Genetic Information Improves Prognosis And Relapse-Free Survival Prediction For Breast Cancer Patients

Editor's Choice
Main Category: Breast Cancer
Also Included In: Genetics;  Cancer / Oncology
Article Date: 02 Apr 2008 - 0:00 PDT

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According to an article published in JAMA, doctors who breast cancer patients and integrate gene expression signatures with clinical and other risk factors may be better at predicting a patient's response to chemotherapy. Chaitanya R. Acharya, M.S. (Duke Institute for Genome Sciences and Policy, Duke University, Durham, N.C.) and colleagues also find that the genetic information helps to refine estimates of relapse-free survival.

There are few studies that investigate how genomic information and traditional clinical risk factors can be combined to improve the assessment of clinical risk and the prediction of how well a patient responds to therapy. The authors note that, "The advent of genomic technology for the analysis of human tumor samples has now added an additional source of information to aid prognosis and clinical decisions. In particular, the development of genomic profiles that accurately assess risk of recurrence offers the hope that this information will more precisely define clinical outcomes in breast cancer. The dimension and complexity of such data provide an opportunity to uncover clinically valid trends that can distinguish subtle phenotypes [physical manifestations] in ways that traditional methods cannot."

Acharya and colleagues set out to determine if improvements could be made in diagnoses and therapeutic strategies for early stage breast cancer the value by integrating genomic information with clinical and pathological risk factors. They procured a sample of patients with early stage breast cancer who were cleared for supplemental chemotherapy. With 573 patients in the initial discovery set and 391 in the validation cohort, a total of 964 breast tumor samples were used. The authors used the participants' clinicopathological features in order to assign relapse risk scores. In addition, the researchers applied gene expression signatures (characteristic profiles) to the results of genetic tests in order to analyze patterns of deregulation. They focused on patterns that are associated with relapse risk scores in order to refine prognosis with only the clinicopathological prognostic model. Lastly, the researchers used predictors of the response to chemotherapy in early stage breast cancer to further characterize clinically important diversity.

The results of the investigation led researchers to find that combining gene expression signatures with clinical risk groups could improve prognosis for patients in low, intermediate, and high risk subgroups. In addition, the integration of genomic information helped to predict relapse-free survival and chemotherapy response.

The researchers conclude: "Pending future prospective clinical validation, these results provide preliminary evidence that the profusion of gene expression signatures in defining breast cancer, if used appropriately, represent less of a paradox and should be viewed as an important complementary approach to current clinicopathological risk stratification systems. Furthermore, knowledge of increased likelihood of sensitivity to specific chemotherapeutic agents from a repertoire of drugs that are commonly used to treat breast cancer is something that could be more immediately used in current clinical practice, once issues regarding cost and accessibility are addressed, in instances wherein multiple chemotherapeutics or chemotherapeutic combinations are Food and Drug Administration approved, as in early stage breast cancer, and are considered the standard of care."

An accompanying editorial, written by Chiang-Ching Huang, Ph.D. and Markus Bredel, M.D., Ph.D. (Feinberg School of Medicine, Northwestern University, Chicago), suggests that the findings by Acharya and colleagues are quite useful:

"In essence, the study by Acharya et al demonstrates the potential value of using microarray-based gene signatures to refine outcome predictions. In an attempt to tailor risk estimation, these investigators shy away from pure metagene predictors but instead focus on genes with mechanistic implication in breast cancer. Because these genes represent potential targets for specific molecular therapy, this approach represents an advance in the changing landscape of oncology toward individualized patient management."

Gene Expression Signatures, Clinicopathological Features, and Individualized Therapy in Breast Cancer
Chaitanya R. Acharya; David S. Hsu; Carey K. Anders; Ariel Anguiano; Kelly H. Salter; Kelli S. Walters; Richard C. Redman; Sascha A. Tuchman; Cynthia A. Moylan; Sayan Mukherjee; William T. Barry; Holly K. Dressman; Geoffrey S. Ginsburg; Kelly P. Marcom; Katherine S. Garman; Gary H. Lyman; Joseph R. Nevins; Anil Potti
JAMA (2008). 299[13]: 1574 - 1587.
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Written by: Peter M Crosta
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