Integrative Approaches to Cancer Immunotherapy

Abstract

Cancer immunotherapy aims to arm patients with cancer-fighting immunity. Many new cancer-specific immunotherapeutic drugs have gained approval in the past several years, demonstrating immunotherapy's efficacy and promise as an anticancer modality. Despite these successes, several outstanding questions remain for cancer immunotherapy, including how to make immunotherapy more efficacious in a broader range of cancer types and patients, and how to predict which patients will respond or not respond to therapy. We present a case for integrative systems approaches that will answer these questions. This involves applying mechanistic and statistical modeling, establishing consistent and widely adopted experimental tools to generate systems-level data, and creating sustained mechanisms of support. If implemented, these approaches will lead to major advances in cancer treatment.

Keywords: bioinformatics; computational modeling; machine learning; systems approaches; systems-level data.

Copyright © 2019 Elsevier Inc. All rights reserved.

Figures

Figure 1.

The development of immunotherapeutic drugs for cancer has led to durable responses in numerous patients. However, many questions remain. Combining mechanistic and statistical modeling, consistent and widely adopted experimental tools to generate systems-level data, and sustained mechanisms to support systems immunotherapy will produce answers to these questions and enable major advances in cancer treatment.