Abstract：
Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.
摘要：
三阴性乳腺癌（TNBC）由于其攻击行为和有限的治疗选择而在肿瘤学中提出了重大挑战。这篇综述探讨了免疫治疗的潜力，特别是基于疫苗的方法，解决TNBC问题。它探讨了免疫信息学在创建针对TNBC的有效疫苗中的作用。该综述首先强调了TNBC的独特属性以及肿瘤抗原在疫苗开发中的重要性。然后阐述了抗原检测技术，如外显子组测序，HLA分型,和RNA测序，这有助于鉴定TNBC特异性抗原和选择候选疫苗。然后讨论转移到计算机疫苗开发过程，包括抗原选择，表位预测，和合理的疫苗设计。此过程将计算模拟与免疫学见解合并。还强调了人工智能（AI）在加速抗原和表位预测中的作用。这篇综述总结了免疫信息学如何增强TNBC疫苗的设计，整合肿瘤抗原，先进的检测方法，计算机内战略，和AI驱动的见解，以推进TNBC免疫疗法。这可能为更有针对性和有效的治疗铺平道路。
