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Gene Expression Signatures, Clinicopathological Features, and Individualized Therapy in Breast Cancer
Chaitanya R. Acharya, MS;
David S. Hsu, MD, PhD;
Carey K. Anders, MD;
Ariel Anguiano, MD;
Kelly H. Salter, BS;
Kelli S. Walters, BS;
Richard C. Redman, MD;
Sascha A. Tuchman, MD;
Cynthia A. Moylan, MD;
Sayan Mukherjee, PhD;
William T. Barry, PhD;
Holly K. Dressman, PhD;
Geoffrey S. Ginsburg, MD, PhD;
Kelly P. Marcom, MD;
Katherine S. Garman, MD;
Gary H. Lyman, MD;
Joseph R. Nevins, PhD;
Anil Potti, MD
JAMA. 2008;299(13):1574-1587.
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.
Author Affiliations: Duke Institute for Genome Sciences and Policy (Drs Hsu, Anguiano, Redman, Tuchman, Moylan, Mukherjee, Barry, Dressman, Ginsburg, Garman, Nevins, and Potti, Mr Acharya, and Mss Salter and Walters) and Institute for Statistics and Decision Sciences (Drs Mukherjee and Barry), Duke University; and Department of Medicine, Duke University Medical Center (Drs Hsu, Anders, Anguiano, Redman, Tuchman, Moylan, Marcom, Garman, Lyman, and Potti), Durham, North Carolina.
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