Cancer vaccines: identification of biomarkers predictive of clinical efficacy

Personalized medicine is playing an increasingly important role in the treatment of patients living with cancer. This landmark shift has been driven in part by statistics emerging from the "one size fits all" approach to the treatment of cancer patients. Some reports suggest that only a minority of individuals actually benefit from treatment and adverse effects of medications remain a major cause of hospitalization, morbidities and deaths. Although the side-effect profile of most immunotherapy treatment modalities is usually fairly benign, there is no reason to believe that immunotherapy is any different from other oncology therapies in that some patients are likely to receive more benefit than others. Indeed, the fact that generation of the therapeutic modality requires translation through multiple complex biological processes for an immunotherapy product to be effective may mean that such approaches require an even better understanding of the patient being treated. Furthermore, the very low success rate of cancer immunotherapy approaches to deliver benefit to patients demands a more detailed understanding of who will benefit and why. The identification of biomarkers predictive of treatment benefit is one route to improve the success rate of cancer vaccines.