Evidence map›Paper›PMID 31937234›Full record

ArticleJournal of the Royal Society, Interface2020

Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data.

Prativa Sahoo, Xin Yang, Daniel Abler, Davide Maestrini, Vikram Adhikarla, David Frankhouser, Heyrim Cho, Vanessa Machuca, Dongrui Wang, Michael Barish and 4 more

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing 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

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

  1. Pooled it
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  20. A Framework for Quantitative Systems Pharmacology Model Execution.Handbook of experimental pharmacology · 2025
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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

14 authors.

Prativa SahooDepartment of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, USA.
Xin YangDepartment of Hematology and Hematopoietic Cell Translation and Immuno-Oncology, City of Hope National Medical Center, Duarte, CA, USA.
Daniel AblerDepartment of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, USA.
Davide MaestriniDepartment of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, USA.
Vikram AdhikarlaDepartment of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, USA.
David FrankhouserDepartment of Diabetes Complications and Metabolism, City of Hope National Medical Center, Duarte, CA, USA.
Heyrim ChoDepartment of Mathematics, University of California, Riverside, CA, USA.
Vanessa MachucaMathematical and Computational Systems Biology, University of California, Irvine, CA, USA.
Dongrui WangDepartment of Hematology and Hematopoietic Cell Translation and Immuno-Oncology, City of Hope National Medical Center, Duarte, CA, USA.
Michael BarishDepartment of Developmental and Stem Cell Biology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, USA.
Margarita GutovaDepartment of Developmental and Stem Cell Biology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, USA.
Sergio BranciamoreDepartment of Diabetes Complications and Metabolism, City of Hope National Medical Center, Duarte, CA, USA.
Christine E BrownDepartment of Hematology and Hematopoietic Cell Translation and Immuno-Oncology, City of Hope National Medical Center, Duarte, CA, USA.
Russell C RockneDepartment of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, USA.

Funding

Transgenic Mouse FacilityP30CA033572 · NCI · CITY OF HOPE/BECKMAN RESEARCH INSTITUTE · PI XIWEI WU · 1985 to 2026
$86.3M
Project 2U54CA285116 · NCI · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI Victoria L. Seewaldt · 2023 to 2026
$6.8M
Clinical evaluation of IL13Ra2-targeted CAR T cell therapy in combination with nivolumab in patients with recurrent malignant gliomaR01CA236500 · NCI · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI BADIE, BEHNAM, BROWN, CHRISTINE · 2019 to 2024
$3.3M
Cancer Metabolism Training ProgramT32CA221709 · NCI · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI David K. Ann, Victoria L. Seewaldt · 2018 to 2026
$1.8M
NCI NIH HHS P30 CA033572NCI NIH HHS R01 CA236500NCI NIH HHS T32 CA221709NCI NIH HHS U54 CA285116
6 · The paper itself

Abstract

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor T-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, proliferation, exhaustion, and persistence. We use patient-derived and engineered cancer cell lines with an

Indexed as

Receptors, Chimeric AntigenCell ProliferationHumansImmunotherapy, AdoptiveReceptors, Antigen, T-CellT-LymphocytesReceptors, Antigen, T-CellReceptors, Chimeric Antigenantigen densityCAR T-cell doseCAR T-cell therapymathematical modellingT-cell exhaustiontreatment efficacy

Identifiers

PMID31937234
PMCPMC7014796

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.