|
70 gene signature much more accurate than adjuvant online in predicting prognosis
for early stage breast cancer. Since adjuvant online still does not include her2 status as one of the deciding factors, it would seem the 70 gene signature is far superior for her2neu + patients seeking to discover their prognosis for decisions regarding treatment alternatives
Gene Signature Assesses Breast Cancer Outcomes [Journal of the National Cancer Institute]
A test that looks at the expression of 70 genes linked to breast cancer can accurately assess a patient's risk of recurrence or death, according to an article in the September 6 issue of the Journal of the National Cancer Institute.
In previous research, scientists at the Netherlands Cancer Institute in Amsterdam identified 231 genes associated with patient outcomes in a group of patients 55 years or younger with breast tumors under 5 cm. The 231 genes were reduced to a core group of 70 that make up a genetic signature to predict outcomes in a separate group of patients from the same institution.
To determine whether the 70-gene signature performed better than traditional risk classifiers, scientists from the international consortium TRANSBIG assigned 307 patients to high- and low-risk groups based on scores from the 70-gene signature and on traditional risk assessment with the Adjuvant Online! software. The patients were followed for 13.6 years and assessed for disease recurrence and death.
The authors found that the 70-gene signature was a more accurate predictor of disease outcomes than assessment with the Adjuvant Online! software. Moreover, statistical analysis indicated that most of the prognostic information provided by traditional risk classifiers was included in the gene classifier.
The authors write, "These results indicate that the gene signature adds independent prognostic information to that provided by a risk assessment based solely on clinico-pathologic factors."
In an accompanying editorial, Richard Simon, D.Sc., of the National Cancer Institute in Rockville, Md., discusses the strengths and limitations of the study by Buyse et al. He suggests improvements that might have been made in the study's design, and writes that the research, "Illustrate[s] many desirable features of gene expression profiling studies for optimizing treatment selection for individual patients."
The 70-gene signature will be prospectively tested in a larger study called MINDACT, a trial coordinated by the European Organization for Research and Treatment of Cancer. This trial will assess whether the 70-gene signature can better identify who can safely be spared adjuvant chemotherapy in 6000 women with node-negative early-stage breast cancer. If validated, the tool could safely spare 1 in 6 women the burden of adjuvant chemotherapy.
OPEN ACCESS: Validation and Clinical Utility of a 70-Gene Prognostic Signature for Women With Node-Negative Breast Cancer [Journal of the National Cancer Institute]
Background: A 70-gene signature was previously shown to have prognostic value in patients with node-negative breast cancer. Our goal was to validate the signature in an independent group of patients.
Methods: Patients (n = 307, with 137 events after a median follow-up of 13.6 years) from five European centers were divided into high- and low-risk groups based on the gene signature classification and on clinical risk classifications. Patients were assigned to the gene signature low-risk group if their 5-year distant metastasis-free survival probability as estimated by the gene signature was greater than 90%. Patients were assigned to the clinicopathologic low-risk group if their 10-year survival probability, as estimated by Adjuvant! software, was greater than 88% (for estrogen receptor [ER]-positive patients) or 92% (for ER-negative patients). Hazard ratios (HRs) were estimated to compare time to distant metastases, disease-free survival, and overall survival in high- versus low-risk groups.
Results: The 70-gene signature outperformed the clinicopathologic risk assessment in predicting all endpoints. For time to distant metastases, the gene signature yielded HR = 2.32 (95% confidence interval [CI] = 1.35 to 4.00) without adjustment for clinical risk and hazard ratios ranging from 2.13 to 2.15 after adjustment for various estimates of clinical risk; clinicopathologic risk using Adjuvant! software yielded an unadjusted HR = 1.68 (95% CI = 0.92 to 3.07). For overall survival, the gene signature yielded an unadjusted HR = 2.79 (95% CI = 1.60 to 4.87) and adjusted hazard ratios ranging from 2.63 to 2.89; clinicopathologic risk yielded an unadjusted HR = 1.67 (95% CI = 0.93 to 2.98). For patients in the gene signature high-risk group, 10-year overall survival was 0.69 for patients in both the low- and high-clinical risk groups; for patients in the gene signature low-risk group, the 10-year survival rates were 0.88 and 0.89, respectively.
Conclusions: The 70-gene signature adds independent prognostic information to clinicopathologic risk assessment for patients with early breast cancer.
[NOTE: For the full paper, please follow the supplied link.]
OPEN ACCESS: EDITORIAL: Development and Evaluation of Therapeutically Relevant Predictive Classifiers Using Gene Expression Profiling [Journal of the National Cancer Institute; Subscribe; Sample]
The studies in this issue, taken together, illustrate many desirable features of gene expression profiling studies for optimizing treatment selection for individual patients. Clinical trial designs for using predictive classifiers in conjunction with the development of new drugs are very different than those described here, however. Although the expression profiles utilized by Asgharzadeh et al. and Buyse et al. provide some biologic information about the nature of these cancers under study and potential molecular targets, it is important to recognize that the objective of predicting outcome for patients receiving a treatment is distinct from the objective of understanding the pathogenesis of the disease. Also, it is a mistake to criticize or reject a predictive classifier because its components are not seen as valid "disease biomarkers." Establishing a biomarker as a valid surrogate for measuring disease progression and clinical benefit is much more difficult than establishing that a classifier has clinical benefit for treatment selection. Because of the very distinct uses of the term "biomarker" and the very distinct kinds of "validation" that are appropriate, these terms should be used very carefully.
|
Linear Mode
