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Advancements in tumor immunotherapy highlight the significant potential of antibody drugs, a key category of biological agents, for treating cancer and autoimmune diseases. This paper begins by defining and classifying key targets in tumor immunity, as well as discussing their structural and functional characteristics. Subsequently, it elaborates on innovative technologies for antibody drug screening, which, when integrated with contemporary molecular biology, biotechnology, and computational biology, have substantially enhanced the efficiency and accuracy of target identification and antibody drug screening processes. Despite the promising prospects of tumor immunotherapy, certain limitations persist in its practical implementation. In conclusion, this paper offers a comprehensive examination of the cutting-edge developments in tumor immunotherapy, focusing on the aspects of tumor immunotherapy itself, critical targets for immunotherapy, and novel technologies and methodologies for antibody screening. This analysis is crucial for advancing the field of tumor immunotherapy and for enhancing both therapeutic efficacy and safety. Furthermore, research and development of antibody drugs in other domains, such as autoimmune and inflammatory diseases, can benefit from it. • By analyzing the molecular properties, functional roles and spatial locations in tumor immunity, a systematic classification method is established to help pinpoint therapeutic targets and enhance the effectiveness of immunotherapy. • Systematic antibody screening frameworks integrate high-throughput technologies (phage display, single-cell omics) with AI-driven prediction models, establishing closed-loop platforms for rapid discovery of tumor-specific antibodies. • Advocates for merging emerging technologies (e.g., nanomaterial delivery, CRISPR-engineered therapies) with traditional antibody engineering to develop multifunctional agents (bispecific/multispecific antibodies), redefining therapeutic paradigms for refractory malignancies. • Provides a critical analysis of clinical translation barriers, including data quality, model interpretability, and ethical dilemmas, while proposing scalable solutions and interdisciplinary validation frameworks to bridge preclinical and clinical gaps.
Zhou et al. (Sun,) studied this question.