Evidence map›Paper›PMID 27556353›Full record

ArticleRadiation research2016

Transcript Analysis for Internal Biodosimetry Using Peripheral Blood from Neuroblastoma Patients Treated with (131)I-mIBG, a Targeted Radionuclide.

David A Edmondson, Erin E Karski, Ayano Kohlgruber, Harsha Koneru, Katherine K Matthay, Shelly Allen, Christine L Hartmann, Leif E Peterson, Steven G DuBois, Matthew A Coleman

Open access · greenAbstract read
In one paragraph

Article in Radiation research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.8field-weighted citation impact, top 15% 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

15 citing papers in PubMed, 24 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Transcriptomics for radiation biodosimetry: progress and challenges.International journal of radiation biology · 2023
    Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
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  13. Article
  14. Gene Expression as a Dosimeter in Irradiated Drosophila melanogaster.Journal of computational biology : a journal of computational molecular cell biology · 2017
    Article
  15. Article
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

10 authors at 4 institutions in 1 country.

David A Edmondsona   School of Health Sciences, Purdue University, West Lafayette, Indiana 47907;
Erin E Karskib   Department of Pediatrics, University of California San Francisco School of Medicine, San Francisco California 94143;
Ayano Kohlgruberc   Lawrence Livermore National Laboratory, Livermore, California 94550;
Harsha Koneruc   Lawrence Livermore National Laboratory, Livermore, California 94550;
Katherine K Matthayb   Department of Pediatrics, University of California San Francisco School of Medicine, San Francisco California 94143;
Shelly Allenb   Department of Pediatrics, University of California San Francisco School of Medicine, San Francisco California 94143;
Christine L Hartmannc   Lawrence Livermore National Laboratory, Livermore, California 94550;
Leif E Petersond   Center for Biostatistics, Houston Methodist Research Institute. Houston, Texas 77030; and.
Steven G DuBoisb   Department of Pediatrics, University of California San Francisco School of Medicine, San Francisco California 94143;
Matthew A Colemanc   Lawrence Livermore National Laboratory, Livermore, California 94550;
Lawrence Livermore National Laboratory · USUniversity of California, San Francisco · USHouston Methodist · USPurdue University West Lafayette · US

Funding

Sample Engineering CoreU19AI067773 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI FORNACE, ALBERT J · 2005 to 2024
$105.4M
Research Training in Childhood CancerT32CA128583 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Eric Alejandro Sweet-Cordero · 2007 to 2026
$6.0M
Optimizing 131I-mIBG Therapy for Children with Advanced NeuroblastomaR01CA172067 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI DUBOIS, STEVEN · 2013 to 2017
$1.6M
NCI NIH HHS R01 CA172067NCI NIH HHS T32 CA128583NIAID NIH HHS U19 AI067773
6 · The paper itself

Abstract

Calculating internal dose from therapeutic radionuclides currently relies on estimates made from multiple radiation exposure measurements, converted to absorbed dose in specific organs using the Medical Internal Radiation Dose (MIRD) schema. As an alternative biodosimetric approach, we utilized gene expression analysis of whole blood from patients receiving targeted radiotherapy. Collected blood from patients with relapsed or refractory neuroblastoma who received (131)I-labeled metaiodobenzylguanidine ((131)I-mIBG) at the University of California San Francisco (UCSF) was used to compare calculated internal dose with the modulation of chosen gene expression. A total of 40 patients, median age 9 years, had blood drawn at baseline, 72 and 96 h after (131)I-mIBG infusion. Whole-body absorbed dose was calculated for each patient based on the cumulated activity determined from injected mIBG activity and patient-specific time-activity curves combined with (131)I whole-body S factors. We then assessed transcripts that were the most significant for describing the mixed therapeutic treatments over time using real-time polymerase chain reaction (RT-PCR). Modulation was evaluated statistically using multiple regression analysis for data at 0, 72 and 96 h. A total of 10 genes were analyzed across 40 patients: CDKN1A; FDXR; GADD45A; BCLXL; STAT5B; BAX; BCL2; DDB2; XPC; and MDM2. Six genes were significantly modulated upon exposure to (131)I-mIBG at 72 h, as well as at 96 h. Four genes varied significantly with absorbed dose when controlling for time. A gene expression biodosimetry model was developed to predict absorbed dose based on modulation of gene transcripts within whole blood. Three transcripts explained over 98% of the variance in the modulation of gene expression over the 96 h (CDKN1A, BAX and DDB2). To our knowledge, this is a novel study, which uses whole blood collected from patients treated with a radiopharmaceutical, to characterize biomarkers that may be useful for biodosimetry. Our data indicate that transcripts, which have been previously identified as biomarkers of external exposures in ex vivo whole blood and in vivo radiotherapy patients, are also good early indicators of internal exposure. However, for internal sources of radiation, the biokinetics and physical decay of the radionuclide strongly influence the gene expression.

Indexed as

3-IodobenzylguanidineAdolescentAdultChildChild, PreschoolDose-Response Relationship, RadiationFemaleHumansMaleNeuroblastomaRadiometryRNA, MessengerTime FactorsTranscriptomeWhole-Body IrradiationYoung Adult3-IodobenzylguanidineRNA, Messenger

Identifiers

PMID27556353
PMCPMC5047008
OpenAlexW2511949539

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.