Impact of Tumor Composition on Breast Cancer Treatment Prediction
Author Information
Author(s): Cleator Susan J, Powles Trevor J, Dexter Tim, Fulford Laura, Mackay Alan, Smith Ian E, Valgeirsson Haukur, Ashworth Alan, Dowsett Mitch
Primary Institution: Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, UK
Hypothesis
The cellular composition of breast tumor biopsies affects the accuracy of predicting clinical response to chemotherapy.
Conclusion
The presence of non-tumor components in breast cancer samples significantly influences gene expression profiles, and accounting for this improves prediction accuracy for treatment response.
Supporting Evidence
- 53% of patients showed a good clinical response to chemotherapy.
- Misclassification error for predicting response was 28% for all samples but improved to 8%-13% for more homogeneous subsets.
- 144 genes were identified that distinguished between high- and low-percentage invasive tumor biopsies.
Takeaway
This study found that the mix of cancer and other cells in breast tumor samples can change how well doctors can predict if chemotherapy will work.
Methodology
Core biopsies were taken from 43 patients, and gene expression was analyzed using microarray technology to predict clinical response to chemotherapy.
Potential Biases
Potential bias due to the selection of samples based on tumor content and the subjective nature of histological assessments.
Limitations
The study had a modest sample size and the results may not be generalizable to all breast cancer types.
Participant Demographics
Median age of participants was 49 years, with a mix of ER-positive and ER-negative tumors.
Statistical Information
P-Value
0.03
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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