Integrative Approaches to Cancer Immunotherapy

Gregory L Szeto; Stacey D Finley

doi:10.1016/j.trecan.2019.05.010

Integrative Approaches to Cancer Immunotherapy

Trends Cancer. 2019 Jul;5(7):400-410. doi: 10.1016/j.trecan.2019.05.010.

Authors

Gregory L Szeto¹, Stacey D Finley²

Affiliations

¹ Department of Chemical, Biochemical, and Environmental Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA; Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, MD 21201, USA. Electronic address: [email protected].
² Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, DRB 140, Los Angeles, CA 90089, USA; Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 925 Bloom Walk, HED 216, Los Angeles, CA 90089, USA; Department of Biological Sciences, University of Southern California, 3616 Trousdale Parkway, AHF 107, Los Angeles, CA 90089, USA. Electronic address: [email protected].

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.

Publication types

Review

MeSH terms

Combined Modality Therapy / methods
Computational Biology / methods
Disease Susceptibility / immunology
Humans
Immunotherapy* / adverse effects
Immunotherapy* / methods
Immunotherapy, Adoptive
Maschinelles Lernen
Models, Theoretical
Neoplasms / immunology
Neoplasms / metabolism
Neoplasms / therapy*
Research Design
Treatment Outcome

Grants and funding

P30 CA014089/CA/NCI NIH HHS/United States