Abstract P1-01-17: Machine learning-based DNA methylation classifiers to predict pathologic nodal stage in breast cancer patients with clinically node positive disease

Abstract

Abstract Introduction: Axillary lymph node dissection (ALND) has no apparent oncologic benefit for breast cancer patients with clinically negative axillae and 1-2 positive sentinel lymph nodes so omission has been considered appropriate for this patient cohort. For patients with clinically positive nodes who are not treated with neoadjuvant chemotherapy (NAC), ALND is routinely recommended. Yet, at ALND, up to 40-57% of patients are found to have ≤ 3 positive nodes (pN1). No accurate method exists for identifying patients with minimal nodal disease who can potentially be spared the morbidity of ALND. DNA methylation (DNAm) has emerged as a potential tool for creating different classifiers to stratify cancer patients using artificial intelligence methods, such as machine learning. This study employed machine learning algorithms to create DNAm-based classifiers that efficiently stratify patients with clinically positive nodes to pN1 vs >pN1 disease. Methodology: We used publically available data from The Cancer Genome Atlas (TCGA) of breast cancer patients (n=1,006). We curated the cohort to select patients with hormone receptor-positive and HER2 negative invasive ductal carcinoma who were not treated with NAC. We only included patients who had undergone an ALND and had available DNAm (HM450 BeadChip array) data (n=58). Two Random Forest methodologies were used to obtain different combinations of DNAm features with high accuracy in stratifying pN1 and >pN1 patients. The most efficient combination of CpG sites was selected according to the Area Under the Curve (AUC) of each combination. Results: First, the differentially methylated sites (DMS, n=218) between pN1 and >pN1 patients were computed. Using all DMS displayed a good predictive potential for the selection of pN1 patients (AUC=0.88). Three different panels were obtained, containing 20 (P20), 15 (P15), and 13 (P13) genomic regions per panel. All three signatures displayed an excellent performance stratifying pN1 from >pN1 patients (AUCP20=0.98, AUCP15=0.98, and AUCP13=0.97). Furthermore, these epigenetic signatures showed a higher discriminative potential than clinical and pathological variables, such as the location of the tumor, tumor size, age, and tumor grade. Discussion: Due to the lack of data, ALND is still recommended for patients with clinically node positive disease. However, over half of patients with clinically positive nodes have minimal (pN1) nodal disease at surgery and could theoretically avoid ALND. We have designed three artificial intelligence DNAm-based panels (P20, P15, and P13) which efficiently stratify pN1 from >pN1 patients. These classifiers are more accurate than clinical and pathological variables for selecting patients with a low number of positive nodes. Furthermore, our method employs a small number of genomic regions (13 to 20), facilitating the use of PCR-based assays, such as quantitative Methylation Specific PCR (qMSP), which decreases cost compared to high-throughput technology, and increases availability and accessibility of this predictive tool. Citation Format: Maggie L. DiNome, Miguel Ensenyat-Mendez, Javier I.J. Orozco, Dennis Rünger, Jennifer L. Baker, Joanne B. Weidhaas, Diego M. Marzese. Machine learning-based DNA methylation classifiers to predict pathologic nodal stage in breast cancer patients with clinically node positive disease [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-01-17.