Bulashevska Alla, Nacsa Zsófia, Lang Franziska, Braun Markus, Machyna Martin, Diken Mustafa, Childs Liam, König Renate
Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany.
TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany.
Front Immunol. 2024 May 29;15:1394003. doi: 10.3389/fimmu.2024.1394003. eCollection 2024.
Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.
近年来,癌症免疫疗法取得了快速进展,尤其关注新抗原作为个性化治疗的有前景靶点。免疫基因组学、生物信息学和人工智能(AI)的融合推动了创新的新抗原发现工具和流程的发展。这些工具彻底改变了我们识别肿瘤特异性抗原的能力,为精准癌症免疫疗法奠定了基础。人工智能驱动的算法可以处理大量数据,识别模式并做出以前难以实现的预测。然而,人工智能的整合也带来了一系列自身的挑战,为进一步研究留下了空间。本文特别关注计算方法,探讨了新抗原预测的当前状况、背后的基本概念、挑战及其潜在解决方案,对这个快速发展的领域进行了全面概述。