varies between tumors that are characterized
by different gene expression patterns is most likely because there are small
“subgroups” of genes within the clusters whose function determines the outcome.
The question remains how to identify these targets. The mechanisms involved in
sensitivity, resistance, or both to therapy are most likely complex and multifactorial;
thus, prognosis, or the risk for a particular outcome, should be assessed as a
vector composed of several parameters: metastatic potential, growth rate of the
tumor, and effect of therapy (1). Different subgroups of tumors from the same
organ may respond differently to therapy: hence, molecular stratification of
tumors is needed to be able to uncover biologically and statistically valid relationships
to treatment response.
5. Molecular Characteristics of the Subtypes
5.1. Proliferation Cluster
The previously defined proliferation cluster is a group of genes whose levels
of expression correlate with cellular proliferation rates (15,16). Expression of