A New Classifier for Identifying Good Prognosis in ER-Negative Breast Cancer
Author Information
Author(s): Teschendorff Andrew E, Caldas Carlos
Primary Institution: Cancer Research UK Cambridge Research Institute
Hypothesis
Can a seven-gene expression classifier accurately identify ER-negative breast cancer patients with a good prognosis?
Conclusion
The seven-gene classifier can effectively identify ER-negative breast cancer patients with a good prognosis, potentially allowing for less aggressive treatment.
Supporting Evidence
- The classifier showed an average predictive value of 94% across test cohorts.
- Overexpression of the immune response module was linked to significantly better clinical outcomes.
- The classifier was validated in six independent cohorts, confirming its robustness.
Takeaway
Scientists created a test that helps doctors find breast cancer patients who are likely to do well, so they can avoid harsh treatments.
Methodology
The study developed a seven-gene expression classifier using Mixture Discriminant Analysis to predict outcomes in ER-negative breast cancer patients.
Potential Biases
Potential biases may arise from the merging of different microarray expression sets.
Limitations
The classifier's performance may vary across different cohorts due to inherent differences in patient populations.
Participant Demographics
The study included ER-negative breast cancer patients from multiple cohorts, with a total of 469 tumors analyzed.
Statistical Information
P-Value
p < 0.000001
Confidence Interval
0.07 to 0.36
Statistical Significance
p < 0.000001
Digital Object Identifier (DOI)
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