Evidence map›Paper›PMID 27185084›Full record

ReviewDrug discovery today2016

Microfluidic models for adoptive cell-mediated cancer immunotherapies.

Giulia Adriani, Andrea Pavesi, Anthony T Tan, Antonio Bertoletti, Jean Paul Thiery, Roger D Kamm

Abstract readReview
In one paragraph

Review in Drug discovery today, 2016. 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. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Review
  10. Review
  11. Article
  12. A Perfusion Model to Evaluate Response to Photodynamic Therapy in 3D Tumors.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  13. APL bioengineering · 2021
    Review
  14. Review
  15. Review
  16. Reconstructing the tumor architecture into organoids.Advanced drug delivery reviews · 2021
    Review
  17. Review
  18. Screening Cancer Immunotherapy: When Engineering Approaches Meet Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2020
    Review
  19. Review
  20. Article
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

6 authors.

Giulia AdrianiSingapore-MIT Alliance for Research and Technology, BioSyM IRG, 1 Create Way, 138602, Singapore.
Andrea PavesiSingapore-MIT Alliance for Research and Technology, BioSyM IRG, 1 Create Way, 138602, Singapore.
Anthony T TanDUKE-NUS Graduate Medical School Singapore, Emerging Infectious Disease Program, 8 College Road, 169857, Singapore.
Antonio BertolettiDUKE-NUS Graduate Medical School Singapore, Emerging Infectious Disease Program, 8 College Road, 169857, Singapore.
Jean Paul ThieryNational University of Singapore, Department of Biochemistry, Yong Loo Lin School of Medicine MD7, 8 Medical Drive, 117597, Singapore.
Roger D KammSingapore-MIT Alliance for Research and Technology, BioSyM IRG, 1 Create Way, 138602, Singapore; Massachusetts Institute of Technology, Department of Biological Engineering, 77 Massachusetts Avenue, 02139 Cambridge, MA, USA. Electronic address: rdkamm@mit.edu.

Funding

Research Supplement - Quantitative analyses of tumor cell extravasationU01CA202177 · NCI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI KAMM, ROGER D · 2015 to 2020
$4.2M
Microfluidic 3D Assays for Metastatic CancerR33CA174550 · NCI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI KAMM, ROGER D · 2013 to 2015
$1.1M
NCI NIH HHS R33 CA174550NCI NIH HHS U01 CA202177
6 · The paper itself

Abstract

Current adoptive T cell therapies have shown promising results in clinical trials but need further development as an effective cancer treatment. Here, we discuss how 3D microfluidic tumour models mimicking the tumour microenvironment could help in testing T cell immunotherapies by assessing engineered T cells and identifying combinatorial therapy to improve therapeutic efficacy. We propose that 3D microfluidic systems can be used to screen different patient-specific treatments, thereby reducing the burden of in vivo testing and facilitating the rapid translation of successful T cell cancer immunotherapies to the clinic.

Indexed as

Immunotherapy, AdoptiveLab-On-A-Chip DevicesAnimalsHumansNeoplasmsT-Lymphocytes

Identifiers

PMID27185084
PMCPMC5035566

What OpenQuestion holds

Textmetadata
LicenceTDM
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.