An Eighteen-Gene Classifier Predicts Locoregional Recurrence in Post-Mastectomy Breast Cancer Patients.

Area of Science:

• Oncology
• Genomics
• Biostatistics

Background:

• Identifying patients who benefit from post-mastectomy radiotherapy (PMRT) is crucial for breast cancer treatment.
• Previous research identified 34 genes of interest (GOI) to assist oncologists in PMRT decisions.
• An independent cohort of 135 patients with primary tumor DNA microarray data was selected for validation.

Purpose of the Study:

• To validate a gene classifier for predicting locoregional recurrence (LRR) risk in breast cancer patients treated with mastectomy.
• To assess the utility of 18 specific genes of interest (GOI) in stratifying patients into high- and low-risk groups for LRR.
• To determine if the gene classifier independently predicts LRR regardless of clinical factors.

Main Methods:

• DNA microarray data from primary tumor tissue of 135 patients (stages I-III, mastectomy first treatment, no PMRT) were analyzed.
• Inter-platform data integration (Affymetrix U95 and U133 Plus 2.0) and quantile normalization were performed.
• An 18-gene classifier was developed to stratify patients into high- and low-risk groups for LRR.

Main Results:

• The 5-year freedom from LRR was 30% in the high-risk group and 99% in the low-risk group (p < 0.0001).
• The 18-gene classifier demonstrated significant predictive power for LRR.
• Multivariate analysis confirmed the classifier's independent predictive value, irrespective of nodal status or cancer subtype.

Conclusions:

• An 18-gene classifier effectively stratifies breast cancer patients treated with mastectomy into distinct LRR risk groups.
• This genomic classifier can aid in personalized treatment decisions, potentially sparing low-risk patients from unnecessary radiotherapy.
• The findings support the use of gene expression profiling for predicting recurrence risk in breast cancer management.