Evidence map›Paper›PMID 33042756›Full record

ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2020

Screening Cancer Immunotherapy: When Engineering Approaches Meet Artificial Intelligence.

Xingwu Zhou, Moyuan Qu, Peyton Tebon, Xing Jiang, Canran Wang, Yumeng Xue, Jixiang Zhu, Shiming Zhang, Rahmi Oklu, Shiladitya Sengupta and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed.

  1. Review
  2. Leveraging Microphysiological Systems to Facilitate Neutrophil-Based Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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  16. Engineering Heterogeneous Tumor Models for Biomedical Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Review
  17. Review
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  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Xingwu ZhouDepartment of Bioengineering University of California, Los Angeles Los Angeles CA 90095 USA.
Moyuan QuDepartment of Bioengineering University of California, Los Angeles Los Angeles CA 90095 USA.
Peyton TebonDepartment of Bioengineering University of California, Los Angeles Los Angeles CA 90095 USA.
Xing JiangDepartment of Bioengineering University of California, Los Angeles Los Angeles CA 90095 USA.
Canran WangDepartment of Bioengineering University of California, Los Angeles Los Angeles CA 90095 USA.
Yumeng XueDepartment of Bioengineering University of California, Los Angeles Los Angeles CA 90095 USA.
Jixiang ZhuDepartment of Bioengineering University of California, Los Angeles Los Angeles CA 90095 USA.
Shiming ZhangDepartment of Bioengineering University of California, Los Angeles Los Angeles CA 90095 USA.
Rahmi OkluMinimally Invasive Therapeutics Laboratory Division of Vascular and Interventional Radiology Mayo Clinic Phoenix AZ 85054 USA.
Shiladitya SenguptaHarvard-Massachusetts Institute of Technology Division of Health Sciences and Technology Harvard Medical School Boston MA 02115 USA.
Wujin SunDepartment of Bioengineering University of California, Los Angeles Los Angeles CA 90095 USA.
Ali KhademhosseiniDepartment of Bioengineering University of California, Los Angeles Los Angeles CA 90095 USA.ORCID https://orcid.org/0000-0001-6322-8852

Funding

Engineering personalized micro-tumor ecosystemsU01CA214411 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI KHADEMHOSSEINI, ALI, SENGUPTA, SHILADITYA · 2017 to 2021
$3.5M
NCI NIH HHS U01 CA214411
6 · The paper itself

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.

Indexed as

artificial intelligencecancer immunotherapydrug screeninghigh‐throughput screeningtissue engineering

Identifiers

PMID33042756
PMCPMC7539186

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.