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

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 di...

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Bibliographic Details
Published in:Annals of surgical oncology 2022-10, Vol.29 (10), p.6407-6414
Main Authors: Orozco, Javier I. J., Le, Julie, Ensenyat-Mendez, Miquel, Baker, Jennifer L., Weidhaas, Joanne, Klomhaus, Alexandra, Marzese, Diego M., DiNome, Maggie L.
Format: Article
Language:English
Subjects:
Axilla - pathology
Breast cancer
Breast Neoplasms - genetics
Breast Neoplasms - pathology
Breast Neoplasms - surgery
Breast Oncology
Carcinoma
DNA methylation
Epigenesis, Genetic
Epigenetics
ErbB-2 protein
Estrogen receptors
Female
Genomes
Humans
Invasiveness
Learning algorithms
Lymph Node Excision
Lymph nodes
Lymph Nodes - pathology
Lymph Nodes - surgery
Lymphatic Metastasis - pathology
Lymphatic system
Machine Learning
Medicine
Medicine & Public Health
Neoplasm Staging
Nomograms
Oncology
Patients
ROC Curve
Sentinel Lymph Node Biopsy
Surgery
Surgical Oncology
Tumors
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Summary: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.
ISSN:1068-9265
1534-4681
DOI:10.1245/s10434-022-12143-6