Baldi Salem, Alnaggar Mohammed, Al-Mogahed Maged, Khalil Khalil A A, Zhan Xianquan
Department of Medical Laboratory Diagnostics, School of Medical Technology, Shaoyang University, Shaoyang, 422000 China.
Department of Medical Laboratory Diagnostics, Al-Thawra General Hospital, Al Hudaydah, Yemen.
EPMA J. 2025 Mar 22;16(2):465-503. doi: 10.1007/s13167-025-00403-w. eCollection 2025 Jun.
Immune checkpoint inhibitors (ICIs), such as anti-PD-1, anti-PD-L1, and anti-CTLA-4 therapies, have revolutionized cancer treatment by harnessing the body's immune system to eliminate cancer cells. Despite their considerable promise, the efficacy of ICIs significantly differs based on tumor types and specific patient conditions, highlighting the necessity for personalized approaches in the framework of predictive preventive personalized medicine (PPPM; 3PM).
This review proposes a stratification instrument within the 3PM framework to enhance the therapeutic efficacy of ICIs across Pan-cancer. Predictive approaches need to be utilized to enhance the effectiveness of ICIs. For example, biomarkers such as particular genetic alterations and metabolic pathways provide key information on patient treatment responses. To predict treatment outcomes, uncover resistance mechanisms, and tailor medications, we examine biomarkers including PDL-1 and CTLA4. Focusing on cancers like melanoma, bladder, and renal cell carcinoma, we highlight advances in combination therapies and cellular approaches to overcome resistance. We conducted an analysis of clinical trials and public datasets (TCGA, GEO) to evaluate ICI responses across number of cancer types. Survival analysis employed Kaplan-Meier curves and Cox regression. Pan-cancer analysis shows response rates ranging from 19.8% in bladder cancer to > 39% in melanoma when combination therapy is used, emphasizing the potential of 3PM to improve outcomes. By exploring resistance mechanisms and emerging therapeutic innovations, we propose a cost-effective model for better patient stratification and care. Validation of this model requires standardized biomarkers and prospective trials, promising a shift toward precision oncology.
Within the 3PM framework, this review addresses the urgent need for cost-effective stratification tools and adaptive combinatorial strategies to optimize outcomes.
免疫检查点抑制剂（ICI），如抗程序性死亡蛋白1（anti-PD-1）、抗程序性死亡配体1（anti-PD-L1）和抗细胞毒性T淋巴细胞相关蛋白4（anti-CTLA-4）疗法，通过利用人体免疫系统消除癌细胞，彻底改变了癌症治疗方式。尽管它们前景广阔，但ICI的疗效因肿瘤类型和特定患者情况而有显著差异，这凸显了在预测性预防个性化医学（PPPM；3PM）框架下采用个性化方法的必要性。
本综述在3PM框架内提出了一种分层工具，以提高ICI在泛癌中的治疗效果。需要利用预测方法来提高ICI的有效性。例如，特定基因改变和代谢途径等生物标志物提供了有关患者治疗反应的关键信息。为了预测治疗结果、揭示耐药机制并量身定制药物，我们研究了包括程序性死亡配体1（PDL-1）和细胞毒性T淋巴细胞相关蛋白4（CTLA4）在内的生物标志物。以黑色素瘤、膀胱癌和肾细胞癌等癌症为重点，我们强调了联合疗法和细胞方法在克服耐药性方面的进展。我们对临床试验和公共数据集（癌症基因组图谱数据库（TCGA）和基因表达综合数据库（GEO））进行了分析，以评估多种癌症类型对ICI的反应。生存分析采用了Kaplan-Meier曲线和Cox回归。泛癌分析表明，联合治疗时，膀胱癌的反应率为19.8%，黑色素瘤的反应率超过39%，这强调了3PM改善治疗结果的潜力。通过探索耐药机制和新兴治疗创新，我们提出了一种具有成本效益的模型，以实现更好的患者分层和护理。该模型的验证需要标准化的生物标志物和前瞻性试验，有望推动向精准肿瘤学的转变。
在3PM框架内，本综述满足了对具有成本效益的分层工具和适应性联合策略的迫切需求，以优化治疗结果。
