Evidence map›Paper›PMID 27587540›Full record

ReviewCancer research2016

Humanized Mouse Xenograft Models: Narrowing the Tumor-Microenvironment Gap.

J Jason Morton, Gregory Bird, Yosef Refaeli, Antonio Jimeno

Open access · greenAbstract readReview
In one paragraph

Review in Cancer research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 139 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
139citing papers in PubMed, 3 pooled it
30.6field-weighted citation impact, top 1% of its field
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

139 citing papers in PubMed, 3 syntheses or guidelines pooled it, 241 citations in OpenAlex.

  1. Pooled it
  2. Patient-Derived Xenograft Models in Cervical Cancer: A Systematic Review.International journal of molecular sciences · 2021
    Pooled it
  3. Pooled it
  4. Review
  5. Review
  6. Humanized immune system animal models and their recent applications.Animal models and experimental medicine · 2026
    Review
  7. Review
  8. Article
  9. Review
  10. Review
  11. Functional significance of miR-218 in lung cancer.Journal of cancer metastasis and treatment · 2026
    Article
  12. Article
  13. Article
  14. Review
  15. Review
  16. Review
  17. Article
  18. Review
  19. Review
  20. Review

79 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

4 authors at 1 institution in 1 country.

J Jason MortonDivision of Medical Oncology, Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado.
Gregory BirdDepartment of Dermatology, University of Colorado School of Medicine, Aurora, Colorado.
Yosef RefaeliDepartment of Dermatology, University of Colorado School of Medicine, Aurora, Colorado.
Antonio JimenoDivision of Medical Oncology, Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado. antonio.jimeno@ucdenver.edu.
University of Colorado Denver · US

Funding

THE REGULATION OF V ALPHA 7 EXPRESSING T CELLSP01AI022295 · NIAID · NATIONAL JEWISH HEALTH · PI REFAELI, YOSEF · 1985 to 2011
$11.7M
Targeting PD-L1 interactome signaling to enhance immunotherapy efficacy in head and neck squamous cell cancerR01CA149456 · NCI · UNIVERSITY OF COLORADO DENVER · PI Antonio Jimeno · 2010 to 2026
$5.6M
Training in Translational Research of Lung, Head and Neck CancerT32CA174648 · NCI · UNIVERSITY OF COLORADO DENVER · PI KARAM, SANA D · 2013 to 2022
$4.2M
Identifying oral cancer stem cell properties affected by the microenvironmentR01DE024371 · NIDCR · UNIVERSITY OF COLORADO DENVER · PI JIMENO, ANTONIO, WANG, XIAO-JING · 2014 to 2017
$2.1M
MYC-induced breach of B-cell anergy in AIDS-related NHLsR01CA117802 · NCI · UNIVERSITY OF COLORADO DENVER · PI REFAELI, YOSEF · 2006 to 2010
$1.5M
Rational generation of a head and neck squamous cell cancer direct xenograft modeR21DE019712 · NIDCR · UNIVERSITY OF COLORADO DENVER · PI JIMENO, ANTONIO · 2009 to 2010
$385k
NCI NIH HHS R01 CA117802NCI NIH HHS R01 CA149456NCI NIH HHS T32 CA174648NIAID NIH HHS P01 AI022295NIDCR NIH HHS R01 DE024371NIDCR NIH HHS R21 DE019712
6 · The paper itself

Abstract

Cancer research has long been hampered by the limitations of the current model systems. Both cultured cells and mouse xenografts grow in an environment highly dissimilar to that of their originating tumor, frequently resulting in promising treatments that are ultimately clinically ineffective. The development of highly immunodeficient mouse strains into which human immune systems can be engrafted can help bridge this gap. Humanized mice (HM) allow researchers to examine xenograft growth in the context of a human immune system and resultant tumor microenvironment, and recent studies have highlighted the increased similarities in attendant tumor structure, metastasis, and signaling to those features in cancer patients. This setting also facilitates the examination of investigational cancer therapies, including new immunotherapies. This review discusses recent advancements in the generation and application of HM models, their promise in cancer research, and their potential in generating clinically relevant treatments. This review also focuses on current efforts to improve HM models by engineering mouse strains expressing human cytokines or HLA proteins and implanting human bone, liver, and thymus tissue to facilitate immune cell maturation and trafficking. Finally, we discuss how these improvements may help direct future HM model cancer studies. Cancer Res; 76(21); 6153-8. ©2016 AACR.

Indexed as

Disease Models, AnimalTumor MicroenvironmentAnimalsCytokinesHumansMiceNeoplasmsNeoplasm TransplantationTransplantation, HeterologousCytokines

Identifiers

PMID27587540
PMCPMC5093075
OpenAlexW2510821666

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.