Crabb Simon J, Cheang Maggie C U, Leung Samuel, Immonen Taina, Nielsen Torsten O, Huntsman David D, Bajdik Chris D, Chia Stephen K
Division of Medical Oncology, British Columbia Cancer Agency, Vancouver, British Columbia, Canada.
Clin Breast Cancer. 2008 Jun;8(3):249-56. doi: 10.3816/CBC.2008.n.028.
Axillary lymph node involvement remains the most important prognostic factor in early-stage breast cancer. We hypothesized that molecular classification based on breast cancer biology would predict the presence of nodal involvement at diagnosis, which might aid treatment decisions regarding the axilla.
From a clinically annotated tissue microarray of 4444 early-stage breast cancers, expression of estrogen receptor (ER), progesterone receptor (PgR), HER2, epidermal growth factor receptor, and cytokeratin 5/6 was determined by immunohistochemistry. Cases were classified by published criteria into molecular subtypes of luminal, luminal/HER2 positive, HER2 positive/ER negative/PgR negative, and basal. Risk of axillary nodal involvement at diagnosis was determined in 2 multivariable logistic regression models: a "core biopsy model" including molecular subtype, age, grade, and tumor size and a "lumpectomy model," which also included lymphovascular invasion. Luminal was used as the reference group. After internal validation of findings in 2 independent sets, we conducted combined analysis of both.
In the core biopsy model, the molecular subtypes had a predictive effect for nodal involvement (P= .000001), with the basal subtype having an odds ratio for axillary lymph node involvement of 0.53 (95% CI, 0.41-0.69). Tumor grade (P=5.43 x 10(-12)) and size (P=8.52 x 10(-35)) were also predictive for nodal involvement. Similar results were found in the lumpectomy model, where lymphovascular invasion was also predictive (P=2.74 x 10(-115)).
These results indicate that the basal breast cancer molecular subtype predicts a lower incidence of axillary nodal involvement, and including biomarker profiles to predict nodal status at diagnosis could help stratification for decisions regarding axillary surgery and locoregional radiation.
腋窝淋巴结受累仍然是早期乳腺癌最重要的预后因素。我们假设基于乳腺癌生物学的分子分类能够预测诊断时淋巴结受累情况，这可能有助于腋窝相关治疗决策。
从4444例早期乳腺癌的临床注释组织芯片中，通过免疫组化测定雌激素受体（ER）、孕激素受体（PgR）、HER2、表皮生长因子受体和细胞角蛋白5/6的表达。根据已发表的标准将病例分为管腔型、管腔/HER2阳性型、HER2阳性/ER阴性/PgR阴性型和基底型分子亚型。在两个多变量逻辑回归模型中确定诊断时腋窝淋巴结受累的风险：一个“粗针活检模型”，包括分子亚型、年龄、分级和肿瘤大小；另一个“乳房切除术模型”，还包括淋巴管浸润。管腔型作为参照组。在两个独立数据集对结果进行内部验证后，我们对两者进行了联合分析。
在粗针活检模型中，分子亚型对淋巴结受累有预测作用（P = 0.000001），基底型亚型腋窝淋巴结受累的比值比为0.53（95%可信区间，0.41 - 0.69）。肿瘤分级（P = 5.43×10⁻¹²）和大小（P = 8.52×10⁻³⁵）对淋巴结受累也有预测作用。在乳房切除术模型中发现了类似结果，其中淋巴管浸润也有预测作用（P = 2.74×10⁻¹¹⁵）。
这些结果表明基底型乳腺癌分子亚型预测腋窝淋巴结受累的发生率较低，纳入生物标志物谱以预测诊断时的淋巴结状态有助于腋窝手术和局部区域放疗决策的分层。
