Evidence map›Paper›PMID 27231717›Full record

ReviewMolecular therapy oncolytics2016

CAR models: next-generation CAR modifications for enhanced T-cell function.

Daniel Abate-Daga, Marco L Davila

Abstract readReview
In one paragraph

Review in Molecular therapy oncolytics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
97citing papers in PubMed, 1 pooled it
–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

97 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Guideline
  2. Article
  3. Review
  4. Review
  5. Review
  6. Review
  7. Review
  8. Article
  9. Review
  10. Review
  11. Review
  12. Review
  13. Review
  14. Review
  15. Review
  16. Colorectal Cancer; Novel Approaches in Chimeric Antigen Receptors (CAR) -T cell.International journal of molecular and cellular medicine · 2025
    Review
  17. Review
  18. Review
  19. Article
  20. Review

37 more citing papers are in PubMed but not listed here.

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

2 authors.

Daniel Abate-DagaDepartment of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA; Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA; Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
Marco L DavilaMorsani College of Medicine, University of South Florida, Tampa, FL, USA; Department of Blood and Marrow Transplantation, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.

Funding

Vanderbilt Clinical Oncology Research Career Development ProgramK12CA090625 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Debra L. Friedman, Paula Jill Hurley · 2001 to 2026
$16.9M
NCI NIH HHS K12 CA090625
6 · The paper itself

Abstract

T cells genetically targeted with a chimeric antigen receptor (CAR) to B-cell malignancies have demonstrated tremendous clinical outcomes. With the proof in principle for CAR T cells as a therapy for B-cell malignancies being established, current and future research is being focused on adapting CAR technology to other cancers, as well as enhancing its efficacy and/or safety. The modular nature of the CAR, extracellular antigen-binding domain fused to a transmembrane domain and intracellular T-cell signaling domains, allows for optimization by replacement of the various components. These modifications are creating a whole new class of therapeutic CARs. In this review, we discuss the recent major advances in CAR design and how these modifications will impact its clinical application.

Identifiers

PMID27231717
PMCPMC4871190

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.