Prediction of axillary lymph node metastasis in triple-negative breast cancer by multi-omics analysis and an integrated model.

ABSTRACT:

Background

To avoid unnecessary postoperative complications, it is essential to select breast cancer patients without axillary lymph node (LN) metastasis who might be eligible for exemption from sentinel lymph node biopsy (SLNB). However, the lymph node metastasis (LNM) of triple-negative breast cancer (TNBC) is difficult to predict if only considering clinical parameters. Hence, by investigating the difference between LN positive and LN negative patients, we aimed to build a multi-omics model able to better predict LNM in TNBC.

Methods

A total of 445 TNBC patients with lymph node status and multi-omics data were enrolled and divided into training and validation sets. We analyzed both clinicopathological characteristics and multi-omics data to search for robust biomarkers, which were used to establish a multi-omics model.

Results

Compared with LN negative patients, LN positive patients had an increasing number of mutational events, while the frequencies of both amplification and deletion in somatic copy number alterations (SCNAs) were lower in LN positive cases. After analyzing upregulated gene-related pathways, neutrophil-related pathways were found to be enriched in LN positive patients. Based on these omics analyses, 5 predictors were utilized to build a multi-omics model, and the area under the receiver operating characteristic curve was 0.790 in the training set and 0.807 in the validation set, showing a better performance than models using individual omics data.

Conclusions

After analyzing the largest TNBC multi-omics cohorts, we identified the potential clinical and molecular characteristics that are related to LNM. A multi-omics model was developed and performed robustly in predicting LNM, with the potential assistance of tailoring unnecessary axillary LN management among TNBC patients.

SUBMITTER: Li SY 

PROVIDER: S-EPMC9263764 | biostudies-literature |

REPOSITORIES: biostudies-literature