According to researchers, patterns of gene expression can help refine prognosis in early breast cancer.
By Michael Smith, North American Correspondent, MedPage Today
Durham, N.C., April 1 — Patterns of gene expression can help refine prognosis in early breast cancer, researchers here said.
In a retrospective study, gene patterns were able to tease out differences in outcomes even among patients with identical prognoses based on clinical and pathological factors, Anil Potti, M.D., of Duke University, and colleagues, reported in the April 2 issue of the Journal of the American Medical Association.
The findings are preliminary but show that genomic technology can be used as “an additional source of information to aid prognosis and clinical decisions,” they said.
Rather than look at disrupted genes, the researchers examined de-regulated gene expression pathways, including genes that defined such things as chromosomal instability, wound healing, and invasiveness.
To study the effects of such pathways, the researchers used tissue samples from 964 women with early stage breast cancer, dividing the samples into an initial discovery set of 573 and a validation set of 391.
They were stratified into low-, intermediate-, and high-risk groups by the use of the Adjuvant! online prognostic model and analysis of outcomes showed a significant difference (P<0.001) in relapse-free survival among the three groups.
Within each group, however, the genomic analysis showed “clusters” of de-regulated pathways that were associated with better or worse outcomes, the researchers found.
For example, among women in the low-risk group, activation of the tumor necrosis factor-α, SRC, RAS, and β-catenin pathways — dubbed cluster one — was associated with better relapse-free survival than activation of the wound healing, invasiveness, and chromosomal instability pathways (cluster four).
The difference was significant with a log-rank P=0.004.
In clinical terms, the patients in the first group had a median relapse-free survival time that was 16 months longer than patients in cluster four.
Another cluster — featuring activation of the chromosomal instability, SRC, MYC, and β-catenin pathways — also conferred greater relapse-free survival than cluster four.
The difference in that case was 19 months and was significant with a log-rank P=0.04.
Similar results — for different clusters of pathways — were seen in the intermediate- and high-risk groups, as well as in the validation cohort.
Clusters of activated pathways also appeared to predict response to various chemotherapy drugs, the researchers found.
For instance, among patients deemed to be high-risk by the clinicopathological model, patients with a poor genomic prognosis (activated tumor necrosis factor-α, SRC, RAS, and β-catenin pathways) were predicted to be sensitive to fluorouracil and resistant to docetaxel compared with patients with a good genomic prognosis (activation of the wound healing, chromosomal instability, epigenetic stem cell, and PI3K pathways).
The converse was also true.
“Taken together, these data further emphasize the heterogeneity within each of the prognostic subclasses of breast cancer, while also suggesting opportunities for therapeutic strategies that could potentially improve patient outcomes,” the researchers said.
“These observations provide an approach to developing strategies for prognosis that build on clinically relevant risk stratification systems, and also identify the potential for novel therapeutic regimens tailored to the individual patterns of gene expression,” they argued.
They added that genomic information could be easily added to current prognostic models if the findings are confirmed prospectively.
The study “demonstrates the potential value of using microarray-based gene signatures to refine outcome predictions,” said Chiang-Ching Huang, Ph.D., and Markus Bredel, M.D., Ph.D., of Northwestern University in Chicago.
Writing in an accompanying editorial, they noted, however, that the current study still needs prospective confirmation, preferably with “sensitive and robust methods.”
But because the researchers studied pathways with mechanistic implications, “this approach represents an advance in the changing landscape of oncology toward individualized patient management.”
The study was supported by the American Cancer Society, the National Cancer Institute, the Emilene Brown Genomic Cancer Research Fund, and the Jimmy V Foundation.
The researchers reported no conflicts.
Drs. Huang and Bredel also reported no conflicts.
Primary source: Journal of the American Medical Association
Source reference: Acharya CR, et al. “Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer.” JAMA 2008; 299(13): 1574-87.
Additional source: Journal of the American Medical Association
Source reference: Huang CC, Bredel M “Use of gene signatures to improve risk estimation in cancer.” JAMA 2008; 299(13): 1605-06.
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Published: April 01, 2008
Reviewed by Dori F. Zaleznik, MD; Associate Clinical Professor of Medicine, Harvard Medical School, Boston.
Additional articles and resources on this topic:
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- Tumor-derived Matrix Metalloproteinase-13 (MMP-13) correlates with poor prognoses of invasive breast cancer
- Obesity Worsens Locally Advanced Breast Cancer Prognosis
- Frequency and Cost of Chemotherapy-Related Serious Adverse Effects in a Population Sample of Women with Breast Cancer
- Florida Breast Cancer Resource Center
- The V Foundation for Cancer Research