Epigenetic Signatures Predict Pathologic Nodal Stage in Breast Cancer Patients with Estrogen Receptor-Positive, Clinically Node-Positive Disease.

Journal Article (Journal Article)

BACKGROUND: Breast cancer patients with clinically positive nodes who undergo upfront surgery are often recommended for axillary lymph node dissection (ALND), yet more than half are found to have limited nodal disease (≤ 3 positive nodes, pN1) at surgery. In this study, we examined the efficiency of molecular classifiers in stratifying patients with clinically positive nodes to pN1 versus > pN1 disease. METHODS: We evaluated the clinical and epigenetic data of patients in The Cancer Genome Atlas with estrogen receptor-positive, human epidermal growth factor receptor 2-negative invasive ductal carcinoma who underwent ALND for node-positive disease. Patients were divided into control (pN1, ≤ 3 positive nodes) and case (> pN1, > 3 positive nodes) groups. Machine learning algorithms were trained on 50% of the cohort and validated on the remaining 50% to identify DNA methylation signatures that predict > pN1 disease. Clinical variables and epigenetic signatures were compared. RESULTS: Controls (n = 34) and case (n = 24) cohorts showed similar mean age (56.4 ± 12.2 vs. 57.6 ± 16.7 years; p = 0.77), number of nodes removed (16.1 ± 7.3 vs. 17.5 ± 6.2; p = 0.45), tumor grade (p = 0.76), presence of lymphovascular invasion (p = 0.18), extranodal extension (p = 0.17), tumor laterality (p = 0.89), and tumor location (p = 0.42). The mean number of positive nodes was significantly different (1.76 ± 0.82, pN1; 8.83 ± 5.36, > pN1; p < 0.001). Three epigenetic signatures (EpiSig14, EpiSig13, EpiSig10) based on DNA methylation patterns of the primary tumors demonstrated high accuracy in predicting > pN1 disease (area under the curve 0.98). CONCLUSIONS: Epigenetic signatures have an excellent diagnostic accuracy for stratifying nodal disease in patients with clinically positive nodes. Validation of this tool is warranted and may provide an accurate and cost-effective method of identifying patients with predicted low nodal burden who could be spared the morbidity of ALND.

Full Text

Duke Authors

Cited Authors

  • Ensenyat-Mendez, M; Rünger, D; Orozco, JIJ; Le, J; Baker, JL; Weidhaas, J; Marzese, DM; DiNome, ML

Published Date

  • August 2022

Published In

Volume / Issue

  • 29 / 8

Start / End Page

  • 4716 - 4724

PubMed ID

  • 35397740

Electronic International Standard Serial Number (EISSN)

  • 1534-4681

Digital Object Identifier (DOI)

  • 10.1245/s10434-022-11684-0


  • eng

Conference Location

  • United States