Stem cell-based cancer therapy: A precision oncology paradigm

Abstract

Stem cell-based approaches have emerged as a promising direction in cancer therapy by combining targeted treatment strategies with regenerative potential. Conventional cancer treatments, including chemotherapy, radiation, and surgery, often lack specificity and may result in significant toxicity, treatment resistance, and disease recurrence. In contrast, stem cells offer unique advantages such as self-renewal, differentiation capacity, and tumortargeting ability, enabling both direct therapeutic applications and support for tissue repair and immune recovery. This review provides a comprehensive overview of stem cell biology and its relevance to cancer therapy, including key applications such as hematopoietic stem cell transplantation and mesenchymal stem cell-mediated drug delivery. It also examines the dual role of stem cells in cancer, highlighting both their therapeutic potential and associated risks, including tumor promotion and immune-related complications. Furthermore, the review discusses recent advances in engineered stem cell systems for gene and drug delivery in oncology. A key focus of this article is the integration of artificial intelligence and machine learning in optimizing stem cell-based therapies. These computational approaches enable improved cell characterization,
treatment personalization, and prediction of therapeutic outcomes using multi-dimensional biological and clinical data. The convergence of stem cell biology with data-driven technologies is accelerating the development of more precise and effective cancer
treatments. Despite ongoing challenges related to safety, ethical considerations, and regulatory constraints, stem cell-based therapies represent a rapidly evolving field with significant clinical potential. Continued interdisciplinary research integrating
biotechnology and computational methods is expected to play a critical role in advancing personalized and durable cancer treatment strategies.