Chimeric antigen receptor (CAR)-T cell therapy has transformed cancer immunotherapy, achieving durable complete remissions in hematologic cancers. Yet its translation to solid tumors like hepatocellular carcinoma (HCC), a leading cause of cancer-related deaths worldwide, faces formidable barriers, including immunosuppressive tumor microenvironments (TMEs), antigen heterogeneity, and risks of on-target/off-tumor toxicity. This review discusses the evolving role of CAR-T therapy in HCC across three domains: (1) foundational concepts in CAR-T design, mechanistic action, and antigen-targeting strategies; (2) breakthroughs from preclinical studies and early-phase clinical trials, such as glypican-3 (GPC3) and alpha-fetoprotein (AFP) directed CAR-T cells that have demonstrated preliminary safety and anti-tumor activity; and (3) innovative strategies to overcome TME-driven resistance, including metabolic reprogramming and stromal modulation. We highlight cutting-edge engineering solutions such as armored CAR-T cells engineered for cytokine support, dual-targeting constructs to mitigate antigen escape, and hypoxia-resistant designs alongside synergistic approaches combining CAR-T with immune checkpoint inhibitors or tyrosine kinase inhibitors. Furthermore, we dissect emerging tactics to disrupt TME immunosuppression. While CAR-T therapy holds promise for redefining HCC management, its success will depend on overcoming biological and logistical barriers through patient-tailored designs and robust translational pipelines. Future directions should prioritize biomarker-driven clinical trials, scalable manufacturing platforms, and integration with existing multimodal HCC therapies to maximize durable responses.
CAR-T cell therapy in hepatocellular carcinoma: from mechanistic insights to clinical translation.
Reza Elahi,Y. A. Idrissi,A. Saeed
Published 2025 in Cancer Treatment Reviews
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- Publication year
2025
- Venue
Cancer Treatment Reviews
- Publication date
2025-11-01
- Fields of study
Medicine
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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