Risk factors for non-sentinel lymph node metastases in breast cancer patients with positive sentinel lymph nodes

571 Background: Sentinel lymph node (SLN) biopsy status is a key prognostic factor for breast cancer patients. Sentinel nodes are believed to receive early disseminating cells from the primary tumor, but little is known about the origin of metastases colonizing the sentinel nodes. We used allelic imbalance (AI) to examine genomic relationships among metastases in the sentinel and non-sentinel axillary lymph nodes from complete axillary dissections in 15 patients with lymph node positive breast cancer. Methods: Sentinel nodes were localized by standard scintigraphic and gamma probe techniques using 1.0 mCi technetium-99m sulfur colloid. Pathologically positive nodes were identified by H&E histology and immunohistochemistry. Primary breast tumors and metastases in sentinel and axillary nodes were isolated by laser microdissection. AI was assessed at 26 chromosomal regions and used to examine the timing and molecular mechanisms of metastatic spread to the sentinel and axillary nodes. Results: Overall AI frequencies were significantly higher (p<0.05) in primary breast tumors compared to lymph node metastases. A high level of discordance was observed in patterns and frequencies of AI events between metastases in the sentinel and non-sentinel axillary nodes. Phylogenetic analyses showed that 1) multiple genetically-divergent lineages of metastatic cells independently colonize the lymph nodes; 2) some lymph node metastases appeared to acquire metastatic potential early in tumorigenesis, while other metastases evolved later; and 3) importantly, lineages colonizing the sentinel nodes appeared to originate at different times and to progress by different molecular mechanisms. Conclusions: Genomic diversity and timing of metastatic nodal spread may be important factors in determining outcomes of breast cancer patients. Metastases colonizing the sentinel nodes appear to arise at different times during disease progression and may not be descendants of progenitor cells that colonize the lymph nodes early in tumorigenesis. Metastatic growth in the sentinel nodes thus may be a consequence of stimulating factors from the primary tumor that affect proliferation of previously disseminated cells rather than the timing of metastatic spread. No significant financial relationships to disclose.

Download Full-text

Clinical Significance of Extracapsular Invasion at Sentinel Lymph Nodes in Breast Cancer Patients with Sentinel Lymph Node Involvement

Annals of Surgical Oncology ◽

10.1245/s10434-014-4269-2 ◽

2014 ◽

Vol 22 (7) ◽

pp. 2365-2371 ◽

Cited By ~ 18

Author(s):

Hideo Shigematsu ◽

Kenichi Taguchi ◽

Hiroko Koui ◽

Shinji Ohno

Keyword(s):

Breast Cancer ◽

Lymph Node ◽

Sentinel Lymph Node ◽

Lymph Nodes ◽

Cancer Patients ◽

Lymph Node Involvement ◽

Sentinel Lymph Nodes ◽

Breast Cancer Patients ◽

Node Involvement ◽

Extracapsular Invasion

Download Full-text

Meta-analysis of predictive factors for non-sentinel lymph node metastases in breast cancer patients with a positive SLN

Breast Diseases A Year Book Quarterly ◽

10.1016/j.breastdis.2011.10.048 ◽

2011 ◽

Vol 22 (4) ◽

pp. 390-391

Author(s):

G. Cserni

Keyword(s):

Breast Cancer ◽

Lymph Node ◽

Sentinel Lymph Node ◽

Cancer Patients ◽

Predictive Factors ◽

Lymph Node Metastases ◽

Meta Analysis ◽

Breast Cancer Patients ◽

Node Metastases

Download Full-text

Development of a prediction model based on LASSO regression to evaluate the risk of non-sentinel lymph node metastasis in Chinese breast cancer patients with 1–2 positive sentinel lymph nodes

Scientific Reports ◽

10.1038/s41598-021-99522-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lei Meng ◽

Ting Zheng ◽

Yuanyuan Wang ◽

Zhao Li ◽

Qi Xiao ◽

...

Keyword(s):

Breast Cancer ◽

Lymph Node ◽

Sentinel Lymph Node ◽

Lymph Nodes ◽

Prediction Model ◽

Cancer Patients ◽

Sentinel Lymph Nodes ◽

Lasso Regression ◽

Breast Cancer Patients ◽

Positive Slns

AbstractThis study aimed to develop an intraoperative prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs). The clinicopathologic data of 714 patients with 1–2 positive SLNs were investigated. Univariate and multivariate analyses were performed to identify the risk factors of NSLN metastasis. A new mathematical prediction model was developed based on LASSO and validated in an independent cohort of 131 patients. The area under the receiver operating characteristic curve (AUC) was used to quantify performance of the model. Patients with NSLN metastasis accounted for 37.3% (266/714) and 34.3% (45/131) of the training and validation cohorts, respectively. A LASSO regression-based prediction model was developed and included the 13 most powerful factors (age group, clinical tumour stage, histologic type, number of positive SLNs, number of negative SLNs, number of SLNs dissected, SLN metastasis ratio, ER status, PR status, HER2 status, Ki67 staining percentage, molecular subtype and P53 status). The AUCs of training and validation cohorts were 0.764 (95% CI 0.729–0.798) and 0.777 (95% CI 0.692–0.862), respectively. We presented a new prediction model with excellent clinical applicability and diagnostic performance for use by clinicians as an intraoperative clinical tool to predict risk of NSLN metastasis in Chinese breast cancer patients with 1–2 positive SLNs and make the final decisions regarding axillary lymph node dissection.

Download Full-text