Background

As the paradigm of cancer treatment shifts toward immunotherapies, strategies that activate the patient's immune system to attack cancer cells have become a central focus in medicine. Existing therapies, including immune checkpoint inhibitors (ICIs), have shown remarkable efficacy in some patients, but their tendency to attack healthy cells indiscriminately has been a major limitation. Additionally, the low response rates observed in only specific patient groups pose a significant barrier to widespread clinical application. This has led to growing interest in identifying target molecules that can precisely strike cancer cells while minimizing side effects. Neoantigens, which arise from the unique genetic mutations of cancer cells, are now considered a core alternative in precision oncology. Since neoantigens are expressed exclusively on cancer cells and not on normal cells, they align well with the goal of enabling immune cells to precisely target tumors. However, the challenge of designing personalized therapies is complicated by tumor heterogeneity, where genetic profiles vary not only between patients but also among cells within a single tumor.

Key Findings

Neoantigens originate not only from single nucleotide variants (SNVs) and insertions/deletions (INDELs), but also from structural variants (SVs), alternative splicing, post-translational modifications, and tumor-derived viral proteins, reflecting complex genomic and proteomic changes within and outside the cell. Recent advancements in genomic analysis have made it possible to identify patient-specific mutations, and machine learning (ML) models now play a key role in selecting neoantigens with a high likelihood of inducing immune responses. These selected neoantigens serve as the foundation for various therapeutic designs, including personalized vaccines, adoptive T-cell therapy (ACT), and combination with immune checkpoint inhibitors. In clinical studies, cancers with high mutation burdens, such as melanoma and non-small cell lung cancer, have shown rapid and robust immune responses to neoantigen-based therapies. In contrast, cancers with low mutation burdens, such as pancreatic and prostate cancers, exhibit limited immune cell activity when treated alone. To address this, combination strategies involving chemotherapy or radiotherapy to artificially enhance the immunogenicity of cancer cells have been explored. Notably, the integration of lipid nanoparticle (LNP) technology, which ensures targeted delivery of therapeutic agents without loss, has also been highlighted as a means to improve delivery efficiency.

Implications and Outlook

This research achievement presents an opportunity to further expand the scope of precision oncology through the development of neoantigen-based therapeutic platforms. The integration of multi-omics data analysis with gene-editing technologies like CRISPR-Cas9 is gaining attention as a means to understand the unique genetic architecture of tumors and suppress treatment resistance. However, many challenges remain before these therapies can be fully established in clinical practice. The difficulty of completely controlling tumor heterogeneity, where cancer cells evolve and change their targets over time, remains a significant hurdle. Additionally, immune evasion mechanisms, by which cancer cells bypass immune surveillance, are also a limiting factor in treatment response rates. Ultimately, the improvement of AI models for predictive accuracy and the advancement of nano-delivery systems for in vivo administration are expected to be key variables in future commercialization. The potential for neoantigen therapy to become a core component of patient-tailored precision medicine is anticipated through its synergistic combination with existing standard cancer therapies and radiation treatments.