Axillary surgery for breast cancer has continually evolved, with sentinel lymph node biopsy for clinically node-negative women with invasive breast cancer having long replaced axillary lymph node dissection. The information obtained from axillary staging has been important in providing prognostic information and guiding adjuvant treatment recommendations. However, recent studies suggest that sentinel lymph node biopsy should be omitted in select low-risk patients whose axillary surgery provides minimal prognostic value. This was highlighted by the Society of Surgical Oncology Choosing Wisely Guidelines, advocating against routine axillary staging in older women with early-stage hormone receptor-positive breast cancer. Since the guideline release, ongoing research has continued to identify the subset of low-risk patients who would benefit from the omission of axillary staging and improve adherence to Choosing Wisely to prevent overtreatment in older people.

Background

In the era of molecular stratification and effective multimodality therapies, surgical staging of the axilla is becoming less relevant for patients with estrogen receptor (ER)-positive early-stage breast cancer (EBC). Therefore, a nonsurgical method for accurately predicting lymph node disease is the next step in the de-escalation of axillary surgery. This study sought to identify epigenetic signatures in the primary tumor that accurately predict lymph node status.

Patients and methods

We selected a cohort of patients in The Cancer Genome Atlas (TCGA) with ER-positive, HER2-negative invasive ductal carcinomas, and clinically-negative axillae (n = 127). Clinicopathological nomograms from the Memorial Sloan Kettering Cancer Center (MSKCC) and the MD Anderson Cancer Center (MDACC) were calculated. DNA methylation (DNAm) patterns from primary tumor specimens were compared between patients with pN0 and those with > pN0. The cohort was divided into training (n = 85) and validation (n = 42) sets. Random forest was employed to obtain the combinations of DNAm features with the highest accuracy for stratifying patients with > pN0. The most efficient combinations were selected according to the area under the curve (AUC).

Results

Clinicopathological models displayed a modest predictive potential for identifying > pN0 disease (MSKCC AUC 0.76, MDACC AUC 0.69, p = 0.15). Differentially methylated sites (DMS) between patients with pN0 and those with > pN0 were identified (n = 1656). DMS showed a similar performance to the MSKCC model (AUC = 0.76, p = 0.83). Machine learning approaches generated five epigenetic classifiers, which showed higher discriminative potential than the clinicopathological variables tested (AUC > 0.88, p < 0.05).

Conclusions

Epigenetic classifiers based on primary tumor characteristics can efficiently stratify patients with no lymph node involvement from those with axillary lymph node disease, thereby providing an accurate method of staging the axilla.