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Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer.

Publication ,  Journal Article
Orozco, JIJ; Le, J; Ensenyat-Mendez, M; Baker, JL; Weidhaas, J; Klomhaus, A; Marzese, DM; DiNome, ML
Published in: Ann Surg Oncol
October 2022

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.

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Published In

Ann Surg Oncol

DOI

EISSN

1534-4681

Publication Date

October 2022

Volume

29

Issue

10

Start / End Page

6407 / 6414

Location

United States

Related Subject Headings

  • Sentinel Lymph Node Biopsy
  • ROC Curve
  • Oncology & Carcinogenesis
  • Nomograms
  • Neoplasm Staging
  • Machine Learning
  • Lymphatic Metastasis
  • Lymph Nodes
  • Lymph Node Excision
  • Humans
 

Citation

APA
Chicago
ICMJE
MLA
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Orozco, J. I. J., Le, J., Ensenyat-Mendez, M., Baker, J. L., Weidhaas, J., Klomhaus, A., … DiNome, M. L. (2022). Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer. Ann Surg Oncol, 29(10), 6407–6414. https://doi.org/10.1245/s10434-022-12143-6
Orozco, Javier I. J., Julie Le, Miquel Ensenyat-Mendez, Jennifer L. Baker, Joanne Weidhaas, Alexandra Klomhaus, Diego M. Marzese, and Maggie L. DiNome. “Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer.Ann Surg Oncol 29, no. 10 (October 2022): 6407–14. https://doi.org/10.1245/s10434-022-12143-6.
Orozco JIJ, Le J, Ensenyat-Mendez M, Baker JL, Weidhaas J, Klomhaus A, et al. Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer. Ann Surg Oncol. 2022 Oct;29(10):6407–14.
Orozco, Javier I. J., et al. “Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer.Ann Surg Oncol, vol. 29, no. 10, Oct. 2022, pp. 6407–14. Pubmed, doi:10.1245/s10434-022-12143-6.
Orozco JIJ, Le J, Ensenyat-Mendez M, Baker JL, Weidhaas J, Klomhaus A, Marzese DM, DiNome ML. Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer. Ann Surg Oncol. 2022 Oct;29(10):6407–6414.
Journal cover image

Published In

Ann Surg Oncol

DOI

EISSN

1534-4681

Publication Date

October 2022

Volume

29

Issue

10

Start / End Page

6407 / 6414

Location

United States

Related Subject Headings

  • Sentinel Lymph Node Biopsy
  • ROC Curve
  • Oncology & Carcinogenesis
  • Nomograms
  • Neoplasm Staging
  • Machine Learning
  • Lymphatic Metastasis
  • Lymph Nodes
  • Lymph Node Excision
  • Humans