Аннотация
CONTEXT: Gene expression profiling may be useful for prognostic and
therapeutic strategies in breast carcinoma. OBJECTIVES: To demonstrate
the value in integrating genomic information with clinical and pathological
risk factors, to refine prognosis, and to improve therapeutic strategies
for early stage breast cancer. DESIGN, SETTING, AND PATIENTS: Retrospective
study of patients with early stage breast carcinoma who were candidates
for adjuvant chemotherapy; 964 clinically annotated breast tumor
samples (573 in the initial discovery set and 391 in the validation
cohort) with corresponding microarray data were used. All patients
were assigned relapse risk scores based on their respective clinicopathological
features. Signatures representing oncogenic pathway activation and
tumor biology/microenvironment status were applied to these samples
to obtain patterns of deregulation that correspond with relapse risk
scores to refine prognosis with the clinicopathological prognostic
model alone. Predictors of chemotherapeutic response were also applied
to further characterize clinically relevant heterogeneity in early
stage breast cancer. MAIN OUTCOME MEASURES: Gene expression signatures
and clinicopathological variables in early stage breast cancer to
determine a refined estimation of relapse-free survival and sensitivity
to chemotherapy. RESULTS: In the initial data set of 573 patients,
prognostically significant clusters representing patterns of oncogenic
pathway activation and tumor biology/microenvironment states were
identified within the low-risk (log-rank P = .004), intermediate-risk
(log-rank P = .01), and high-risk (log-rank P = .003) model cohorts,
representing clinically important genomic subphenotypes of breast
cancer. As an example, in the low-risk cohort, of 6 prognostically
significant clusters, patients in cluster 4 had an inferior relapse-free
survival vs patients in cluster 1 (log-rank P = .004) and cluster
5 (log-rank P = .03). Median relapse-free survival for patients in
cluster 4 was 16 months less than for patients in cluster 1 (95%
CI, 7.5-24.5 months) and 19 months less than for patients in cluster
5 (95% CI, 10.5-27.5 months). Multivariate analyses confirmed the
independent prognostic value of the genomic clusters (low risk, P
= .05; high risk, P = .02). The reproducibility and validity of these
patterns of pathway deregulation in predicting relapse risk was established
using related but not identical clusters in the independent validation
cohort. The prognostic clinicogenomic clusters also have unique sensitivity
patterns to commonly used cytotoxic therapies. CONCLUSIONS: These
results provide preliminary evidence that incorporation of gene expression
signatures into clinical risk stratification can refine prognosis.
Prospective studies are needed to determine the value of this approach
for individualizing therapeutic strategies.
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