Abstract
The success that vaccines have had in the fight with infectious diseases has not been mirrored in their use in the fight against cancer. The major differences are that cancer vaccines have been tested in the therapeutic rather than the prophylactic setting, and in older adults rather than in the pediatric population. Cancers, as well as current standard treatments, are highly immunosuppressive, which further compromises the success of therapeutic vaccines. Cancer is considered to be primarily a disease of the older age and yet many children suffer from or succumb to cancers such as leukemias, glioblastomas, neuroblastomas and sarcomas. Standard therapy, even when curative, is accompanied by serious side effects, including secondary tumors later in life. Due to the greater capacity of a young immune system to recover after cancer treatment, therapeutic vaccines are expected to have a better chance to elicit protective immunity and prevent cancer recurrence in children. In this review, we discuss the current efforts at designing and testing cancer vaccines in children with the focus on specific tumor antigens expressed by pediatric cancers.
Keywords: Cancer, immunotherapy, pediatric, tumor, vaccine
Call for Papers in Thematic Issues
Accelerating Cancer Drug Discovery Using Artificial Intelligence and In Silico Methods
Artificial intelligence and in silico methods speed up cancer drug discovery, transforming how new treatments are developed. Artificial intelligence, along with in silico methods, allows for quick investigation of large biological datasets, helping identify potential drug targets with remarkable speed and accuracy. Machine learning models help us understand how molecules ...read more
Artificial Intelligence and Computational Approaches in Drug Discovery
Computer-aided drug design (CADD) and artificial intelligence (AI) are fundamentally reshaping drug discovery pipelines by significantly enhancing efficiency in molecular screening, rational drug design, and natural product development. In the field of molecular screening, the integration of virtual high-throughput screening with advanced AI models enables rapid analysis of million-compound libraries, ...read more
Artificial Intelligence for Transformative Healthcare: From Prediction to Prevention
Machine learning and deep learning are transforming biomedical diagnosis by allowing for quicker and more accurate disease detection, ultimately enhancing patient care. Using advanced algorithms and extensive medical data, these approaches uncover patterns that may not be visible to humans, resulting in better detection of conditions such as cancer and ...read more
Mapping Dynamic Sites and Designing Novel Therapeutics
The concept of allostery, where ligand binding at a site distinct from the active or orthosteric site modulates protein function, has emerged as a transformative paradigm in modern drug discovery. Unlike conventional orthosteric drugs that often face limitations in selectivity and off-target effects, allosteric modulators offer a promising alternative. Their ...read more
Related Journals
Anti-Cancer Agents in Medicinal Chemistry
Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry
Current Bioactive Compounds
Current Cancer Drug Targets
Combinatorial Chemistry & High Throughput Screening
Current Cancer Therapy Reviews
Current Diabetes Reviews
Current Drug Safety
Current Drug Targets
Current Drug Therapy
Related Books
The Role of Nutraceuticals in Degenerative Diseases (Part 2)
Herb-Drug Interactions: A Medicinal Approach
The Role of Nutraceuticals in Degenerative Diseases (Part 1)
Autoimmune Disorders and Secondary Plant Metabolites (Part 2)
Autoimmune Disorders and Secondary Plant Metabolites (Part 1)
Brain Tumor Drug Development: Current Advances and Strategies (Part 2)
The Role of Asymmetric Synthesis in Drug Discovery
Brain Tumor Drug Development: Current Advances and Strategies (Part 1)
Handbook of Practical Pharmacology - I
Drug Repurposing for Antivirals
85