File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1002/advs.202001447
- Scopus: eid_2-s2.0-85089397135
- PMID: 33042756
- WOS: WOS:000567383600001
Supplementary
- Citations:
- Appears in Collections:
Article: Screening Cancer Immunotherapy: When Engineering Approaches Meet Artificial Intelligence
Title | Screening Cancer Immunotherapy: When Engineering Approaches Meet Artificial Intelligence |
---|---|
Authors | |
Keywords | cancer immunotherapy high-throughput screening drug screening artificial intelligence tissue engineering |
Issue Date | 2020 |
Citation | Advanced Science, 2020, v. 7, n. 19, article no. 2001447 How to Cite? |
Abstract | © 2020 The Authors. Published by Wiley-VCH GmbH. 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. |
Persistent Identifier | http://hdl.handle.net/10722/295435 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhou, Xingwu | - |
dc.contributor.author | Qu, Moyuan | - |
dc.contributor.author | Tebon, Peyton | - |
dc.contributor.author | Jiang, Xing | - |
dc.contributor.author | Wang, Canran | - |
dc.contributor.author | Xue, Yumeng | - |
dc.contributor.author | Zhu, Jixiang | - |
dc.contributor.author | Zhang, Shiming | - |
dc.contributor.author | Oklu, Rahmi | - |
dc.contributor.author | Sengupta, Shiladitya | - |
dc.contributor.author | Sun, Wujin | - |
dc.contributor.author | Khademhosseini, Ali | - |
dc.date.accessioned | 2021-01-18T15:46:52Z | - |
dc.date.available | 2021-01-18T15:46:52Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Advanced Science, 2020, v. 7, n. 19, article no. 2001447 | - |
dc.identifier.uri | http://hdl.handle.net/10722/295435 | - |
dc.description.abstract | © 2020 The Authors. Published by Wiley-VCH GmbH. 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. | - |
dc.language | eng | - |
dc.relation.ispartof | Advanced Science | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | cancer immunotherapy | - |
dc.subject | high-throughput screening | - |
dc.subject | drug screening | - |
dc.subject | artificial intelligence | - |
dc.subject | tissue engineering | - |
dc.title | Screening Cancer Immunotherapy: When Engineering Approaches Meet Artificial Intelligence | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1002/advs.202001447 | - |
dc.identifier.pmid | 33042756 | - |
dc.identifier.pmcid | PMC7539186 | - |
dc.identifier.scopus | eid_2-s2.0-85089397135 | - |
dc.identifier.volume | 7 | - |
dc.identifier.issue | 19 | - |
dc.identifier.spage | article no. 2001447 | - |
dc.identifier.epage | article no. 2001447 | - |
dc.identifier.eissn | 2198-3844 | - |
dc.identifier.isi | WOS:000567383600001 | - |
dc.identifier.issnl | 2198-3844 | - |