From text to insight: A natural language processing-based analysis of burst and research trends in HER2-low breast cancer patients.

Journal: Ageing research reviews
PMID:

Abstract

With the intensification of population aging, the proportion of elderly breast cancer patients is continuously increasing, among which those with low HER2 expression account for approximately 45 %-55 % of all cases within traditional HER2-negative breast cancer. Concurrently, the significant therapeutic effect of T-DXd on patients with HER2-low tumors has brought this group into the public spotlight. Since the clinical approval of T-DXd in 2019, there has been a significant vertical surge in the volume of publications within this domain. We analyzed 512 articles on HER2-low breast cancer from the Web of Science Core Collection using bibliometrics, topic modeling, and knowledge graph techniques to summarize the current state and trends of research in this domain. Research efforts are particularly concentrated in the United States and China. Our analysis revealed six main research directions: HER2 detection, omics and clinical biomarkers, basic and translational research, neoadjuvant therapy and prognosis, progress of ADC drugs and clinical trials. To enhance the therapeutic efficacy and safety of antibodydrug conjugates (ADCs), researchers are actively exploring potential drug candidates other than T-DXd, with numerous ADC drugs emerging in clinical practice and trials. By incorporating emerging treatment strategies such as immunotherapy and employing circulating tumor cell (CTC) detection techniques, progress has been made toward improving the prognosis of patients with low HER2 expression. We believe that these research efforts hold promise as compelling evidence that HER2-low breast cancer may constitute a distinct and independent subtype.

Authors

  • Muyao Li
    School of Mathematical Sciences, Ocean University of China, Qingdao, 266000, Shandong, China.
  • Ang Zheng
    Department of Breast Surgery, the First Hospital of China Medical University, Shenyang, Liaoning 110001, China. Electronic address: azheng@cmu.edu.cn.
  • Mingjie Song
    Department of General Medicine, the First Hospital of China Medical University, Shenyang, Liaoning 110001, China. Electronic address: songmingjie97@163.com.
  • Feng Jin
    IBM Research China, Beijing, China.
  • Mengyang Pang
    Department of Breast Surgery, the First Hospital of China Medical University, Shenyang, Liaoning 110001, China. Electronic address: 15326117391@163.com.
  • Yuchong Zhang
    Robotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology Stockholm, 114 17, Stockholm, Sweden.
  • Ying Wu
    School of Nursing, Capital Medical University, Beijing, China.
  • Xin Li
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Mingfang Zhao
    Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
  • Zhi Li
    Department of Nursing, Zhongshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine, Zhongshan, China.