
pmid: 17030074
To date cancer vaccines have yet to show efficacy in a phase III trial. However, the clinical benefit seen with monoclonal antibody mediated therapies (e.g., Herceptin) has provided proof of principle that immune responses directed against tumour-associated antigens could have therapeutic potential. The failure of past cancer vaccine trials is likely due to several factors including the inappropriate choice of tumour antigen, use of an unoptimised antigen delivery system or vaccination schedule or selection of the wrong patient group. Any one of these variables could potentially result in the induction of an immune response of insufficient magnitude to deliver clinical benefit. Live recombinant viral vaccines have been used in the development of cancer immunotherapy approaches for the past 10 years. Though such vectors are self-adjuvanted and offer the ability to express multiple tumour-associated antigens (TAAs) along with an array of immune co-factors, arguably, they have yet to demonstrate convincing efficacy in pivotal clinical trials. However, in recent years, more coordinated studies have revealed mechanisms to optimise current vectors and have lead to the development of new advantageous vector systems. In this review, we highlight that live recombinant viral vectors provide a versatile and effective antigen delivery system and describe the optimal properties of an effective viral vector. Additionally, we discuss the advantages and disadvantages of the panel of recombinant viral systems currently available to cancer vaccinologists and how they can work in synergy in heterologous prime boost protocols and with other treatment modalities.
Neoplasms, Genetic Vectors, DNA Viruses, Animals, Humans, Viral Vaccines, Cancer Vaccines
Neoplasms, Genetic Vectors, DNA Viruses, Animals, Humans, Viral Vaccines, Cancer Vaccines
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 85 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
