Cancer Genetics and Epigenetics, 2025, Vol.13, No.1, 32-40 http://medscipublisher.com/index.php/cge 37 camrelizumab and apatinib combined for the treatment of advanced cervical cancer. Genetic changes such as PIK3CA and PTEN may be related to treatment efficacy, and they may be markers for predicting treatment efficacy (Huang et al., 2021). The NCI-MATCH trial found that approximately 28.4% of cervical cancer patients can choose corresponding targeted therapy based on their genetic characteristics (Crowley et al., 2021). Not everyone can benefit, but some people may indeed get better treatment through genotyping. 6.3 Lessons learned from trial results and challenges faced Although genotyping brings new ideas to treatment, there are also many challenges in practical application. The CLAP trial reminds us that it is important to find key genes that predict efficacy. Some specific genetic changes may be associated with better survival time (Huang et al., 2021). Some patients respond well to medication at first, but develop drug resistance after a period of time, and the treatment effect decreases (Sawada et al., 2021). Different regions and populations have different genetic differences, so one treatment plan may not be suitable for everyone. For example, related studies in Japan have found that differences in genetic background may affect the formulation of treatment strategies (Kuno et al., 2019; Kim et al., 2023). 7 Barriers to Implementation in Clinical Practice 7.1 Socioeconomic and ethical challenges in the adoption of genotyping The use of genotyping in the treatment of cervical cancer often encounters some social and ethical issues. A major difficulty is that not all patients in all regions can use advanced genetic testing. These technologies are usually only available in hospitals with good economic conditions, and patients living in lower-income areas are less likely to have access to these potentially life-saving tools (Crowley et al., 2021; Friedman et al., 2023). People are also worried that genetic information will be leaked or used for discrimination, such as by employers or insurance companies (Meric-Bernstam et al., 2015). 7.2 Technical issues such as data interpretation and storage Genotyping also has some technical difficulties. Interpreting genetic data is not easy and requires experienced professionals, but not every hospital has such talents (Meric-Bernstam et al., 2015; Friedman et al., 2023). The amount of genetic data is very large, and it takes powerful computer equipment to save this data, which will bring considerable pressure and require hospitals to spend a lot of money on information systems (Áyen et al., 2020). 7.3 Cost-effectiveness and patient accessibility issues Although genotyping can help doctors tailor treatment plans for patients, this technology is not cheap. Sometimes, the cost is too high and many patients cannot afford it (Kuno et al., 2019; Crowley et al., 2021). Especially for those patients who do not have good medical insurance, it is even more difficult to afford such expenses. If we want genotyping to be truly used in daily diagnosis and treatment, we must control the cost and make it available to more people (Meric-Bernstam et al., 2015). 8 Future Prospects of Cervical Cancer Genotyping 8.1 Innovations in genotyping and real-time patient monitoring Genotyping technology has made significant progress, making it easier to identify specific gene mutations associated with cervical cancer. Now, next-generation sequencing (NGS) tools such as MSK-IMPACT can fully analyze the genome of tumors. These tools can find some mutations that can be controlled by drugs, thereby helping doctors formulate treatment plans (Friedman et al., 2023). HPV genotyping technology is also developing, and the detection of cervical intraepithelial neoplasia is becoming more accurate. This technology can not only better assess cancer risk, but also be used to track patients' response during treatment (Broquet et al., 2022; Li et al., 2022). 8.2 Application of artificial intelligence and machine learning in patient stratification Artificial intelligence (AI) and machine learning (ML) technologies have also begun to be used in the stratified management of cervical cancer patients. They can analyze a lot of complex genetic information, find useful
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