Evidence map›Paper›PMID 34060613›Full record

ArticleIntegrative biology : quantitative biosciences from nano to macro2021

A time-resolved experimental-mathematical model for predicting the response of glioma cells to single-dose radiation therapy.

Junyan Liu, David A Hormuth, Tessa Davis, Jianchen Yang, Matthew T McKenna, Angela M Jarrett, Heiko Enderling, Amy Brock, Thomas E Yankeelov

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In one paragraph

Article in Integrative biology : quantitative biosciences from nano to macro, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

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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

9 authors.

Junyan LiuDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
David A HormuthOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.
Tessa DavisDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
Jianchen YangDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
Matthew T McKennaDepartment of Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Angela M JarrettOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.
Heiko EnderlingDepartment of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.
Amy BrockDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
Thomas E YankeelovDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.

Funding

Postdoctoral Training in Biomedical MRI and MRST32EB001628 · NIBIB · VANDERBILT UNIVERSITY MEDICAL CENTER · PI John C Gore · 2003 to 2026
$7.0M
INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCERU24CA226110 · NCI · BAYLOR COLLEGE OF MEDICINE · PI LEWIS, MICHAEL T., RUBIN, DANIEL L · 2019 to 2023
$3.2M
Quantitative Multiscale Imaging to Optimize Cancer Treatment StrategiesR01CA186193 · NCI · VANDERBILT UNIVERSITY · PI QUARANTA, VITO, RERICHA, ERIN C · 2014 to 2019
$3.1M
Personalizing immunotherapy in HER2+ breast cancer through quantitative imagingR01CA240589 · NCI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI SORACE, ANNA C · 2020 to 2023
$1.6M
NCI NIH HHS R01 CA186193NCI NIH HHS R01CA240589NCI NIH HHS U24 CA226110NIBIB NIH HHS T32 EB001628
6 · The paper itself

Abstract

purposeTo develop and validate a mechanism-based, mathematical model that characterizes 9L and C6 glioma cells' temporal response to single-dose radiation therapy in vitro by explicitly incorporating time-dependent biological interactions with radiation.

methodsWe employed time-resolved microscopy to track the confluence of 9L and C6 glioma cells receiving radiation doses of 0, 2, 4, 6, 8, 10, 12, 14 or 16 Gy. DNA repair kinetics are measured by γH2AX expression via flow cytometry. The microscopy data (814 replicates for 9L, 540 replicates for C6 at various seeding densities receiving doses above) were divided into training (75%) and validation (25%) sets. A mechanistic model was developed, and model parameters were calibrated to the training data. The model was then used to predict the temporal dynamics of the validation set given the known initial confluences and doses. The predictions were compared to the corresponding dynamic microscopy data.

resultsFor 9L, we obtained an average (± standard deviation, SD) Pearson correlation coefficient between the predicted and measured confluence of 0.87 ± 0.16, and an average (±SD) concordance correlation coefficient of 0.72 ± 0.28. For C6, we obtained an average (±SD) Pearson correlation coefficient of 0.90 ± 0.17, and an average (±SD) concordance correlation coefficient of 0.71 ± 0.24.

conclusionThe proposed model can effectively predict the temporal development of 9L and C6 glioma cells in response to a range of single-fraction radiation doses. By developing a mechanism-based, mathematical model that can be populated with time-resolved data, we provide an experimental-mathematical framework that allows for quantitative investigation of cells' temporal response to radiation. Our approach provides two key advances: (i) a time-resolved, dynamic death rate with a clear biological interpretation, and (ii) accurate predictions over a wide range of cell seeding densities and radiation doses.

Indexed as

GliomaHumansModels, Theoreticalbrain cancercomputational biologymathematical modelingoncologyradiotherapy

Identifiers

PMID34060613
PMCPMC8271006

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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.