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Published on: April 30, 2021
Screening Cancer Immunotherapy: When Engineering Approaches Meet Artificial Intelligence
Xingwu Zhou1,2,3, Moyuan Qu1,2,4, Peyton Tebon1,2
1Department of Bioengineering University of California, Los Angeles Los Angeles CA 90095 USA.
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.
Insights
Engineering advanced screening platforms, including organoids and organs-on-a-chip, can improve cancer immunotherapy efficacy prediction. These methods aid in developing personalized treatments and overcoming patient response variability in cancer immunotherapy.
Area of Science:
- Biomedical Engineering
- Cancer Research
- Immunology
Background:
- Immunotherapy shows promise for cancer treatment, with increasing FDA approvals.
- Clinical use is hindered by variable patient responses and lack of efficacy predictors.
- Predicting patient response and developing new immunotherapies require advanced screening platforms.
Purpose of the Study:
- To highlight engineering approaches for screening cancer immunotherapies.
- To discuss future perspectives and challenges in advancing cancer immunotherapy.
- To improve the prediction of patient-specific responses to immunotherapeutic agents.
Main Methods:
- Utilizing advanced engineering platforms such as sequencing, gene editing, tumor organoids, bioprinted tissues, and organs-on-a-chip.
- Recreating tumor and microenvironment features to screen immunotherapies.
- Leveraging high-throughput platforms and artificial intelligence for efficient screening.
Main Results:
- Engineering approaches enable comprehensive screening of cancer immunotherapies.
- These platforms can model tumor complexity and microenvironment interactions.
- AI and high-throughput methods enhance screening efficiency and accuracy.
Conclusions:
- Advanced engineering platforms are crucial for overcoming challenges in cancer immunotherapy.
- These technologies facilitate personalized treatment strategies and improved clinical outcomes.
- Further development is needed to fully realize the potential of engineering in cancer immunotherapy.
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