Cancer Genetics and Epigenetics, 2025, Vol.13, No.6, 300-309 http://medscipublisher.com/index.php/cge 307 7.2 Develop more precise and reliable biomarker systems Searching for and verifying effective biomarkers is the key to achieving personalized and precise medication in immunotherapy. Although PD-L1 expression and tumor mutational burden are commonly used as predictive indicators at present, their sensitivity and specificity are limited and cannot fully reflect the true situation of immune response. In recent years, researchers have been committed to integrating multi-level data such as genomic, transcriptomic and proteomic data to establish more accurate biomarker systems (Cristescu et al., 2018). Some new detection methods are being developed, such as immune-related gene expression characteristics, circulating tumor DNA detection in the blood, and in situ analysis of the tumor microenvironment, all of which are expected to help better screen patients and monitor therapeutic effects (Binnewies et al., 2018). The development of high-quality and standardized biomarker detection methods is of vital importance for guiding clinical medication and enhancing the value of immunotherapy. 7.3 Trends in the development of next-generation immunotherapy To overcome the shortcomings of existing immunotherapy drugs and further enhance the therapeutic effect, new immunotherapy approaches are constantly evolving. Bispecific antibodies can act on two immune-related targets simultaneously or help T cells better recognize tumors, showing good potential in preclinical and early clinical studies. In addition, individualized immunotherapy, such as neoantigen vaccines and customized T-cell therapy, is also constantly advancing, hoping to formulate more precise treatment plans based on the tumor characteristics of different patients and enhance the immune system's ability to attack tumors. Artificial intelligence and machine learning are also increasingly being used to process complex bioinformatics, predict therapeutic responses, and assist in formulating new immunotherapy strategies (Esteva et al., 2019). The application of artificial intelligence in treatment design is expected to accelerate the development of new drugs, optimize combination therapy methods, and achieve more flexible adjustments during the treatment process, laying the foundation for more efficient and personalized tumor immunotherapy in the future. 8 Concluding Remarks Immune checkpoint inhibitors have significantly transformed the treatment approaches for various solid tumors. In cancer types such as melanoma, non-small cell lung cancer, and renal cell carcinoma, they help patients control their conditions over the long term and prolong their survival time. However, due to differences in tumor types, drug resistance, and the lack of universal predictive indicators, only a portion of patients can benefit from it at present. This indicates that we need to continue in-depth research, expand the beneficiary population and overcome the problem of drug resistance. Safe medication management is of great significance as immunotherapy may cause immune-related side effects ranging from mild to severe, which require close monitoring and timely handling. Combining precision medicine strategies-such as screening suitable patients through biomarkers or adopting individualized dosing regimens-can help improve treatment outcomes while reducing the risk of adverse reactions and ensuring that patients receive the most appropriate immunotherapy regimens. In the future, combining immunotherapy with chemotherapy, targeted therapy, radiotherapy and new drugs, while promoting the development of personalized medicine, is expected to break through the current treatment bottleneck. Driven by a more complete biomarker system and emerging technologies, the development of multi-modal and individualized treatment strategies will further improve the immunotherapy outcomes for patients with solid tumors, benefiting more patients. Acknowledgments The author extends sincere thanks to Mrs. Shou for her feedback on the manuscript. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.
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