Monoclonal antibody immune therapy response instrument for stratification and cost-effective personalized approaches...

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.