Antibody–drug conjugates for breast cancer: a bibliometric study and clinical trial analysis
Authors: Xing Mengjie , Li Zhiyi , Cui Yuwan , He Minghua , Xing Yang , Yang Lei , Liu Ziling , Luo Linzhi , Wang Hong , Guo Rui 
Published: August 2024

Abstract
Background
Breast cancer (BC) remains the most commonly malignancy among women worldwide. Although early-stage BC typically presents with curative possibilities, advanced-stage disease, especially with metastasis, is significantly limited in terms of effective therapeutic interventions, thereby establishing it as the second leading cause of cancer-related deaths in women. Antibody–Drug Conjugates (ADCs) establish a groundbreaking class of anti-neoplastic agents characterized by high specificity and targeting precision. These agents have been significant in reshaping the therapeutic approach to breast cancer, especially those subtypes with overexpression of the Human Epidermal Growth Factor Receptor 2 (HER2). Comprising monoclonal antibodies, cytotoxic payloads, and conjugative linkers, ADCs function by specifically targeting antigens on cancer cells, thereby facilitating the intracellular delivery of the toxic payload. The present investigation endeavors to synthesize existing primary research outcomes through rigorous bibliometric and data analytical approaches, thereby elucidating the current research landscape, delineating research foci, and identifying potential avenues for future innovation.

Methods
For bibliometric analysis, a comprehensive data set comprising 2181 entries related to ADCs in breast cancer was retrieved from the Web of Science Core Collection (WoSCC) spanning the years 1999 to 2023. This data was further filtered from the Science Citation Index Expanded (SCI-Expanded). Analysis software tools such as CiteSpace and VOSviewer were employed for multifaceted analyses such as trends of publications, contributions of countries, and burst analytics. In the dimension of clinical trials, we interrogated databases including ClinicalTrials.gov (https://www.clinicaltrials.gov) and the WHO International Clinical Trials Registry Platform (ICTRP) (https://trialsearch.who.int). A total of 239 clinical trials were initially sourced, among which, 175 were from ClinicalTrials.gov and 64 from ICTRP. After repetitive and correlation-based screening, 119 trials specifically addressing ADC therapeutic strategies in breast cancer were included. Analytical algorithms were executed using Microsoft-based software to evaluate treatment paradigms, emergent research themes, and progress.

Results
Our investigations signify a growing trend of research on ADCs, with consistent advancements in scientific achievements. The analysis revealed that variables such as economic stratification of nations, healthcare investment paradigms, and disease incidence rates serve as significant determinants in shaping research output. Geographically, the United States emerged as the predominant contributor to the research corpus (36.56%), closely followed by China (21.33%). The underpinning of research accomplishments was found to be significantly bolstered by advancements in molecular biology, immunology, and genetic research. Moreover, the advent of nuclear magnetic resonance diagnostic modalities has contributed saliently to the diagnostic and therapeutic management of breast cancer.

Conclusion
Our study provides a comprehensive overview of the ADC research landscape through rigorous bibliometric and clinical trial evaluations. At present, the ADC arena has witnessed the successful development and FDA approval of 14 distinct agents, substantially improving the clinical outcomes for a broad spectrum of oncological patients. Future research imperatives may include the exploration of ADCs targeting mutated oncoproteins, dual-specificity ADCs, combination payload strategies, peptide-drug conjugates (PDCs), and non-internalizing ADC modalities. With sustained academic and clinical focus, the ADC domain is poised for transformative advancements in targeted therapeutics across a variety of malignancies.