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Gene Expression Plus Conventional Predictors of Breast Cancer
In a retrospective study, researchers combined conventional predictors of breast
cancer outcomes − factors such as patient age, tumor size, and so on − with informa-
tion about gene expression profi les in nearly a thousand breast cancer tumor sam-
ples (Acharya et al. 2008 ). Their fi ndings suggest that incorporation of gene
expression signatures into clinical risk stratifi cation can refi ne prognosis and poten-
tially guide treatment of breast cancer. Identifi cation of subgroups may not only
refi ne predictions about patient outcomes, but also provides information about the
underlying biology and the tumor microenvironment because gene expression pat-
terns reveal different genetic pathways that are activated or silenced in different
tumors. Tumors in the high-risk group with the best outcomes tended to have low
expression of cancer risk genes, chromosomal instability, etc. On the other hand,
tumors that have high expression of genes associated with oncogenic pathway acti-
vation, wound healing, etc., tend to be associated with poorer outcomes. Genetic
signatures within high-, medium-, and low-risk groups were associated with differ-
ent responses to chemotherapy treatments. Prospective studies are needed to deter-
mine the value of this approach for individualizing therapeutic strategies.
Typically, ER-positive tumors, which are more common in older women, can be
treated with drugs that inhibit estrogen production. However, not all tumors that
start out estrogen-receptor positive remain so. Some estrogen-receptor positive
tumors respond to anti-estrogen therapy at fi rst, but eventually become estrogen-
receptor negative and resistant to these drugs. This transition is associated with
patient relapse and poor overall outcomes. It is possible to classify ER-positive
tumors into low-, medium-, and high-risk groups depending on the genetic signa-
ture in the tumors after patients start treatment, rather than just looking for the spe-
cifi c gene signature in tumors before treatment. In case of treatment with letrozole
(Novartis’ Femara), a drug that blocks estrogen production, clinical trials have
shown that ~10 % to 15 % of estrogen-receptor positive tumors behave in a com-
pletely hormone refractory way. This approach can predict which seemingly low-
risk tumors are destined to become high risk and help guide treatment accordingly.
This may eventually change the way that physicians design ER receptor positive
breast cancer therapies. For example, it may be possible to target aggressive, post-
surgery chemotherapy to those with higher-risk tumors.
Earlier studies at NCI using mouse models and human breast cancer populations
have shown that metastasis susceptibility is an inherited trait. This same combined
approach facilitated the identifi cation of a number of candidate genes that, when
dysregulated, have the potential to induce prognostic gene expression profi les in
human data sets. A further series of expression profi ling experiments in a mouse
model of metastatic breast cancer have shown that both the tumor epithelium and
invading stromal tissues contribute to the development of prognostic gene signa-
tures (Lukes et al. 2009 ). Furthermore, analysis of normal tissues and tumor trans-
plants suggests that prognostic signatures result from both somatic and inherited
components, with the inherited components being more consistently predictive.
10 Personalized Therapy of Cancer