Personal approach for cancer treatment: A meta-analysis of Phase II clinical trials - Mikhail B. Potievskiy; Elena P. Zharova; Lidia A. Nekrasova; Airat I. Garifullin; Ivan V. Korobov; Nikita E. Shevchenko; Anastasia A. Zabolotneva; Dmitriy N. Atochin; Andrei D. Kaprin; Peter V. Shegai (PLoS ONE, 2025)
Background To date, no meta-analysis has studied the general outcomes of personalized cancer drug therapy with a focus on current targeted, immunotherapy, and multi-agent phase II clinical trials. Objective We conducted a systematic review and meta-analysis to provide a comprehensive overview of outcomes in patients undergoing personalized genomics-based versus non-personalized treatment in oncology. Data source We searched for publications in PubMed, dedicated to specific cancer drug treatment and phase II clinical trials, and published from 2010 to 2021. The search dates were from 20.10.23 to 20.11.23. The final data check was on 20.12.23. Selection criteria Studies of chemotherapy, immunotherapy, and targeted therapy were included. Only trials, including adults (more than 18 y.o.) were selected. The personalization was evaluated based on genetic markers and study design. Data collection and analysis The study was performed following PRISMA guidelines. Two reviewers worked independently to select studies and arms, and one checked the results. Three reviewers extracted the data, and another reviewer independently checked it. The proportional meta-analysis, random-effects model, and meta-regression were employed to evaluate the effects of genomics-based personalization and other study design parameters (randomization, multi-central protocol, pre-treatment, therapy type, number of patients per arm, and journal impact factor) on the treatment outcomes. Mann-Whitney was employed to compare survival medians, p < 0.05. Results We evaluated 50 studies, having 81 arms and 6536 patients. Response Rate (RR) and 1-year Progression-Free Survival (PFS) were significantly higher in personalized arms (p = 0.009 and p = 0.011). Medians of PFS and Overall Survival (OS) were also higher in personalized arms (p = 0.018 and p = 0.032). Proportional meta-analysis and meta-regression detected a significant positive association between personalized treatment and RR (p = 0.037). The same results were obtained for 1-year PFS and OS rates (p = 0.043 and p = 0.022). Previous drug treatment, type of therapy (targeted therapy/immunotherapy/cytotoxic chemotherapy), study design, and journal impact factor did not affect RR, PFS, and OS. Personalization only affected the treatment outcomes. Conclusion This study discovered the benefits of a personal approach to cancer treatment using genomic data. The personalized approach improved cancer outcomes and offers promising therapeutic potential for the further development of cancer treatment. Registration PROSPERO record ID CRD42024504021