Intraoperative prediction of non‑sentinel lymph node metastases in breast cancer using cytokeratin 19 mRNA copy number: A retrospective analysis

Diagnostic value of one-step nucleic acid amplification for sentinel lymph node metastasis in cytokeratin 19-positive tumors: evidence from bioinformatics and meta-analysis

Background

The status of the sentinel lymph nodes (SLNs) was an important prognostic factor in varies cancers. A one-step nucleic acid amplification (OSNA) assay, a molecular-based whole-node analysis method based on CK19 mRNA copy number, was developed to diagnose lymph node metastases. We aimed to evaluate the value of OSNA for the diagnosis of sentinel lymph node metastasis in CK19 positive cancers. CK19 mRNA and protein expression for pan-caner analysis were obtained from TCGA and the Human protein atlas database.

Methods

Two researchers independently searched the PubMed, Cochrane Library and Web of Science databases for qualified articles published before December 1, 2023. A meta-analysis was performed using MetaDisc and STATA. Risk bias and quality assessments of the included studies were evaluated, and a subgroup analysis was performed. Ten cancer types were found to be CK19 positively expressed and 7 of 10 had been reported to use OSNA for SLN detection.

Results

After literature review, there were 61 articles included in the meta-analysis, which consisted of 7115 patients with 18007 sentinel lymph nodes. The pooled sensitivity and specificity of OSNA were 0.87 and 0.95 in overall patients. Moreover, we found the background CK19 expression in normal tissue affected the diagnostic accuracy of OSNA. In breast cancer, we performed subgroup analysis. OSNA exhibited to be a stable method across different population groups and various medical centers. In addition, when 250 copies/μl was chosen as the cutoff point of CK19 mRNA, there were a relatively higher sensitivity and AUC in detecting SLN micro-metastasis than 5000 copies/μl.

Discussion

OSNA can predict the occurrence of SLN metastasis accurately in CK19 positive cancers, especially in breast cancer, colorectal cancer, lung cancer, gastric cancer and endometrial cancer. Our study warrants future studies investigating the clinical application of OSNA in pancreatic, ovarian and bladder cancers.

One Step Nucleic Acid Amplification (OSNA) Lysate Samples Are Suitable to Establish a Transcriptional Metastatic Signature in Patients with Early Stage Hormone Receptors-Positive Breast Cancer

The One Step Nucleic Acid Amplification (OSNA) is being adopted worldwide for sentinel lymph nodes (SLNs) staging in breast cancer (BC). As major disadvantage, OSNA precludes prognostic information based on structural evaluation of SLNs. Our aim is to identify biomarkers related to tumor-microenvironment interplay exploring gene expression data from the OSNA remaining lysate. This study included 32 patients with early stage hormone receptors-positive BC. Remaining OSNA lysates were prepared for targeted RNA-sequencing analysis. Identification of differentially expressed genes (DEGs) was performed by DESeq2 in R and data analysis in STATA. The results show that, in metastatic SLNs, several genes were upregulated: KRT7, VTCN1, CD44, GATA3, ALOX15B, RORC, NECTIN2, LRG1, CD276, FOXM1 and IGF1R. Hierarchical clustering analysis revealed three different clusters. The identified DEGs codify proteins mainly involved in cancer aggressiveness and with impact in immune response. The overexpression of the immune suppressive genes VTCN1 and CD276 may explain that no direct evidence of activation of immune response in metastatic SLNs was found. We show that OSNA results may be improved incorporating microenvironment-related biomarkers that may be useful in the future for prognosis stratification and immunotherapy selection. As OSNA assay is being implemented for SLNs staging in other cancers, this approach could also have a wider utility.

A New Possible Cut-Off of Cytokeratin 19 mRNA Copy Number by OSNA in the Sentinel Node of Breast Cancer Patients to Avoid Unnecessary Axillary Dissection: A 10-Year Experience in a Tertiary Breast Unit

Simple Summary

This manuscript aims to investigate the features of patients with metastatic sentinel lymph node (SLN), evaluated by OSNA, and to predict which patients have a high risk of positive ALND. The finding of the present study suggests a new cut-off of CK19 mRNA copy number in the sentinel lymph node useful to personalize surgical treatments and avoid unnecessary axillary surgical treatments.

Abstract

(1) Background: The main discriminant in breast cancer prognosis is axillary lymph node status. In a select cohort of patients, axillary lymph node dissection (ALND) may be safely spared. This study aimed to determine a new possible cut-off of cytokeratin (CK) 19 mRNA copy number in the SLN to predict cases at high risk of positive ALND. (2) Methods: Clinical records of 1339 patients were retrospectively reviewed and were separated into two groups according to the axillary status (negative: ALNs− and positive ALNs+). Receiver operative characteristic (ROC) curves were used to identify a new optimal cut-off of CK19 mRNA copy number in SLN; (3) Results: Large tumor size and high grade were found mostly in ALNs+. Results from the ROC analyses, with an AUC of 82.1%, identified a new cut-off (9150 CK19 mRNA copies) showing 94% sensitivity, 67.3% specificity, 61.2% positive, and 95.3% negative predictive values; (4) OSNA remains the most-important intra-operative tool to identify patients who can benefit from ALND but with the traditional cut-off, many patients undergo needless ALND. The results of the present study suggest a new cut-off helpful to personalize surgical treatment and avoid unnecessary invasive procedures.

Application of the Machine-Learning Model to Improve Prediction of Non-Sentinel Lymph Node Metastasis Status Among Breast Cancer Patients

Background

Performing axillary lymph node dissection (ALND) is the current standard option after a positive sentinel lymph node (SLN). However, whether 1–2 metastatic SLNs require ALND is debatable. The probability of metastasis in non-sentinel lymph nodes (NSLNs) can be calculated using nomograms. In this study, we developed an individualized model using machine-learning (ML) methods to select potential variables, which influence NSLN metastasis.

Materials and Methods

Cohorts of patients with early breast cancer who underwent SLN biopsy and ALND between 2012 and 2021 were created (training cohort, N 157 and validation cohort, N 58) for the development of the nomogram. Three ML methods were trained in the training set to create a strong predictive model. Finally, the multiple iterations of the least absolute shrinkage and selection operator regression method were used to determine the variables associated with NSLN status.

Results

Four independent variables (positive SLN number, absence of lymph node hilum, lymphovascular invasion (LVI), and total number of SLNs harvested) were combined to generate the nomogram. The area under the receiver operating characteristic curve (AUC) value of 0.759 was obtained in the entire set. The AUC values for the training set and the test set were 0.782 and 0.705, respectively. The Hosmer-Lemeshow test of the model fit accuracy was identified with p = 0.759.

Conclusion

This study developed a nomogram that incorporates ultrasound (US)-related variables using the ML method and serves to clinically predict the non-metastatic status of NSLN and help in the selection of the appropriate treatment option.