Screening Cancer Immunotherapy: When Engineering Approaches Meet Artificial Intelligence

• Main Authors: Xingwu Zhou, Moyuan Qu, Peyton Tebon, Xing Jiang, Canran Wang, Yumeng Xue, Jixiang Zhu, Shiming Zhang, Rahmi Oklu, Shiladitya Sengupta, Wujin Sun, Ali Khademhosseini
• Format: Article
• Language: English
• Published: Wiley 2020-10-01
• Series: Advanced Science
• Subjects: artificial intelligence; cancer immunotherapy; drug screening; high‐throughput screening; tissue engineering
• Online Access: https://doi.org/10.1002/advs.202001447

Abstract Immunotherapy is a class of promising anticancer treatments that has recently gained attention due to surging numbers of FDA approvals and extensive preclinical studies demonstrating efficacy. Nevertheless, further clinical implementation has been limited by high variability in patient response to different immunotherapeutic agents. These treatments currently do not have reliable predictors of efficacy and may lead to side effects. The future development of additional immunotherapy options and the prediction of patient‐specific response to treatment require advanced screening platforms associated with accurate and rapid data interpretation. Advanced engineering approaches ranging from sequencing and gene editing, to tumor organoids engineering, bioprinted tissues, and organs‐on‐a‐chip systems facilitate the screening of cancer immunotherapies by recreating the intrinsic and extrinsic features of a tumor and its microenvironment. High‐throughput platform development and progress in artificial intelligence can also improve the efficiency and accuracy of screening methods. Here, these engineering approaches in screening cancer immunotherapies are highlighted, and a discussion of the future perspectives and challenges associated with these emerging fields to further advance the clinical use of state‐of‐the‐art cancer immunotherapies are provided.