Evaluating Genomic Biomarkers in Cancer Genomics
Author Information
Author(s): Lucas Joseph E., Carvalho Carlos M., Chen Julia Ling-Yu, Chi Jen-Tsan, West Mike
Primary Institution: Duke University
Hypothesis
Can in vitro gene expression signatures be effectively translated to predict clinical outcomes in cancer?
Conclusion
The study demonstrates that derived factors from in vitro gene expression signatures can improve predictions of clinical outcomes in breast cancer.
Supporting Evidence
- Factors derived from in vitro signatures improved prediction of clinical outcomes.
- Seven out of ten factors were significant in predicting the original signature.
- Factors can differentiate key biological phenotypes in breast cancer.
- Factors were able to predict survival outcomes across multiple cancer datasets.
- Factors showed predictive ability for Tamoxifen resistance.
Takeaway
Scientists found a way to use lab results to better understand and predict how breast cancer behaves in real patients.
Methodology
The study used Bayesian Factor Regression Modeling to analyze gene expression data from breast cancer samples.
Potential Biases
Potential biases from using in vitro data to predict in vivo outcomes.
Limitations
The study may not account for all biological variability present in different cancer types.
Participant Demographics
The study focused on breast cancer patients, but specific demographics were not detailed.
Statistical Information
P-Value
<10−13
Statistical Significance
p<0.01
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website