Journal of Translational Medicine (Dec 2022)

Noninvasive prediction of axillary lymph node status in breast cancer using promoter profiling of circulating cell-free DNA

  • Zhi-Wei Guo,
  • Qing Liu,
  • Xu Yang,
  • Geng-Xi Cai,
  • Bo-Wei Han,
  • Li-Min Huang,
  • Chun-Xi Li,
  • Zhi-Kun Liang,
  • Xiang-Ming Zhai,
  • Li Lin,
  • Kun Li,
  • Min Zhang,
  • Tian-Cai Liu,
  • Rui-lin Pan,
  • Ying-Song Wu,
  • Xue-Xi Yang

DOI
https://doi.org/10.1186/s12967-022-03724-w
Journal volume & issue
Vol. 20, no. 1
pp. 1 – 11

Abstract

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Abstract Background Lymph node metastasis (LNM) is one of the most important factors affecting the prognosis of breast cancer. The accurate evaluation of lymph node status is useful to predict the outcomes of patients and guide the choice of cancer treatment. However, there is still lack of a low-cost non-invasive method to assess the status of axillary lymph node (ALN). Gene expression signature has been used to assess lymph node metastasis status of breast cancer. In addition, nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of its original tissues, so it may be used to evaluate the axillary lymph node status in breast cancer. Methods In this study, we found that the cfDNA nucleosome footprints between the ALN-positive patients and ALN-negative patients showed different patterns by implementing whole-genome sequencing (WGS) to detect 15 ALN-positive and 15 ALN-negative patients. In order to further evaluate its potential for assessing ALN status, we developed a classifier with multiple machine learning models by using 330 WGS data of cfDNA from 162 ALN-positive and 168 ALN-negative samples to distinguish these two types of patients. Results We found that the promoter profiling between the ALN-positive patients and ALN-negative patients showed distinct patterns. In addition, we observed 1071 genes with differential promoter coverage and their functions were closely related to tumorigenesis. We found that the predictive classifier based on promoter profiling with a support vector machine model, named PPCNM, produced the largest area under the curve of 0.897 (95% confidence interval 0.86–0.93). Conclusions These results indicate that promoter profiling can be used to distinguish ALN-positive patients from ALN-negative patients, which may be helpful to guide the choice of cancer treatment.

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