Evidence map›Paper›PMID 36231127›Full record

ArticleCells2022

Investigating Two Modes of Cancer-Associated Antigen Heterogeneity in an Agent-Based Model of Chimeric Antigen Receptor T-Cell Therapy.

Tina Giorgadze, Henning Fischel, Ansel Tessier, Kerri-Ann Norton

Open access · goldAbstract read
In one paragraph

Article in Cells, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

8 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
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  4. Article
  5. Mathematical Modeling Support for Lung Cancer Therapy-A Short Review.International journal of molecular sciences · 2023
    Review
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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.

Tina GiorgadzeBard College, 30 Campus Road, Annandale-on-Hudson, NY 12504, USA.
Henning FischelBard College, 30 Campus Road, Annandale-on-Hudson, NY 12504, USA.
Ansel TessierBard College, 30 Campus Road, Annandale-on-Hudson, NY 12504, USA.
Kerri-Ann NortonBard College, 30 Campus Road, Annandale-on-Hudson, NY 12504, USA.
Bard College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chimeric antigen receptor (CAR) T-cell therapy has been successful in treating liquid tumors but has had limited success in solid tumors. This work examines unanswered questions regarding CAR T-cell therapy using computational modeling, such as, what percentage of the tumor must express cancer-associated antigens for treatment to be successful? The model includes cancer cell and vascular and CAR T-cell modules that interact with each other. We compare two different models of antigen expression on tumor cells, binary (in which cancer cells are either susceptible or are immune to CAR T-cell therapy) and gradated (where each cancer cell has a probability of being killed by a CAR T-cell). We vary the antigen expression levels within the tumor and determine how effective each treatment is for the two models. The simulations show that the gradated antigen model eliminates the tumor under more parameter values than the binary model. Under both models, shielding, in which the low/non-antigen-expressing cells protect high antigen-expressing cells, reduced the efficacy of CAR T-cell therapy. One prediction is that a combination of CAR T-cell therapies that targets the general population of cells as well as one that specifically targets cancer stem cells should increase its efficacy.

Indexed as

NeoplasmsReceptors, Chimeric AntigenAntigens, Tumor-Associated, CarbohydrateCell- and Tissue-Based TherapyHumansImmunotherapy, AdoptiveT-LymphocytesAntigens, Tumor-Associated, CarbohydrateReceptors, Chimeric Antigenagent-based modelcancer stem cellsCAR T-cellscomputational modelimmunotherapytriple-negative breast cancertumor heterogeneity

Identifiers

PMID36231127
PMCPMC9561977
OpenAlexW4303946543

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.