Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d'Investigació Sanitària Illes Balears (IdISBa), Palma, Spain.
Statistics Core, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Ann Surg Oncol. 2022 Aug;29(8):4716-4724. doi: 10.1245/s10434-022-11684-0. Epub 2022 Apr 9.
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.
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.
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).
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.
临床淋巴结阳性的乳腺癌患者常被建议行 upfront 手术，然而其中一半以上的患者在手术时发现淋巴结疾病有限（≤3 个阳性淋巴结，pN1）。本研究旨在探讨分子分类器在区分临床淋巴结阳性患者的 pN1 与>pN1 疾病中的效率。
我们评估了接受 ALND 治疗淋巴结阳性疾病的癌症基因组图谱中雌激素受体阳性、人表皮生长因子受体 2 阴性浸润性导管癌患者的临床和表观遗传学数据。患者分为对照组（pN1，≤3 个阳性淋巴结）和病例组（>pN1，>3 个阳性淋巴结）。机器学习算法在队列的 50%上进行训练，并在剩余的 50%上进行验证，以确定预测>pN1 疾病的 DNA 甲基化特征。比较了临床变量和表观遗传学特征。
对照组（n=34）和病例组（n=24）的平均年龄（56.4±12.2 vs. 57.6±16.7 岁；p=0.77）、切除的淋巴结数量（16.1±7.3 vs. 17.5±6.2；p=0.45）、肿瘤分级（p=0.76）、淋巴管侵犯（p=0.18）、淋巴结外侵犯（p=0.17）、肿瘤侧别（p=0.89）和肿瘤位置（p=0.42）相似。阳性淋巴结的平均数量差异显著（1.76±0.82，pN1；8.83±5.36，>pN1；p<0.001）。基于原发肿瘤 DNA 甲基化模式的三个表观遗传学特征（EpiSig14、EpiSig13、EpiSig10）在预测>pN1 疾病方面具有很高的准确性（曲线下面积 0.98）。
表观遗传学特征在区分临床淋巴结阳性患者的淋巴结疾病方面具有出色的诊断准确性。该工具的验证是必要的，并且可能提供一种准确且具有成本效益的方法，以识别预测低淋巴结负担的患者，从而避免 ALND 的发病率。
